| 1. |
- 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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| 2. |
- Salahirad, Alireza, et al.
(author)
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Mapping the structure and evolution of software testing research over the past three decades
- 2023
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In: Journal of Systems and Software. - : Elsevier BV. - 0164-1212. ; 195
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Journal article (peer-reviewed)abstract
- Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Methods: We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics examine how topics connect, and examine how the field is changing. Results: Testing research can be divided into 16 high-level topics and 18 subtopics. Creation guidance, automated test generation, evolution and maintenance, and test oracles have particularly strong connections to other topics, highlighting their multidisciplinary nature. Emerging keywords relate to web and mobile apps, machine learning, energy consumption, automated program repair and test generation, while emerging connections have formed between web apps, test oracles, and machine learning with many topics. Random and requirements-based testing show potential decline. Conclusions: Our observations, advice, and map data offer a deeper understanding of the field and inspiration regarding challenges and connections to explore. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
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| 4. |
- Mirchooli, Fahimeh, et al.
(author)
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How do data-mining models consider arsenic contamination in sediments and variables importance?
- 2019
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In: Environmental Monitoring & Assessment. - : SPRINGER. - 0167-6369 .- 1573-2959. ; 191:12
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Journal article (peer-reviewed)abstract
- Arsenic (As) is one of the most important dangerous elements as more than 100 million of people are exposed to risk, globally. The permissible threshold of As for drinking water is 10 mu g/L according to both the WHO's drinking water guidelines and the Iranian national standard. However, several studies have indicated that As concentrations exceed this threshold value in several regions of Iran. This research evaluates an As-susceptible region, the Tajan River watershed, using the following data-mining models: multivariate adaptive regression splines (MARS), functional data analysis (FDA), support vector machine (SVM), generalized linear model (GLM), multivariate discriminant analysis (MDA), and gradient boosting machine (GBM). This study considers 12 factors for elevated As concentrations: land use, drainage density, profile curvature, plan curvature, slope length, slope degree, topographic wetness index, erosion, village density, distance from villages, precipitation, and lithology. The susceptibility mapping was conducted using training (70%) and validation (30%). The results of As contamination in sediment showed that classifications into 4 levels of concentration are very similar for two models of GLM and FDA. The GBM calculated the areas of highest arsenic contamination risk by MARS and SVM with percentages of 30.0% and 28.7%, respectively. FDA, GLM, MARS, and MDA models calculated the areas of lowest risk to be 3.3%, 23.0%, 72.0%, 25.2%, and 26.1%, respectively. The results of ROC curve reveal that the MARS, SVM, and MDA had the highest accuracies with area under the curve ROC values of 84.6%, 78.9%, and 79.5%, respectively. Land use, lithology, erosion, and elevation were the most important predictors of contamination potential with a value of 0.6, 0.59, 0.57, and 0.56, respectively. These are the most important factors. Finally, these data-mining methods can be used as appropriate, inexpensive, and feasible options to identify As-susceptible areas and can guide managers to reduce contamination in sediment of the environment and the food chain.
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| 5. |
- Salahirad, Alireza, et al.
(author)
-
Mapping the structure and evolution of software testing research over the past three decades
- 2023
-
In: Journal of Systems and Software. - 0164-1212. ; 195
-
Journal article (peer-reviewed)abstract
- Background: The field of software testing is growing and rapidly-evolving. Aims: Based on keywords assigned to publications, we seek to identify predominant research topics and understand how they are connected and have evolved. Methods: We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics examine how topics connect, and examine how the field is changing. Results: Testing research can be divided into 16 high-level topics and 18 subtopics. Creation guidance, automated test generation, evolution and maintenance, and test oracles have particularly strong connections to other topics, highlighting their multidisciplinary nature. Emerging keywords relate to web and mobile apps, machine learning, energy consumption, automated program repair and test generation, while emerging connections have formed between web apps, test oracles, and machine learning with many topics. Random and requirements-based testing show potential decline. Conclusions: Our observations, advice, and map data offer a deeper understanding of the field and inspiration regarding challenges and connections to explore. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
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| 6. |
- Mohammadi, Ali, 1983-, et al.
(author)
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Joint Life Cycle Assessment and Data Envelopment Analysis for the benchmarking of environmental impacts in rice paddy production
- 2015
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In: Journal of Cleaner Production. - : Elsevier. - 0959-6526 .- 1879-1786. ; 106, s. 521-532
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Journal article (peer-reviewed)abstract
- The combined implementation of Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) has been identified as a suitable tool for the evaluation of the environmental and economic performance of multiple similar entities. In this study, a total of 82 rice paddy fields for spring and summer growing seasons in north of Iran were assessed using a combined LCA and DEA methodology to estimate the technical efficiency of each farmer. Furthermore, the environmental consequences of operational inefficiencies were quantified and target performance values benchmarked for inefficient units so that ecoefficiency criteria were verified. Results showed average reduction levels of up to 20% and 25% per material input for spring and summer systems, leading to impact reductions which ranged from 8% to 11% for spring farms and 19% to 25% for summer farms depending on the chosen impact category. Additionally, the potential economic savings from efficient farming operations were also determined. The economic results indicate that an added annual gross margin of 0.045 $ per 1 kg rice paddy could be achieved if inefficient units converted to an efficient operation. (C) 2014 Elsevier Ltd. All rights reserved.
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