Search Results (580)

The Internet’s development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public’s demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian–Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network—Bidirectional Long Short-Term Memory (CNN-BiLSTM) model’s hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters. Full article

Freshwater, vital for life and ecosystems, accounts for only 2.5% of Earth’s water, and is primarily located in polar caps, underground reservoirs, and surface water. Its quality varies due to environmental interactions, especially in groundwater. Tenerife, located in the Canary Islands, Spain, relies mainly on underground aquifers and tunnels capturing 51.6 cubic hectometers annually. Ensuring safe drinking water is a global challenge due to health risks from poor water quality, including diseases and cancer. Fluoride, sodium, and potassium are essential for health, and are mainly derived from groundwater as fluoride ions (F⁻) and sodium and potassium cations (Na⁺, K⁺). However, excessive F⁻, Na⁺, and K⁺ in drinking water is harmful. The World Health Organization limits F⁻ to 1.5 mg/L, Na⁺ to 8.70 meq/L, and K⁺ to 0.31 meq/L. Geological, climatic, and human factors control the presence and transport of F⁻, Na⁺, and K⁺ in groundwater. Seismic events can impact water quality, with long-term effects linked to aquifer structure and transient effects from gas and fluid expansion during earthquakes. This study was motivated by a 3.8 mbLg earthquake in Tenerife in 2012, which allowed its impact on groundwater quality, specifically F⁻, Na⁺, and K⁻ concentrations, to be examined. Post-earthquake, F⁻ levels alarmingly increased to 8.367 meq/L, while Na⁺ and K⁺ showed no significant changes. This research quantifies the influence of earthquakes on increasing F⁻ levels and evaluates F⁻ reduction during low seismic activity, emphasizing the importance of water management on volcanic islands. Full article

This study is an additional investigation of stratosphere–troposphere coupling based on the recent stratospheric winter descriptions in five distinct modes: January, February, Double, Dynamical, and Radiative. These modes, established in a previous study, categorize the main stratospheric winter typologies modulated by the timing of important sudden stratospheric warmings (SSWs) and final stratospheric warmings (FSWs). The novelty of this research is to investigate the Northern Annular Mode, mean sea level pressure (MSLP) anomalies in the Ural and Aleutian regions, and the decomposition of Eliassen–Palm flux into wavenumbers 1 and 2 within each mode. The results show that the January and Double modes exhibit similar pre-warming surface signals, characterized by Ural blocking and Aleutian trough events preceding weak polar vortex events. The January mode displays a positive MSLP anomaly of +395 hPa (−191 hPa) in the Ural (Aleutian) region in December, while the Double mode shows +311 hPa (−89 hPa) in November. These modes are primarily wave-1 driven, generating tropospheric responses via negative Arctic Oscillation patterns. Conversely, the February and Dynamical modes show opposite signals, with Aleutian blocking and Ural trough events preceding strong polar vortex events. In December, the February mode exhibits MSLP anomalies of +119 hPa (Aleutian) and −180 hPa (Ural), while the Dynamical mode shows +77 hPa and −184 hPa, respectively. These modes, along with important SSWs in February and dynamical FSWs, are driven by both wave-1 and wave-2 and do not significantly impact the troposphere. The Radiative mode’s occurrence is strongly related to the Aleutian blocking presence. These findings confirm that SSW timing is influenced by specific dynamical forcing related to surface precursors and underscore its importance in subsequent tropospheric responses. This study establishes a connection between early winter tropospheric conditions and upcoming stratospheric states, potentially improving seasonal forecasts in the northern hemisphere. Full article

