Probabilistic Load Forecasting with Deep Conformalized Quantile Regression

The establishment of smart grids and the introduction of distributed generation posed new challenges in energy analytics that can be tackled with machine learning algorithms. The latter, are able to handle a combination of weather and consumption data, grid measurements, and their historical records to compute inference and make predictions. An accurate energy load forecasting is essential to assure reliable grid operation and power provision at peak times when power consumption is high. However, most of the existing load forecasting algorithms provide only point estimates or probabilistic forecasting methods that construct prediction intervals without coverage guarantee. Nevertheless, information about uncertainty and prediction intervals is very useful to grid operators to evaluate the reliability of operations in the power network and to enable a risk-based strategy for configuring the grid over a conservative one. There are two popular statistical methods used to generate prediction intervals in regression tasks: Quantile regression is a non-parametric probabilistic forecasting technique producing prediction intervals adaptive to local variability within the data by estimating quantile functions directly from the data. However, the actual coverage of the prediction intervals obtained via quantile regression is not guaranteed to satisfy the designed coverage level for finite samples. Conformal prediction is an on-top probabilistic forecasting framework producing symmetric prediction intervals, most often with a fixed length, guaranteed to marginally satisfy the designed coverage level for finite samples. This thesis proposes a probabilistic load forecasting method for constructing marginally valid prediction intervals adaptive to local variability and suitable for data characterized by temporal dependencies. The method is applied in conjunction with recurrent neural networks, deep learning architectures for sequential data, which are mostly used to compute point forecasts rather than probabilistic forecasts. Specifically, the use of an ensemble of pinball-loss guided deep neural networks performing quantile regression is used together with conformal prediction to address the individual shortcomings of both techniques

#Autism: A Cross-Sectional Study of the Quality of Diagnostic Information on TikTok

TikTok is one of the most popular social media applications worldwide. In this study, we aimed to investigate the quality of diagnostic information about Autism Spectrum Disorder (ASD) on TikTok. Although the amount of research on this topic is lacking, previous studies indicate that the quality of diagnostic information on TikTok is questionable. We conducted a cross-sectional content analysis, searching for the hashtag #Autism on TikTok and analyzing the 100 most liked videos. The videos were classified into three categories: misleading (M), personal experience (P) or useful (U). Results showed that 34% of the videos were misleading, 61% were personal experience, and 5% were useful. None of the videos were created or published by healthcare providers (HCPs). Lastly, there was a clear pattern in frequently highlighted ASD symptoms discussed on the application. Thus, it is important for clinicians and other HCPs to be aware of the dissemination of health misinformation on social media, as well as the potential impact this might have on clinical care.TikTok is one of the most popular social media applications worldwide. In this study, we aimed to investigate the quality of diagnostic information about Autism Spectrum Disorder (ASD) on TikTok. Although the amount of research on this topic is lacking, previous studies indicate that the quality of diagnostic information on TikTok is questionable. We conducted a cross-sectional content analysis, searching for the hashtag #Autism on TikTok and analyzing the 100 most liked videos. The videos were classified into three categories: misleading (M), personal experience (P) or useful (U). Results showed that 34% of the videos were misleading, 61% were personal experience, and 5% were useful. None of the videos were created or published by healthcare providers (HCPs). Lastly, there was a clear pattern in frequently highlighted ASD symptoms discussed on the application. Thus, it is important for clinicians and other HCPs to be aware of the dissemination of health misinformation on social media, as well as the potential impact this might have on clinical care

Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as generic machine learning algorithms performing quantile regression (QR), which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on QR or conformal prediction (CP), and it provides sharper, more informative, and valid PIs

Plasmonic properties of aluminium nanowires in amorphous silicon

Plasmonic structures can help enhance optical activity in the ultraviolet (UV) region and therefore enhancing photocatalytic reactions and the detection of organic and biological species. Most plasmonic structures are composed of Ag or Au. However, producing structures small enough for optical activity in the UV region has proved difficult. In this study, we demonstrate that aluminium nanowires are an excellent alternative. We investigated the plasmonic properties of the Al nanowires as well as the optoelectronic properties of the surrounding a − Si matrix by combining scanning transmission electron microscopy imaging, electron energy loss spectroscopy and electrodynamic modelling. We have found that the Al nanowires have distinct plasmonic modes in the UV and far UV region, from 0.75 eV to 13 eV. In addition, simulated results found that the size and spacing of the Al nanowires, as well as the embedding material were shown to have a large impact on the type of surface plasmon energies that can be generated in the material. Using electromagnetic modelling, we have identified the modes and illustrated how they could be tuned further.publishedVersio

