Time-series product and substance flow analyses of end-of-life electrical and electronic equipment in China

ABSTRACT Consumer electronics have caused an unsustainable amount of waste electrical and electronic equipment (WEEE). Organic electronics, by means of eco-design, represent an opportunity to manufacture compostable electronic devices. Waste electrical and electronic equipment (WEEE), or e-waste, is defined as the waste of any device that uses a power source and that has reached its end of life. Disposing of WEEE at landfill sites has been identified as an inefficient solid waste processing strategy as well as a threat to human health and the environment. In the effort to mitigate the problem, practices such as (i) designing products for durability, reparability, and safe recycling, and (ii) promoting closed-loop systems based on systematic collection and reuse/refurbishment have been identified. In this perspective, we introduce a complementary route to making electronics more sustainable: organic electronics based on biodegradable materials and devices. Biodegradable organic electronics lie at the intersection of research in chemistry, materials science, device engineering, bioelectronics, microbiology, and toxicology. The design of organic electronics for standardized biodegradability will allow composting to be an end-of-life option.

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End-of-life management protocol offered within emergency room (EMPOWER): study protocol for a multicentre study

BMJ Open ◽

10.1136/bmjopen-2019-036598 ◽

2020 ◽

Vol 10 (4) ◽

pp. e036598 ◽

Cited By ~ 1

Author(s):

Rakhee Yash Pal ◽

Win Sen Kuan ◽

Ling Tiah ◽

Ranjeev Kumar ◽

Yoko Kin Yoke Wong ◽

...

Keyword(s):

Time Series ◽

Multicentre Study ◽

End Of Life ◽

Study Protocol ◽

Quantitative Methods ◽

Local Context ◽

Resource Utilisation ◽

Care Protocol ◽

Management Protocol ◽

Eol Care

BackgroundPatients at their end-of-life (EOL) phase frequently visit the emergency department (ED) due to their symptoms, yet the environment and physicians in ED are not traditionally equipped or trained to provide palliative care. This multicentre study aims to measure the current quality of EOL care in ED to identify gaps, formulate improvements and implement the improved EOL care protocol. We shall also evaluate healthcare resource utilisation and its associated costs.Methods and analysisThis study employs a quasiexperimental interrupted time series design using both qualitative and quantitative methods, involving the EDs of three tertiary hospitals in Singapore, over a period of 3 years. There are five phases in this study: (1) retrospective chart reviews of patients who died within 5 days of ED attendance; (2) pilot phase to validate the CODE questionnaire in the local context; (3) preimplementation phase; (4) focus group discussions (FGDs); and (5) postimplementation phase. In the prospective cohort, patients who are actively dying or have high likelihood of mortality this admission, and whose goal of care is palliation, will be eligible for inclusion. At least 140 patients will be recruited for each preimplementation and postimplementation phase. There will be face-to-face interviews with patients’ family members, review of medical records and self-administered staff survey to evaluate existing knowledge and confidence. The FGDs will involve hospital and community healthcare providers. Data obtained from the retrospective cohort, preimplementation phase and FGDs will be used to guide prospective improvement and protocol changes. Patient, family and staff relevant outcomes from these changes will be measured using time series regression.Ethics and disseminationThe study protocol has been reviewed and ethics approval obtained from the National Healthcare Group Domain Specific Review Board, Singapore. The results from this study will be actively disseminated through manuscript publications and conference presentations.Trial registration numberNCT03906747.

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Explore Big Data and Forecasting Future Values using Univariate Arima Model in R

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.12300 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 1107 ◽

Cited By ~ 1

Author(s):

S Sagar Imambi ◽

P Vidyullatha ◽

M V.B.T.Santhi ◽

P Haran Babu

Keyword(s):

Time Series ◽

Big Data ◽

Real Time ◽

Arima Model ◽

Electronic Equipment ◽

Experimental Results ◽

Business Organization ◽

Huge Amount ◽

Diagnostic Data ◽

Using Data

Electronic equipment and sensors spontaneously create diagnostic data that needs to be stocked and processed in real time. It is not only difficult to keep up with huge amount of data but also reasonably more challenging to analyze it. Big Data is providing many opportunities for organizations to evolve their processes they try to move beyond regular BI activities like using data to populate reports. Predicting future values is one of the requirements for any business organization. The experimental results shows that time series model with ARIMA (3,0,1)(1,0,0) is best fitted for predicting future values of the sales.

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