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Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R2 improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands.

Climate change is expected to increasingly intensify the water stress that directly impacts crop productivity in the near future. This study integrates the crop water stress index (CWSI) with high-resolution regional climate simulations produced by the weather research and forecasting (WRF) model to evaluate water stress that winter wheat and olive trees will potentially experience in Greece in the future. Decadal, high-resolution climate simulations were generated for both the present and near-future periods using the most recent shared socioeconomic pathways (SSP) framework. A bias-corrected dataset based on 18 models from the Coupled Model Intercomparison Project 6 was used for boundary conditions to mitigate errors associated with individual global model biases. Projections indicated a mean air temperature increase of 1.1–1.7 °C and a relative humidity decrease of up to 3.5%. Mean CWSI increases of up to 6% and 4% were projected in most of the country for winter wheat and olive trees, respectively. The water stress of the winter wheat was also assessed over the three growing stages defined by the FAO. The analysis showed that water stress may occur during all growing stages, inducing potential impacts on tillering, photosynthetic efficiency, biomass accumulation, or yield. Additionally, a water stress threshold (i.e., CWSI > 0.5) was applied for both species in order to carry out a spatial assessment of the water stress that is projected to occur in the future in key winter wheat-, olive oil- and table olive-producing Greek regions. The findings of this study can support the irrigation scheduling and the development of climate-resilient agricultural practices in Greece. The modeling framework that was established in this study can also be applied to other crops and regions in the Mediterranean.

It is well established that exposure to heat-stress conditions significantly impacts the physiology, health, welfare, and productivity of both sheep and cattle. The aim of this study was to apply the Temperature Humidity Index (THI) in order to assess the impact of future climate conditions on the thermal stress exposure of sheep and cattle in Greece. The Weather Research and Forecasting (WRF) model was used as a high-resolution regional climate model to simulate climate conditions for two decades in Greece at a 10 Km spatial resolution and a 1 h temporal resolution. The WRF model was applied to two emission scenarios, namely SSP2-4.5 (intermediate) and SSP5-8.5 (worst-case). Projections were made for the near-future decade (2046–2055), with the decade (2005–2014) serving as the reference period for comparative analysis. The data analysis indicated that under the SSP2-4.5 emission scenario, the mean temperature is projected to increase by 1.2–1.4 °C and 1.4–1.6 °C across 38% and 58% of the country’s territory, respectively. Increases higher than 1.6 °C are projected across 32% of the Greek territory under the SSP5-8.5 emission scenario. The mean THI (sheep) and mean THI (adj) (cattle) are projected to increase by 5–10% and by 4% across 74% and 82% of the Greek territory, respectively, when considering the SSP2-4.5 emission scenario. Slightly more severe mean heat-stress conditions were projected when considering the SSP5-8.5 emission scenario. The analysis of the hourly THI values showed that sheep and cattle are expected to experience heat-stress conditions during extended periods in the future, in which hot weather will prevail. Specifically, the number of severe/danger heat-stress hours is projected to double in the greater part of the country. To mitigate the adverse effects of climate-change-induced thermal stress on animal productivity, health, and welfare, the implementation of adaptation measures and best management practices is strongly recommended for sheep and cattle farmers. These measures encompass improvements in breeding strategies, livestock housing and microclimate management, nutritional interventions, and the adoption of precision livestock farming technologies. Given the outstanding economic, social, and environmental importance of sheep and cattle farming in Greece, effective adaptation to and mitigation of climate change impacts represent urgent priorities to ensure the long-term sustainability and resilience of the livestock sector.

The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final (NCEP FNL) Operational Global Analysis data. Simulations were conducted over central Europe for the year 2015 using 36, 12 and 4 km grid resolutions. Evaluation is done using daily E-OBS data. Several performance metrics and the bias adjusted equitable threat score (BAETS) for precipitation are used. Results show that model performance for mean, maximum and minimum temperature improves when increasing the spatial resolution from 36 to 12 km, with no significant added value when further increasing it to 4 km. Model performance for precipitation is slightly worsened when increasing the spatial resolution from 36 to 12 km while further increasing it to 4 km has minor effect. However, simulated and observed precipitation data are in quite good agreement in areas with precipitation rates below 3 mm/day for all three grid resolutions. The annual mean fraction of observed and/or forecast events that were correctly predicted (BAETS), when increasing the grid size resolution from 36 to 12 and 4 km, suggests a slight modification on average over the domain. During summer the model presents significantly lower BAETS skill score compared to the rest of the seasons.

