The foundation of the inquiry dialogue system is rooted in natural language understanding (NLU), where NLU involves the extraction of intent and entity information from conversational data, transforming it into a structured representation. This process primarily encompasses two tasks: intent recognition and slot filling. Intent recognition, a typical text classification task, aims to discern the underlying purpose of the dialogue, while slot filling utilizes sequential algorithms to extract corresponding slot values based on predefined positions within the conversation. Conventional approaches often build separate models for intent recognition and slot filling, subsequently performing slot filling based on the recognized intent. However, this methodology is susceptible to error propagation. To address this issue, this paper proposes a fine-grained intent recognition method that integrates dialogue intent classification and semantic slot value extraction using a contrastive learning approach. The method combines intent classification and slot value tasks, leveraging BART as the backbone model for improvement and innovation. This model, employing an encoder-decoder architecture, shares an encoding layer for intent recognition and slot filling tasks. Additionally, it adopts character-level labels in the decoding layer, thereby integrating intent information into the slot filling task. Contrastive learning is introduced during the sample construction process. Experimental results demonstrate that the proposed algorithm achieves an intent recognition accuracy of 81.96% and a slot filling F₁ score of 85.26% on a medical dialogue dataset, showing significant performance improvements compared with other algorithms. The paper also conducts ablation experiments on contrastive learning, historical information, and sentence-level intent to further substant the effectiveness of the proposed method.

Personalized speech synthesis aims to generate speech with specific speaker’s characteristics. Traditional approaches often exhibit noticeable timbre disparities when synthesizing speech from unseen speakers, making it challenging to disentangle speaker-specific timbre features. This paper proposes a multi-level disentangled personalized speech synthesis approach designed for out-of-domain speakers. By fusing features at different granularities, the proposed method effectively enhances the performance of synthesizing speech from unseen speakers under zero-resource conditions. This is achieved by utilizing fast Fourier convolution to extract global speaker features, thereby enhancing the model's generalization to unseen speakers and enabling sentence-level speaker decoupling. Additionally, leveraging a speech recognition model, the method decouples speaker features at the phoneme level and captures phoneme-level timbre features through an attention mechanism, achieving phoneme-level speaker disentanglement. Experimental results on the publicly available dataset AISHELL3 demonstrate that the proposed approach achieves a cosine similarity of 0.697 for speaker feature vectors of cross-speaker adaptation, indicating a 6.25% improvement compared with the baseline model. This enhancement shows the method’s capability in modeling timbre features for speech from unseen speakers in cross-speaker adaptation scenarios.

Functional morphology is the study of the mutual adaptation of function and form. Primates (including human and non-human primates) are an extremely special group in the animal kingdom. They show strong adaptability to the environment in terms of morphology, physiology, and behavior, making them often as model animals for the study of ecological issues. In order to adapt to the diverse habitats, primates have evolved different locomotion patterns, and their skeletal and muscular systems have also evolved accordingly to adapt to different locomotion patterns. At the same time, primates are similar to humans in ecological niche, social structure, geographical distribution and evolution. Research on the functional morphology of their locomotion will not only help to understand the ecology and adaptation of primates, but also provide an understanding on the role of motor functions played in human evolution. This paper summarizes the development history of functional morphology of locomotion in primates and explores the movement patterns of primates and the corresponding characteristics and functions of the skeletal and muscular systems. In the future, new technologies and methods will continue to be integrated into the study of locomotion functional morphology in primates, which will further enrich people’s understanding of primates and reveal the adaptation and evolution rules of their structures and functions.

Tetramethylpyrazine (C₈H₁₂N₂), a natural compound, was firstly extracted from Ligusticum chuanxiong Hort., a plant of Umbelliferae family. It has the effects of dilating blood vessels, activating blood circulation and improving microcirculation. The research of tetramethylpyrazine has been a hot spot in physiology and clinical medicine. Tetramethylpyrazine has a special flavour, which can improve bitterness, acidity, and provide aroma to the baked foods and fermented foods. Tetramethylpyrazine can also be used as a food flavour enhancer in food production industry. This paper discussed the metabolic pathways, through which sugars were metabolized to produce tetramethylpyrazine in microorganisms. The common methods used to increase the yield of tetramethylpyrazine from microorganisms were summarized. The advantages and disadvantages of microbial fermentation to produce tetramethylpyrazine were discussed.

