Short-term Events Research Articles

Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches.

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.

TrajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning

：Short-term rainfall prediction is a crucial and practical research area, with the accuracy of rainfall prediction, particularly for heavy rainfall, significantly impacting people's lives, property, and even their safety. Existing models, such as ConvLSTM, TrajGRU, and PredRNN, exhibit limitations in capturing fine-grained appearances due to insufficient memory units or addressing positional misalignment issues, thereby compromising the accuracy of model predictions. In this study, we propose trajPredRNN+, an innovative approach that integrates the trajectory segmentation model and the PredRNN deep learning model to address both limitations in nowcasting precipitation using weather radar echo images. By incorporating attention mechanisms, the model demonstrates an enhanced focus on short-term and imminent heavy rainfall events. To ensure improved stability during training, a residual network is introduced. Lastly, a more rational and effective training loss function is proposed, encompassing weight mechanism, SSIM index, and GAN loss. To validate the proposed model, we conducted a comparative experiment and an ablation experiment using the radar echo map dataset obtained from the Shenzhen Meteorological Bureau. The results of these experiments demonstrate that our model has achieved significant improvements across multiple key performance indicators.

Seasonal drought treatments impact plant and microbial uptake of nitrogen in a mixed shrub grassland on the Colorado Plateau.

For many drylands, both long- and short-term drought conditions can accentuate landscape heterogeneity at both temporal (e.g., role of seasonal patterns) and spatial (e.g., patchy plant cover) scales. Furthermore, short-term drought conditions occurring over one season can exacerbate long-term, multidecadal droughts or aridification, by limiting soil water recharge, decreasing plant growth, and altering biogeochemical cycles. Here, we examine how experimentally altered seasonal precipitation regimes in a mixed shrub grassland on the Colorado Plateau impact soil moisture, vegetation, and carbon and nitrogen cycling. The experiment was conducted from 2015 to 2019, during a regional multidecadal drought event, and consisted of three precipitation treatments, which were implemented with removable drought shelters intercepting ~66% of incoming precipitation including: control (ambient precipitation conditions, no shelter), warm season drought (sheltered April-October), and cool season drought (sheltered November-March). To track changes in vegetation, we measured biomass of the dominant shrub, Ephedra viridis, and estimated perennial plant and ground cover in the spring and the fall. Soil moisture dynamics suggested that warm season experimental drought had longer and more consistent drought legacy effects (occurring two out of the four drought cycles) than either cool season drought or ambient conditions, even during the driest years. We also found that E. viridis biomass remained consistent across treatments, while bunchgrass cover declined by 25% by 2019 across all treatments, with the earliest declines noticeable in the warm season drought plots. Extractable dissolved inorganic nitrogen and microbial biomass nitrogen concentrations appeared sensitive to seasonal drought conditions, with dissolved inorganic nitrogen increasing and microbial biomass nitrogen decreasing with reduced soil volumetric water content. Carbon stocks were not sensitive to drought but were greater under E. viridis patches. Additionally, we found that under E. viridis, there was a negative relationship between dissolved inorganic nitrogen and microbial biomass nitrogen, suggesting that drought-induced increases in dissolved inorganic nitrogen may be due to declines in nitrogen uptake from microbes and plants alike. This work suggests that perennial grass plant-soil feedbacks are more vulnerable to both short-term (seasonal) and long-term (multiyear) drought events than shrubs, which can impact the future trajectory of dryland mixed shrub grassland ecosystems as drought frequency and intensity will likely continue to increase with ongoing climate change.

Direct Bilirubin, but not Indirect Bilirubin, is Associated with Short-term Adverse Events in HFpEF.

