Refine
Year of publication
- 2019 (18) (remove)
Document Type
- Article (specialist journals) (18) (remove)
Language
- English (18)
Has Fulltext
- yes (18)
Is part of the Bibliography
- no (18) (remove)
Keywords
- Maschinelles Lernen (3)
- machine learning (3)
- Künstliche Intelligenz (2)
- Personenkraftwagen (2)
- Rückenschmerz (2)
- Sport (2)
- back pain (2)
- sports (2)
- Aguascalientes (Staat) (1)
- Analyse (1)
Institute
- FB Umweltplanung/-technik (UCB) (7)
- FB Informatik + Therapiewissenschaft (5)
- ISS - Institut für Softwaresysteme in Wirtschaft, Umwelt und Verwaltung (2)
- FB Bauen + Leben (1)
- FB Wirtschaft (1)
- IfaS - Institut für angewandtes Stoffstrommanagement (1)
- LaROS - Labor für Radiotechnologie und optische Systeme (1)
Driven by falling photovoltaic (PV) installation costs and potential support policies, rooftop PV is expected to expand rapidly in Thailand. As a result, the relevant stakeholders, especially utilities, have concerns about the net economic impacts of high PV adoption. Using a cost–benefit analysis, this study quantifies the net economic impacts of rooftop PV systems on three utilities and on ratepayers in Thailand by applying nine different PV adoption scenarios with various buyback rates and annual percentages of PV cost reduction. Under Thailand’s current electricity tariff structure, Thai utilities are well-protected and able to pass all costs due to PV onto the ratepayers in terms of changes in retail rates. We find that when PV adoption is low, the net economic impacts on both the utilities and retail rates are small and the impacts on each utility depend on its specific characteristics. On the other hand, when PV adoption ranges from 9–14% in energy basis, five-year retail rate impacts become noticeable and are between 6% and 11% as compared to the projected retail rates in 2036 depending on the PV adoption level. Thus, it is necessary for Thailand to make tradeoffs among the stakeholders and maximize the benefits of rooftop PV adoption.
Background: To facilitate access to evidence-based care for back pain, a German private medical insurance offered a health program proactively to their members. Feasibility and long-term efficacy of this approach were evaluated.
Methods: Using Zelen’s design, adult members of the health insurance with chronic back pain according to billing data were randomized to the intervention (IG) or the control group (CG). Participants allocated to the IG were invited to participate in the comprehensive health program comprising medical exercise therapy and life style coaching, and those allocated to the CG to a longitudinal back pain survey. Primary outcomes were back pain severity (Korff’s Chronic Pain Grade Questionnaire) as well as health-related quality of life (SF-12) assessed by identical online questionnaires at baseline and 2-year follow-up in both study arms. In addition to analyses of covariance, a subgroup analysis explored the heterogeneity of treatment effects among different risks of back pain chronification (STarT Back Tool).
Results: Out of 3462 persons selected, randomized and thereafter contacted, 552 agreed to participate. At the 24-month follow-up, data on 189 of 258 (73.3%) of the IG were available, in the CG on 255 of 294 (86.7%). Significant, small beneficial effects were seen in primary outcomes: Compared to the CG, the IG reported less disability (1.6 vs 2.0; p = 0.025; d = 0.24) and scored better at the SF-12 physical health scale (43.3 vs 41.0; p < 0.007; d = 0.26). No effect was seen in back pain intensity and in the SF-12 mental health scale. Persons with medium or high risk of back pain chronification at baseline responded better to the health program in all primary outcomes than the subgroup with low risk at baseline.
Conclusions: After 2 years, the proactive health program resulted in small positive long-term improvements. Using risk screening prior to inclusion in the health program might increase the percentage of participants deriving benefits from it.
This article presents experience curves and cost-benefit analyses for electric and plug-in hybrid cars sold in Germany. We find that between 2010 and 2016, the prices and price differentials relative to conventional cars declined at learning rates of 23 ± 2% and 32 ± 2% for electric cars and 6 ± 1% and 37 ± 2% for plug-in hybrids. If trends persist, price beak-even with conventional cars may be reached after another 7 ± 1 million electric cars and 5 ± 1 million plug-in hybrids are produced. The user costs of electric and plug-in hybrid cars relative to their conventional counterparts are declining annually by 14% and 26%. Also the costs for mitigating CO2 and air pollutant emissions through the deployment of electrified cars tend to decline. However, at current levels, NOX and particle emissions are still mitigated at lower costs by state-of-the-art after-treatment systems than through the electrification of powertrains. Overall, the observation of robust technological learning suggests policy makers should focus their support on non-cost market barriers for the electrification of road transport, addressing specifically the availability of recharging infrastructure.