In order to improve the level of remote parking passenger shuttle service at airports, an autonomous remote shuttle system using modular vehicle technology is constructed, with the objective of minimizing the total economic cost for passengers and operators, while considering the constraints of system safety, number of people conservation and operation quality, a dispatching model is constructed with unequal departure time by using a commercial solver Gurobi. To improve the speed in model-solving, a genetic algorithm is also designed, and the speed and accuracy in model-solving of the two methods are compared and analyzed. The results show that the departure solution by Gurobi solver is optimal, and the genetic algorithm obtains an approximate solution with a difference of only 3.4%, but the computation time required by the genetic algorithm is 1% of that of Gurobi; and the total cost with a small difference can basically meet the actual operation needs in terms of the quality of the solution. In addition, by comparing with the shuttle bus departure scheme, it is found that the total cost of the departure scheme based on modular vehicle technology is reduced by 64.50% and the average passenger waiting time is reduced by 76.35%, which proves that the scheme has better economy than the traditional scheme and can significantly improve the service level of the system.

At present, there are some problems in parameter identification of photovoltaic cells, such as low precision, slow speed and poor stability, which need to be improved. To solve these problems, a method of parameter identification of photovoltaic cells based on dung beetle optimization algorithm is proposed in this paper. By introducing Tent chaotic mapping to initialize the population, the initial solution is distributed as evenly as possible in the solution space. Levy flight strategy is added to update individual position of dung beetle during ball rolling, jump out of the local optimal solution, and expand the search scope. The adaptive T-distribution and dynamic selection strategies are adopted, and the T-distribution mutation operator with the number of iterations as the degree of freedom parameter is used to perturbate the dung beetle position, which enhances the global development ability and local exploration ability of the algorithm, and accelerates the convergence speed. The experimental results show that the root-mean square errors of RTC France’s single diode model, double diode model and Photowatt-PWP 201 model are respectively 0.000 986, 0.000 983 and 0.002 425. The method proposed in this paper can identify the parameters of photovoltaic cells faster and more accurately, and has a small error and high stability.

The large-scale integration of distributed power sources complicates the structure of the distribution network, resulting in increased difficulty in fault localization. A distribution network partition fault location method with improved chimpanzee algorithm is proposed. Firstly, Iteration mapping is introduced to improve the initialized population quality, and then Cauchy variation and backward learning strategy as well as simplex method are added to improve the local exploitation capability and exploration capability of the algorithm. The switching function and objective function containing distributed power supply are established, and the region is divided according to the correspondence mechanism between the fault point and the switching function. Finally, it is verified by simulation that the proposed method improves the solution speed by 43.05% on average and the accuracy by 1.17% on average compared with the traditional chimpanzee algorithm partitioning localization method. It is shown that the method can accurately and rapidly locate the faulty zones with high fault tolerance.

Radar bird detection is a hot issue in the fields of aviation safety and environmental ecology. The radar scattering cross section (RCS) of bird targets is small and the scattering features are single, which brings many challenges to bird detection. To solve these problems, a dynamic electromagnetic scattering feature extraction method based on wavelet transform and singular value decomposition for flying bird targets is proposed. Firstly, the dynamic RCS sequence of the flying bird with flapping frequency of 2-20 Hz is wavelet transformed to obtain the wavelet coefficients of each branch, and then the wavelet coefficients of each branch are reconstructed. The feature matrix composed of wavelet coefficients is decomposed by singular value to quantitatively describe the dynamic electromagnetic scattering features of the flying bird with eigenvalues. To verify the validity of the method, numerical experiments with incident frequencies of 0.5, 1, and 3 GHz are conducted in this paper under circling and leveling trajectories, horizontal polarization, and vertical polarization, respectively. The results show that the eigenvalues show obvious linear correlation with the flapping frequency of the flying bird, which can effectively reflect the motion characteristics of the flying bird and provide new perspectives and ideas for the radar detection and identification of bird targets.

The multiphase flow model based on the lattice Boltzmann method has the advantages of automatic evolution of the phase interface and no boundary integration. It has been widely studied and applied in the simulation of complex multiphase fluid systems. In this paper, a multiphase flow model satisfying thermodynamic consistency and Galilean invariance is proposed by introducing the free energy density to calculate the interaction force between molecules. The results of using this model to predict the liquid-gas two-phase coexistence densities are in good agreement with theoretical values. At low temperatures, they are better than the improved pseudopotential model. At the same time, the model can also be used for the simulation of multi-phase flow systems with large density ratios. The model's compliance with Galilean invariance is verified by a series of numerical simulations such as speckle map and droplet impact on the liquid film. The model is physically clear and easy to implement, then, it can simulate multiphase flow systems with different equations of state, which has a better potential for practicality and application.

