Anthropogenic Influence on Decadal Changes in Concurrent Hot and Dry Events over China around the Mid-1990s

The frequency and duration of observed concurrent hot and dry events(HDEs) over China during the growing season(April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China(SEC), northern China(NC), and northeastern China(NEC). The frequency of HDEs averaged over China in the present day(PD,1994–2011) is double that in the early period(EP, 1964–81);the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model(MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes,suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas(GHG) concentrations dominates the simulated decadal changes,increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol(AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed.

Prediabetes: An overlooked risk factor for major adverse cardiac and cerebrovascular events in atrial fibrillation patients

BACKGROUND Prediabetes is a well-established risk factor for major adverse cardiac and cerebrovascular events(MACCE).However,the relationship between prediabetes and MACCE in atrial fibrillation(AF)patients has not been extensively studied.Therefore,this study aimed to establish a link between prediabetes and MACCE in AF patients.AIM To investigate a link between prediabetes and MACCE in AF patients.METHODS We used the National Inpatient Sample(2019)and relevant ICD-10 CM codes to identify hospitalizations with AF and categorized them into groups with and without prediabetes,excluding diabetics.The primary outcome was MACCE(all-cause inpatient mortality,cardiac arrest including ventricular fibrillation,and stroke)in AF-related hospitalizations.RESULTS Of the 2965875 AF-related hospitalizations for MACCE,47505(1.6%)were among patients with prediabetes.The prediabetes cohort was relatively younger(median 75 vs 78 years),and often consisted of males(56.3%vs 51.4%),blacks(9.8%vs 7.9%),Hispanics(7.3%vs 4.3%),and Asians(4.7%vs 1.6%)than the non-prediabetic cohort(P<0.001).The prediabetes group had significantly higher rates of hypertension,hyperlipidemia,smoking,obesity,drug abuse,prior myocardial infarction,peripheral vascular disease,and hyperthyroidism(all P<0.05).The prediabetes cohort was often discharged routinely(51.1%vs 41.1%),but more frequently required home health care(23.6%vs 21.0%)and had higher costs.After adjusting for baseline characteristics or comorbidities,the prediabetes cohort with AF admissions showed a higher rate and significantly higher odds of MACCE compared to the non-prediabetic cohort[18.6%vs 14.7%,odds ratio(OR)1.34,95%confidence interval 1.26-1.42,P<0.001].On subgroup analyses,males had a stronger association(aOR 1.43)compared to females(aOR 1.22),whereas on the race-wise comparison,Hispanics(aOR 1.43)and Asians(aOR 1.36)had a stronger association with MACCE with prediabetes vs whites(aOR 1.33)and blacks(aOR 1.21).CONCLUSION This population-based study found a significant association between prediabetes and MACCE in AF patients.Therefore,there is a need for further research to actively screen and manage prediabetes in AF to prevent MACCE.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

Sex and racial disparities in non-alcoholic fatty liver disease-related cardiovascular events: National inpatient sample analysis (2019)

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)increases cardiovascular disease(CVD)risk irrespective of other risk factors.However,large-scale cardiovascular sex and race differences are poorly understood.AIM To investigate the relationship between NAFLD and major cardiovascular and cerebrovascular events(MACCE)in subgroups using a nationally representative United States inpatient sample.METHODS We examined National Inpatient Sample(2019)to identify adult hospitalizations with NAFLD by age,sex,and race using ICD-10-CM codes.Clinical and demographic characteristics,comorbidities,and MACCE-related mortality,acute myocardial infarction(AMI),cardiac arrest,and stroke were compared in NAFLD cohorts by sex and race.Multivariable regression analyses were adjusted for sociodemographic characteristics,hospitalization features,and comorbidities.RESULTS We examined 409130 hospitalizations[median 55(IQR 43-66)years]with NFALD.NAFLD was more common in females(1.2%),Hispanics(2%),and Native Americans(1.9%)than whites.Females often reported non-elective admissions,Medicare enrolment,the median age of 55(IQR 42-67),and poor income.Females had higher obesity and uncomplicated diabetes but lower hypertension,hyperlipidemia,and complicated diabetes than males.Hispanics had a median age of 48(IQR 37-60),were Medicaid enrollees,and had non-elective admissions.Hispanics had greater diabetes and obesity rates than whites but lower hypertension and hyperlipidemia.MACCE,all-cause mortality,AMI,cardiac arrest,and stroke were all greater in elderly individuals(P<0.001).MACCE,AMI,and cardiac arrest were more common in men(P<0.001).Native Americans(aOR 1.64)and Asian Pacific Islanders(aOR 1.18)had higher all-cause death risks than whites.CONCLUSION Increasing age and male sex link NAFLD with adverse MACCE outcomes;Native Americans and Asian Pacific Islanders face higher mortality,highlighting a need for tailored interventions and care.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Grassland-type ecosystem stability in China differs under the influence of drought and wet events

