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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 better deal with time series data. Recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (CNN)-based models, bi-directional long short-term memory (BLSTM)-based models, and a combination of both neural networks, CNN–BLSTM. However, these models have encountered issues due to the lack of time data integration during the training phase, which also lead to the persistence of a long-term dependency problem in the LSTM models. These limitations have culminated in a performance deterioration when the prediction time length was extended. To overcome these issues, the proposed model first incorporates time-periodic information into the learning process by generating time-periodic information and inputting it into the model. Second, the proposed model replaces the LSTM with an Informer as an alternative to mitigating the long-term dependency problem. Third, a series of fusion operations between AWS and LDAPS data are executed to examine the effect of each dataset on the temperature prediction performance. The performance of the proposed temperature prediction model is evaluated via objective measures, including the root-mean-square error (RMSE) and mean absolute error (MAE) over different timeframes, ranging from 6 to 336 h. The experiments showed that the proposed model relatively reduced the average RMSE and MAE by 0.25 °C and 0.203 °C, respectively, compared with the results of the CNN–BLSTM-based model.

Lipid droplet-associated proteins (LDAPs) play essential roles in tissue growth and development and in drought stress responses in plants. Cotton is an important fiber and cash crop; however, the LDAP family has not been characterized in cotton. In this study, a total of 14, six, seven, and seven genes were confirmed as LDAP family members in Gossypium hirsutum, Gossypium raimondii, Gossypium arboreum, and Gossypium stocksii, respectively. Additionally, expansion in the LDAP family occurred with the formation of Gossypium, which is mirrored in the number of LDAPs found in five Malvaceae species (Gossypioides kirkii, Bombax ceiba, Durio zibethinus, Theobroma cacao, and Corchorus capsularis), Arabidopsis thaliana, and Carica papaya. The phylogenetic tree showed that the LDAP genes in cotton can be divided into three groups (I, II, and III). The analysis of gene structure and conserved domains showed that LDAPs derived from group I (LDAP1/2/3) are highly conserved during evolution, while members from groups II and III had large variations in both domains and gene structures. The gene expression pattern analysis of LDAP genes showed that they are expressed not only in the reproductive organs (ovule) but also in vegetative organs (root, stem, and leaves). The expression level of two genes in group III, GhLDAP6_(A)t/Dt, were significantly higher in fiber development than in other tissues, indicating that it may be an important regulator of cotton fiber development. In group III, GhLDAP2_(A)t/Dt, especially GhLDAP2_(D)t was strongly induced by various abiotic stresses. Decreasing the expression of GhLDAP2_(D)t in cotton via virus-induced gene silencing increased the drought sensitivity, and the over-expression of GhLDAP2_(D)t led to increased tolerance to mannitol-simulated osmotic stress at the germination stage. Thus, we conclude that GhLDAP2_(D)t plays a positive role in drought tolerance.

Ocean fog, a meteorological phenomenon characterized by reduced visibility due to tiny water droplets or ice particles, poses significant safety risks for maritime activities and coastal regions. Accurate prediction of ocean fog is crucial but challenging due to its complex formation mechanisms and variability. This study proposes an advanced ocean fog prediction model for the Yellow Sea region, leveraging satellite-based detection and high-performance data-driven methods. We used Himawari-8 satellite data to obtain a lot of spatiotemporal ocean fog references and employed AutoML to integrate numerical weather prediction (NWP) outputs and sea surface temperature (SST)-related variables. The model demonstrated superior performance compared to traditional NWP-based methods, achieving high performance in both quantitative—probability of detection of 81.6%, false alarm ratio of 24.4%, f1 score of 75%, and proportion correct of 79.8%—and qualitative evaluations for 1 to 6 h lead times. Key contributing variables included relative humidity, accumulated shortwave radiation, and atmospheric pressure, indicating the importance of integrating diverse data sources. The study emphasizes the potential of using satellite-derived data to improve ocean fog prediction, while also addressing the challenges of overfitting and the need for more comprehensive reference data.

This study evaluates a time-lagged ensemble averaging strategy to improve the accuracy and robustness of short-range streamflow point forecasts when hydrological simulations are driven by deterministic numerical weather prediction (NWP) forcing. We implemented WRF-Hydro in standalone mode for the Geumho River basin, South Korea, using Local Data Assimilation and Prediction System (LDAPS) forecasts initialized every 6 h with lead times up to 48 h. Time-lagged ensembles were constructed by averaging overlapping WRF-Hydro predictions from successive LDAPS initializations. Across two contrasting flood-producing storms, ensemble-mean forecasts consistently reduced lead-time-dependent skill degradation relative to single-initialization forecasts; the event-wise median Nash–Sutcliffe efficiency at the downstream gauge improved from 0.39 to 0.81 at 48 h (Event 2020) and from 0.48 to 0.85 at 24 h (Event 2022), while RMSE decreased by up to 48%. The most effective ensemble window varied with storm evolution and forecast horizon, indicating additional gains from adaptive time-lag selection. Overall, time-lagged ensemble averaging provides a practical, low-cost post-processing approach to enhance operational short-range streamflow prediction with NWP forcings.

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day−1 in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment.

