Search Results (80)

To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple categories of information are embedded. This research used large language models (LLMs) and traditional transformer-based models, such as BERT, to classify news and social media events using the example of the Sudan Conflict. A systematic evaluation framework was introduced to test GPT models using Zero-Shot prompting, Retrieval-Augmented Generation (RAG), and RAG with In-Context Learning (ICL) against standard and hyperparameter-tuned bert-based and bert-large models. BERT outperformed GPT in F1-score and accuracy for multi-label classification (MLC) while GPT outperformed BERT in accuracy for Single-Label classification from Multi-Label Ground Truth (SL-MLG). The results illustrate that a larger model size improves classification accuracy for both BERT and GPT, while BERT benefits from hyperparameter tuning and GPT benefits from its enhanced contextual comprehension capabilities. By addressing challenges such as overlapping semantic categories, task-specific adaptation, and a limited dataset, this study provides a deeper understanding of LLMs’ applicability in constrained, real-world scenarios, particularly in highlighting the potential for integrating NLP with other applications such as GIS in future conflict analyses. Full article

Cognitive impairment is common but often overlooked in patients with inflammatory demyelinating diseases such as multiple sclerosis and neuromyelitis optica spectrum disorder. The conventional assessments may fail to detect subtle deficits and require substantial time and expertise. We collected neuropsychological scores and real-time handwriting data across nine drawing tasks and tasks from the Symbol Digit Modalities Test in 93 patients. Temporal, pressure, and kinematic features were extracted, and machine learning classifiers were trained using five-fold cross-validation with bootstrap confidence intervals. The response timing and pen pressure metrics correlated significantly with global cognitive scores (|r| = 0.30–0.37, p < 0.01). A support vector machine using eight selected features achieved an area under the receiver-operating characteristic curve (AUC) of 0.910, and a streamlined five-feature variant maintained an equivalent performance (AUC = 0.921) while reducing the assessment time by 35%. These results indicate that digital handwriting metrics can complement the standard screening by capturing fine motor and temporal characteristics overlooked in conventional testing. Validation in larger, disease-balanced, and longitudinal cohorts is needed to confirm their clinical utility. Full article

The airflow within industrial buildings under natural ventilation is influenced by both internal conditions and external environments. Multi-layer vertically connected plants include a vertically connected space and multiple heat sources distributed on different floors. Due to its complex internal conditions, airflow patterns under natural ventilation in this type of plant are not clear. In this work, we numerically investigate the influence of outdoor wind and thermal pressure on the airflow patterns within this type of plant. The findings indicate that with no outdoor thermal and wind pressure, the airflow crosses the layers from the bottom to the top, while intermediate layers tend to present independent airflows. As the ratio of the Grashof numbers of outdoor thermal pressure and indoor heat source (Gr_i−o/Gr_s) increases from 0 to 0.2, the airflow in the plant changes pattern from a middle layer alone type to the pattern of each layer mixed. Furthermore, when the ratio of the natural ventilation Reynolds number to the indoor heat source Grashof number (Re_o/Gr_s) rises from 0 to 9.7 × 10⁻⁸, the airflow pattern in the plant radically changes from a middle layer alone type to straight through flow. This study provides an important reference for optimizing the natural ventilation environment in such plants. Full article

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R²). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. However, LSTM models may be too slow (1.5 h) or computation-intensive for applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m³) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m³) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. These findings suggest that AI/ML models, particularly LSTM models, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is among the most comprehensive studies on AI/ML for air pollutant calibration. We also discussed limitations, applicability to other sensors, and the explanations for good model performances. This research can be adapted to enhance air quality monitoring for public health risk assessments, support broader environmental health initiatives, and inform policy decisions. Full article

Breathing in fine particulate matter of diameter less than 2.5 µm (PM_2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM_2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m³ and 35.5 µg/m³) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m³ threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R² of 0.914, particularly excelling in forecasting high PM_2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM_2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins. Full article

Data dashboards provide a means for sharing multiple data products at a glance and were ubiquitous during the COVID-19 pandemic. Data dashboards tracked global and country-specific statistics and provided cartographic visualizations of cases, deaths, vaccination rates and other metrics. We examined the role of geospatial data on COVID-19 dashboards in the form of maps, charts, and graphs. We organize our review of 193 COVID-19 dashboards by region and compare the accessibility and operationality of dashboards over time and the use of web maps and geospatial visualizations. We found that of the dashboards reviewed, only 17% included geospatial visualizations. We observe that many of the COVID-19 dashboards from our analysis are no longer accessible (66%) and consider the ephemeral nature of data and dashboards. We conclude that coordinated efforts and a call to action to ensure the standardization, storage, and maintenance of geospatial data for use on data dashboards and web maps are needed for long-term use, analyses, and monitoring to address current and future public health and other challenging issues. Full article