Ni-doped A-site excess SrTiO3 thin films modified with Au nanoparticles by a thermodynamically-driven restructuring for plasmonic activity

Plasmonically active nanoparticles offer a promising pathway to extend the absorption range of photocatalysts. While not necessarily catalytically active themselves, these particles allow the absorption of lower energy photons in wide band gap photocatalysts. Here, we present A-site excess SrTiO3 thin films, doped with Ni, where through a subsequent exsolution process we created well socketed Ni nanoparticles in the surface of SrTiO3. These were galvanically replaced by Au, resulting in well-socketed Au nanoparticles with variable size on the surface, depending on the galvanic replacement time. Photoelectrochemical measurements and electron energy loss spectroscopy revealed the improved photoresponse of the thin films by plasmonic activity of the nanoparticles. The energy of the plasmon peak suggests that the main improvement results from the injection of hot charge carriers. Our study opens new avenues for the design and synthesis of the next generation of photocatalytic materials

Plasmonic properties of aluminium nanowires in amorphous silicon

Plasmonic structures can help enhance optical activity in the ultraviolet (UV) region and therefore enhancing photocatalytic reactions and the detection of organic and biological species. Most plasmonic structures are composed of Ag or Au. However, producing structures small enough for optical activity in the UV region has proved difficult. In this study, we demonstrate that aluminium nanowires are an excellent alternative. We investigated the plasmonic properties of the Al nanowires as well as the optoelectronic properties of the surrounding aSi matrix by combining scanning transmission electron microscopy (STEM) imaging, electron energy loss spectroscopy (EELS) and electrodynamic modelling. We have found that the Al nanowires have distinct plasmonic modes in the UV and far UV region, from 0.75 eV to 13 eV. In addition, the size and spacing of the Al nanowires, as well as the embedding material were shown to have a large impact on the type of surface plasmons energies that can be generated in the material. Using electromagnetic modelling, we have identified the modes and illustrated how they could be tuned further

Probabilistic Load Forecasting with Deep Conformalized Quantile Regression

The establishment of smart grids and the introduction of distributed generation posed new challenges in energy analytics that can be tackled with machine learning algorithms. The latter, are able to handle a combination of weather and consumption data, grid measurements, and their historical records to compute inference and make predictions. An accurate energy load forecasting is essential to assure reliable grid operation and power provision at peak times when power consumption is high. However, most of the existing load forecasting algorithms provide only point estimates or probabilistic forecasting methods that construct prediction intervals without coverage guarantee. Nevertheless, information about uncertainty and prediction intervals is very useful to grid operators to evaluate the reliability of operations in the power network and to enable a risk-based strategy for configuring the grid over a conservative one. There are two popular statistical methods used to generate prediction intervals in regression tasks: Quantile regression is a non-parametric probabilistic forecasting technique producing prediction intervals adaptive to local variability within the data by estimating quantile functions directly from the data. However, the actual coverage of the prediction intervals obtained via quantile regression is not guaranteed to satisfy the designed coverage level for finite samples. Conformal prediction is an on-top probabilistic forecasting framework producing symmetric prediction intervals, most often with a fixed length, guaranteed to marginally satisfy the designed coverage level for finite samples. This thesis proposes a probabilistic load forecasting method for constructing marginally valid prediction intervals adaptive to local variability and suitable for data characterized by temporal dependencies. The method is applied in conjunction with recurrent neural networks, deep learning architectures for sequential data, which are mostly used to compute point forecasts rather than probabilistic forecasts. Specifically, the use of an ensemble of pinball-loss guided deep neural networks performing quantile regression is used together with conformal prediction to address the individual shortcomings of both techniques

