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Introduction: Injury prevention programs (IPPs) are an inherent part of training in recreational and professional sports. Providing performance-enhancing benefits in addition to injury prevention may help adjust coaches and athletes’ attitudes towards implementation of injury prevention into daily routine. Conventional thinking by players and coaches alike seems to suggest that IPPs need to be specific to one’s sport to allow for performance enhancement. The systematic literature review aims to firstly determine the IPPs nature of exercises and whether they are specific to the sport or based on general conditioning. Secondly, can they demonstrate whether general, sports-specific or even mixed IPPs improve key performance indicators with the aim to better facilitate long-term implementation of these programs?
Methods: PubMed and Web of Science were electronically searched throughout March 2018. The inclusion criteria were randomized control trials, publication dates between Jan 2006 and Feb 2018, athletes (11–45 years), injury prevention programs and included predefined performance measures that could be categorized into balance, power, strength, speed/agility and endurance. The methodological quality of included articles was assessed with the Cochrane Collaboration assessment tools.
Results: Of 6619 initial findings, 22 studies met the inclusion criteria. In addition, reference lists unearthed a further 6 studies, making a total of 28. Nine studies used sports specific IPPs, eleven general and eight mixed prevention strategies. Overall, general programs ranged from 29–57% in their effectiveness across performance outcomes. Mixed IPPs improved in 80% balance outcomes but only 20–44% in others. Sports-specific programs led to larger scale improvements in balance (66%), power (83%), strength (75%), and speed/agility (62%).
Conclusion: Sports-specific IPPs have the strongest influence on most performance indices based on the significant improvement versus control groups. Other factors such as intensity, technical execution and compliance should be accounted for in future investigations in addition to exercise modality.
Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.
Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.
Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.
Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power).
Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed.
Fuzzy system based on two-step cascade genetic optimization strategy for tobacco tar prediction
(2019)
There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs of development are three fuzzy structures optimized with genetic algorithms resulting in genetic algorithm (GA)-FUZZY, GA-adaptive neuro fuzzy inference system (ANFIS), GA-GA-FUZZY algorithms. Proposed algorithms are used for the tar prediction in the cigarette production process. The results of prediction are compared with gas chromatograph (high-performance liquid chromatography (HPLC)) readings.
Aim: The aim of the study was to identify common orthopedic sports injury profiles in adolescent elite athletes with respect to age, sex, and anthropometrics.
Methods: A retrospective data analysis of 718 orthopedic presentations among 381 adolescent elite athletes from 16 different sports to a sports medical department was performed. Recorded data of history and clinical examination included area, cause and structure of acute and overuse injuries. Injury-events were analyzed in the whole cohort and stratified by age (11–14/15–17 years) and sex. Group differences were tested by chi-squared-tests. Logistic regression analysis was applied examining the influence of factors age, sex, and body mass index (BMI) on the outcome variables area and structure (α = 0.05).
Results: Higher proportions of injury-events were reported for females (60%) and athletes of the older age group (66%) than males and younger athletes. The most frequently injured area was the lower extremity (47%) followed by the spine (30.5%) and the upper extremity (12.5%). Acute injuries were mainly located at the lower extremity (74.5%), while overuse injuries were predominantly observed at the lower extremity (41%) as well as the spine (36.5%). Joints (34%), muscles (22%), and tendons (21.5%) were found to be the most often affected structures. The injured structures were different between the age groups (p = 0.022), with the older age group presenting three times more frequent with ligament pathology events (5.5%/2%) and less frequent with bony problems (11%/20.5%) than athletes of the younger age group. The injured area differed between the sexes (p = 0.005), with males having fewer spine injury-events (25.5%/34%) but more upper extremity injuries (18%/9%) than females. Regression analysis showed statistically significant influence for BMI (p = 0.002) and age (p = 0.015) on structure, whereas the area was significantly influenced by sex (p = 0.005).
Conclusion: Events of soft-tissue overuse injuries are the most common reasons resulting in orthopedic presentations of adolescent elite athletes. Mostly, the lower extremity and the spine are affected, while sex and age characteristics on affected area and structure must be considered. Therefore, prevention strategies addressing the injury-event profiles should already be implemented in early adolescence taking age, sex as well as injury entity into account.
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.