Abnormal live function tests have been identified as independent risk factors for ominous prognosis in patients with heart failure. However, most of the previous studies have failed to determine the contribution of direct bilirubin (DBIL) and indirect bilirubin (IBIL) separately. Hence, we aimed to explore whether DBIL or IBIL is correlated with the prognosis of heart failure with preserved ejection fraction (HFpEF). A total of 19837 patients were hospitalized for HFpEF between January 2012 and January 2022 in Fuqing City Hospital affiliated with Fujian Medical University. The primary endpoint was in-hospital all-cause mortality. Secondary endpoints included in-hospital cardiovascular mortality and 30-day re-admission for heart failure. Univariable analysis indicated that patients with elevated DBIL or IBIL were exposed to a higher risk of mortality and re-admission. However, in multivariable models, both ln-transformed DBIL and TBIL, but not IBIL, were independent risk factors for in-hospital all-cause mortality (hazard ratio (HR)=1.796, 95% confidential interval (CI)=1.477-2.183, P<0.001; HR=1.854, 95% CI=1.461-2.352, P.0.001; HR=1.161, 95% CI=0.959-1.407, P=0.126) and in-hospital cardiovascular mortality (HR=1.831, 95% CI=1.345-2.492, P.0.001; HR=1.899, 95% CI=1.300-2.773, P=0.001; HR=1.145, 95% CI=0.841-1.561, P=0.389). Only DBIL remained independently associated with 30-day readmission for heart failure (HR=1.361, 95% CI=1.036-1.787, P=0.027). Adding ln-transformed DBIL to model 1 increased its discriminatory capacity (C-statistic: 0.851 to 0.869, respectively), whereas adding ln-transformed IBIL yielded little increment (C-statistic: 0.851 to 0.852, respectively). DBIL, but not IBIL, was associated with short-term ominous prognosis in patients with HFpEF. Hence, DBIL may be the superior predictor for prognosis in HFpEF.

The impact of natural disasters on US business credit markets: a comparative analysis of short-term and long-duration events

The impact of natural disasters on US business credit markets: a comparative analysis of short-term and long-duration events

Assessing thermal resilience to overheating in a Belgian apartment: impact of building parameters

Globally and in European Union, climate change and heatwaves (HWs) challenges the thermal resilience performance (buildings’ ability to withstand and recover from a shock) of airtight, highly insulated buildings with no cooling strategies. Ensuring thermal resilience in buildings is crucial for maintaining habitability amid climate change uncertainties. This study, under the framework of IEA EBC Annex 80, aims to explore the impact of design parameters (thermal mass, window to wall ratio (WWR), operation of solar shading and natural night ventilation) on the thermal resilience in a Belgian apartment. Although a number of recent studies explored impact of building design on overheating, there is still a lack in the state-of-the-art exploring and varying ranges of the design parameters that impact the thermal resilience performance of apartments during short-term shocks such as heatwaves. In order to reach the aim of this study, building energy simulations were performed. Initially, the default apartment was evaluated during current weather scenario varying the parameters. Results showed overheating risk in default apartment during current weather scenario. Then the worst, improved and, the optimized variation of the building is evaluated against 6 days HW. The apartments’ thermal resilience performance assessment conclude that high window-to-wall ratios (WWR >30%) without solar shading and lighter thermal mass decrease thermal resilience during shocks, highlighting WWR’s significance. For short-term overheating events, heavy thermal mass improves the thermal resilience by pro-longing the absorptivity time (withstanding capability) of the excess heat. However, if excess heat is absorbed by the building (higher WWR resulting in higher solar gains), lighter thermal mass enhances the thermal resilience performance (recovery capability) by flushing out the excess heat. Solar shading and passive cooling strategy like natural night ventilation aids in temperature management, improving the buildings’ thermal resilience performance by prolonging the absorptivity and speeding the recovery from the shock.

Effects of minimum dissolved oxygen setpoints for aeration in semi-intensive pond production of Pacific white shrimp (Litopenaeus vannamei)