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

In this study, a radiation component calculation algorithm was developed using channel data from the Himawari-8 Advanced Himawari Imager (AHI) and meteorological data from the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS). In addition, the energy budget of the Korean Peninsula region in 2016 was calculated and its regional differences were analyzed. Radiation components derived using the algorithm were calibrated using the broadband radiation component data from the Clouds and the Earth’s Radiant Energy System (CERES) to improve their accuracy. The calculated radiation components and the CERES data showed an annual mean percent bias of less than 3.5% and a high correlation coefficient of over 0.98. The energy budget of the Korean Peninsula region was −2.4 Wm−2 at the top of the atmosphere (RT), −14.5 Wm−2 at the surface (RS), and 12.1 Wm−2 in the atmosphere (RA), with regional energy budget differences. The Seoul region had a high surface temperature (289.5 K) and a RS of −33.4 Wm−2 (surface emission), whereas the Sokcho region had a low surface temperature (284.7 K) and a RS of 5.0 Wm−2 (surface absorption), for a difference of 38.5 Wm−2. In short, regions with relatively high surface temperatures tended to show energy emission, and regions with relatively low surface temperatures tended to show energy absorption. Such regional energy imbalances can cause weather and climate changes and bring about meteorological disasters, and thus research on detecting energy budget changes must be continued.

Data mining algorithms have essential methods and rules that can contribute in detecting and preventing various types of network attacks. These methods are utilized with the intrusion detection systems that can be designed and developed preserve the information in organizations from damage. Specifically, the data mining technique allows users to effectively distinguish between normal and malicious traffic with good accuracy. In this paper, a methodology for revealing and detecting (DDOS) network attack was suggested using DM algorithms. The utilized methodology is divided especially into four parts, each part has its own rules, as the following: First one is the pre-processing which consists of three sub-steps: (i) encoding, (ii) log2, and (iii) PCA. Encoding is used by converting the original nominal packets into numeric features. Standardization of data was performed using logarithmic algorithm. Finally the PCA technique is applied eight times for several different features to reduce the dimensions of the dataset. The second stage is an anomaly detection model, (RF) algorithm is implemented for the extraction of data patterns while classification the types of the given features in training step, (NB) algorithm was also used in classifying the data to compare the results of its classification with the results of using the classifier (RF). In the third stage, the outcomes were tested by implementing the already trained datasets. In the fourth stage, the proposed system performance evaluation metrics were collected such as the rates of accuracy, false alarm, detection, precision, and F.measure. MIX dataset were utilized to train and test the proposed model which resulted from merging two datasets (PORTMAP+LDAP), which are used from the CICDDOS2019 datasets, each consisting of several types of attack packets, and benign packets. Several metrics were utilized in the evaluation of the proposed system. The best outcomes were obtained for detection by using the log2 algorithm and PCA technique in the preprocessing step and using (RF)classifier to classify the dataset. the accuracy when using MIX dataset was 99.9764%, the detection rate was 100%, false alarm rate ≍ 0, and the F.measure was 99.9% when PCA = 25.

Lipid droplet-associated proteins (LDAPs) play essential roles in tissue growth and development and in drought stress responses in plants. Cotton is an important fiber and cash crop; however, the LDAP family has not been characterized in cotton. In this study, a total of 14, six, seven, and seven genes were confirmed as LDAP family members in Gossypium hirsutum , Gossypium raimondii , Gossypium arboreum , and Gossypium stocksii , respectively. Additionally, expansion in the LDAP family occurred with the formation of Gossypium , which is mirrored in the number of LDAPs found in five Malvaceae species ( Gossypioides kirkii , Bombax ceiba , Durio zibethinus , Theobroma cacao , and Corchorus capsularis ), Arabidopsis thaliana , and Carica papaya . The phylogenetic tree showed that the LDAP genes in cotton can be divided into three groups (I, II, and III). The analysis of gene structure and conserved domains showed that LDAPs derived from group I ( LDAP1 / 2 / 3 ) are highly conserved during evolution, while members from groups II and III had large variations in both domains and gene structures. The gene expression pattern analysis of LDAP genes showed that they are expressed not only in the reproductive organs (ovule) but also in vegetative organs (root, stem, and leaves). The expression level of two genes in group III, GhLDAP6_At/Dt , were significantly higher in fiber development than in other tissues, indicating that it may be an important regulator of cotton fiber development. In group III, GhLDAP2_At / Dt , especially GhLDAP2_Dt was strongly induced by various abiotic stresses. Decreasing the expression of GhLDAP2_Dt in cotton via virus-induced gene silencing increased the drought sensitivity, and the over-expression of GhLDAP2_Dt led to increased tolerance to mannitol-simulated osmotic stress at the germination stage. Thus, we conclude that GhLDAP2_Dt plays a positive role in drought tolerance.

Alerting has been hypothesized to affect spatial orienting either by accelerating the speed of attentional shift toward the cued target location (the accelerating hypothesis) or by enhancing the orienting effect without changing its time course (the enhancing hypothesis). To investigate the neural underpinnings of the effect of phasic alerting on endogenous orienting, we recorded the electroencephalogram (EEG) in a variant of the spatial cueing task with a tone presented 100 ms before the cue as a phasic alerting signal, and calculated cue-evoked event-related lateralizations (ERLs) providing a precise assessment of preparatory visuospatial attention. Behavioral results showed that the spatial orienting effect was increased under the phasic alerting condition, as expected. The EEG results showed that an orienting-related ERL component called a late directing attention positivity (LDAP) had shorter onset latency and larger amplitude in the alerting condition than in the no-alerting (no-tone) condition. In conclusion, phasic alerting seems to both accelerate and enhance orienting-related preparatory modulations within the ventral visual stream.