| 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. |
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| 3. |
- Murray, Christopher J. L., et al.
(author)
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Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017
- 2018
-
In: The Lancet. - 1474-547X .- 0140-6736. ; 392:10159, s. 1995-2051
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Journal article (peer-reviewed)abstract
- Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49·4% (95% uncertainty interval [UI] 46·4–52·0). The TFR decreased from 4·7 livebirths (4·5–4·9) to 2·4 livebirths (2·2–2·5), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83·8 million people per year since 1985. The global population increased by 197·2% (193·3–200·8) since 1950, from 2·6 billion (2·5–2·6) to 7·6 billion (7·4–7·9) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2·0%; this rate then remained nearly constant until 1970 and then decreased to 1·1% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2·5% in 1963 to 0·7% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2·7%. The global average age increased from 26·6 years in 1950 to 32·1 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59·9% to 65·3%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1·0 livebirths (95% UI 0·9–1·2) in Cyprus to a high of 7·1 livebirths (6·8–7·4) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0·08 livebirths (0·07–0·09) in South Korea to 2·4 livebirths (2·2–2·6) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0·3 livebirths (0·3–0·4) in Puerto Rico to a high of 3·1 livebirths (3·0–3·2) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2·0% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill & Melinda Gates Foundation.
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| 4. |
- Albano, Michele, et al.
(author)
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Energy Saving by Blockchaining Maintenance.
- 2018
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In: Journal of Industrial Engineering and Management Science. - : River Publishers. - 2446-1822. ; 2018:1, s. 63-88
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Journal article (peer-reviewed)abstract
- The development and interest in Industry 4.0 together with rapid development of Cyber Physical Systems has created magnificent opportunities to develop maintenance to a totally new level. The Maintenance 4.0 vision considers massive exploitation of information regarding factories and machines to improve maintenance efficiency and efficacy, for example by facilitating logistics of spare parts, but on the other hand this creates other logistics issues on the data itself, which only exacerbate data management issues that emerge when distributed maintenance platforms scale up. In fact, factories can be delocalized with respect to the data centers, where data has to be transferred to be processed. Moreover, any transaction needs communication, be it related to purchase of spare parts, sales contract, and decisions making in general, and it has to be verified by remote parties. Keeping in mind the current average level of Overall Equipment Efficiency (50%) i.e. there is a hidden factory behind every factory, the potential is huge. It is expected that most of this potential can be realised based on the use of the above named technologies, and relying on a new approach called blockchain technology, the latter aimed at facilitating data and transactions management. Blockchain supports logistics by a distributed ledger to record transactions in a verifiable and permanent way, thus removing the need for multiple remote parties to verify and store every transaction made, in agreement with the first “r” of maintenance (reduce, repair, reuse, recycle). Keeping in mind the total industrial influence on the consumption of natural resources, such as energy, the new technology advancements can allow for dramatic savings, and can deliver important contributions to the green economy that Europe aims for. The paper introduces the novel technologies that can support sustainability of manufacturing and industry at large, and proposes an architecture to bind together said technologies to realise the vision of Maintenance 4.0.
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| 5. |
- Baglee, David, et al.
(author)
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How can SMEs adopt a new method to advanced maintenance strategies : A Case study approach
- 2018
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In: Conference Proceedings: 30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017). - : University of Central Lancashire. - 9781909755154 ; , s. 155-162
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Conference paper (peer-reviewed)abstract
- Maintenance is crucial to manufacturing operations. In many organisations, the production equipmentrepresents the majority of invested capital, and deterioration of these facilities and equipment increasesproduction costs, reduces product quality. Over recent years the importance of maintenance, and thereforemaintenance management, within manufacturing organisations has grown. The maintenance function hasbecome an increasingly important and complex activity, particularly as automation increases. Theopportunity exists for many organisations to benefit substantially through improvements to theircompetitiveness and profitability by adopting a new approach to maintenance management. Several toolsand technologies including Condition Based Maintenance (CBM), Reliability Centred Maintenance (RCM)and more recently e-maintenance have developed under the heading of Advanced Maintenance Strategies.However, the adoption of advanced maintenance strategies and their potential benefits are usuallydemonstrated in large organisations. Unfortunately, the majority of organisations are constrained by thelack of knowledge and understanding on the requirements, which need to be in place before adopting anadvanced maintenance strategy. These are usually classified as Small and Medium Sized Enterprises(SMEs).The research strategy is based on ‘empirical iterations’ using survey secondary data, experts’ interviewsinformation and multiple case studies. The results show that there is a set of recommendations, whichstrongly influence the implementation of an Advanced Maintenance Strategy (AMS) with a Small toMedium Enterprise (SME). Organisations require a structured and integrative approach in order to takeadvantage of a new approach to maintenance management. This paper will propose recommendations forintegrating an AMS into the organisation and provide evidence of a successful implementation.
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| 6. |
- Baglee, David, et al.
(author)
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How Does CBM Function in the Real World?
- 2016
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In: MFPT 2015 and ISA’s 61st International Instrumentation Symposium, At Dayton. Ohio.
