Accurate and robust indoor location prediction plays an important role in indoor location services. Markov chains (MCs) have been widely adopted for location prediction due to their strong interpretability. However, multi-order Markov chains (k-MCs) are not suitable for predicting long sequences due to problems of dimensionality. This study proposes a hybrid Markov model for location prediction that integrates a long short-term memory model (LSTM); this hybrid model is referred to as the Markov-LSTM. First, a multi-step Markov transition matrix is defined to decompose the k-MC into multiple first-order MCs. The LSTM is then introduced to combine multiple first-order MCs to improve prediction performance. Extensive experiments are conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.

To accelerate the ecological civilization in the new era and coordinate the region’s ecological economic growth, land-use benefit pre-evaluations and coordination analyses must be carried out. These should be based on a land-use structure layout that is low-carbon oriented to ensure the land is utilized efficiently and sustainably. This paper studies Fujian Province, the national ecological civilization test area, and constructs a framework for land-use benefit evaluation and coupling coordination relationship analysis based on land-use structure from the low-carbon perspective. Based on land-use status data from 2000 to 2020, land-use simulation data under two development scenarios in 2030, and accounting for the different types of main functional areas, this paper establishes the land-use benefit evaluation model. The model uses aspects of carbon emission benefit, economic benefit, and ecological benefit to calculate the land-use benefit of counties (cities and districts) in Fujian Province from 2000 to 2030 by combining with the grey prediction model. The coupling coordination degree model is used to explore the coordination relationship between the above three types of benefits in different districts and counties in 2030 and to identify the main factors restricting the improvement of coupling coordination relationships in different regions in the future. The following results are found: (1) From the perspective of land-use efficiency, the coupling and coordination of land-use efficiency and benefits of counties (cities and districts) in Fujian Province continued to rise from 2000 to 2030 under the low-carbon development scenario, and the spatial agglomeration characteristics were obvious. By 2030, the coupling and coordination relationship of regional land-use efficiency was gradually optimized, and the development pattern with the Fuzhou metropolitan area and Xiamen-Zhangquan metropolitan area as the core was formed. (2) From the perspective of restrictive factors of land-use efficiency coupling coordination, the improvement of land-use efficiency coupling coordination relationship in each county (city and district) is affected by multiple factors. The influence degree is economic benefit > ecological benefit > carbon emissions. These results will help to promote the efficient and sustainable use of land resources and realize the comprehensive and coordinated development of a low-carbon economy and society.

This paper proposes a novel model for named entity recognition of Chinese crop diseases and pests. The model is intended to solve the problems of uneven entity distribution, incomplete recognition of complex terms, and unclear entity boundaries. First, a robustly optimized BERT pre-training approach-whole word masking (RoBERTa-wwm) model is used to extract diseases and pests’ text semantics, acquiring dynamic word vectors to solve the problem of incomplete word recognition. Adversarial training is then introduced to address unclear boundaries of diseases and pest entities and to improve the generalization ability of models in an effective manner. The context features are obtained by the bi-directional gated recurrent unit (BiGRU) neural network. Finally, the optimal tag sequence is obtained by conditional random fields (CRF) decoding. A focal loss function is introduced to optimize conditional random fields (CRF) and thus solve the problem of unbalanced label classification in the sequence. The experimental results show that the model’s precision, recall, and F1 values on the crop diseases and pests corpus reached 89.23%, 90.90%, and 90.04%, respectively, demonstrating effectiveness at improving the accuracy of named entity recognition for Chinese crop diseases and pests. The named entity recognition model proposed in this study can provide a high-quality technical basis for downstream tasks such as crop diseases and pests knowledge graphs and question-answering systems.

Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.

The accurate extraction of agricultural parcels from remote sensing images is crucial for advanced agricultural management and monitoring systems. Existing methods primarily emphasize regional accuracy over boundary quality, often resulting in fragmented outputs due to uniform crop types, diverse agricultural practices, and environmental variations. To address these issues, this paper proposes DSTBA-Net, an end-to-end encoder–decoder architecture. Initially, we introduce a Dual-Stream Feature Extraction (DSFE) mechanism within the encoder, which consists of Residual Blocks and Boundary Feature Guidance (BFG) to separately process image and boundary data. The extracted features are then fused in the Global Feature Fusion Module (GFFM), utilizing Transformer technology to further integrate global and detailed information. In the decoder, we employ Feature Compensation Recovery (FCR) to restore critical information lost during the encoding process. Additionally, the network is optimized using a boundary-aware weighted loss strategy. DSTBA-Net aims to achieve high precision in agricultural parcel segmentation and accurate boundary extraction. To evaluate the model’s effectiveness, we conducted experiments on agricultural parcel extraction in Denmark (Europe) and Shandong (Asia). Both quantitative and qualitative analyses show that DSTBA-Net outperforms comparative methods, offering significant advantages in agricultural parcel extraction.