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Since the outbreak of the Russia-Ukraine conflict in 2022, Ukraine has experienced different types of abandoned cropland, such as unused and unattended cropland, as a result of war damage, agricultural infrastructure destruction, and refugee outflows. Common methods for detecting abandoned cropland have difficulty effectively identifying and distinguishing these different types. This study proposes a Dual-period Change Detection method to reveal the spatial distribution and changes of different types of abandoned cropland in Ukraine, which can aid in agricultural assessments and international assistance in conflict-affected areas. The method mainly utilizes time-series NDVI data to fit the crop curves corresponding to cropland on a pixel-by-pixel basis, and then establishes discrimination rules for different types of abandoned cropland based on the crop curves, so as to detect unused cropland in the pre-conflict period (2015–2021) as well as unused cropland and unattended cropland in the post-conflict period (2022–2023). Finally, the detection results are validated and accuracy assessed using medium and high resolution spatiotemporal remote sensing imagery interpretation. The results show that the overall accuracy of the abandoned cropland extraction in Ukraine ranges from 83 to 96% during the study period. Before the conflict, the national average unused rate was 1.6%, with the lowest in 2021 and the highest in 2018. In 2022, the unused cropland area was approximately twice the average unused area before the conflict, and it was widely distributed, with the area of unattended cropland reaching 462,000 hectares, mainly in the eastern part of Ukraine. In 2023, compared to 2022, the unused cropland area decreased by 67.8%, while unattended cropland increased by 116.7%. Both types of abandoned cropland exhibited spatial clustering, with major clusters identified in the Crimea region, Kherson Oblast, Zaporizhzhia Oblast, and Donetsk Oblast.

Enhancing the accuracy of short-term wind power forecasting can be effectively achieved by considering the spatial–temporal correlation among neighboring wind turbines. In this study, we propose a short-term wind power forecasting model based on 3D CNN-GRU. First, the wind power data and meteorological data of 24 surrounding turbines around the target turbine are reconstructed into a three-dimensional matrix and inputted into the 3D CNN and GRU encoders to extract their spatial–temporal features. Then, the power predictions for different forecasting horizons are outputted through the GRU decoder and fully connected layers. Finally, experimental results on the SDWPT datasets show that our proposed model significantly improves the prediction accuracy compared to BPNN, GRU, and 1D CNN-GRU models. The results show that the 3D CNN-GRU model performs optimally. For a forecasting horizon of 10 min, the average reductions in RMSE and MAE on the validation set are about 10% and 11%, respectively, with an average improvement of about 1% in R. For a forecasting horizon of 120 min, the average reductions in RMSE and MAE on the validation set are about 6% and 8%, respectively, with an average improvement of about 14% in R.

The study of cross-border transport connectivity is significant for the development of regional integration and insight into global patterns. Comprehensive connectivity evaluations are lacking and insufficient attention has been paid to Latin American connectivity, so it is of great practical importance to comprehensively and rationally evaluate Latin American connectivity. In this article, based on the four modes of transport, namely, sea, road, air and railroad, and using the actual trade volume as a comparison, a connectivity evaluation index system with considerable reliability and generalization ability was constructed using the expert scoring method, QAP correlation analysis, QAP regression, and statistics, and the connectivity calculations of Latin America were obtained. Analyzing the connectivity structure of Latin America, it was found that cross-border passenger and cargo transport in the region was dominated by sea transport and supplemented by road and air transport, with railroads used the least. The overall connectivity of Latin America was low, and the overall development was unbalanced, with a strong law of spatial differentiation, which was mainly manifested in the strongest connectivity of the integrated coastal countries, followed by the island countries, and the lowest connectivity of the landlocked countries. Different countries assumed different roles in regional connectivity, which could be categorized into global hub type, local hub type and non-hub type based on the calculations. There was a spatial pattern of decreasing connectivity with distance in typical countries, but the rate of decline was closely related to their geographic location and the role they played in the connectivity network. This study can provide reference and inspiration for regional connectivity evaluation, improvement, and sustainable development.

Myanmar is among the regions with the most frequent activities of International Non-Government Organizations (INGOs). Analyzing the spatio-temporal patterns of these activities holds crucial importance for optimizing organizational coordination and enhancing governmental oversight. This study focuses on the spatio-temporal evolution characteristics and influencing factors of INGO activities in Myanmar from 2010 to 2021, utilizing spatial autocorrelation and regression analysis. The results show that the number of INGOs in Myanmar has shown a gradual slowdown in growth trends, with the number of activities exhibiting a wave-like pattern, primarily driven by spontaneous activities of INGOs. The spatial distribution of INGO activities in Myanmar is concentrated in the southern plains, with the core located in Yangon, Naypyitaw, and Loilen. Furthermore, there is significant spatial polarization in the hotspot area of INGO acticities. The hotspots followed an evolutionary path from “South Myanmar” to “North Myanmar” and then back to “South Myanmar”. INGO activities in Myanmar are more focused on the local economic level, urbanization level, medical level, education level, and total population size, providing the necessary support and services for the local society and making up for the “government malfunction” and “market malfunction”.

To overcome the limitations of long-term prediction of PM2.5 concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM2.5 sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM2.5 time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R2 values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction.