Aeration is considered one of the most critical factors in shrimp farming as it affects the metabolism of shrimp and other living organisms within a pond. Maintenance of proper dissolved oxygen (DO) concentrations is critical, as low DO can cause stress, lower resistance to disease, and inhibit growth. Although the effects of single, short-term hypoxia events have been widely studied, little to no research has examined effects of repeated exposure to hypoxia during multiple diurnal cycles that shrimp may experience in aquaculture ponds. Even though the concept is simple, proper DO management is not simple. It is dependent on numerous factors and often based on anecdotal recommendations. The present trial was conducted to help elucidate the effects of DO management strategies on shrimp performance. The trial examined the effects of three different DO set points for automatic aeration on shrimp production and water quality parameters in earthen ponds. In sixteen earthen ponds (0.1 ha), juvenile Pacific white shrimp (⁓0.030 g) were stocked at a density of 25ind/m2. Minimal DO setpoints that activated automatic aeration in each of the treatments were 2.5, 3.5, and 4.5 mg O2/L, respectively. Shrimp were fed using the AQ1 passive acoustic monitoring system that was set to not feed while DO was below the threshold of a given treatment. Each of the treatments was applied to 5 ponds, except for treatment 1 (2.5 mg/L) which was applied 6 ponds as it was considered of higher risk. Shrimp growth performance and water quality indicators were monitored weekly. At the end of the 79–81-day trial, results showed that different aeration control treatments had no significant effect in terms of growth performance, feed inputs or productivity parameters. The final weight of the shrimp ranged between 33.3 and 33.6 g, with average final yields of 7500–8500 kg per ha. Nonetheless, mean electrical costs differed significantly between treatments, with higher DO setpoints treatments exhibiting a higher aeration cost with no discernible benefit. Water quality parameters showed no significant difference, except for morning and afternoon DO concentrations.

Air Quality Monitoring with Low-Cost Sensors: A Record of the Increase of PM2.5 during Christmas and New Year’s Eve Celebrations in the City of Queretaro, Mexico

The high concentrations of particulate matter (PM) in the atmosphere have adverse effects on both the environment and human health, as well as on urban and faunal biodiversity. Short-term events, such as the burning of fireworks, attributable to human activity, result in a significant and rapid increase in PM concentration levels within the atmosphere. We present findings derived from observations made using low-cost PM sensors deployed as part of the Environmental Monitoring Network in Querétaro, Mexico. These sensors utilize the Environmental Geo Monitoring Stations (EMGA) device, developed by the Institute of Geosciences at UNAM. The observation period spanned from 10 December 2023 to 10 January 2024, encompassed the Christmas and New Year holidays, and focused on PM2.5 concentrations, and a comparison with a similar period from 10 February 2024 to 10 March 2024 considered a typical activity period in the area. The results reveal two substantial increases in PM2.5 concentrations (from less than 50 µg/m3 to more than 340 µg/m3) over time and spatial distribution during this period, with notable increases observed during the festive season coincident with the Christmas and New Year celebrations. Specifically, prolonged periods exceeding up to 6 h were noted on festivity days. This research offers insight into the effects, trends, and spatial–temporal distribution of pollutants within the city of Queretaro, which may be used as a reference for other cities around the world.

No Difference in Short-Term Surgical Outcomes from Semaglutide Treatment for Type 2 Diabetes Mellitus after Cervical Decompression and Fusion: A Propensity Score-Matched Analysis.

Retrospective cohort. To evaluate the impact of semaglutide treatment for Type 2 Diabetes Mellitus (T2DM) on the risk of short-term (<6 months) postoperative complications in patients undergoing primary cervical spine decompression and fusion (CSDF). Semaglutide, a GLP-1 receptor agonist, is gaining popularity as a weekly injectable medication for the treatment of T2DM and obesity. Existing research indicates that higher levels of HbA1c and obesity are linked to fewer positive results after undergoing spine surgery, particularly cervical decompression and fusion. Nevertheless, there is a scarcity of publications evaluating the influence of semaglutide therapy on surgical complications, including surgical site infection, wound complications, and reoperation within 6 months, which were aggregated into a composite measure. The PearlDiver Database was queried from January 2010 to December 2021 for patients with a primary diagnosis of T2DM who underwent CSDF for degenerative pathology. Patients with semaglutide treatment within 6 months before index surgery were propensity score-matched to patients without the treatment, employing age, gender, and Charlson comorbidity index (CCI) as matching covariates. A multivariate regression model was used to investigate the impact of semaglutide treatment on postoperative surgical complications. The propensity score-matched cohort included 596 patients (semaglutide cohort: 298, control cohort: 298). There were no statistically significant differences between cohorts in the composite measure of postoperative surgical complications following index CSDF (OR 1.26, 95% CI 0.83-1.93, P=0.331). Similarly, both 30-day (OR 0.83, 95% CI 0.49-1.42, P=0.589) and 90-day readmission rate (OR 0.89, 95% CI 0.56-1.42, P=0.724) were similar between both cohorts. This study suggests that in patients with T2DM, semaglutide treatment is not associated with higher rates of short-term adverse events after CSDF. The effect of semaglutide use on long-term outcomes remains unknown.