The inefficiency of grain refinement processes has traditionally been attributed to the limited utilization of heterogeneous nucleation particles within master alloy systems, resulting in the formation of abundant inactive particles. This study aims to investigate the alternative influences of particles by incorporating external micrometer-sized TiB₂ particles into the grain refinement process. Through a series of experiments, the refinement efficiency, grain refinement mechanism, and resultant microstructure of TiB₂ particle-induced grain refinement specimens are comprehensively examined using various microscopy and analytical techniques, including polarization microscopy (OM), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and transmission electron microscopy (TEM). Our findings demonstrate a direct correlation between increased levels of TiB₂ particles and enhanced grain refinement efficiency. Moreover, the microstructure analysis reveals the distribution of TiB₂ particles along grain boundaries, forming a coating due to self-assembly phenomena, while regions with a lower particle content may exhibit irregular grain structures. DSC analysis further confirms reduced undercooling, indicating the occurrence of heterogeneous nucleation events. However, TEM observations suggest that heterogeneous nucleation is not significantly influenced by the growth restriction factor attributed to TiAl₃ 2DC compounds. The grain refinement mechanism involving TiB₂ particles is elucidated to entail both heterogeneous nucleation and physical growth restriction effects. Specifically, a reduction in average grain size is attributed not only to heterogeneous nucleation but also to the physical growth restriction effect facilitated by the TiB₂ particle coating. This study offers insights into leveraging particles that do not participate in heterogeneous nucleation within master alloy-based grain refinement systems. Full article

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δp-Gf method based on the particle depolarization ratio at 355 nm (δp355) and the fluorescence capacity (Gf), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10⁻⁴ to 0.63 × 10⁻⁴. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10⁻⁴ to 1.10 × 10⁻⁴ and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10⁻⁴ to 1.23 × 10⁻⁴, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δp355-Gf method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δp355-Gf classification method and the δp355-Gf distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing. Full article

As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March and May, 2024. More precisely, on 24 March 2024, a G4 (according to the NOAA Space Weather Scale for Geomagnetic Storms) geomagnetic storm was registered, with the corresponding geomagnetic indices Kp and Dst equal to 8 and −130 nT, respectively. On the same day, the majority of ground-based neutron monitor stations recorded an unusual Forbush decrease. This event stands out from a typical Forbush decrease because of its high amplitude decrease phase and rapid recovery phase, i.e., 15% decrease and an extremely rapid recovery of 10% within 1.5 h, as recorded at the Oulu neutron monitor station. Furthermore, on 10–13 May 2024, an unusual G5 geomagnetic storm (geomagnetic indices Kp = 9 and Dst = −412 nT) was registered (the last G5 storm had been observed in 2003). In addition, the polar neutron monitor stations recorded a Ground Level Enhancement (GLE74) during the recovery phase of a large Forbush decrease of 15%, which started on 10 May 2024. In this study, a detailed analysis of these two severe events in regard to the accompanying solar activity, interplanetary conditions and solar energetic particle events is provided. Moreover, the results of the NKUA “GLE Alert++ system”, the NKUA/IZMIRAN “FD Precursory Signals” method and the NKUA “ap Prediction tool” concerning these events are presented. Full article

In this study, we used the Sanya Incoherent Scatter Radar (SYISR) to observe the altitude profiles of traveling ionospheric disturbances (TIDs) during a moderate magnetic storm from 13 to 15 March 2022. Three TIDs were recorded, including two large-scale TIDs (LSTIDs) and one medium-scale TID (MSTID). These LSTIDs occurred during the storm recovery phase, characterized by periods of ~110–155 min, downward phase velocities of 22–60 m/s, and a relative amplitude of 17–25%. A nearly vertical front was noted at ~350–550 km, differing from AGW theory predictions. This structure is more attributed to the combined effects of sunrise-induced electron density changes and pre-sunrise uplift. Moreover, GNSS observations linked this LSTID to high-latitude origins, indicating a connection to polar magnetic storm excitation. However, the second LSTID was observed at lower altitudes (150–360 km) with a higher elevation angle (~17°). This LSTID, observed by the SYISR, was absent in the GNSS data from mainland China and Japan, suggesting a potential local source. The MSTID exhibited a larger relative amplitude of 29–36% at lower altitudes (130–210 km) with severe upward attenuation. The MSTID may be related to atmospheric gravity waves from the lower atmosphere. AGWs are considered to be the perturbation source for this MSTID event. Full article

