Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-sever...
Solar flare prediction models typically use classification, predicting only the probability of categorized events within a time window. This misses critical information, such as how many flares occur, their precise timings, and their intensities. To...
Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea...
A small number of robust studies have explored the association between cognitive tests and marital status levels of mild cognitive impairment (MCI) patients using the TADPOLE dataset. Rey Auditory Verbal Learning Test (RAVLT) cognition performance co...
Surface heat source (SHS) is a crucial factor affecting local weather systems. Particularly SHS on the Tibetan Plateau (TP) significantly influences East Asian atmospheric circulation and global climate. Accurate prediction of summer SHS on the TP is...
Background/Objective: Accurately identifying women at high risk for preterm birth among those with threatened preterm labor (PTL) is crucial for effective interventions or tocolytic management to reduce preterm birth and its complications. This study...
Flash drought is a type of drought that develops quickly (usually within 2–4 weeks) in contrast to conventional, slowly evolving drought. Due to its sudden onset, flash drought is more difficult to predict and can cause major agricultural losse...
In urban areas where populations commonly reside, particle mass concentrations in street canyons can pose significant risks to human health. This study aimed to investigate the diffusion mechanism of particle mass concentrations in urban street canyo...
Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate...
Predicting mineral deposits accurately requires capturing the complex interactions among geological structures, geochemical anomalies, and alteration patterns. To address this challenge, this study develops a Knowledge–Data Collaboration Graph...
Accurate forecasting of typhoon evolution from satellite cloud imagery is critical for disaster preparedness and mitigation, yet remains challenging due to the complex spatiotemporal dynamics of typhoon systems. While deep learning models have shown...
Using multi-source monitoring data to model and predict the displacement behavior of landslides is of great significance for the judgment and decision-making of future landslide risks. This research proposes a landslide displacement prediction model...
The application of sub-seasonal prediction error analysis and interpretation remains a key research frontier of climate prediction. Related studies are essential for deepening the understanding of model performance, investigating error sources, and i...
Short-term (with a lead time of about one week) earthquake (EQ) prediction is one of the most challenging subjects in geoscience and applied science; however, it is highly required by society because it is of essential importance in mitigating the hu...
The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we pro...
Based on the operational regional ensemble prediction system (REPS) in China Meteorological Administration (CMA), this paper carried out comparison of two initial condition perturbation methods: an ensemble transform Kalman filter (ETKF) and a dynam...
Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9...
A time–space shift method was recently developed for relocating the best ensemble member predicted tornado vortex to the radar-observed location, aiming to improve the model’s initial condition and subsequent prediction of tornadoes. To f...
Accurate prediction of wind speed is critical for wind power generation and bias correction serves as an effective tool to enhance the precision of climate model forecasts. This study evaluates the effectiveness of three bias correction methods&mdash...
Typhoon wind prediction is important for disaster prevention. ERA5 reanalysis provides spatially continuous wind fields but may smooth local structures, whereas FY-3E WindRAD, a dual-frequency scatterometer, observes ocean surface winds through C-ban...
Fine spectral detection can basically solve the problem of low vertical resolution at the 183 GHz water-vapor absorption line, and it is expected to become one of the main methods for next-generation geostationary and polar-orbiting satellites. Here,...
Background: Luminal gastrointestinal (GI) tract cancers, including esophageal, gastric, small bowel, colorectal, and anal cancers, are often diagnosed at late stages. These tumors can cause gradual GI bleeding, which may be unrecognized but detectabl...
The uncertainty of two recently proposed methods, “new fluid factor” and “delta K”, is analyzed under different water saturation and noise conditions through Monte Carlo modelling. The new fluid factor performs reliably (all m...
Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for manage...
The empirical or semi-empirical model is widely used for vehicle simulation because of its high accuracy but relies on massive experimental data of tire force and moment. Therefore, tire mechanical prediction is of great significance to improve tire...