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

A novel deep learning approach for estimating and classifying short-term voltage stability events in modern power systems with composite load and distributed energy resources

Occurrence and emission characteristics of microplastics in agricultural surface runoff under different natural rainfall and short-term fertilizer application

Land-based microplastics (MPs) are considered the primary source of MPs in aquatic environments, with runoff being a major pathway for their transfer from soil to surface water. However, the transportation characteristics of MPs via agricultural surface runoff remain unclear. In this study, we investigated the occurrence and emission characteristics of MPs in agricultural surface runoff under various short-term fertilizer applications and natural rainfall events using laser direct infrared imaging analysis (LDIR). MPs from fertilizers and soils co-migrated with the agricultural runoff. The abundance and concentration of MPs in runoff were 145.90 ± 22.48–2043.38 ± 89.51 items·L−1 and 39.17 ± 21.94–523.04 ± 47.85 µg·L−1, respectively. Small and low-density MPs, such as polyethylene (PE), chlorinated polyethylene (CPE), and polyurethane (PU) in film/fragment form with 20–50 µm exhibited a higher mobility. No statistical differences were observed in the distribution of runoff MPs with the application of different fertilizers. There was a significant positive relationship between runoff MP abundance and rainfall intensity. The annual emission load in this study area was 116.73 g·hm−2, indicating that the transportation of MPs via agricultural surface runoff cannot be ignored. This study is conducive to understanding the migration behavior of MPs in soil-water environments in a better manner.

A nomogram prediction model for short-term aortic-related adverse events in patients with acute Stanford type B aortic intramural hematoma: development and validation.

This study is to examine the factors associated with short-term aortic-related adverse events in patients with acute type B aortic intramural hematoma (IMH). Additionally, we develop a risk prediction nomogram model and evaluate its accuracy. This study included 197 patients diagnosed with acute type B IMH. The patients were divided into stable group (n = 125) and exacerbation group (n = 72) based on the occurrence of aortic-related adverse events. Logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) method for variables based on baseline assessments with significant differences in clinical and image characteristics were employed to identify independent predictors. A nomogram risk model was constructed based on these independent predictors. The nomogram model was evaluated using various methods such as the receiver operating characteristic curve, calibration curve, decision analysis curve, and clinical impact curve. Internal validation was performed using the Bootstrap method. A nomogram risk prediction model was established based on four variables: absence of diabetes, anemia, maximum descending aortic diameter (MDAD), and ulcer-like projection (ULP). The model demonstrated a discriminative ability with an area under the curve (AUC) of 0.813. The calibration curve indicated a good agreement between the predicted probabilities and the actual probabilities. The Hosmer-Lemeshow goodness of fit test showed no significant difference (χ 2 = 7.040, P = 0.532). The decision curve analysis (DCA) was employed to further confirm the clinical effectiveness of the nomogram. This study introduces a nomogram prediction model that integrates four important risk factors: ULP, MDAD, anemia, and absence of diabetes. The model allows for personalized prediction of patients with type B IMH.

Stem cell therapy in diabetic men with erectile dysfunction: a 24-month follow-up of safety and efficacy of two intracavernous autologous bone marrow derived mesenchymal stem cells injections, an open label phase 2 clinical trial

