| 1. |
- Kanai, M, et al.
(author)
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- 2023
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swepub:Mat__t
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| 2. |
- Ahmadlou, Mohammad, et al.
(author)
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Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks
- 2021
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In: Journal of Flood Risk Management. - UK : John Wiley & Sons. - 1753-318X. ; 14:1
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Journal article (peer-reviewed)abstract
- Floods are one of the most destructive natural disasters causing financial dam-ages and casualties every year worldwide. Recently, the combination of data-driven techniques with remote sensing (RS) and geographical information sys-tems (GIS) has been widely used by researchers for flood susceptibility map-ping. This study presents a novel hybrid model combining the multilayerperceptron (MLP) and autoencoder models to produce the susceptibility mapsfor two study areas located in Iran and India. For two cases, nine, and twelvefactors were considered as the predictor variables for flood susceptibility map-ping, respectively. The prediction capability of the proposed hybrid model wascompared with that of the traditional MLP model through the area under thereceiver operating characteristic (AUROC) criterion. The AUROC curve for theMLP and autoencoder-MLP models were, respectively, 75 and 90, 74 and 93%in the training phase and 60 and 91, 81 and 97% in the testing phase, for Iranand India cases, respectively. The results suggested that the hybridautoencoder-MLP model outperformed the MLP model and, therefore, can beused as a powerful model in other studies for flood susceptibility mapping.
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| 3. |
- Arora, Aman, et al.
(author)
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Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
- 2021
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In: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 750
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Journal article (peer-reviewed)abstract
- This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.
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| 4. |
- Holmqvist, Anna Sällfors, et al.
(author)
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Assessment of Late Mortality Risk after Allogeneic Blood or Marrow Transplantation Performed in Childhood
- 2018
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In: JAMA Oncology. - : American Medical Association (AMA). - 2374-2437. ; 4:12
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Journal article (peer-reviewed)abstract
- Importance: Allogeneic blood or marrow transplantation (BMT) is a curative option for malignant and nonmalignant diseases of childhood. However, little is known about trends in cause-specific late mortality in this population during the past 3 decades. Objectives: To examine cause-specific late mortality among individuals who have lived 2 years or more after allogeneic BMT performed in childhood and whether rates of late mortality have changed over time. Design, Setting, and Participants: A retrospective cohort study was conducted of individuals who lived 2 years or more after undergoing allogeneic BMT performed in childhood between January 1, 1974, and December 31, 2010. The end of follow-up was December 31, 2016. Exposure: Allogeneic BMT performed in childhood. Main Outcomes and Measures: All-cause mortality, relapse-related mortality, and non-relapse-related mortality. Data on vital status and causes of death were collected using medical records, the National Death Index Plus Program, and Accurint databases. Results: Among 1388 individuals (559 females and 829 males) who lived 2 years or more after allogeneic BMT performed in childhood, the median age at transplantation was 14.6 years (range, 0-21 years). In this cohort, there was a total of 295 deaths, yielding an overall survival rate of 79.3% at 20 years after BMT. The leading causes of death were infection and/or chronic graft-vs-host disease (121 of 244 [49.6%]), primary disease (60 of 244 [24.6%]), and subsequent malignant neoplasms (45 of 244 [18.4%]). Overall, the cohort had a 14.4-fold increased risk for death (95% CI, 12.8-16.1) compared with the general population (292 deaths observed; 20.3 deaths expected). Relative mortality remained elevated at 25 years or more after BMT (standardized mortality ratio, 2.9; 95% CI, 2.0-4.1). The absolute excess risk for death from any cause was 12.0 per 1000 person-years (95% CI, 10.5-13.5). The cumulative incidence of non-relapse-related mortality exceeded that of relapse-related mortality throughout follow-up. The 10-year cumulative incidence of late mortality decreased over time (before 1990, 18.9%; 1990-1999, 12.8%; 2000-2010, 10.9%; P =.002); this decrease remained statistically significant after adjusting for demographic and clinical factors (referent group: <1990; 1990-1999: hazard ratio, 0.64; 95% CI, 0.47-0.89; P =.007; 2000-2010: hazard ratio, 0.49; 95% CI, 0.31-0.76; P =.002; P <.001 for trend). Conclusions and Relevance: Late mortality among children undergoing allogeneic BMT has decreased during the past 3 decades. However, these patients remain at an elevated risk of late mortality even 25 years or more after transplantation when compared with the general population, necessitating lifelong follow-up.