WRF is used to simulate eight extreme precipitation events that occurred over the regions of Schleswig–Holstein and Baden–Wurttemberg in Germany. The events were chosen from the German Weather Service (DWD) catalog and exceeded the DWD’s warning level 3 (i.e., rainfall > 40 mm/h). A two-way nesting approach is used with 9 and 3 km spatial resolutions. Initial and boundary conditions are obtained from the ERA5 dataset at 0.25° × 0.25°. To model each event, thirty different parameterization configurations were used, accounting for all possible combinations of five microphysics (MP), three cumulus (CU), and two planetary boundary layer (PBL) parameterization methods, yielding a total of 240 simulations. TOPSIS multicriteria analysis technique is employed to determine the performance skill of each setup and rank them, using six categorical and five statistical metrics. Resolution increase from 9 to 3 km did not improve forecasting accuracy temporally or in intensity. According to TOPSIS ranking, when treating each event individually, the ideal parameterizations combination is spatiotemporally dependent, with certain members ranking higher. When all events are considered, the Morrison double-moment MP–Grell–Freitas CU–YSU PBL combination works best with a frequency of occurrence in the top five performing scenarios of 30%, 47.5%, and 57.5% respectively.

The Weather Research and Forecasting (WRF) mesoscale meteorological model is used to dynamically downscale data from the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) CMIP5 version (Model E2-R) over Europe at a 0.25° grid size resolution, for the period of 1951 to 2010. The model configuration is single nested with grid resolutions of 0.75° to 0.25°. Two 30-year datasets are produced for the periods of 1951–1980 and 1981–2010, representing the historic and current periods, respectively. Simulated changes in climate normals are estimated and compared against the change derived from the E-OBS gridded dataset at 0.25° spatial analysis. Results indicate that the model consistently underpredicts the temperature fluctuations observed across all subregions, indicative of a colder model climatology. Winter has the strongest bias of all seasons, with the northeastern part of the domain having the highest. This is largely due to the land–atmosphere interactions. Conversely, spring and summer have the lowest regional biases, owing to a combination of low snow cover (relative to winter) and milder radiation effects (as opposed to summer). Precipitation has a negative bias in most cases, regardless of the subregion analyzed, due to the physical mechanism employed and the topographic features of each region. Both the change in the number of days when the temperature exceeds 25 °C and the change in the number of days when precipitation exceeds 5 mm/day are captured by the model reasonably well, exhibiting similar characteristics with their counterpart means.

Aim. To evaluate the effect of quinapril on diabetic cardiovascular autonomic neuropathy (CAN) and peripheral neuropathy (DPN). Patients and Methods. Sixty-three consecutive patients with diabetes mellitus [43% males, 27 with type 1 DM, mean age 52 years (range 22–65)], definite DCAN [abnormal results in 2 cardiovascular autonomic reflex tests (CARTs)], and DPN were randomized to quinapril 20 mg/day (group A, n=31) or placebo (group B, n=32) for 2 years. Patients with hypertension or coronary heart disease were excluded. To detect DPN and DCAN, the Michigan Neuropathy Screening Instrument Questionnaire and Examination (MNSIQ and MNSIE), measurement of vibration perception threshold with biothesiometer (BIO), and CARTs [R-R variation during deep breathing [assessed by expiration/inspiration ratio (E/I), mean circular resultant (MCR), and standard deviation (SD)], Valsalva maneuver (Vals), 30 : 15 ratio, and orthostatic hypotension (OH)] were used. Results. In group A, E/I, MCR, and SD increased (p for all comparisons < 0.05). Other indices (Vals, 30 : 15, OH, MNSIQ, MNSIE, and BIO) did not change. In group B, all CART indices deteriorated, except Vals, which did not change. MNSIQ, MNSIE, and BIO did not change. Conclusions. Treatment with quinapril improves DCAN (mainly parasympathetic dysfunction). Improved autonomic balance may improve the long-term outcome of diabetic patients.