The flexible operation of carbon capture units is crucial for the economic performance of coal-fired power plants equipped with CO₂ capture systems. This paper aims to investigate the impact of electricity, CO₂, and fuel prices on the economic operation of such plants. A novel economic optimization model is proposed, integrating a static model of the carbon capture system with a particle swarm optimization algorithm. A new concept, the CO₂ boundary price, is introduced as a key metric for determining the operating conditions of CO₂ capture units. The CO₂ boundary price rises when the power load decreases due to the decline in power generation efficiency, and it also increases with rising fuel prices, as the cost of steam for CO₂ capture increases. Additionally, when the objective is to meet power load demand, CO₂ prices have a great influence on the operation of CO₂ capture units, assuming fixed coal and electricity prices. However, when the primary goal is to maximize plant profitability, the system’s operational conditions are strongly influenced by the relative prices of electricity and CO₂. The proposed optimization model and the uncovered price-effect mechanisms provide valuable insights into the economic operation of carbon capture power plants. Full article

Banana crowns’ intricate vascular systems facilitate nutrient transport for fruit growth and provide mechanical support. Analyzing vascular bundle morphology facilitates understanding of its influence on the banana de-handing process. In this study, we employed X-ray Computed Tomography (CT) scanning and microscopic observation of paraffin sections to characterize the morphological traits of the banana crown’s vascular tissue system and reconstructed its 3D vascular tissue system throughout the banana bunch. Based on the internal tissue characteristics and external morphology, the banana crown is categorized into three regions: the central stalk–crown transition region (CSCTR), the crown expansion region (CER), and the crown–finger transition region (CFTR). Cutting experiments indicated that variations in the cutting strength and specific cutting energy across positions within the banana bunch were insignificant but significantly distinct among the three regions. Specifically, the CER showed a 19.7% reduction in cutting strength and a 15.5% decrease in energy consumption compared to the other regions. This was due to the unique cross-distribution of fibers within the CER, which were primarily parallel to the cutting blade, significantly reducing cutting forces and energy consumption, making the CER the optimal region for cutting. The orientation of vascular bundles relative to the blade is key to optimizing plant cutting mechanics. Full article

The improper treatment of crop straw not only leads to resource wastage but also adversely impacts the ecological environment. However, the application of microorganisms can accelerate the decomposition of crop straw and improve its utilization. In this study, cellulose-degrading microbial strains were isolated from naturally decayed corn straw and screened using Congo red staining, along with assessing variations in carboxymethyl cellulase (CMCase) activity, filter paper enzyme (FPase) activity and β-glucosidase (β-Gase) activity, as well as the degradation rate. The eight strains, namely Neurospora intermedia isolate 29 (A1), Streptomyces isolate FFJC33 (A2), Gibberella moniliformis isolate FKCB-009 (A3), Fusarium fujikuroi isolate EFS3(2) (A4), Fusarium Fujikuroi isolate FZ04 (A5), Lysine bacillus macroides strain LNHL43 (B1), Bacillus subtilis strain MPF30 (B2) and Paenibacilli lautus strain ALEB-P1 (C), were identified and selected for microbial strain consortium design based on their high activities of CMCase, FPase and β-Gase. The fungi, bacteria and actinomycete strains were combined without antagonistic effects on corn straw decomposition. The results showed the A2B2 combination had a significantly higher FPase at 55.44 U/mL and β-Gase at 25.73 U/mL than the other two strain combinations (p < 0.05). Additionally, the degradation rate of this combination was 40.33%, which was considerably higher than that of the other strains/consortia. The strain combination A4B2C also had superior enzyme activity, including CMCase with a value of 35.03 U/mL, FPase with a value of 63.59 U/mL and β-Gase with a value of 26.15 U/mL, which were significantly different to those of the other three strain combinations (p < 0.05). Furthermore, seven single microbial strains with high cellulase activities were selected to construct various microbial consortiums for in situ composting in order to evaluate their potential. Taken as a whole, the results of composting, including temperature, moisture content, pH, E4/E6 value and seed germination index, indicated that the microbial strain consortium consisting of Neurospora intermediate isolate 29, Fusarium fujikuroi isolate EFS3(2), Fusarium fujikuroi isolate FZ04, Lysinibacillus macrolides, Lysinibacillus sphaericus, Bacillus subtilis and Paenibacillus lautus was advantageous for corn straw decomposition and yielded high-quality compost. The screened flora was able to effectively degrade corn straw. This study provides a novel solution for the construction of a microbial consortium for the composting of corn straw. Full article