The genomes and annotated genes of allotetraploid cotton Gossypium hirsutum have been extensively studied in recent years. However, the expression, regulation, and evolution of intergenic genes (ITGs) have not been completely deciphered. In this study, we identified a novel set of actively expressed ITGs in G. hirsutum cotton, through transcriptome profiling based on deep sequencing data, as well as chromatin immunoprecipitation, followed by sequencing (ChIP-seq) of histone modifications and how the ITGs evolved. Totals of 17,567 and 8249 ITGs were identified in G. hirsutum and Gossypium arboreum, respectively. The expression of ITGs in G. hirsutum was significantly higher than that in G. arboreum. Moreover, longer exons were observed in G. hirsutum ITGs. Notably, 42.3% of the ITGs from G. hirsutum were generated by the long terminal repeat (LTR) insertions, while their proportion in genic genes was 19.9%. The H3K27ac and H3K4me3 modification proportions and intensities of ITGs were equivalent to genic genes. The H3K4me1 modifications were lower in ITGs. Additionally, evolution analyses revealed that the ITGs from G. hirsutum were mainly produced around 6.6 and 1.6 million years ago (Mya), later than the pegged time for genic genes, which is 7.0 Mya. The characterization of ITGs helps to elucidate the evolution of cotton genomes and shed more light on their biological functions in the transcriptional regulation of eukaryotic genes, along with the roles of histone modifications in speciation and diversification.

Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and storage, but the detection result also lacks the directional information of the cargo ship. In order to address the above problems, we propose a novel cargo ship detection and directional discrimination method for remote sensing images based on a lightweight network. Specifically, we design an efficient and lightweight feature extraction network called the one-shot aggregation and depthwise separable network (OSADSNet), which is inspired by one-shot feature aggregation modules and depthwise separable convolutions. Additionally, we combine the RPN with the K-Mean++ algorithm to obtain the K-RPN, which can produce a more suitable region proposal for cargo ship detection. Furthermore, without introducing extra parameters, the directional discrimination of the cargo ship is transformed into a classification task, and the directional discrimination is completed when the detection task is completed. Experiments on a self-built remote sensing image cargo ship dataset indicate that our model can provide relatively accurate and fast detection for cargo ships (mAP of 91.96% and prediction time of 46 ms per image) and discriminate the directions (north, east, south, and west) of cargo ships, with fewer parameters (model size of 110 MB), which is more suitable for autonomous operation on spaceborne platforms. Therefore, the proposed method can meet the needs of cargo ship detection and directional discrimination in remote sensing images on spaceborne platforms.

The uncertainty of COVID-19 and the spatial inequality of anti-pandemic materials have made international aid an important means for many countries to cope with this global public health crisis. It is of far-reaching significance to analyze the spatiotemporal characteristics and influencing factors of international aid events for the global joint fight against COVID-19 and the sustainability of global public health business. The data on aid events from 23 January 2020 to 31 October 2021, were from the GDELT database. China, the United States, the United Kingdom, and Canada were selected as the study objects because they provided more aid. Their spatiotemporal characteristics of main aid flows, the response characteristics of the aid requests, and the characteristics of verbal aid to cash in were studied using spatial statistical analysis methods. The influencing factors of aid allocation also were studied by regression analysis. The results found that: the international aid flow of each country was consistent in spatial distribution, mainly to countries with severe pandemics and neighboring countries. However, there were differences in the recipients. China mainly aided developing countries, while the United States, the United Kingdom, and Canada mainly aided developed countries. Relatively speaking, China was more responsive to aid requests and more aggressive in cashing in on verbal aid. The countries considered the impact of their economic interests when they planned to aid. At the same time, there were obvious “bandwagon effect” and “small country tendency” on the aid events.

Foreign trade is an important part of the national economy. Promoting the development of foreign trade can regulate the optimal allocation of resources, raise the level of domestic productivity, and accelerate economic development. As a traditional inland agricultural province, Henan Province has inherent disadvantages in developing foreign trade due to its geographical location. However, it has characteristic advantages in terms of population and transportation, so it is necessary to study the pattern of foreign trade and the factors affecting it in this region. In this research study, statistical data were assessed with methods such as the foreign trade dependence, geographical detector, and gravity models to analyze the trade scale, pattern, spatio-temporal variation characteristics, and foreign trade mechanisms in Henan Province. The results show that the trade pattern of Henan Province from 2002 to 2021 has evident spatial and temporal heterogeneity, with a relatively homogeneous overall commodity structure, weak competitive advantages, and a high degree of dependence on US trade. Innovation and transportation are essential internal factors, while the external factors are positively affected by the GDP of both Henan Province and the trading countries, FTAs, trade openness, and the population in the long run and are negatively impacted by distance. This study provides suggestions and decision support for formulating foreign trade policies for Henan Province. It also provides a research basis for related corresponding studies of other regions with similar characteristics.

To overcome the limitations of long-term prediction of PM[sub.2.5] concentration, a multi-factor information flow causality analysis method is used to screen suitable meteorological and air pollutant-related factors and concatenate them with a PM[sub.2.5] sequence as the dataset. A modal decomposition algorithm is used as a module to be integrated into the autoformer (transformer improved with autocorrelation mechanism) model to improve it, and the modal autoformer (empirical modal decomposition combined with autoformer) is proposed. The constructed model decomposes the sequence into several components by using the modal decomposition module and uses the self-correlation mechanism and decomposition structure to decompose and extract features of different components at the time-feature level. Based on the matching method, the model is adjusted for different component features to improve the long-term prediction effect. The model is applied to three cities in Henan Province, Zhengzhou, Luoyang, and Zhumadian, as examples for experiments, and gated neural unit (GRU), informer, autoformer, and modal GRU (empirical modal decomposition combined with GRU model) are constructed for comparative verification. The results show that the modal autoformer can better cope with the complex characteristics of long-term prediction of the PM[sub.2.5] time series, has strong spatial adaptability and that its various indicators are optimal for the three cities, with R[sup.2] values being all above 0.96, where the highest is 0.987 in Zhengzhou; MAPE (Mean absolute percentage error) values all being less than 10, where the best is 7.602 in Zhumadian; and MAE (Mean absolute error) values all being less than 4. The prediction effect is stable enough, showing its feasibility and adaptability in long-term prediction.