AIM To evaluate the risk profile of sulfur hexafluoride in voiding urosonography(VUS)based on a large cohort of children.METHODS Since 2011 sulfur hexafluoride(SH,SonoV ue?,Bracco,Italy)is the only ultrasound contrast...AIM To evaluate the risk profile of sulfur hexafluoride in voiding urosonography(VUS)based on a large cohort of children.METHODS Since 2011 sulfur hexafluoride(SH,SonoV ue?,Bracco,Italy)is the only ultrasound contrast available in the European Union and its use in children has not been approved.Within a 4-year-period,531 children with suspected or proven vesicoureteral reflux(f/m=478/53;mean age 4.9 years;1 mo-25.2 years)following parental informed consent underwent VUS with administration of 2.6±1.2 mL SH in a two-center study.A standardizedtelephone survey on adverse events was conducted three days later.RESULTS No acute adverse reactions were observed.The survey revealed subacute,mostly self-limited adverse events in 4.1%(22/531).The majority of observed adverse events(17/22)was not suspected to be caused by an allergic reaction:Five were related to catheter placement,three to reactivated urinary tract infections,five were associated with perineal disinfection before voiding urosonography or perineal dermatitis and four with a common cold.In five patients(0.9%)hints to a potential allergic cause were noted:Perineal urticaria was reported in three interviews and isolated,mild fever in two.These were minor self-limited adverse events with a subacute onset and no hospital admittance was necessary.Ninety-six point two percent of the parents would prefer future VUS examinations with use of SH.CONCLUSION No severe adverse events were observed and indications of self-limited minor allergic reactions related to intravesical administration of SH were reported in less than 1%.展开更多
Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption optimization.Due to the increasing automation of these processes,meanin...Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption optimization.Due to the increasing automation of these processes,meaningful and transparent forecasts become more and more important.Still,at the same time,the complexity of the used machine learning models and architectures increases.Because there is an increasing interest in interpretable and explainable load forecasting methods,this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine Learning.Based on extensive literature research covering eight publication portals,recurring modeling approaches,trends,and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load forecasts.The results on interpretability show an increase in the use of probabilistic models,methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable models.Dominant explainable approaches are Feature Importance and Attention mechanisms.The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF.Compared to other applications of explainable and interpretable methods such as clustering,there are currently relatively few research results,but with an increasing trend.展开更多
Background Diffusion-weighted imaging(DWI)of synovitis has been suggested as a possible non-invasive alternative to contrast-enhanced T1w imaging(ce-T1w).We aimed to study DWI for diagnosing synovitis in the knee join...Background Diffusion-weighted imaging(DWI)of synovitis has been suggested as a possible non-invasive alternative to contrast-enhanced T1w imaging(ce-T1w).We aimed to study DWI for diagnosing synovitis in the knee joint of pediatric patients,to quantify inter-observer agreement on DWI and ce-T1w and to calculate quantitative measures of synovial diffusivity and conspicuity.Methods Forty consecutive patients with known or suspected arthritis of the knee(25 girls,median age 12 years)underwent routine 1.5T MRI with ce-T1w and transverse DWI with b values 50 and 800 s/mm2.Mean apparent diffusion coefficient(ADC)values and signal intensity of inflamed synovium,joint effusion and muscle were measured with regions of interest retrospectively.Post-contrast T1 w images(diagnostic standard)and diffusion-weighted images at b=800 s/mm2 with ADC map were separately rated by three independent and blinded readers with different levels of expertise for the presence and degree of synovitis along with the level of diagnostic confidence.Results Thirty-one(78%)patients showed at least some synovial contrast enhancement,17(43%)children were diagnosed with synovitis on ce-T1w.Ratings by the 1st reader on ce-T1w and on DWI for synovitis showed very good agreement(kappa=0.90).Inter-observer agreement on DWI ranged from moderate to substantial with kappa values between 0.68 and 0.79(all P<0.001).Agreement and diagnostic confidence were generally lower in patients with mild and without synovial enhancement,compared to patients with synovitis.DWI yielded higher signal of inflamed synovium vs.muscle tissue,but lower signal vs.joint effusion,compared to ce-T1 w(all P<0.001).Conclusions Diffusion-weighted imaging is a promising,though reader-dependent alternative to contrast-enhanced imaging in patients with arthritis of the knee,based on our preliminary findings.It holds potential for increasing patient safety and comfort.展开更多
This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The mod...This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.展开更多
This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on e...This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.展开更多
文摘AIM To evaluate the risk profile of sulfur hexafluoride in voiding urosonography(VUS)based on a large cohort of children.METHODS Since 2011 sulfur hexafluoride(SH,SonoV ue?,Bracco,Italy)is the only ultrasound contrast available in the European Union and its use in children has not been approved.Within a 4-year-period,531 children with suspected or proven vesicoureteral reflux(f/m=478/53;mean age 4.9 years;1 mo-25.2 years)following parental informed consent underwent VUS with administration of 2.6±1.2 mL SH in a two-center study.A standardizedtelephone survey on adverse events was conducted three days later.RESULTS No acute adverse reactions were observed.The survey revealed subacute,mostly self-limited adverse events in 4.1%(22/531).The majority of observed adverse events(17/22)was not suspected to be caused by an allergic reaction:Five were related to catheter placement,three to reactivated urinary tract infections,five were associated with perineal disinfection before voiding urosonography or perineal dermatitis and four with a common cold.In five patients(0.9%)hints to a potential allergic cause were noted:Perineal urticaria was reported in three interviews and isolated,mild fever in two.These were minor self-limited adverse events with a subacute onset and no hospital admittance was necessary.Ninety-six point two percent of the parents would prefer future VUS examinations with use of SH.CONCLUSION No severe adverse events were observed and indications of self-limited minor allergic reactions related to intravesical administration of SH were reported in less than 1%.