Ecological stability is a core issue in ecological research and holds significant implications forhumanity. The increased frequency and intensity of drought and wet climate events resulting from climatechange pose a major threat to global ecological stability. Variations in stability among different ecosystemshave been confirmed, but it remains unclear whether there are differences in stability within the sameterrestrial vegetation ecosystem under the influence of climate events in different directions and intensities.China's grassland ecosystem includes most grassland types and is a good choice for studying this issue.This study used the Standardized Precipitation Evapotranspiration Index-12 (SPEI-12) to identify thedirections and intensities of different types of climate events, and based on Normalized DifferenceVegetation Index (NDVI), calculated the resistance and resilience of different grassland types for 30consecutive years from 1990 to 2019 (resistance and resilience are important indicators to measurestability). Based on a traditional regression model, standardized methods were integrated to analyze theimpacts of the intensity and duration of drought and wet events on vegetation stability. The resultsshowed that meadow steppe exhibited the highest stability, while alpine steppe and desert steppe had thelowest overall stability. The stability of typical steppe, alpine meadow, temperate meadow was at anintermediate level. Regarding the impact of the duration and intensity of climate events on vegetationecosystem stability for the same grassland type, the resilience of desert steppe during drought was mainlyaffected by the duration. In contrast, the impact of intensity was not significant. However, alpine steppewas mainly affected by intensity in wet environments, and duration had no significant impact. Ourconclusions can provide decision support for the future grassland ecosystem governance.

IJO Press Event Photos

Pof.G.H.Naumamn,Pesidet ofICO and Pror Bruce Spivey,Peidenr-Elet of iCO moct Prof Xiu-Wen Hu midle Chier Eaitor of JOIES a Woc 2006 in Sao Paulo,Brazil.

Predictive Effect of CA125 on Adverse Cardiovascular Events in Patients with Chronic Heart Failure

Objective: To study the expression of CA125 in the serum of patients with CHF and the relationship between CA125 level and the occurrence of adverse cardiovascular events. Methods: The clinical data of 132 patients with CHF admitted to Shizuishan Second People’s Hospital from January 2023 to December 2023 were collected and divided into heart function II group, heart function III group, heart function IV group according to cardiac function. 44 healthy subjects who underwent physical examination during the same period were selected as the control group. The clinical data of CA125, NT-proBNP, echocardiography and other clinical data of the four groups were compared, and the incidence of major adverse cardiovascular events was followed up for 12 months. Results: Compared with the control group, the CA125 level in the CHF group was significantly increased (P Conclusion: Serum CA125 level is related to the cardiac function level in CHF patients and increases with the deterioration of cardiac function. The increase of the index is related to the mortality rate and re-hospitalization rate, suggesting that CA125 can be used as an indicator to reflect the severity of heart failure and prognosis monitoring.

Potential morphological responses of an artificial beach to a flood in extreme events: field observation and numerical modelling

Conch Island is a typical artificial island at the Tanghe Estuary in Bohai Sea,China.To improve natural environment and boost local tourism,beach nourishment will be applied to its north-western shore.The projected beach is landward and opposite to the Jinmeng Bay Beach.Nowadays,with climate changes,frequent heavy rainfalls in Hebei Province rise flood hazards at the Tanghe Estuary.Under this circumstance,potential influences on the projected beach of a flood are investigated for sustainable managements.A multi-coupled model is established and based on the data from field observations,where wave model,flow model and multifraction sediment transport model are included.In addition,the impacts on the projected beach of different components in extreme events are discussed,including the spring tides,storm winds,storm waves,and sediment inputs.The numerical results indicate the following result.(1)Artificial islands protect the coasts from erosion by obstructing landward waves,but rise the deposition risks along the target shore.(2)Flood brings massive sediment inputs and leads to scours at the estuary,but the currents with high sediment concentration contribute to the accretions along the target shore.(3)The projected beach mitigates flood actions and reduces the maximum mean sediment concentration along the target shore by 20%.(4)The storm winds restrict the flood and decrease the maximum mean sediment concentration by 21%.With the combined actions of storm winds and waves,the maximum value further declines by 38%.(5)A quadratic polynomial relationship between the deposition depths and the maximum sediment inputs with flood is established for estimations on the potential morphological changes after the flood process in extreme events.For the uncertainty of estuarine floods,continuous monitoring on local hydrodynamic variations and sediment characteristics at Tanghe Estuary is necessary.