Precise prediction of wireless communication network traffic is indispensable in the operational deployment of base station resources and improvement of the user experience. Cellular wireless network traffic has both spatial and temporal characteristics. The existing modeling algorithms have achieved good results in extracting the spatial features, but there are still deficiencies in the extraction models for the time dependencies. To resolve these problems, this paper proposes a novel hybrid neural network prediction model, called WVETT-Net. Firstly, variational mode decomposition (VMD) is used to preprocess network traffic, and the whale optimization algorithm (WOA) is used to select the optimal parameters for VMD. Secondly, the local and global features are extracted from each subsequence by a temporal convolutional network (TCN) and an improved Transformer network with a multi-head ProbSparse self-attention mechanism (Pe-Transformer), respectively. Finally, the extracted feature representation is enhanced by using an efficient channel attention (ECA) mechanism to achieve accurate wireless network traffic predictions. Experimental results on two wireless network traffic datasets show that the proposed model (WVETT-Net) outperforms the traditional single or combined models in wireless network traffic prediction. Full article

Due to the widespread use of intensive cropping patterns, the problem of continuous cropping obstacle, which is dominated by autotoxicity, has been becoming more and more prominent. Although many methods have been proposed to overcome the continuous cropping obstacle of cucumber, no study has reported the screening and evaluation of cucumber germplasm resistant to autotoxicity. In this study, 28 physiological indices related to the cucumber bud stage under cinnamic acid (CA) treatment were determined. In total, 45 cucumber cultivars were classified into three groups using principal component analysis and cluster analysis, and a model for evaluating cucumber resistance to autotoxicity was developed. The evaluation model was validated using autotoxicity-tolerant and non-autotoxicity-tolerant cultivars. The results showed that the growth of non-autotoxicity-tolerant cultivars was significantly inhibited compared to autotoxicity-tolerant cultivars. This indicated that the evaluation model of cucumber autotoxicity tolerance is reliable. The results of this study provide a valuable reference for the application of cucumber autotoxicity-tolerant germplasm resources and the development of autotoxicity-tolerant genes. Full article

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm. Full article

With recent advancements, large language models (LLMs) such as ChatGPT and Bard have shown the potential to disrupt many industries, from customer service to healthcare. Traditionally, humans interact with geospatial data through software (e.g., ArcGIS 10.3) and programming languages (e.g., Python). As a pioneer study, we explore the possibility of using an LLM as an interface to interact with geospatial datasets through natural language. To achieve this, we also propose a framework to (1) train an LLM to understand the datasets, (2) generate geospatial SQL queries based on a natural language question, (3) send the SQL query to the backend database, (4) parse the database response back to human language. As a proof of concept, a case study was conducted on real-world data to evaluate its performance on various queries. The results show that LLMs can be accurate in generating SQL code for most cases, including spatial joins, although there is still room for improvement. As all geospatial data can be stored in a spatial database, we hope that this framework can serve as a proxy to improve the efficiency of spatial data analyses and unlock the possibility of automated geospatial analytics. Full article

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation. Full article

Quantitative trait locus (QTL) mapping based on a genetic map is a very effective method of marker-assisted selection in breeding, and whole-genome resequencing is one of the useful methods to obtain high-density genetic maps. In this study, the hybrid assembly of Illumina, PacBio, and chromatin interaction mapping data was used to construct high-quality chromosomal genome sequences of Paulownia fortunei, with a size of 476.82 Mb, a heterozygosity of 0.52%, and a contig and scaffold N50s of 7.81 Mb and 21.81 Mb, respectively. Twenty scaffolds with a total length of 437.72 Mb were assembled into 20 pseudochromosomes. Repeat sequences with a total length of 243.96 Mb accounted for 51.16% of the entire genome. In all, 26,903 protein-coding gene loci were identified, and 26,008 (96.67%) genes had conserved functional motifs. Further comparative genomics analysis preliminarily showed that the split of P. fortunei with Tectona grandis likely occurred 38.8 (33.3–45.1) million years ago. Whole-genome resequencing was used to construct a merged genetic map of 20 linkage groups, with 2993 bin markers (3,312,780 SNPs), a total length of 1675.14 cm, and an average marker interval of 0.56 cm. In total, 73 QTLs for important phenotypic traits were identified (19 major QTLs with phenotypic variation explained ≥ 10%), including 10 for the diameter at breast height, 7 for the main trunk height, and 56 for branch-related traits. These results not only enrich P. fortunei genomic data but also form a solid foundation for fine QTL mapping and key marker/gene mining of Paulownia, which is of great significance for the directed genetic improvement of these species. Full article