BackgroundRecently we reported results of phase 1 pilot clinical trial of 2 consecutive intracavernous (IC) injection of autologous bone marrow-derived mesenchymal stem cells (BM-MSCs) for the first time in the treatment of diabetic patients with erectile dysfunction (DM-ED). In phase 2 of this study our aim is to evaluate long term safety and efficacy of IC injections of BM-MSC on additional eight patients with DM-ED.ResultsEach patient received 2 consecutive IC injections of BM-MSC and evaluated at 1, 3, 6, 12, and 24-month time points. Primary outcome was the tolerability and safety of stem cells therapy (SCT), while the secondary outcome was improvement of erectile function (EF) as assessed using the International Index of Erectile Function-5 (IIEF-5), Erection Hardness Score (EHS) questionnaires, and Color Duplex Doppler Ultrasound (CDDU). IC injections of BM-MSCs was safe and well-tolerated. Minor local and short-term adverse events related to the bone marrow aspiration and IC injections were observed and treated conservatively. There were significant improvement in mean IIEF-5, EHS, all over the follow-up time points in comparison to the baseline. At 24-month follow up there were significant decline in the mean IIEF-5, and EHS compared to the baseline. The mean basal and 20-min peak systolic velocity was significantly higher at 3-month after the IC injections compared to baseline.ConclusionsThis phase 2 clinical trial confirmed that IC injections of BM-MSC are safe and improve EF. The decline in EF over time suggests a need for assessing repeated injections.Clinical trial registrationNCT02945462

KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy.

The aim of this project was to create time-aware, individual-level risk score models for adverse drug events related to multiple sclerosis disease-modifying therapy and to provide interpretable explanations for model prediction behavior. We used temporal sequences of observational medical outcomes partnership common data model (OMOP CDM) concepts derived from an electronic health record as model features. Each concept was assigned an embedding representation that was learned from a graph convolution network trained on a knowledge graph (KG) of OMOP concept relationships. Concept embeddings were fed into long short-term memory networks for 1-year adverse event prediction following drug exposure. Finally, we implemented a novel extension of the local interpretable model agnostic explanation (LIME) method, knowledge graph LIME (KG-LIME) to leverage the KG and explain individual predictions of each model. For a set of 4859 patients, we found that our model was effective at predicting 32 out of 56 adverse event types (P < .05) when compared to demographics and past diagnosis as variables. We also assessed discrimination in the form of area under the curve (AUC = 0.77 ± 0.15) and area under the precision-recall curve (AUC-PR = 0.31 ± 0.27) and assessed calibration in the form of Brier score (BS = 0.04 ± 0.04). Additionally, KG-LIME generated interpretable literature-validated lists of relevant medical concepts used for prediction. Many of our risk models demonstrated high calibration and discrimination for adverse event prediction. Furthermore, our novel KG-LIME method was able to utilize the knowledge graph to highlight concepts that were important to prediction. Future work will be required to further explore the temporal window of adverse event occurrence beyond the generic 1-year window used here, particularly for short-term inpatient adverse events and long-term severe adverse events.

Projection of Changes in Rainfall and Drought Based on CMIP6 Scenarios on the Ca River Basin, Vietnam

In this study, future precipitation and drought in the Ca river basin, Vietnam, were projected based on an ensemble of 27 CMIP6 models for four climate change scenarios. The impact of climate change on precipitation and drought was investigated. Monthly precipitation observation data were adjusted using the bias correction method. To detect drought events, the Standard Precipitation Index (SPI) was employed. Changes in drought were assessed using SPI3, SPI6, and SPI12. Although the amount of annual total precipitation slightly increased, the drought events may become more severe. There is a high likelihood of increased drought intensity and severity in Vietnam due to climate change. The frequency of droughts is likely to change depending on the location and climate change scenario. We found that the frequency and severity of droughts may be altered depending on the window size of SPI. The short-term drought events will be more frequent and severe, and long-term drought events will become more severe in the Ca river basin.