Adverse Events following AstraZeneca COVID-19 Vaccination: A Case Study in Abidjan, Côte d’Ivoire

Introduction: Pharmaceutical companies have boosted vaccine production following the global COVID-19 pandemic. In Côte d’Ivoire, the first vaccination campaign with the AstraZeneca vaccine began on March 1, 2021, as part of the Covax program. Despite the positive benefit/risk balance, the adverse effects of vaccination should not be minimized. Objective: To identify adverse events of AstraZeneca’s COVID-19 vaccination for better management. Materials and Methods: This is a case of a 57-year-old obese (BMI = 39 kg/m2) female health care worker who experienced adverse events in March 2021 after the second dose of AstraZeneca vaccine administered 4 weeks apart. These were subject to mandatory case reporting. Results: Major post-vaccination events occurred in a noisy systemic picture with parameters showing significant disturbances. Biological surveillance remains costly and makes the accountability of the vaccine complex. Conclusion: Vaccination remains the ultimate weapon in the fight against endemic diseases but should not overshadow the reporting of adverse events.

Power equipment vibration visualization using intelligent sensing method based on event-sensing principle

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification.Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior.Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor,this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal.A vibration measurement test platform is constructed,and feasibility and effectiveness tests are performed for the vibration motor and other power equipment.The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range.Furthermore,the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency.The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment.

Evaluation of the Impact of Driver Behavior on Back of Queues Events in Work Zones Using the SHRP2 Naturalistic Driving Study Data

The SHRP2 Naturalistic Driving Study was used to evaluate the impact of various work zone and driver characteristics on back of queue safety critical events (crash, near-crash, or conflicts) The model included 43 SCE and 209 “normal” events which were used as controls. The traces included representing 209 unique drivers. A Mixed-Effects Logistic Regression model was developed with probability of a SCE as the response variable and driver and work zone characteristics as predictor variables. The final model indicated glances over 1 second away from the driving task and following closely increased risk of an SCE by 3.8 times and 2.9 times, respectively. Average speed was negatively correlated to crash risk. This is counterintuitive since in most cases, it is expected that higher speeds are related to back of queue crashes. However, most queues form under congested conditions. As a result, vehicles encountering a back of queue would be more likely to be traveling at lower speeds.

Characteristics and Mechanisms of Persistent Wet–Cold Events with Different Cold-air Paths in South China

We investigate the characteristics and mechanisms of persistent wet–cold events(PWCEs)with different types of coldair paths.Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part.The pattern of single-station frequency of the PWCEs are“Yangtze River(YR)uniform”and“east–west inverse”.The YR uniform pattern is the dominant mode,so we focus on this pattern.The cold-air paths for PWCEs of the YR uniform pattern are divided into three types—namely,the west,northwest and north types—among which the west type accounts for the largest proportion.The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious.The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs.The positive water vapor budget for the three types of PWCEs mainly appears at the southern boundary.

Performance of the EDACS-ADP incorporating high-sensitivity troponin assay:Do components of major adverse cardiac events matter?

BACKGROUND:The accelerated diagnostic protocol(ADP)using the Emergency Department Assessment of Chest pain Score(EDACS-ADP),a tool to identify patients at low risk of a major adverse cardiac event(MACE)among patients presenting with chest pain to the emergency department,was developed using a contemporary troponin assay.This study was performed to validate and compare the performance of the EDACS-ADP incorporating high-sensitivity cardiac troponin I between patients who had a 30-day MACE with and without unstable angina(MACE I and II,respectively).METHODS:A single-center prospective observational study of adult patients presenting with chest pain suggestive of acute coronary syndrome was performed.The performance of EDACS-ADP in predicting MACE was assessed by calculating the sensitivity and negative predictive value.RESULTS:Of the 1,304 patients prospectively enrolled,399(30.6%;95%confidence interval[95%CI]:27.7%–33.8%)were considered low-risk using the EDACS-ADP.Among them,the rates of MACE I and II were 1.3%(5/399)and 1.0%(4/399),respectively.The EDACS-ADP showed sensitivities and negative predictive values of 98.8%(95%CI:97.2%–99.6%)and 98.7%(95%CI:97.0%–99.5%)for MACE I and 98.7%(95%CI:96.8%–99.7%)and 99.0%(95%CI:97.4%–99.6%)for MACE II,respectively.CONCLUSION:EDACS-ADP could help identify patients as safe for early discharge.However,when unstable angina was added to the outcome,the 30-day MACE rate among the designated lowrisk patients remained above the level acceptable for early discharge without further evaluation.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio:Markers predicting immune-checkpoint inhibitor efficacy and immune-related adverse events