The adjustment empirical likelihood inference of spatial autoregressive models with spatial autoregressive errors is studied in this paper. Using adjusted empirical likelihood method, an adjusted empirical likelihood ratio statistic is constructed for spatial autoregressive models with spatial autoregressive errors. It is shown that the limit distribution of the adjusted empirical likelihood statistic is a chi-squared distribution. The advantages and disadvantages of the adjusted empirical likelihood method are compared with the general empirical likelihood method through simulations. Simulation results show that the adjusted empirical likelihood region has higher coverage accuracy and is faster in implementation than that of the general empirical likelihood. When the sample size is large, both general empirical likelihood method and adjusted empirical likelihood method can be used whether the error term follows a normal distribution or not. But when the sample size is small, it is recommended to use the adjusted empirical likelihood method.

Medical insurance reform and medical expenses are people’s important livelihood issues. People pay attention to whether there are differences in the proportion of medical expenses paid by different types of medical insurance and the influencing factors of medical expenses. In this paper, the discharge settlement data of type 2 diabetes patients in the secretion department of a tertiary class-Ⅲ-A hospital in Chengdu, Sichuan Province are analyzed from January 2020 to December 2021 by using the high-dimensional permutation test and the interaction effect model to explore whether there is a significant difference in the medical expense out-of-pocket ratio between urban and rural resident medical insurance patients and urban employee medical insurance patients and the influencing factors of the medical expense out-of-pocket ratio, so as to provide a basis for the subsequent reform of medical insurance. The results of the study show that there are significant differences in the medical expense out-of-pocket ratio of patients with two types of medical insurance, and the increase of various expense ratios such as examination expense ratio would increase the medical expense out-of-pocket ratio. Moreover, under low dimensions, the results of the high-dimensional permutation test method used in this paper are consistent with those of the Hotelling T² permutation test.

Computation for the Hilbert transform of a given signal over a finite interval is an important problem in data analysis. In the existing method, the best algorithm has the computational complexity O(nlog n). In order to obtain a faster computational speed, this paper proposes a fast algorithm based upon cubic spline wavelets, which can reduces the computational complexity from O(nlog n) to O(n).Numerical results show that the proposed method does have faster computational speed, while keeping a comparable computational accuracy with the best existing method.

Although graph regularized non-negative matrix factorization (GNMF) has become the basic framework for a large number of multi-view clustering methods, it is undoubtedly a great challenge to fuse complex data relationships from different views with a simple graph and obtain a consistent discriminative representation at the same time. In order to better deal with the clustering task of multi-view data,a semi-supervised multi-view clustering method based on hypergraph regularized non-negative matrix factorization is proposed. Specifically,by constructing a hypergraph,this method learns the high-order relationships of data from multiple views. In order to make reasonable use of the label information available in the real world,the label constraint is introduced for semi-supervised learning. In addition,this method considers the learning of consistency information and complementarity information at the same time,adopts adaptive measures to distinguish the contributions of different views,and uses an alternating iterative algorithm to optimize the objective function. The comparative experimental results on 7 real datasets show that the proposed algorithm is superior to other classical algorithms and current advanced algorithms in ACC and NMI indicators on 6 datasets.

Aiming at the shortcomings of the hunter-prey optimization algorithm, which is easy to fall into local optimum and low convergence precision, a multi-strategy improved hunter-prey optimization algorithm is proposed. The proposed algorithm is based on the idea of dynamic search, and the adaptive adjustment algorithm is transformed from global search to local search; the historical information of the population is used for differential evolution to improve the diversity of the population; combined with the elite pool strategy and nonlinear step size, the algorithm is prevented from falling into local optimum, to improve the convergence accuracy of the algorithm. The improved algorithm and six other classical or state-of-the-art optimization algorithms are evaluated for their performance on 10 large-scale (10 000-dimensional) test functions. The experimental results show that the proposed algorithm has better global optimization capabilities, optimization accuracy and stability, and can effectively solve high-dimensional optimization problems. Finally, the multi-strategy improved hunter-prey optimization algorithm is applied to the path planning problem of 3D UAV. The simulation experiment results show that the algorithm can also solve the optimal result in the 3D path planning optimization problem of UAVs.