Previous theories have established the mental model activation of processing different types of conditionals, stating that counterfactual conditionals expressing events that contradict known facts (e.g., “If it had rained, then they would not go to the park.”) are considered to trigger two mental models: (1) a hypothetical but factually wrong model (e.g., “rain” and “did not go to the park”) and (2) a corresponding real-world model (e.g., “did not rain” and “went to the park”). This study aimed to investigate whether pragmatic factors differentially influence readers’ comprehension and distinction between counterfactual and hypothetical conditional sentences in Mandarin Chinese. Participants were required to read and judge the comprehensibility of Chinese hypothetical and counterfactual conditionals, which were different in temporal indicators (past vs. future temporal indicators) in the antecedent. Different polarities (with vs. without negators) and different moving directions (different directional verbs: lai2 [come] vs. qu4 [go]) in the consequent were also manipulated. Linear mixed-effects models (LMEM) revealed that hypothetical conditionals (with future temporal indicators) were more comprehensible than counterfactual conditionals (with past temporal indicators). The semantic similarities within the subordinate clause revealed future temporal indicators had higher lexical–semantic co-occurrence than past indicators, suggesting that temporal indicators impact comprehension partly through lexical semantics in the premise, and hypothetical conditionals are more easily processed. However, the semantic similarity analysis of the main and the subordinate clauses showed no effect of temporal indicators, suggesting that lexical–semantic co-occurrence across clauses may not substantially contribute to the distinction between hypothetical conditionals and counterfactual conditionals. In conclusion, this study offers insights into the comprehension of Chinese conditional sentences by shedding light on the pragmatic factors influencing the activation of different mental models. Full article

Solar proton events (SPEs) affect the Earth’s atmosphere, causing additional ionization in the high-latitude mesosphere and stratosphere. Ionization rates from such solar proton events maximize in the stratosphere, but the formation of ozone-depleting nitrogen and hydrogen oxides begins at mesospheric altitudes. The destruction of mesospheric ozone is associated with protons with energies of about 10 MeV and higher and will strongly depend on the intensity of the flux of these particles. Most studies investigating the impact of SPEs on the characteristics of the middle atmosphere have been based on either simulations or reanalysis datasets, and some studies have used satellite observations to validate model results. We study the impact of SPEs on cold-season ozone loss in both the northern and southern hemispheres using Aura MLS mesospheric ozone measurements over the 2004 to 2024 period. Here, we show how strongly SPEs can deplete polar mesospheric ozone in different hemispheres and attempt to evaluate this dependence on the intensity of solar proton events. We found that moderate SPEs consisting of protons with an energy of more than 10 MeV and a flux intensity of more than 100 pfu destroy mesospheric ozone in the northern hemisphere up to 47% and in the southern hemisphere up to 33%. For both hemispheres, the peak of winter ozone loss was observed at about 76 km. In the northern hemisphere, maximum winter ozone loss was observed on the second day after a solar proton event, but in the southern hemisphere, winter ozone depletion was already detected on the first day. In the southern hemisphere, mesospheric ozone concentrations return to pre-event levels on the ninth day after a solar proton event, but in the northern hemisphere, even on the tenth day after a solar proton event, the mesospheric ozone layer may not be fully recovered. The strong SPEs with a proton flux intensity of more than 1000 pfu lead to a maximum winter ozone loss of up to 85% in the northern hemisphere, and in the southern hemisphere winter, ozone loss reaches 73%. Full article

With the rapid progress in deciphering the pathogenesis of Alzheimer’s disease (AD), it has been widely accepted that the accumulation of misfolded amyloid β (Aβ) in the brain could cause the neurodegeneration in AD. Although much evidence demonstrates the neurotoxicity of Aβ, the role of Aβ in the nervous system are complex. However, more comprehensive studies are needed to understand the physiological effect of Aβ₄₀ monomers in depth. To explore the physiological mechanism of Aβ, we employed mass spectrometry to investigate the altered proteomic events induced by a lower submicromolar concentration of Aβ. Human neuroblastoma SH-SY5Y cells were exposed to five different concentrations of Aβ_1-40 monomers and collected at four time points. The proteomic analysis revealed the time–course behavior of proteins involved in biological processes, such as RNA splicing, nuclear transport and protein localization. Further biological studies indicated that Aβ₄₀ monomers may activate PI3K/AKT signaling to regulate p-Tau, Ezrin and MAP2. These three proteins are associated with dendritic morphogenesis, neuronal polarity, synaptogenesis, axon establishment and axon elongation. Moreover, Aβ₄₀ monomers may regulate their physiological forms by inhibiting the expression of BACE1 and APP via activation of the ERK1/2 pathway. A comprehensive exploration of pathological and physiological mechanisms of Aβ is beneficial for exploring novel treatment. Full article