As mineral resources become increasingly scarce, the search for potential ore deposits presents a significant challenge in geological exploration. Subjective factors often constrain traditional knowledge-driven approaches, while purely data-driven me...
Earthquakes threaten people, homes, and infrastructure. Early warning systems provide prior warning of oncoming significant shaking to decrease seismic risk by providing location, magnitude, and depth information of the event. Their usefulness depend...
Precise prediction of a cyclone track with wind speed, pressure, landfall point, and the time of crossing the land are essential for disaster management and mitigation, including evacuation processes. In this paper, we use an artificial neural networ...
Quantitative 3D spatial association of geological factors and geophysical fields with orebodies is critical for ore prediction. The Anqing orefield, a principal Cu–Fe orefield in China, is closely associated with the Yueshan intrusion. By compi...
As an indispensable part of the power system, transformers need to be continuously monitored to detect anomalies or faults in a timely manner to avoid serious damage to the power grid and society. This article proposes a combined model for transforme...
Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observa...
This paper proposes an Informer-based temperature prediction model to leverage data from an automatic weather station (AWS) and a local data assimilation and prediction system (LDAPS), where the Informer as a variant of a Transformer was developed to...
Roping is a hydrocyclone failure mode that reduces separation efficiency, negatively impacting both the comminution circuit and downstream flotation processes. Therefore, detection of roping as early as possible is crucial in maintaining the normal p...
In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model...
The adequate representation of interactions between the land surface and the atmosphere is of crucial importance in modern numerical weather prediction (NWP) systems. In this context, this study examines how errors in the planetary boundary layer (PB...
Since the implementation of the sustainable development strategy, China has made great efforts to save water resources. Therefore, effective prediction and analysis of regional water consumption are very important for the regional economy. In order t...
Accurate prediction of future air traffic situations is an essential task in many applications in air traffic management. This paper presents a new framework for predicting air traffic situations as a sequence of images from a deep learning perspecti...
Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model...
Accurately and rapidly predicting marine drifter trajectories under conditions of information scarcity is critical for addressing maritime emergencies and conducting marine surveys with resource-limited unmanned vessels. Machine learning-based tracki...
A forecast from a numerical weather prediction (NWP) model can be decomposed into model climate and anomaly. Each part contributes to forecast error. To avoid errors from model climate, an anomaly, rather than a full field, should be used in a model....
Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesti...
This study aims to develop a theoretical formula to help bus operators easily predict electricity consumption while introducing a certain type of electric bus on a predetermined route. The formula requires vehicle-side information (such as air resist...
Typhoon is one of the most destructive natural disasters, and it affects human society significantly. To reduce the negative impacts, many deep learning models for predicting future typhoon tracks have appeared. However, most of these models use all...
The new generation video coding standard Versatile Video Coding (VVC) has adopted many novel technologies to improve compression performance, and consequently, remarkable results have been achieved. In practical applications, less data, in terms of b...
This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management cap...
Based on 35-year (1981–2015) ensemble (24 members) hindcasts of the IAP/LASG global seasonal prediction system named FGOALS-f2 V1.0 (FGOALS-f2), the tropical cyclone (TC) seasonal prediction skills over the South China Sea (SCS) during the TC p...
Emergency department (ED) information accumulates asynchronously, making prediction landmarks, population eligibility, and input availability central to admission-prediction evaluation. In a retrospective, single-center internal evaluation of previou...
Ships carrying large amounts of cargo and passengers are larger and slower than other modes of transportation. They are mostly foreign flagged and operate at sea far from coasts for 20 years or more, incurring more operating costs than construction c...
Rising incidences of cutaneous melanoma have fueled the development of statistical models that predict individual melanoma risk. Our aim was to assess the validity of published prediction models for incident cutaneous melanoma using a standardized pr...
Machine learning and artificial intelligence models have become popular for climate change prediction. Forested regions in California and Western Australia are increasingly facing intense wildfires, while other parts of the world face various climate...
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