In this paper, the invasive plant Myriophyllum aquaticum was taken as the target plant, and the interference factors were set with the competition of Vallisneria natans and the herbivory of Pomacea canaliculata to explore the effects of herbivory of P. canaliculata and competition of V. natans on the invasion of M. aquaticum. The results showed as follows: (1) The plant height and biomass of M. aquaticum were significantly decreased by P. canaliculata herbivory and V. natans competition, and the growth of M. aquaticum was inhibited. (2) Compared with the control, the biomass and plant height of M. aquaticum were significantly reduced under the combined treatment of herbivory of P. canaliculata and competition of V. natans. The superposition of these two factors enhanced the inhibition of M. aquaticum, but there was no significant interaction between herbivory and competition. (3) Under the combined influence of competition and herbivory, the logarithmic response ratio (ln RR) of M. aquaticum was not significantly different among different herbivory densities of P. canaliculata, and all were negative, indicating that V. natans inhibited the growth of M. aquaticum than that of P. canaliculata herbivory. Therefore, in the future water ecosystem restoration, the reasonable dense planting of V. natans can be used to control the invasion of M. aquaticum.

The Lijiang River is one of the 15 most beautiful rivers in the world, with high aquatic biodiversity. However, the water quality has been affected due to the rapid economic development. This study aims to develop a biotic index based on macroinvertebrates and to evaluate the ecological health status of the Lijiang River. Based on the identification of macroinvertebrates and water physicochemical data from 26 monitoring sites in Guilin from August, November 2021 to August 2022, a complete set of water quality evaluation index was constructed to scientifically evaluate the water quality of Lijiang River. The Lijiang Zoobenthic Biotic Index (LJBI) proposed in this study analyzed the physicochemical values of water quality at monitoring points using the principal component analysis method and was associated with the distribution of macroinvertebrates. The LJBI values were divided into four level by 25%, 50%, and 75% quantiles, representing the health, sub-health, average and poor, respectively. This index divided the Lijiang River water quality health into four levels: excellent, good, medium, and general. The result showed that most of the macroinvertebrates distributed in the Lijiang River were moderately pollution-tolerant species, while the species adapted to clean water quality, such as Nebrioporus spp., Microvelia spp., and Aciagrion spp. were only distributed in the upstream. Therefore, it was important to protect the upstream and tributary streams. Based on the classification level of the genus, certain sensitive values were assigned to each genus, and the health status of water quality in the Lijiang River was assessed according to the average value of sensitive values at each monitoring point. The results showed that the water quality in the upper reaches of the Lijiang River was good, for example Qingshitan, Gaozai, and Huajiang sample sites were evaluated as excellent, but the water quality in the lower reaches of the Lijiang River was badly affected by human activities, such as the water body of Jingpingshan sample site was cloudy due to the influence of bridge construction, and the water ecology of this site was evaluated as general by LJBI. The benefits of LJBI contain: (1) It remains unaffected by the quantity of individual species; (2) For the users, is is simple in operation, convenience in calculation, and quick in conclusions; (3) It is convenient to promote the popularization of scientific knowledge of ecological health status of water quality within the Lijiang River basin.

Calamaria jinggangensis Cai, 2023, which belonging to the family of Colubridae, was only known to be distributed in Jinggang Mountains, Jiangxi, China. On September 3, 2021 and June 2, 2022, two specimens (No. GXNU20210909007 and GXNU20220613012) presented similar morphological features with C. jinggangensis were collected from Guilin, Guangxi Zhuang Autonomous Region. Phylogenetic analysis based on the mitochondrial cytochrome b (cyt b) gene showed that specimens from Guilin clustered into a single unit with the type specimen of C. jinggangensis. Genetic distance between the collected samples and C. jinggangensis was 0.005-0.030, significantly smaller than the genetic distance between other species within the genus Calamaria 0.052-0.237. Through morphological comparison and phylogenetic analysis, it is confirmed that the two specimens are C. jinggangensis which belongs to the genus Calamaria. This discovery is the first record of this species in Guangxi, which could provide more information on the geographic distribution and phylogeny of Calamaria species.

In recent years, the intensification of human activities has resulted in the decline of water quality for Jianghan Plain, the water quality series presents complicated evolution phenomena in middle and lower reaches of the main canal. Based on annual water quality monitoring data of Four Lakes (from 2016 to 2021), this study analyzed the distribution characteristics, variation rules and main environmental impact factors of the characteristic pollutants; diagnosed the spatiotemporal variation of water quality sequence, and recognized a set of variation elements and impact factors of regional system. The results showed that: ①The water quality of the middle and lower reaches were remarkable, and the series had obvious concentration gradient. ②Excessive aquaculture had led to the phenomenon of seasonal over-standard prominently. ③Non-point source pollution and ecological flow were the leading factors of water quality for the main canal. It indicated that this method could be used for variation analysis of water quality.

