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The size of the texture extraction window impacts image tree species classification, and the determination of the optimal texture extraction window requires the supervision of a specific classifier for accuracy. Therefore, it is necessary to analyse which kind of classifier is more suitable and should be to choose. In this study, we extracted eight types of textures, namely mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation, changed the window size by gradient increase and used maximum likelihood classification (MLC) and random forest (RF) to supervise and determine their optimal extraction windows, respectively. Finally, the optimised time consumption and classification accuracy for tree species classification was identified. The time consumption of MLC was significantly less than that of RF; however, neither was very long; for most textures, the optimal texture extraction window determined by MLC supervision was larger than that determined by RF supervision; in the classification of most feature sets, the overall accuracy obtained by MLC was less than that of RF. Because the time consumption of the texture extraction was much greater than that of the image classification, the comprehensive trade-off indicates that using RF supervision to determine the optimal window for texture extraction was more conducive to tree species recognition.

Background: Carbapenem-resistant Klebsiella pneumoniae (CRKP) increases the difficulty of clinical treatment of giant pandas. This study aimed to investigate the antibiotic susceptibility, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), virulence genes, and molecular epidemiology of CRKP strains isolated from giant pandas. A total of 187 nonduplicated Klebsiella pneumoniae (KP) isolates were collected from fresh feces of captive giant pandas at the Chengdu Research Base of Giant Panda Breeding. Then CRKP were isolated and identified through carbapenase Carba NP assay. Subsequently, the antimicrobial susceptibility testing and antibiotic resistance genes of CRKP isolates were studied by disk diffusion (K-B) and HT-qPCR, respectively. Then both the MGEs and virulence genes of CRKP isolates were analyzed by PCR. In addition, molecular epidemiology was analyzed among the CRKP strains using pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). Results: Eight strains of CRKP (4.5%) were isolated and identified among the 187 KP strains, and seven of eight CRKP strains both exhibited resistance to imipenem, while one strain showed resistance to meropenem, and one demonstrated multiple resistance; eight CRKP strains carried a large amount of ARGs, among which ampC/blaDHA, blaSHV−01, blaSHV−02, tetB−01, tetB−02, tetC−01, and tetC−02 were the most abundant. The MGEs analysis revealed the presence of intI1 in all strains, while the detection rates of other MGEs varied, and strain 24 exhibited the highest diversity of MGE species. Seven virulence genes, including wabG, uge, ycf, entB, kpn, alls, and wcaG, showed positive results with different proportions across the strains. In addition, PFGE patterns indicated a high level of genetic diversity among the CRKP strains. MLST analysis classified the strains into different sequence types (STs). Conclusions: This study highlighted the diversity of CRKP strains isolated from giant panda feces, which exhibited varying levels of antibiotic resistance along with multiple ARGs, MGEs and virulence genes present. These findings emphasized the importance of monitoring and researching antibiotic resistance within wildlife populations to protect the health status of these conservation dependent animals. [ABSTRACT FROM AUTHOR]

Background: Bacteroides fragilis is a prevalent anaerobic bacterium typically resides in the human vagina. It is known to potentially induce infections under specific conditions. Interestingly, there have been no previous reports of B. fragilis being isolated from the vagina of giant pandas. Case presentation: A novel strain of anaerobic bacteria was isolated from the vaginal tract of a giant panda exhibiting symptoms of vaginitis. This strain, designated as GPBF01, was identified as Bacteroides fragilis, a species commonly found in the vaginal microbiome of humans and other animals. After purifying of the single colony, a series of evaluations were conducted including morphological examination, physiological and biochemical identification, antibiotic resistance analysis, resistance genes detection, 16S rRNA sequence, and phylogenetic tree sequence analysis to investigate its biological characteristics. The findings indicated the presence of a predominant anaerobic bacterium, which was identified as B. fragilis and temporarily named GPBF01 with unique biological traits not previously. Conclusions: This study is the first to report B. fragilis in the vaginal tract of giant pandas. The analysis of antibiotic resistance patterns among anaerobic bacteria, as conducted in this research, is critical for informing the selection of appropriate antimicrobial agents in the clinical treatment of vaginitis in this species. The findings of this report substantially enhance the scientific basis needed to understand the etiology and refine therapeutic approaches for vaginitis in giant pandas. [ABSTRACT FROM AUTHOR]

Embedded sensing devices that are battery-operated and resource-limited are increasingly utilizing recurrent neural networks (RNNs) to attain real-time perception of the complex environment. Nevertheless, the majority of RNNs typically handle substantial amounts of time-series data, resulting in computational intensity and high energy consumption. Hence, the development of energy-efficient RNN models is a crucial focus. This paper introduces IS-RNN, an energy-efficient RNN model featuring importance-based sparsification, aimed at enhancing the energy efficiency of RNN-based in-sensor inference. The principal motivation for this research arises from the recognition that time series data obtained from sensor sensing often exhibit inherent redundancy. Leveraging input sequence sparsification allows for the elimination of invalid input accesses and computations, thus optimizing efficiency. As a result, optimizing the sparsity of the input sequence through adaptive control allows us to improve inference performance while adhering to energy constraints. In particular, we build an importance score analysis method to quantify the contribution of each input toward the RNN inference performance. Building upon the proposed quantitative importance score analysis, we introduce a dynamic efficient RNN architecture, which enables the model to make decisions based on important data to obtain the highest accuracy under energy constraints. Moreover, we introduce an adaptive RNN inference method to enhance prediction accuracy under energy constraints by making a trade-off between computational cost and model performance. We evaluated IS-RNN on five datasets and the results show that it achieves a higher inference accuracy with the same amount of data processed compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Faculty mobility is one of the most important research issues in the field of higher education. Reasonable faculty mobility can actively promote the fair, coordinated, balanced, healthy, and sustainable development of higher education. Scientific impact is the best proof of faculty members' research abilities and is often represented by the quality of their articles. In particular, the gradual increase in citations of high-quality papers is undoubtedly an important reflection of healthy development in the academic field. This paper aims to explore the influence of faculty mobility on scientific impact, while comparative analysis is used to investigate whether there are disciplinary differences in the relationship between faculty mobility and scientific impact. Four major disciplines—sociology, mathematics, mechanical engineering, and philosophy—are selected as the scope of this study. Articles in these four major disciplines from 2000 to 2020 are obtained from the Web of Science, and Spearman's rank correlation coefficient and the Wilcoxon signed-rank test are used to analyze the collected data. The results indicate the following: (1) faculty mobility has increased, with differences across disciplines; (2) mobility leads to a decrease in the number of citations, which decreases significantly with increased mobility frequency; and (3) the impact of mobility has disciplinary differences, with a relatively obvious decrease in mechanical engineering. [ABSTRACT FROM AUTHOR]

With the improvement of equipment reliability, human factors have become the most uncertain part in the system. The standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H) method is a reliable method in the field of human reliability analysis (HRA) to evaluate human reliability and assess risk in large complex systems. However, the classical SPAR-H method does not consider the dependencies among performance shaping factors (PSFs), which may cause overestimation or underestimation of the risk of the actual situation. To address this issue, this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient. First, the dependence between every two PSFs is measured by the Pearson correlation coefficient. Second, the weights of the PSFs are obtained by considering the total dependence degree. Finally, PSFs' multipliers are modified based on the weights of corresponding PSFs, and then used in the calculating of human error probability (HEP). A case study is used to illustrate the procedure and effectiveness of the proposed method. Graphic Abstract [ABSTRACT FROM AUTHOR]