Transcatheter Versus Surgical Aortic Valve Replacement in Recipients of Solid Organ Transplants and Liver Cirrhosis: A Propensity-Matched Analysis of National Readmission Data

Transcatheter aortic valve replacement (TAVR) continues to grow in the United States. There are limited data on recipients of solid organ transplant (SOT) and patients with liver cirrhosis who undergo aortic valve replacement (AVR). Our study aims to evaluate outcomes in these populations. Using the national readmission database (2016 to 2020), we identified recipients of SOT and patients with liver cirrhosis without previous liver transplants who were admitted for severe aortic stenosis and underwent either TAVR or surgical AVR (SAVR). We used multivariable regression for adjusted analysis and the propensity score matching model, implementing complete Mahalanobis distance matching within the Propensity Score Caliper (0.2) to match TAVR and SAVR cohorts for outcomes. Of 3,394 hospitalizations for AVR in recipients of SOT, 2,181 underwent TAVR, and 1,213 underwent SAVR. On propensity-matched analysis, SAVR was associated with more adverse events than was TAVR, including in-hospital mortality (5.2% vs 1.1%, adjusted odds ratio [aOR] 4.49, p <0.001), acute kidney injury (43.7% vs 10.2%, p <0.001), cardiogenic shock (9.0% vs 1.6%, p <0.001), sudden cardiac arrest (15.9 vs 6.0%, p <0.001), major adverse cardiac and cerebrovascular events (28% vs 10.4%, p <0.001), and net adverse events (72.8 vs 37.6%, p <0.001). A greater median length of stay (10 vs 2 days, p <0.001) and adjusted cost ($80,842 vs $57,014, p <0.001) were also observed. The readmission rates were the same for both cohorts after a 6-month follow-up. Similarly, in 14,763 hospitalizations for AVR in liver cirrhosis, 7,109 patients underwent TAVR, and 7,654 underwent SAVR. In propensity-matched cohorts (n = 2,341), SAVR was found to be associated with greater adverse events, including in-hospital mortality (19.8% vs 10%), stroke (6.7% vs 2%), acute kidney injury (67.7% vs 30.3%), cardiogenic shock (41.9% vs 19.9%), sudden cardiac arrest (31.8% vs 13.2%, aOR 2.89), major adverse cardiac and cerebrovascular events (66.2% vs 35.7%), and net adverse events (86% vs 59.5%) (p <0.001). A greater median length of stay (16 vs 3 days) and cost ($500,218 vs $263,383) were also observed (p <0.001). However, the rate of readmissions at 30-day (9% vs 11.1%) and 180-day intervals (33.4% vs 39.8%) was lower for the SAVR cohort (p <0.05). In recipients of SOT and patients with liver cirrhosis, SAVR is associated with greater short-term mortality, adverse events, and healthcare burden than is TAVR. TAVR is a relatively safer alternative to SAVR in these patient populations, although further studies are warranted to compare the long-term outcomes.

Impact of early bleeding on long-term mortality after left atrial appendage occlusion

BackgroundShort-term antithrombotic therapy is recommended after left atrial appendage occlusion(LAAO) to reduce the risk of device-related thrombosis(DRT). However, patients referred for LAAO have excess bleeding risk. Yet, short-term bleeding events post-LAAO are not well characterized. ObjectiveWe aimed to evaluate the incidence of early bleeding post-LAAO and its association with long-term mortality risk. We also sought to identify predictors of early bleeding. MethodsPatients who underwent LAAO at Mayo Clinic between 2018 and 2023 were included. Early bleeding was defined as bleeding that occurred within three months of discharge. A multivariable Cox proportional hazards model was used to assess the association between early bleeding and death and to estimate the hazard ratio(HR). Multivariable regression analysis was used to identify predictors of early bleeding. ResultsAmong the 592 patients included, 389(66%) were male, and mean age was 75.6 years. Eighty-three patients(14.0%) experienced early bleeding, with the majority having minimal(63.4%) or minor(17.3%) bleeding. At a median follow-up of 14.4 months(IQR 4.2-27.9), early bleeding after LAAO was associated with an increased risk of all-cause mortality (adjustedHR, 2.07; 95%CI, 1.15-3.75; P=0.02). A history of intracranial bleeding, non-paroxysmal atrial fibrillation, CHA2DS2VASc score, and early DRT were independent predictors of early bleeding. Anti-thrombotic therapy on discharge was not associated with early non-procedural bleeding. ConclusionsOne in seven patients experience a non-procedural bleeding event within 90 days after LAAO. Early bleeding after LAAO is associated with a higher all-cause mortality during mid-term follow-up. Further studies are needed to optimize the management of patients undergoing LAAO.

