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Ahmad et al. in their paper for the first time proposed to apply sharp function for classification of images. In continuation of their work, in this paper we investigate the use of sharp function as an edge detector through well known diffusion models. Further, we discuss the formulation of weak solution of nonlinear diffusion equation and prove uniqueness of weak solution of nonlinear problem. The anisotropic generalization of sharp operator based diffusion has also been implemented and tested on various types of images.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
Background: The environmental impact of electric scooters has been the subject of critical debate in the scientific community for the past 5 years. The data published so far are very inhomogeneous and partly methodologically incomplete. Most of the data available in the literature suffer from an average bias of 34%, because end-of-life (EOL) impacts have not been modelled, reported or specified. In addition, the average lifetime mileage of shared fleets of e-scooters, as they are operated in cities around the world, has recently turned out to be much lower than expected. This casts the scooters in an unfavourable light for the necessary mobility transition. Data on impact categories other than the global warming potential (GWP) are scarce. This paper aims to quantify the strengths and weaknesses of e-scooters in terms of their contribution to sustainable transport by more specifically defining and extending the life cycle assessment (LCA) modelling conditions: the modelling is based on two genuine material inventories obtained by dismantling two different e-scooters, one based on a traditional aluminium frame and another, for the first time, based on plastic material.
Results: This study provides complete inventory data to facilitate further LCA modelling of electric kick scooters. The plastic scooter had a 26% lower lifetime GWP than the aluminium vehicle. A favourable choice of electric motor promises a further reduction in GWP. In addition to GWP, the scooter's life cycles were assessed across seven other impact categories and showed no critical environmental or health impacts compared to a passenger car. On the other hand, only the resource extraction impact revealed clear advantages for electric scooters compared to passenger cars.
Conclusions: Under certain conditions, scooters can still be an important element of the desired mobility transition. To assure a lifetime long enough is the crucial factor to make the electric scooter a favourable or even competitive vehicle in a future sustainable mobility system. A scooter mileage of more than 5400 km is required to achieve lower CO2eq/pkm emissions compared to passenger cars, which seems unlikely in today's standard use case of shared scooter fleets. In contrast, a widespread use of e-scooters as a commuting tool is modelled to be able to save 4% of greenhouse gas (GHG) emissions across the German mobility sector.
Numerous research methods have been developed to detect anomalies in the areas of security and risk analysis. In healthcare, there are numerous use cases where anomaly detection is relevant. For example, early detection of sepsis is one such use case. Early treatment of sepsis is cost effective and reduces the number of hospital days of patients in the ICU. There is no single procedure that is sufficient for sepsis diagnosis, and combinations of approaches are needed. Detecting anomalies in patient time series data could help speed the development of some decisions. However, our algorithm must be viewed as complementary to other approaches based on laboratory values and physician judgments. The focus of this work is to develop a hybrid method for detecting anomalies that occur, for example, in multidimensional medical signals, sensor signals, or other time series in business and nature. The novelty of our approach lies in the extension and combination of existing approaches: Statistics, Self Organizing Maps and Linear Discriminant Analysis in a unique and unprecedented way with the goal of identifying different types of anomalies in real-time measurement data and defining the point where the anomaly occurs. The proposed algorithm not only has the full potential to detect anomalies, but also to find real points where an anomaly starts.
While the contribution of renewable energy technologies to the energy system is increasing, so is its level of complexity. In addition to new types of consumer systems, the future system will be characterized by volatile generation plants that will require storage technologies. Furthermore, a solid interconnected system that enables the transit of electrical energy can reduce the need for generation and storage systems. Therefore, appropriate methods are needed to analyze energy production and consumption interactions within different system constellations. Energy system models can help to understand and build these future energy systems. However, although various energy models already exist, none of them can cover all issues related to integrating renewable energy systems. The existing research gap is also reflected in the fact that current models cannot model the entire energy system for very high shares of renewable energies with high temporal resolution (15 min or 1-h steps) and high spatial resolution. Additionally, the low availability of open-source energy models leads to a lack of transparency about exactly how they work. To close this gap, the sector-coupled energy model (UCB-SEnMod) was developed. Its unique features are the modular structure, high flexibility, and applicability, enabling it to model any system constellation and can be easily extended with new functions due to its software design. Due to the software architecture, it is possible to map individual buildings or companies and regions, or even countries. In addition, we plan to make the energy model UCB-SEnMod available as an open-source framework to enable users to understand the functionality and configuration options more easily. This paper presents the methodology of the UCB-SEnMod model. The main components of the model are described in detail, i.e., the energy generation systems, the consumption components in the electricity, heat, and transport sectors, and the possibilities of load balancing.