基金supported by the German Federal Ministry of Economic Affairs and Climate Action(BMWK)through the project“FlexGUIde”(grant number 03EI6065D).
文摘Electric Load Forecasting(ELF)is the central instrument for planning and controlling demand response programs,electricity trading,and consumption optimization.Due to the increasing automation of these processes,meaningful and transparent forecasts become more and more important.Still,at the same time,the complexity of the used machine learning models and architectures increases.Because there is an increasing interest in interpretable and explainable load forecasting methods,this work conducts a literature review to present already applied approaches regarding explainability and interpretability for load forecasts using Machine Learning.Based on extensive literature research covering eight publication portals,recurring modeling approaches,trends,and modeling techniques are identified and clustered by properties to achieve more interpretable and explainable load forecasts.The results on interpretability show an increase in the use of probabilistic models,methods for time series decomposition and the use of fuzzy logic in addition to classically interpretable models.Dominant explainable approaches are Feature Importance and Attention mechanisms.The discussion shows that a lot of knowledge from the related field of time series forecasting still needs to be adapted to the problems in ELF.Compared to other applications of explainable and interpretable methods such as clustering,there are currently relatively few research results,but with an increasing trend.
基金This study was supported by the German Research Foundation(DFG)(No.NE1953/1-1).
文摘Background Diffusion-weighted imaging(DWI)of synovitis has been suggested as a possible non-invasive alternative to contrast-enhanced T1w imaging(ce-T1w).We aimed to study DWI for diagnosing synovitis in the knee joint of pediatric patients,to quantify inter-observer agreement on DWI and ce-T1w and to calculate quantitative measures of synovial diffusivity and conspicuity.Methods Forty consecutive patients with known or suspected arthritis of the knee(25 girls,median age 12 years)underwent routine 1.5T MRI with ce-T1w and transverse DWI with b values 50 and 800 s/mm2.Mean apparent diffusion coefficient(ADC)values and signal intensity of inflamed synovium,joint effusion and muscle were measured with regions of interest retrospectively.Post-contrast T1 w images(diagnostic standard)and diffusion-weighted images at b=800 s/mm2 with ADC map were separately rated by three independent and blinded readers with different levels of expertise for the presence and degree of synovitis along with the level of diagnostic confidence.Results Thirty-one(78%)patients showed at least some synovial contrast enhancement,17(43%)children were diagnosed with synovitis on ce-T1w.Ratings by the 1st reader on ce-T1w and on DWI for synovitis showed very good agreement(kappa=0.90).Inter-observer agreement on DWI ranged from moderate to substantial with kappa values between 0.68 and 0.79(all P<0.001).Agreement and diagnostic confidence were generally lower in patients with mild and without synovial enhancement,compared to patients with synovitis.DWI yielded higher signal of inflamed synovium vs.muscle tissue,but lower signal vs.joint effusion,compared to ce-T1 w(all P<0.001).Conclusions Diffusion-weighted imaging is a promising,though reader-dependent alternative to contrast-enhanced imaging in patients with arthritis of the knee,based on our preliminary findings.It holds potential for increasing patient safety and comfort.
基金The research has received funding from the German Federal Ministry for Economic Affairs and Energy(Project number 03EI6019B-Machine learning for power load profile prediction and energy flexibility man-agement strategies).
文摘This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.
文摘This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.