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| 5. |
- Kassebaum, Nicholas J., et al.
(author)
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Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015 : a systematic analysis for the Global Burden of Disease Study 2015
- 2016
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In: The Lancet. - 0140-6736 .- 1474-547X. ; 388:10053, s. 1603-1658
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Journal article (peer-reviewed)abstract
- Background Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development. Methods We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate. Findings Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs off set by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY rates due to several high-burden NCDs (including osteoarthritis, drug use disorders, depression, diabetes, congenital birth defects, and skin, oral, and sense organ diseases) either increased or remained unchanged, leading to increases in their relative ranking in many geographies. From 2005 to 2015, HALE at birth increased by an average of 2.9 years (95% uncertainty interval 2.9-3.0) for men and 3.5 years (3.4-3.7) for women, while HALE at age 65 years improved by 0.85 years (0.78-0.92) and 1.2 years (1.1-1.3), respectively. Rising SDI was associated with consistently higher HALE and a somewhat smaller proportion of life spent with functional health loss; however, rising SDI was related to increases in total disability. Many countries and territories in central America and eastern sub-Saharan Africa had increasingly lower rates of disease burden than expected given their SDI. At the same time, a subset of geographies recorded a growing gap between observed and expected levels of DALYs, a trend driven mainly by rising burden due to war, interpersonal violence, and various NCDs. Interpretation Health is improving globally, but this means more populations are spending more time with functional health loss, an absolute expansion of morbidity. The proportion of life spent in ill health decreases somewhat with increasing SDI, a relative compression of morbidity, which supports continued efforts to elevate personal income, improve education, and limit fertility. Our analysis of DALYs and HALE and their relationship to SDI represents a robust framework on which to benchmark geography-specific health performance and SDG progress. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives for all countries along the development continuum.
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| 6. |
- Lozano, Rafael, et al.
(author)
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Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017
- 2018
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In: The Lancet. - : Elsevier. - 1474-547X .- 0140-6736. ; 392:10159, s. 2091-2138
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Journal article (peer-reviewed)abstract
- Background: Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods: We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings: The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation: The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030.
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| 7. |
- Prashant, Kumar, et al.
(author)
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Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders
- 2023
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In: Buildings. - : MDPI. - 2075-5309. ; 13:4
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Journal article (peer-reviewed)abstract
- The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.
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| 8. |
- Singh, Rohan, et al.
(author)
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Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms
- 2022
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In: Materials. - : MDPI. - 1996-1944. ; 15:23
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Journal article (peer-reviewed)abstract
- The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect the ultimate load-carrying capacity of reinforced concrete (RC) structures. Therefore, the prediction of accurate bond strength has become an important parameter for the safety measurements of RC structures. However, the analytical models are not enough to estimate the bond strength, as they are built using various assumptions and limited datasets. The machine learning (ML) techniques named artificial neural network (ANN) and support vector machine (SVM) have been used to estimate the bond strength between concrete and corroded steel reinforcement bar. The considered input parameters in this research are the surface area of the specimen, concrete cover, type of reinforcement bars, yield strength of reinforcement bars, concrete compressive strength, diameter of reinforcement bars, bond length, water/cement ratio, and corrosion level of reinforcement bars. These parameters were used to build the ANN and SVM models. The reliability of the developed ANN and SVM models have been compared with twenty analytical models. Moreover, the analyzed results revealed that the precision and efficiency of the ANN and SVM models are higher compared with the analytical models. The radar plot and Taylor diagrams have also been utilized to show the graphical representation of the best-fitted model. The proposed ANN model has the best precision and reliability compared with the SVM model, with a correlation coefficient of 0.99, mean absolute error of 1.091 MPa, and root mean square error of 1.495 MPa. Researchers and designers can apply the developed ANN model to precisely estimate the steel-to-concrete bond strength.
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| 9. |
- Wadhwa, Aman, et al.
