Study of Holocene glacier degradation in central Asia by isotopic methods for long-term forecast of climate changes

This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

CALTM:A Context-Aware Long-Term Time-Series Forecasting Model

Time series data plays a crucial role in intelligent transportation systems.Traffic flow forecasting represents a precise estimation of future traffic flow within a specific region and time interval.Existing approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series forecasting.However,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term forecastings.Consequently,the effectiveness of existing methods diminishes in such scenarios.Therefore,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic flow.Our model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final forecasting.Experimental results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method

To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

Long-term thinning decreases the contribution of heterotrophic respiration to soil respiration in subalpine plantations

Interest in the dynamics of soil respiration(R_(S))in subalpine forest ecosystems is increasing due to their high soil carbon density and potential sensitivity to environmental changes.However,as a principal silvicultural practice,the long-term impacts of thinning on R_(S) and its heterotrophic and autotrophic respiration components(R_(h) and Ra,respectively)in subalpine plantations are poorly understood,espe-cially in winter.A 3-year field observation was carried out with consideration of winter CO_(2) efflux in middle-aged sub-alpine spruce plantations in northwestern China.A trench-ing method was used to explore the long-term impacts of thinning on Rs,Rn and R_(a).Seventeen years after thinning,mean annual Rs,Rn and R_(a) increased,while the contribu-tion of R_(h) to R_(s) decreased with thinning intensity.Thinning significantly decreased winter R,because of the reduction in R_(n) but had no significant effect on Ra.The temperature sensitivity(Q_(10))of R_(h) and R_(a) also increased with thinning intensity,with lower Q_(10) values for R_(h)(2.1-2.6)than for Ra(2.4-2.8).The results revealed the explanatory variables and pathways related to R_(n) and R_(a) dynamics.Thinning increased soil moisture and nitrate nitrogen(NO_(3)^(-)-N),and the enhanced nitrogen and water availability promoted R_(h) and R_(a) by improving fine root biomass and microbial activity.Our results highlight the positive roles of NO_(3)^(-)-N in stimulating R_(s) components following long-term thinning.Therefore,applications of nitrogen fertilizer are not recommended while thinning subalpine spruce plantations from the perspective of reducing soil CO_(2) emissions.The increased Q_(10) values of R_(s) components indicate that a large increase in soil CO_(2) emissions would be expected following thinning because of more pronounced climate warming in alpineregions.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

Soil bacterial and fungal communities resilience to long-term nitrogen addition in subtropical forests in China

Atmospheric nitrogen(N)deposition is predicted to increase,especially in the subtropics.However,the responses of soil microorganisms to long-term N addition at the molecular level in N-rich subtropical forests have not been clarified.A long-term nutrient addition experiment was conducted in a subtropical evergreen old-growth forest in China.The four treatments were:control,low N(50 kg N ha^(-1)a^(-1)),high N(100 kg N ha^(-1)a^(-1)),and combined N and phosphorus(P)(100 kg N ha^(-1)a^(-1)+50 kg P ha^(-1)a^(-1)).Metagenomic sequencing characterized diversity and composition of soil microbial communities and used to construct bacterial/fungal co-occurrence networks.Nutrient-treated soils were more acidic and had higher levels of dissolved organic carbon than controls.There were no significant differences in microbial diversity and community composition across treatments.The addition of nutrients increased the abundance of copiotrophic bacteria and potentially beneficial microorganisms(e.g.,Gemmatimonadetes,Chaetomium,and Aureobasidium).Low N addition increased microbiome network connectivity.Three rare fungi were identified as module hubs under nutrient addition,indicating that low abundance fungi were more sensitive to increased nutrients.The results indicate that the overall composition of microbial communities was stable but not static to long-term N addition.Our findings provide new insights that can aid predictions of the response of soil microbial communities to long-term N addition.