Aim. To compare the cardiovascular autonomic reflex tests (CARTs) with cardiac sympathetic innervation imaging with 123I-metaiodobenzylguanidine (MIBG) in patients with type 1 diabetes mellitus (T1DM). Patients and Methods. Forty-nine patients (29 males, mean age 36 ± 10 years, mean T1DM duration 19 ± 6 years) without cardiovascular risk factors were prospectively enrolled. Participants were evaluated for autonomic dysfunction by assessing the mean circular resultant (MCR), Valsalva maneuver (Vals), postural index (PI), and orthostatic hypotension (OH). Within one month from the performance of these tests, patients underwent cardiac MIBG imaging and the ratio of the heart to upper mediastinum count density (H/M) at 4 hours postinjection was calculated (abnormal values, H/M < 1.80). Results. Twenty-nine patients (59%) had abnormal CARTs, and 37 (76%) patients had an H/M_4 < 1.80 (p=0.456). MCR, PI, Vals, and OH were abnormal in 29 (59%), 8 (16%), 5 (10%), and 11 (22%) patients, respectively. When using H/M_4 < 1.80 as the reference standard, a cutoff point of ≥2 abnormal CARTs had a sensitivity of 100% but a specificity of only 33% for determining CAN. Conclusions. CARTs are not closely associated with 123I-MIBG measurements, which can detect autonomic dysfunction more efficiently than the former. In comparison to semiquantitative cardiac MIBG assessment, the recommended threshold of ≥2 abnormal CARTs to define cardiovascular autonomic dysfunction is highly sensitive but of limited specificity and is independently determined by the duration of T1DM.

We analyzed the medical records of 100 patients with type 2 diabetes to evaluate the effects of new antidiabetic drugs on the TG/HDL-C ratio and the TG-glucose index. We found that GLP-1 RA treatment significantly improved both markers, while SGLT2 inhibitors led to significant reductions only in the latter. •The need for surrogate markers of insulin resistance is increasingly recognized•The TG/HDL-C ratio and the TyG index predict cardiovascular risk and mortality•We comparatively evaluated the impact of SGLT2i and GLP-1 RAs on these biomarkers

Accurate air quality forecasting remains challenging due to persistent biases in chemical transport models. Addressing this challenge, the current study develops pollutant-specific deep learning frameworks that correct systematic errors in the Community Multiscale Air Quality (CMAQ) simulations of nitrogen dioxide (NO2) and coarse particulate matter (PM10) over California. Building upon a previous study on ozone bias correction, a hybrid CNN–Attention–LSTM architecture is adapted, and a weighted Huber loss function is introduced for PM10 to enhance the detection of extreme pollution events through a gated tail-weighting mechanism. Using data from twenty EPA monitoring stations (ten per pollutant) for 2010–2014, the proposed approach achieves substantial performance gains over the CMAQ baseline. For NO2, RMSE decreases by ~51% with an average systematic bias reduction of ~80% and a random error reduction of ~42%. For PM10, RMSE improves by ~49% while the systematic and random errors decrease by ~94% and ~33%, respectively. The PM10 model also shows high consistency with observations (Index of Agreement improvement of ~105%) and a strong ability to capture peak events (F1 score improvement of ~270%), while the NO2 model achieves large gains in explanatory power (R2 improvement averaging ~816%). Both pollutants also demonstrate enhanced temporal agreement between predictions and observations, as confirmed by the Dynamic Time Warping analysis (NO2: ~55%, PM10: ~58%). These results indicate that pollutant-specific loss functions and architectural tuning can significantly improve both accuracy and event sensitivity, offering a transferable framework for bias correction across multiple pollutants and regions.