A comprehensive overview is provided evaluating direct real-world CO2 emissions of both diesel and petrol cars newly registered in Europe between 1995 and 2015. Before 2011, European diesel cars emitted less CO2 per kilometre than petrol cars, but since then there is no appreciable difference in per-km CO2 emissions between diesel and petrol cars. Real-world CO2 emissions of diesel cars have not declined appreciably since 2001, while the CO2 emissions of petrol cars have been stagnant since 2012. When adding black carbon related CO2-equivalents, such as from diesel cars without particulate filters, diesel cars were discovered to have had much higher climate relevant emissions until the year 2001 when compared to petrol cars. From 2001 to 2015 CO2-equivalent emissions from new diesel cars and petrol cars were hardly distinguishable. Lifetime use phase CO2-equivalent emissions of all European passenger vehicles were modelled for 1995–2015 based on three scenarios: the historic case, another scenario freezing percentages of diesel cars at the low levels from the early 1990s (thus avoiding the observed “boom” in new diesel registrations), and an advanced mitigation scenario based on high proportions of petrol hybrid cars and cars burning gaseous fuels. The difference in CO2-equivalent emissions between the historical case and the scenario avoiding the diesel car boom is only 0.4%. The advanced mitigation scenario would have been able to achieve a 3.4% reduction in total CO2-equivalent emissions over the same time frame. The European diesel car boom appears to have been ineffective at reducing climate-warming emissions from the European transport sector.
Passenger cars in Europe have become both heavier and more powerful over the past decades. This trend has increased vehicle utility but it might have also offset technical improvements in powertrain efficiency. Here, we analyze efficiency trade-offs and CO2 emissions for three popular compact cars in Germany. We find that mass, power, and front area of model variants has increased by 66%, 147%, and 22%, respectively between 1980 and 2018. In the same period, fuel consumption decreased 14% for gasoline models but it increased 9% for diesel models. However, if vehicle mass, power, and front area had remained at 1980 levels, technical efficiency improvements would have decreased the fuel consumption of gasoline and diesel models by 23% and 24%, respectively. The related efficiency trade-offs amount to 24 g CO2/km or 13% of the current fuel consumption for gasoline models and 40 g CO2/km or 25% of the current fuel consumption for diesel models. These findings suggest that about half of the technical efficiency improvements in gasoline models and all of the technical efficiency improvements in diesel models are offset through other vehicle attributes. By accounting for the observed efficiency trade-offs, climate policy could become more effective.
Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.
Background: Stratified care is an up-to-date treatment approach suggested for patients with back pain in several guidelines. A comprehensively studied stratification instrument is the STarT Back Tool (SBT). It was developed to stratify patients with back pain into three subgroups, according to their risk of persistent disabling symptoms. The primary aim was to analyse the disability differences in patients with back pain 12 months after inclusion according to the subgroups determined at baseline using the German version of the SBT (STarT-G). Moreover, the potential to improve prognosis for disability by adding further predictor variables, an analysis for differences in pain intensity according to the STarT-Classification, and discriminative ability were investigated.
Methods: Data from the control group of a randomized controlled trial were analysed. Trial participants were members of a private medical insurance with a minimum age of 18 and indicated as having persistent back pain. Measurements were made for the risk of back pain chronification using the STarT-G, disability (as primary outcome) and back pain intensity with the Chronic Pain Grade Scale (CPGS), health-related quality of life with the SF-12, psychological distress with the Patient Health Questionnaire-4 (PHQ-4) and physical activity. Analysis of variance (ANOVA), multiple linear regression, and area under the curve (AUC) analysis were conducted.
Results: The mean age of the 294 participants was 53.5 (SD 8.7) years, and 38% were female. The ANOVA for disability and pain showed significant differences (p < 0.01) among the risk groups at 12 months. Post hoc Tukey tests revealed significant differences among all three risk groups for every comparison for both outcomes. AUC for STarT-G’s ability to discriminate reference standard ‘cases’ for chronic pain status at 12 months was 0.79. A prognostic model including the STarT-Classification, the variables global health, and disability at baseline explained 45% of the variance in disability at 12 months.
Conclusions: Disability differences in patients with back pain after a period of 12 months are in accordance with the subgroups determined using the STarT-G at baseline. Results should be confirmed in a study developed with the primary aim to investigate those differences.
The increasing availability of off-the-shelf high-frequency components makes radar measurement become popular in mainstream industrial applications. We present a cooperative FM radar for strongly reflective environments, being devised for a range of up to approx. 120 m. The target is designed with an unambiguous signature method and satisfies coherence. A prototype is built with commercial semiconductor components that operates in the 24 GHz industrial, scientific and medical band. First experimental results taken in sewage pipes are presented, using the target prototype and a standard FMCW radio station. An overview on four data acquisition procedures is given.