Weather-Driven Solar Power Forecasting Using D-Informer:Enhancing Predictions with Climate Variables

Precise forecasting of solar power is crucial for the development of sustainable energy systems.Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic(PV)power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data.To overcome these challenges,this research presents a cutting-edge,multi-stage forecasting method called D-Informer.This method skillfully merges the differential transformation algorithm with the Informer model,leveraging a detailed array of meteorological variables and historical PV power generation records.The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,achieving on average a 67.64%reduction in mean squared error(MSE),a 49.58%decrease in mean absolute error(MAE),and a 43.43%reduction in root mean square error(RMSE).Moreover,it attained an R2 value as high as 0.9917 during the winter season,highlighting its precision and dependability.This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism,which greatly enhances the model’s ability to identify complex interactions among diverse input variables,and the inclusion of weather variables,enriching the model’s input data and strengthening its predictive accuracy in time series analysis.Additionally,the experimental results confirm the effectiveness of the proposed approach.

Decadal Forecasts of Large Earthquakes along the Northern San Andreas Fault System, California: Increased Activity on Regional Creeping Faults Prior to Major and Great Events

The three largest earthquakes in northern California since 1849 were preceded by increased decadal activity for moderate-size shocks along surrounding nearby faults. Increased seismicity, double-difference precise locations of earthquakes since 1968, geodetic data and fault offsets for the 1906 great shock are used to re-examine the timing and locations of possible future large earthquakes. The physical mechanisms of regional faults like the Calaveras, Hayward and Sargent, which exhibit creep, differ from those of the northern San Andreas, which is currently locked and is not creeping. Much decadal forerunning activity occurred on creeping faults. Moderate-size earthquakes along those faults became more frequent as stresses in the region increased in the latter part of the cycle of stress restoration for major and great earthquakes along the San Andreas. They may be useful for decadal forecasts. Yearly to decadal forecasts, however, are based on only a few major to great events. Activity along closer faults like that in the two years prior to the 1989 Loma Prieta shock needs to be examined for possible yearly forerunning changes to large plate boundary earthquakes. Geodetic observations are needed to focus on identifying creeping faults close to the San Andreas. The distribution of moderate-size earthquakes increased significantly since 1990 along the Hayward fault but not adjacent to the San Andreas fault to the south of San Francisco compared to what took place in the decades prior to the three major historic earthquakes in the region. It is now clear from a re-examination of the 1989 mainshock that the increased level of moderate-size shocks in the one to two preceding decades occurred on nearby East Bay faults. Double-difference locations of small earthquakes provide structural information about faults in the region, especially their depths. The northern San Andreas fault is divided into several strongly coupled segments based on differences in seismicity.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Investigating Periodic Dependencies to Improve Short-Term Load Forecasting

With a further increase in energy flexibility for customers,short-term load forecasting is essential to provide benchmarks for economic dispatch and real-time alerts in power grids.The electrical load series exhibit periodic patterns and share high associations with metrological data.However,current studies have merely focused on point-wise models and failed to sufficiently investigate the periodic patterns of load series,which hinders the further improvement of short-term load forecasting accuracy.Therefore,this paper improved Autoformer to extract the periodic patterns of load series and learn a representative feature from deep decomposition and reconstruction.In addition,a novel multi-factor attention mechanism was proposed to handle multi-source metrological and numerical weather prediction data and thus correct the forecasted electrical load.The paper also compared the proposed model with various competitive models.As the experimental results reveal,the proposed model outperforms the benchmark models and maintains stability on various types of load consumers.

Development of a new Cox model for predicting long-term survival in hepatitis cirrhosis patients underwent transjugular intrahepatic portosystemic shunts