The study of landscape pattern evolution is an important means of urban sustainable development research. It can reveal the dynamic changes of landscape development and control the scale between urban development and ecological protection. In this paper, the landscape pattern change and its driving sub-factors in Lingui District of Guilin City for 20 years were analyzed based on the landscape index method and binary logistic regression analysis method and some suggestions were put forward. The results showed that the urbanization level of Lingui District had been steadily improved from 2000 to 2020, and the urbanization process was the fastest between 2015 and 2020. Although the proportion of construction land was not large, its change rate was the largest among all land types, from 1.94 % in 2000 to 3.64 % in 2020, with a net increase of 0.88 times. This was the result of rapid urbanization in Lingui District. Urbanization had caused a certain tendency of landscape fragmentation, but the overall structure of the landscape had changed little, and the overall trend was stable. The distribution of various types in Lingui District was clustered, and there were giant patches in forest land and cultivated land. Forest land was mainly distributed in the mountains on the north and south sides. Cultivated land, grassland, construction land and water area were concentrated in the plain area centered on Lingui Town. The new construction land was concentrated in Lingui Town. A total of 15 driving factors of natural factors and human factors were selected, and all the driving factors were assigned to grids. Binary logistic regression analysis was carried out after random sampling using ArcGIS10.7. The analysis results showed that the landscape pattern change in Lingui District was affected by natural conditions, overall planning of land use, urban development policies, agricultural production activities, urbanization level improvement and other factors.

In order to study the response mechanism of soil net nitrogen (N) transformation rates to temperature and moisture in subtropical forests, the study focused on two kinds of subtropical forest soils, an acidic red soil and a neutral limestone soil, which were widely distributed in Guangxi. Soil incubation experiments were conducted to compare the responses of net ammonification, nitrification, and N mineralization rates to different soil temperature (5, 15, 25, 35 ^oC) and moisture levels (20%, 40%, 60%, 80% water holding capacity, WHC). The results showed that net N transformation rates of both soils responded strongly to temperature changes. The net ammonification, nitrification and N mineralization rates of red soil decreased with increasing temperature, leading to a decrease in soil net N supply capacity. Conversely, the net nitrification and mineralization rates in limestone soil increased with increasing temperature, thereby increasing the bioavailable N flux as well as the risk of soil nitrogen leaching. Temperature sensitivity values (Q₁₀) of soil N transformations indicated that the sensitivity temperature range of net ammonification and N mineralization rates of acidic red soil was 25 to 35 ℃. The sensitivity range of nitrification rates of red soil and net N transformation rates of limestone soil was 15 to 25 ℃. The increase of soil moisture promoted net nitrification and mineralization rates of limestone soil, but had no significant effect on the net N transformation rates of red soil. Soil calcium content had the highest explanatory rate (28.0%) in predicting N transformation rates at different temperatures. In conclusion, it is crucial to consider the varying responses of N cycling among different soil types to climate change in forest conservation, restoration and land use management of subtropical forests under the global change.

The difficulty in the construction of the ecological compensation mechanism lies in clarifying the subject and object of compensation, evaluating the priority sequence of compensation and accurately calculating the compensation standard. The construction of a scientific and effective ecological compensation mechanism is conducive to the rational distribution of ecological protection responsibilities in the upstream and downstream of the basin. Taking the prefecture-level cities in the Pearl River-West River Economic Belt as the research unit, this paper combines the improved three-dimensional ecological footprint model with the estimation of ecosystem service value to measure the value and standard of ecological compensation in each city from 2005 to 2020, and introduces the priority of ecological compensation, and finally delineates five types of sub-districts: priority payment, potential payment, balance, potential compensation and priority compensation. The results showed that: 1) In 2020, the ecological compensation deficit in the economic belt was 28.809 billion yuan, the highest payment value in Guangzhou was 18.514 billion yuan, and the per capita ecological compensation standard was 987.92 yuan, and only Chongzuo received ecological compensation, with a compensation value of 68 million yuan and a per capita compensation standard of 32.62 yuan. 2) Guangzhou, Foshan and Nanning had long been priority payment areas; Chongzuo had long been a priority compensation area; Zhaoqing, Liuzhou and Baise had been transformed into potential payment areas in recent years; Laibin, Guigang, Wuzhou and Yunfu were balanced areas; The priority compensation area and potential compensation area of the economic belt had gradually changed to the priority payment area, potential payment area and balance area. 3) Guangzhou, Foshan and Nanning should take the lead in paying ecological compensation to balance the inter-regional ecological protection responsibilities of the economic belt, and coordinate the inter-regional ecological protection and co-governance relationship; Priority should be given to Chongzuo ecological compensation to benefit from ecological protection and obtain economic support for ecological and environmental governance.