Soil freeze-thaw (FT) cycles over agricultural lands are of great importance due to their vital role in controlling soil moisture distribution, nutrient availability, health of microbial communities, and water partitioning during flood events. Active microwave sensors such as C-band Sentinel-1 synthetic aperture radar (SAR) can serve as powerful tools to detect field-scale soil FT state. Using Sentinel-1 SAR observations, this study compares the performance of two FT detection approaches, a commonly used seasonal threshold approach (STA) and a computationally inexpensive general threshold approach (GTA) at an agricultural field in New Hampshire, US. It also explores the applicability of an interferometric coherence approach (ICA) for FT detection. STA and GTA achieved 85% and 78% accuracy, respectively, using VH polarization. We find a marginal degradation in the performance of STA (82%) and GTA (76%) when employing VV-polarized data. While there was approximately a 6 percentage point difference between STA’s and GTA‘s overall accuracy, we recommend GTA for FT detection using SAR images at sub-field-scale over extended regions because of its higher computational efficiency. Our analysis shows that interferometric coherence is not suitable for detecting FT transitions under mild and highly dynamic winter conditions. We hypothesize that the relatively mild winter conditions and therefore the subtle FT transitions are not able to significantly reduce the correlation between the phase values. Also, the ephemeral nature of snowpack in our study area, further compounded by frequent rainfall, could cause decorrelation of SAR images even in the absence of a FT transition. We conclude that despite Sentinel-1’s ~80% mapping accuracy at a mid-latitude site, understanding the cause of misclassification remains challenging, even when detailed ground data are readily available and employed in error attribution efforts. Full article

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the “regression to average” issue of autoregressive model leads to the “blurring” phenomenon. The evolution method’s generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method’s generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the “regression to average” issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1. Full article

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km⁻¹sr⁻¹, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km⁻¹sr⁻¹. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. Full article

Contrasting the X-band phased array radar (XPAR) with the conventional S-Band dual-polarization mechanical scanning radar (SMSR), the XPAR offers superior temporal and spatial resolution, enabling a more refined depiction of the internal dynamics within convective systems. While both SMSR and XPAR data are extensively used in monitoring and alerting for severe convective weather, their comparative application in numerical weather prediction through data assimilation remains a relatively unexplored area. This study harnesses the Weather Research and Forecasting Model (WRF) and its data assimilation system (WRFDA) to integrate radial velocity and reflectivity from the Guangzhou SMSR and nine XPARs across Guangdong Province. Utilizing a three-dimensional variational approach at a 1 km convective-scale grid, the assimilated data are applied to forecast a rainstorm event in the Pearl River Delta (PRD) on 6 June 2022. Through a comparative analysis of the results from assimilating SMSR and XPAR data, it was observed that the assimilation of SMSR data led to more extensive adjustments in the lower- and middle-level wind fields compared to XPAR data assimilation. This resulted in an enlarged convergence area at lower levels, prompting an overdevelopment of convective systems and an excessive concentration of internal hydrometeor particles, which in turn led to spurious precipitation forecasts. However, the sequential assimilation of both SMSR and XPAR data effectively reduced the excessive adjustments in the wind fields that were evident when only SMSR data were used. This approach diminished the generation of false echoes and enhanced the precision of quantitative precipitation forecasts. Additionally, the lower spectral width of XPAR data indicates its superior detection accuracy. Assimilating XPAR data alone yields more reasonable adjustments to the low- to middle-level wind fields, leading to the formation of small-to-medium-scale horizontal convergence lines in the lower levels of the analysis field. This enhancement significantly improves the model’s forecasts of composite reflectivity and radar echoes, aligning them more closely with actual observations. Consequently, the Threat Score (TS) and Equitable Threat Score (ETS) for heavy-rain forecasts (>10 mm/h) over the next 5 h are markedly enhanced. This study underscores the necessity of incorporating XPAR data assimilation in numerical weather prediction practices and lays the groundwork for the future joint assimilation of SMSR and XPAR data. Full article

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M = 7.8 and M = 7.5 Kahramanmaras earthquake sequence on 6 February 2023, a closed strong magnetic storm occurred on 7 November 2022, SYM/H = −117 nT. The storm started at 08:04 UT. At this time, the high-latitudinal part of Turkey’s longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth’s environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from days to months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable. Full article