BACKGROUND Transjugular intrahepatic portosystemic shunt(TIPS)placement is a procedure that can effectively treat complications of portal hypertension,such as variceal bleeding and refractory ascites.However,there have been no specific studies on predicting long-term survival after TIPS placement.AIM To establish a model to predict long-term survival in patients with hepatitis cirrhosis after TIPS.METHODS A retrospective analysis was conducted on a cohort of 224 patients who un-derwent TIPS implantation.Through univariate and multivariate Cox regression analyses,various factors were examined for their ability to predict survival at 6 years after TIPS.Consequently,a composite score was formulated,encompassing the indication,shunt reasonability,portal venous pressure gradient(PPG)after TIPS,percentage decrease in portal venous pressure(PVP),indocyanine green retention rate at 15 min(ICGR15)and total bilirubin(Tbil)level.Furthermore,the performance of the newly developed Cox(NDC)model was evaluated in an in-ternal validation cohort and compared with that of a series of existing models.RESULTS The indication(variceal bleeding or ascites),shunt reasonability(reasonable or unreasonable),ICGR15,post-operative PPG,percentage of PVP decrease and Tbil were found to be independent factors affecting long-term survival after TIPS placement.The NDC model incorporated these parameters and successfully identified patients at high risk,exhibiting a notably elevated mortality rate following the TIPS procedure,as observed in both the training and validation cohorts.Additionally,in terms of predicting the long-term survival rate,the performance of the NDC model was significantly better than that of the other four models[Child-Pugh,model for end-stage liver disease(MELD),MELD-sodium and the Freiburg index of post-TIPS survival].CONCLUSION The NDC model can accurately predict long-term survival after the TIPS procedure in patients with hepatitis cirrhosis,help identify high-risk patients and guide follow-up management after TIPS implantation.

Long-Term Mortality of Children with Congenital Heart Disease Admitted to the Departmental University Hospital of Borgou/Alibori from 2011 to 2022

Background: Congenital heart disease is a public health issue due to its incidence and mortality rate. The aim of this study was to investigate the long-term mortality of children with congenital heart disease admitted to the Departmental University Hospital of Borgou/Alibori (CHUD-B/A) from 2011 to 2022. Methods: This descriptive longitudinal study with analytical aims covered 11 years (April 1, 2011 to December 31, 2022). It consisted of a review of the records of children under 15 years of age with echocardiographically confirmed congenital heart disease. This was followed by an interview with the parents to assess the children’s current condition. Data were entered using Kobocollect software and analyzed using R Studio 4.2.2. software. Results: A total of 143 complete files were retained. The median age at diagnosis was 14 months (IIQ: Q1 = 4;Q3 = 60) with a range of 2 days and 175 months, and the sex-ratio (M/F) was 0.96. Left-to-right shunts were the most frequent cardiopathy group (62.9%). Only 35 children (24.5%) benefited from restorative treatment. The mortality rate was 31.5%. Median survival under the maximum bias assumption was 114 months and 216 months under the assumption of minimum bias. Survival was significantly better in children with right-to-left shunts (p = 0.0049) under the assumption of minimum bias. The death risk factors were: age at diagnosis less than 12 months (aHR = 7.58;95% CI = 3.36 - 17.24;p Conclusion: The long-term mortality of congenital heart disease is high and favoured by the absence of restorative treatment. Local correction of congenital heart disease and medical follow-up will help to reduce this mortality.

Long-term performance of recycled asphalt mixtures containing high RAP and RAS

The application of reclaimed asphalt pavement(RAP)and reclaimed asphalt shingles(RAS)on asphalt pavement can reduce the asphalt paving cost,conserve energy and protect the environment.However,the use of high contents of RAP and RAS in asphalt pavement may lead to durability issues,especially the fatigue cracking and thermal cracking.It is necessary to conduct a series of analyses on asphalt mixtures containing high RAP and RAS,and seek methods to enhance their long-term performance.This paper provides a comprehensive over-view of the long-term performance of recycled asphalt mixtures containing high contents of RAP and RAS.The findings in this research show that rutting resistance of high recycled asphalt mixtures is not a concern,whereas their resistance to fatigue and thermal cracking is not conclusive.Recycling agents can be used to improve the thermal cracking resistance of high recycled asphalt mixtures.An optimum decision on recycling agents will improve the durability properties of high recycled asphalt mixtures.It is recommended that to use a balanced mixture design approach with testing of the blended asphalt binders will provide better understanding of long-term performance of recycled asphalt mixtures containing high RAP and RAS.