When does digital merger and acquisition create shareholder value? An empirical investigation in the Chinese context

In China, acquiring firms are increasingly focused on the immediate financial returns that digital mergers and acquisitions (DM&A) can help them achieve in the stock market, but there is little literature that examines which acquiring firms achieve greater returns. Based on signaling theory, this study conceptualizes DM&A announcements as signals released by the acquiring firms to the stock market and explores the factors influencing the Chinese stock market's reaction to such signals. This research empirically investigates potential influencing factors using a short-term event methodology together with regression analysis based on the data collected in China's Shanghai and Shenzhen stock markets during 2012–2021. The research finds that the Chinese stock market reacts more positively to DM&A announcements for acquiring firms with high executive shareholdings, high executive openness, strong digital innovation capabilities, and in regions with higher levels of investor protection. This study is the first attempt to explore the factors influencing the stock market's response to DM&A in the Chinese context.

Carbon isotope geochemistry and its geological significance in the Jixian system of the Qishan section, Ordos Basin

Carbon isotopes are important criteria for determining the underground diagenetic evolution and the evolution characteristics of the Earth's surface environment. Because the Mesoproterozoic was between the two great oxidation events, the changes in inorganic carbon isotopes were relatively gentle, making high-resolution sections of inorganic and organic carbon particularly important for global comparisons. To better constrain the isotopic variation and origins of the Mesoproterozoic, in this study, the Qishan section in the Ordos Basin of China was selected for investigation of its sedimentology, petrology, and geochemistry. The results show that the carbon isotope variations of the Jixian Formation carbonates in the Qishan section were generally not significant, with an average value close to 0‰. Similar characteristics were observed in the Luonan section, the Jixian section and the Jixian Group section (a combined section located at the north margin of North China) in the Yanliao Basin. This indicates that the carbon pool in the seawater had not undergone large-scale changes, and that the aquatic environment remained relatively consistent. A short-term negative excursion that appeared in the L2 member of the Longjiayuan Formation, at the same time may be closely related to greater organic carbon retention caused by a short-term event of biological death. It was not ruled out that carbonate minerals converted by 12C-rich produced by diagenetic evolution may had an impact on carbon isotopes during the rock evolution process.

Modelowanie hydrologiczne jako wsparcie dla projektowania i utrzymania infrastruktury

One of the many concerns related to climate change is its impact on infrastructure, for example in the case of structures such as bridges or culverts which are more frequently exposed to conditions like significant differences in water table levels or floods. Varying weather conditions can also cause deterioration of properties of the materials which they are made of, which coupled with higher loads (water, wind or temperature variation, depending on the structure) decreases their durability and hinders their operation. Existing infrastructure facilities were designed with the use of historical data, and those design assumptions can be out of date at the moment, moreover their validity decreases with further climate changes. In order to support the design of new infrastructure facilities and the maintenance of already existing ones, climate change should be taken into account. One of the means that can be used to assess the impact of climate change on bridges and other river infrastructure is hydrological modelling. In this paper, the authors present a hydrological model of the flow in the Ślęza River, a 78.6 km long, left- bank tributary of the Odra River, as well as in its tributaries, with a particular focus on the points where bridges are located. The model was performed with QSWAT software, taking into account two scenarios of climate change: SSP2-4.5 and SSP5-8.5 (obtained with the use of NorESM2-LM model) and calibrated with the use of historical meteorological data. The results of the model include daily flows for years 2023-2050, which allows to compare characteristic (statistical) flows and observe trends; also a change in the dynamics of the river caused by thaws can also be observed. A greater number of extreme events can be seen in the results for the SSP2-4.5 scenario, the values of flood flows are also higher for this scenario, whereas the average flows are higher for the SSP5-8.5 scenario, which is due to higher rainfall in this scenario – although the threat of short-term extreme events is lower, but nevertheless, due to increased flows, scouring development can occur in this scenario, which when left uncontrolled can pose great risks to bridges. The obtained results would be helpful for engineers who plan the maintenance of infrastructure facilities, as this analysis would provide additional data in order to choose optimal solutions for the costs of exploitation and for the environment.