We conducted a comprehensive review of existing prediction models pertaining to the efficacy of immune-checkpoint inhibitor(ICI)and the occurrence of immune-related adverse events(irAEs).The predictive potential of neutrophil-to-lymphocyte ratio(NLR)and platelet-to-lymphocyte ratio(PLR)in determining ICI effectiveness has been extensively investigated,while limited research has been conducted on predicting irAEs.Furthermore,the combined model incor-porating NLR and PLR,either with each other or in conjunction with additional markers such as carcinoembryonic antigen,exhibits superior predictive capabilities compared to individual markers alone.NLR and PLR are promising markers for clinical applications.Forthcoming models ought to incorporate established efficacious models and newly identified ones,thereby constituting a multifactor composite model.Furthermore,efforts should be made to explore effective clinical application approaches that enhance the predictive accuracy and efficiency.

Studies of an event-building algorithm of the readout system for the twin TPCs in HFRS

The High-energy Fragment Separator(HFRS),which is currently under construction,is a leading international radioactive beam device.Multiple sets of position-sensitive twin time projection chamber(TPC)detectors are distributed on HFRS for particle identification and beam monitoring.The twin TPCs'readout electronics system operates in a trigger-less mode due to its high counting rate,leading to a challenge of handling large amounts of data.To address this problem,we introduced an event-building algorithm.This algorithm employs a hierarchical processing strategy to compress data during transmission and aggregation.In addition,it reconstructs twin TPCs'events online and stores only the reconstructed particle information,which significantly reduces the burden on data transmission and storage resources.Simulation studies demonstrated that the algorithm accurately matches twin TPCs'events and reduces more than 98%of the data volume at a counting rate of 500 kHz/channel.

Assessing the Variability of Extreme Weather Events and Its Influence on Marine Accidents along the Northern Coast of Tanzania

The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.

Increased retinal venule diameter as a prognostic indicator for recurrent cerebrovascular events:a prospective observational study

Microvasculature of the retina is considered an alternative marker of cerebral vascular risk in healthy populations.However,the ability of retinal vasculature changes,specifically focusing on retinal vessel diameter,to predict the recurrence of cerebrovascular events in patients with ischemic stroke has not been determined comprehensively.While previous studies have shown a link between retinal vessel diameter and recurrent cerebrovascular events,they have not incorporated this information into a predictive model.Therefore,this study aimed to investigate the relationship between retinal vessel diameter and subsequent cerebrovascular events in patients with acute ischemic stroke.Additionally,we sought to establish a predictive model by combining retinal veessel diameter with traditional risk factors.We performed a prospective observational study of 141 patients with acute ischemic stroke who were admitted to the First Affiliated Hospital of Jinan University.All of these patients underwent digital retinal imaging within 72 hours of admission and were followed up for 3 years.We found that,after adjusting for related risk factors,patients with acute ischemic stroke with mean arteriolar diameter within 0.5-1.0 disc diameters of the disc margin(MAD_(0.5-1.0DD))of≥74.14μm and mean venular diameter within 0.5-1.0 disc diameters of the disc margin(MVD_(0.5-1.0DD))of≥83.91μm tended to experience recurrent cerebrovascular events.We established three multivariate Cox proportional hazard regression models:model 1 included traditional risk factors,model 2 added MAD_(0.5-1.0DD)to model 1,and model 3 added MVD0.5-1.0DD to model 1.Model 3 had the greatest potential to predict subsequent cerebrovascular events,followed by model 2,and finally model 1.These findings indicate that combining retinal venular or arteriolar diameter with traditional risk factors could improve the prediction of recurrent cerebrovascular events in patients with acute ischemic stroke,and that retinal imaging could be a useful and non-invasive method for identifying high-risk patients who require closer monitoring and more aggressive management.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.