Prehospital ivaretakelse av eldre pasienter utsatt for lavenergifall

Bakgrunn: Det blir stadig flere eldre i befolkningen, og dermed også flere eldre traumepasienter. Erfaringsmessig er lavenergifall en vanlig skademekanisme blant eldre og en hyppig problemstilling vi møter prehospitalt. Formålet med oppgaven er å undersøke om den eldre pasienten som har blitt utsatt for lavenergifall, blir ivaretatt prehospitalt. Med bakgrunn i forskning i tillegg til kunnskap om aldersforandringer ønsker vi også å finne ut om eldre får lik behandling som yngre, og om den eldre pasientens reelle tilstand blir fanget opp av dagens nasjonale traumeplan etter lavenergifall. Metode: Metoden vi har anvendt er litteraturstudie. Fire av de inkluderte studiene har blitt plukket ut etter systematisk søk i de anerkjente databasene Medline og Cinahl. Én av de inkluderte studiene har blitt plukket ut etter usystematisk søk. Totalt har vi inkludert 5 artikler i oppgaven. Resultater: Resultatene fra de utvalgte studiene viser at det skjer en undertriagering av eldre traumepasienter prehospitalt. Flere eldre pasienter blir levert til lavere behandlingsnivå sammenlignet med yngre, til tross for skader med tilsynelatende lik alvorlighetsgrad. Lavenergifall viser seg å være en utbredt skademekanisme blant eldre som kan forårsake alvorlige skader. Eldre har høyere dødelighet i etterkant av traumer, og færre blir skrevet ut til hjemmet etter sykehusopphold, sammenlignet med yngre. Konklusjon: Den prehospitale prosedyren for traume er mangelfull og i liten grad tilpasset eldre pasienter. Dette fører til at pasientgruppen ikke blir tilstrekkelig ivaretatt prehospitalt etter lavenergifall. Vi anser implementering av kriterier tilpasset den eldre pasienten i dagens nasjonale traumeplan, økt kunnskap om aldersforandringer og pasientgruppen, som gunstig for å redusere undertriagering og dødelighet. Background: The older population is growing and therefore there is also an increasing proportion of elderly trauma patients. We experience low-energy falls to be an usual mechanism of injury among the elderly, and therefore this is a frequent problem that we encounter prehospital. Our aim is to investigate whether the elderly patient who has been exposed to low-energy falls, is cared for prehospital. Based on research in addition to knowledge about age related changes, we also want to find out whether the elderly receive the same treatment as the younger. Besides whether the elderly patient’s real state is captured by today’s national trauma plan after low-energy falls. Methods: Our method of choice is literature study. Four of the included studies were found through systematic search in acknowledged databases like Medline and Cinahl. One of the studies was found through unsystematic search. In total, we have included 5 articles. Results: The chosen articles show that there is an undertriage of elderly trauma patients prehospital. Many elderly trauma patients get delivered to lower levels of care, compared to younger age groups, despite seemingly equal severity. Low-energy falls are proving to be a widespread mechanism of injury among the elderly and can lead to serious injuries. The elderly has a higher mortality rate after trauma and fewer are discharged to their homes after being hospitalized, compared to younger age groups. Conclusions: The prehospital procedures for trauma are deficient and only to a small extent adapted to elderly patients. This means that elderly patients are not adequately cared for prehospitally after a low-energy fall. Therefor we consider implementation of criteria adapted to elderly patients in the current national trauma plan, in addition to increased knowledge about age related changes and the patient group, to be beneficial in reducing undertriage and mortality

Is Dental Anxiety Associated with Oral Health-Related Quality of Life? Assessment of Statistical Significance and Clinical Meaningfulness in a Sample of Russian Medical and Dental Students

Information about oral health-related quality of life (OHRQoL) and dental anxiety (DA) in Russian young adults is scarce. We investigated how DA is associated with OHRQoL in a group of medical and dental students in North-West Russia. The study had a cross-sectional design and included 807 students aged 18–25 years who attended the Northern State Medical University in Arkhangelsk. OHRQoL and DA were measured by the Oral Health Impact Profile (OHIP-14) and Corah’s Dental Anxiety Scale (DAS), respectively. A questionnaire collected information on socio-demographics and self-reported oral health (OH) characteristics. A dental examination was executed to assess dental caries and oral hygiene. We observed differences in the OHIP-14 scores between dentally anxious and non-anxious students: unadjusted incidence rate ratio [IRR] = 1.65, 95% confidence interval [CI]: 1.29–2.12; after adjustment for socio-demographics and clinically assessed OH: IRR = 1.58, 95% CI: 1.23–2.02; after adjustment for socio-demographics, clinically assessed OH, and self-reported OH characteristics: IRR = 1.27, 95% CI: 0.99–1.63. The differences between estimated marginal means for the DAS categories in the models were 2.92, 2.51, and 1.24, respectively. Minimal clinically important differences of OHIP-14 fell between 1.68 and 2.51. We found a negative statistical association between DA and OHRQoL in our study sample, but after adjustment for potential confounders, the association lost its clinical importance