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Conference paper (peer-reviewed)abstract
- Manufacturing organizations are under increasing pressure to meet customer and corporate demands by implementing improved maintenance initiatives to reduce costs, improve equipment availability, and protect against failure of critical equipment. Condition Based Maintenance (CBM) is widely accepted and used as a financially effective maintenance strategy which is used to anticipate equipment or component failure. Recent technological advances in component sensitivities, size reductions, and most importantly, cost has opened up an entirely new area of diagnostics. The economic benefit of CBM is achieved if the approach to maintenance is applied to the right equipment and through appropriate tools. In particular the degradation behavior of the equipment needs to be understood to correctly deploy a CBM approach and specific actions to specific equipment or components. Failure modes can be applied to support and optimise the decision making process. Using failure modes can be an efficient low-risk tool process for the prevention of problems, and is referred to as a deductive technique that consists of failure identification in each component. However, the literature is limited regarding the importance and the role of various failure models in different industrial sectors. Thus, if failure models are not known, understood and utilised correctly the use of CBM will not lead to financial benefits. The paper examines the relationship between the failure patterns observed in industrial maintenance practice and the corresponding impact on adoption and potential benefits of Condition-Based Maintenance (CBM). The paper will explain the need for accurate and up to date equipment information to support the correct maintenance approach. The paper suggests the importance of further supporting such investments by appropriately addressing the need to collect relevant data as a basis upon which to make the right decisions.
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| 7. |
- Baglee, David, et al.
(author)
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Optimizing Condition Monitoring of Big Data Systems
- 2017
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In: Proceedings of the 2017 International Conference on Data Mining. - : CSREA Press. - 1601324537 ; , s. 127-131
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Conference paper (peer-reviewed)abstract
- Industrial communication networks are common in a number of manufacturing organisations. The high availability of these networks is crucial for smooth plant operations. Therefore local and remote diagnostics of these networks is of primary importance in determining issues relating to plant reliability and availability. Condition Monitoring (CM) techniques when connected to a network provide a diagnostic system for remote monitoring of manufacturing equipment. The system monitors the health of the network and the equipment and is therefore able to predict performance. However, this leads to the collection, storage and analyses of large amounts of data, which must provide value. These large data sets are commonly referred to as Big Data. This paper presents a general concept of the use of condition monitoring and big data systems to show how they complement each other to provide valuable data to enhance manufacturing competiveness.
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| 8. |
- Campos, Jaime, et al.
(author)
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A big data analytical architecture for the Asset Management
- 2017
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In: Industrial Product/Service-Systems (IPSS) Conference. - : Elsevier. ; , s. 369-374
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Conference paper (peer-reviewed)abstract
- The paper highlights the characteristics of data and big data analytics in manufacturing, more specifically for the industrial asset management. The authors highlight important aspects of the analytical system architecture for purposes of asset management. The authors cover the data and big data technology aspects of the domain of interest. This is followed by application of the big data analytics and technologies, such as machine learning and data mining for asset management. The paper also presents the aspects of visualisation of the results of data analytics. In conclusion, the architecture provides a holistic view of the aspects and requirements of a big data technology application system for purposes of asset management. The issues addressed in the paper, namely equipment health, reliability, effects of unplanned breakdown, etc., are extremely important for today's manufacturing companies. Moreover, the customer's opinion and preferences of the product/services are crucial as it gives an insight into the ways to improve in order to stay competitive in the market. Finally, a successful asset management function plays an important role in the manufacturing industry, which is dependent on the support of proper ICTs for its further success. (C) 2017 The Authors Published by Elsevier B.V.
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| 9. |
- Campos, Jaime, et al.
(author)
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Arrowhead Framework services for condition monitoring and maintenance based on the open source approach
- 2019
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In: 6th International Conference on Control Decision Information Technologies (CoDIT 2019). - : IEEE. - 9781728105215 - 9781728105208 - 9781728105222 ; , s. 697-702
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Conference paper (peer-reviewed)abstract
- The emergence of new Information and Communication Technologies, such as the Internet of Things and big data and data analytics provides opportunities as well as challenges for the domain of interest, and this paper discusses their importance in condition monitoring and maintenance. In addition, the Open system architecture for condition-based maintenance (OSA-CBM), and the Predictive Health Monitoring methods are gone through. Thereafter, the paper uses bearing fault data from a simulation model with the aim to produce vibration signals where different parameters of the model can be controlled. In connection to the former mentioned a prototype was developed and tested for purposes of simulated rolling element bearing fault systems signals with appropriate fault diagnostic and analytics. The prototype was developed taking into consideration recommended standards (e.g., the OSA-CBM). In addition, the authors discuss the possibilities to incorporate the developed prototype into the Arrowhead framework, which would bring possibilities to: analyze various equipment geographically dispersed, especially in this case its rolling element bearing; support servitization of Predictive Health Monitoring methods and large-scale interoperability; and, to facilitate the appearance of novel actors in the area and thus competition.
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| 10. |
- Campos, Jaime, et al.
(author)
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Business Performance Measurements in Asset Management with the Support of Big Data Technologies
- 2017
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In: Management Systems in Production Engineering. - : De Gruyter Open. - 2299-0461 .- 2450-5781. ; 25:3, s. 143-149
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Journal article (peer-reviewed)abstract
- The paper reviews the performance measurement in the domain of interest. Important data in asset management are further, discussed. The importance and the characteristics of today’s ICTs capabilities are also mentioned in the paper. The role of new concepts such as big data and data mining analytical technologies in managing the performance measurements in asset management are discussed in detail. The authors consequently suggest the use of the modified Balanced Scorecard methodology highlighting both quantitative and qualitative aspects, which is crucial for optimal use of the big data approach and technologies.
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