(author)
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Burden of Morbidity after Allogeneic Blood or Marrow Transplantation for Inborn Errors of Metabolism : A BMT Survivor Study Report
- 2022
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In: Transplantation and cellular therapy. - : Elsevier BV. - 2666-6367. ; 28:3, s. 1-157
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Journal article (peer-reviewed)abstract
- Survival after blood or marrow transplantation (BMT) for inborn errors of metabolism (IEM) is excellent; however, the burden of morbidity in long-term survivors of BMT for IEM remains understudied. This study examined the risk of chronic health conditions (CHC) in ≥2-year survivors of allogeneic BMT for IEM performed between 1974 and 2014 using the BMT Survivor Study. In this retrospective cohort study, participants (or their parents; n = 154) reported demographic data and CHCs (graded using Common Terminology Criteria for Adverse Events version 5), and transplantation characteristics were obtained from institutional databases. Unaffected siblings (n = 494) served as a comparison group. Logistic regression was used to estimated the odds of severe/life-threatening CHCs compared with siblings. Cox proportional hazards regression was used to estimate factors associated with severe/life-threatening/fatal CHCs in survivors of BMT for IEM. Survivors of allogeneic BMT for IEM (leukodystrophies, 43.5%; mucopolysaccharidoses, 41.0%) were at 12.5-fold higher odds of severe/life-threatening CHCs (95% confidence interval [CI], 5.4 to 28.9) compared with their siblings. The mean 10-year post-BMT cumulative incidence of grade 3-5 CHCs was 47.5 ± 4.0%. Reduced-intensity conditioning (RIC) was associated with a 2.7-fold higher risk (95% CI, 1.2 to 6.2; P = .02) of any grade 3-5 CHC, a 6.7-fold higher risk of grade 3-5 cardiopulmonary conditions (95% CI, 1.3 to 35.4), and a 3.0-fold higher risk of severe hearing/vision deficits (95% CI, 1.4 to 6.6). Older (age >26 years) BMT survivors were significantly less likely to graduate from college (odds ratio [OR], 0.3; 95% CI, 0.1 to 0.7) or marry (OR, 0.01; 95% CI, 0.004 to 0.07) compared with their siblings. Survivors of BMT for IEM carry a significant burden of morbidities, which affects their ability to attain adult milestones. Efforts to reduce chronic health conditions in this population are needed.
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| 10. |
- Wadhwa, Aman, et al.
(author)
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Late Mortality after Allogeneic Blood or Marrow Transplantation for Inborn Errors of Metabolism : A Report from the Blood or Marrow Transplant Survivor Study-2 (BMTSS-2)
- 2019
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In: Biology of Blood and Marrow Transplantation. - : Elsevier BV. - 1083-8791. ; 25:2, s. 328-334
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Journal article (peer-reviewed)abstract
- Allogeneic blood or marrow transplantation (BMT) is currently considered the standard of care for patients with specific inborn errors of metabolism (IEM). However, there is a paucity of studies describing long-term survival and cause-specific late mortality after BMT in these patients with individual types of IEM. We studied 273 patients who had survived ≥2 years after allogeneic BMT for IEM performed between 1974 and 2014. The most prevalent IEM in our cohort were X-linked adrenoleukodystrophy (ALD; 37.3%), Hurler syndrome (35.1%), and metachromatic leukodystrophy (MLD; 10.2%). Conditional on surviving ≥2 years after BMT, the overall survival for the entire cohort was 85.5 ± 2.4% at 10 years and 73.5 ± 3.7% at 20 years. The cohort had a 29-fold increased risk of late death compared with an age- and sex-matched cohort from the general US population (95% CI, 22- to 38-fold). The increased relative mortality was highest in the 2- to 5-year period after BMT (standardized mortality ratio [SMR], 207; 95% confidence interval [CI], 130 to 308) and declined with increasing time from BMT, but remained elevated for ≥21 years after BMT (SMR, 9; 95% CI, 4 to 18). Sequelae from the progression of primary disease were the most common causes of late mortality in this cohort (76%). The use of T cell-depleted grafts in patients with ALD and Hurler syndrome was a risk factor for late mortality. Younger age at BMT and use of busulfan and cyclosporine were protective in patients with Hurler syndrome. Our findings demonstrate relatively favorable overall survival in ≥2-year survivors of allogeneic BMT for IEM, although primary disease progression continues to be responsible for the majority of late deaths.
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