| 1. |
- Lozano, Rafael, et al.
(författare)
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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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Ingår i: The Lancet. - : Elsevier. - 1474-547X .- 0140-6736. ; 392:10159, s. 2091-2138
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Tidskriftsartikel (refereegranskat)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. |
- Campos, Jaime, et al.
(författare)
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A big data analytical architecture for the Asset Management
- 2017
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Ingår i: Industrial Product/Service-Systems (IPSS) Conference. - : Elsevier. ; , s. 369-374
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Konferensbidrag (refereegranskat)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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| 3. |
- Campos, Jaime, et al.
(författare)
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An Open Source Framework Approach to Support Condition Monitoring and Maintenance
- 2020
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 10:18, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.
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| 4. |
- Campos, Jaime, et al.
(författare)
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The Use of Relational and NoSQL Databases in Industrial Asset Management
- 2020
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Ingår i: Engineering Assets and Public Infrastructures in the Age of Digitalization. - Cham : Springer. - 9783030480219 - 9783030480202 ; , s. 302-308
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Bokkapitel (refereegranskat)abstract
- The advancements concerning the development of ICT systemsincluding Internet of Things (IoT), big data, cloud computing and NoSQLdatabases provide new opportunities and challenges for industrial asset management.The use of NoSQL databases has emerged due to the limitations of therelational databases, in particular, the inability to scale-up horizontally and tomanage the data that is constantly generated by industry. The current workhighlights the key aspects of both relational and NoSQL databases. The paperprovides a review of the database technologies mentioned above. In this context,in order to demonstrate the effectiveness and adequacy of NoSQL databases, areal industrial case study is presented. The authors also discuss the differentdatabase technologies and their suitability in the domain of interest.
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| 5. |
- Sharma, Pankaj, et al.
(författare)
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Big data collection and analysis for manufacturing organisations
- 2017
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Ingår i: Big Data and Information Analytics. - : American Institute of Mathematical Sciences. - 2380-6966 .- 2380-6974. ; 2:2, s. 127-139
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Tidskriftsartikel (refereegranskat)abstract
- Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale benefits for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.
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| 6. |
- Baglee, David, et al.
(författare)
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How Does CBM Function in the Real World?
- 2016
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Ingår i: MFPT 2015 and ISA’s 61st International Instrumentation Symposium, At Dayton. Ohio.
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Konferensbidrag (refereegranskat)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. |
- Bishnoi, Sunita, et al.
(författare)
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Electrochemical Sensing of Chlorpyrifos, a Carcinogen Responsible for Breast Cancer, in Milk and Plasma of Lactating Mothers
- 2023
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Ingår i: Electroanalysis. - : Wiley. - 1040-0397 .- 1521-4109. ; 35:2
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Tidskriftsartikel (refereegranskat)abstract
- In view of the increase in breast cancer cases at the global level, electrochemical sensing of the carcinogenic pesticide, chlorpyrifos (CPF) in breast milk is proposed. The determination is based on the nucleophilic substitution reaction of pralidoxime (PAM) with CPF. The proposed method offers a linear concentration range of 0.002 to 0.08 μmol/L. The limit of detection and limit of quantification was found to be 0.05×10−9 and 0.167×10−9M, respectively. The offered “unmodified edge plane pyrolytic graphite sensor” proved to be a better substrate than the earlier reported modified sensors. The limit of detection for the proposed method was found to be nearly fifty times lower than reported at modified electrodes. The interference study proved the adequate selectivity of the offered sensor. The sensor has good stability and reproducibility along with high sensitivity. The offered sensor is very useful for cancer hospitals, pesticide industries, and the study of environmental toxicity-related issues.
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| 8. |
- Campos, Jaime, et al.
(författare)
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The challenges of cybersecurity frameworks to protect data required for the development of advanced maintenance
- 2016
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Ingår i: Product-Service Systems Across Life Cycle. - : Elsevier. ; , s. 222-227
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Konferensbidrag (refereegranskat)abstract
- The main objective of the paper is to highlight the important aspects of the data management in condition monitoring and maintenance, especially when the emergent technologies, such as the cloud computing and big data, are to be considered in the maintenance department. In addition, one of the main data management elements highlighted in the current work are the cybersecurity issues which might be one of the biggest obstacles hindering the development of cloud based big data for condition-based maintenance (CBM) purposes. Further, the benefits and current risks of storing a company's data in the cloud are highlighted. The authors discuss as well different data needs in various processes in the area of asset management. In addition, the challenges and issues to be addressed for the optimal use of the company data at the cloud together with the big data approach are addressed. This is seen as an important part in an effort to achieve sustainable information and communication technologies for the industry.
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| 9. |
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| 10. |
- Kumar, Nitish, et al.
(författare)
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Functional surface layers in relaxor ferroelectrics
- 2020
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Ingår i: Journal of Materials Chemistry C. - : Royal Society of Chemistry (RSC). - 2050-7534 .- 2050-7526. ; 8:23, s. 7663-7671
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Tidskriftsartikel (refereegranskat)abstract
- Relaxor ferroelectrics are technologically important materials for applications in, for example, high-temperature capacitors, transducers and nano-positioning systems. These materials have often been reported to exhibit surface or skin layers with distinct physical properties to the bulk. The control of formation and functionality of these skin layers has remained elusive and is becoming increasingly critical due to device miniaturization, where the surface contribution to overall material properties becomes significant. We recently demonstrated that the distinct structural distortion of the skin layer is intimately related to the internal chemical pressure applied by oxygen vacancies and the plane stress conditions at the surface. (S. Kong, N. Kumar, S. Checchia, C. Cazorla and J. Daniels, Adv. Funct. Mater., 2019, 29, 1900344) Here, we demonstrate a unique capability to control the formation and properties of the skin layer through the control of defect concentration. Most interestingly, the skin layer is polar and both electrically and optically active, making it functional and a new candidate for low operating voltage and/or optoelectronic devices. The surface domains in the skin could be altered by applying a small voltage bias (1000 times lower than bulk) or light illumination. A reversible optical change in surface domains provides a new non-contact external control to tune the material polarisation.
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