Diadromous fish have exhibited a dramatic decline since the end of the 20th century. The allis shad (Alosa alosa) population in the Gironde-Garonne-Dordogne (GGD) system, once considered as a reference in Europe, remains low despite a fishing ban in 2008. One hypothesis to explain this decline is that the downstream migration and growth dynamics of young stages have changed due to environmental modifications in the rivers and estuary. We retrospectively analysed juvenile growth and migration patterns using otoliths from adults caught in the GGD system 30 years apart during their spawning migration, in 1987 and 2016. We coupled otolith daily growth increments and laser ablation inductively-coupled plasma mass spectrometry measurements of Sr:Ca, Ba:Ca, and Mn:Ca ratios along the longest growth axis from hatching to an age of 100 days (i.e., during the juvenile stage). A back-calculation allowed us to estimate the size of juveniles at the entrance into the brackish estuary. Based on the geochemistry data, we distinguished four different zones that juveniles encountered during their downstream migration: freshwater, fluvial estuary, brackish estuary, and lower estuary. We identified three migration patterns during the first 100 days of their life: (a) Individuals that reached the lower estuary zone, (b) individuals that reached the brackish estuary zone, and (c) individuals that reached the fluvial estuary zone. On average, juveniles from the 1987 subsample stayed slightly longer in freshwater than juveniles from the 2016 subsample. In addition, juveniles from the 2016 subsample entered the brackish estuary at a smaller size. This result suggests that juveniles from the 2016 subsample might have encountered more difficult conditions during their downstream migration, which we attribute to a longer exposure to the turbid maximum zone. This assumption is supported by the microchemical analyses of the otoliths, which suggests based on wider Mn:Ca peaks that juveniles in 2010s experienced a longer period of physiological stress during their downstream migration than juveniles in 1980s. Finally, juveniles from the 2016 subsample took longer than 100 days to exit the lower estuary than we would have expected from previous studies. Adding a new marker (i.e., Ba:Ca) helped us refine the interpretation of the downstream migration for each individual.
This paper presents a feasibility study for the production of recycled glycol modified polyethylene terephthalate (PETG) material for additive manufacturing. Past studies showed a variety of results for the recycling of 3D-printing material, therefore the precise effect on the material properties is not completely clear. For this work, PETG waste of the same grade was recycled once and further processed into 3D printing filament. The study compares three blend ratios between purchased plastic pellets and recycled pellets to determine the degradation effect of one recycling cycle and possible blend ratios to counter these effects. Furthermore, the results include a commercially available filament. The comparison uses the filament diameter, the dimensional accuracy of the printed test specimen and mechanical properties as quality criteria. The study shows that the recycled material has a minor decrease concerning the tensile strength and Young’s modulus.
Additive manufacturing is an essential tool in innovative production processes. The extended degrees of freedom offer much potential in usage, construction, and product design. Rising raw material and energy costs, constantly increasing environmental requirements, and the increasing demand for resource-saving products represent a paradigm shift in classic production processes.
In addition to the purely energetic evaluation, developing energy models is a method to determine energy consumption and reduce it in the long term. The specific energy consumption model, also known as the SEC model, allows a quick estimation of energy consumption by multiplying the SEC with a unit like the mass of the workpiece, the manufacturing time, or the exposed area. Here, high dependence on the used machine, the considered peripheral devices, and the geometry are noticeable.
Previous studies, such as those by Kellens et al. and Baumers et al., have laid the basis for understanding the energy demands of PBF-LB/M processes. Various energy models have subsequently been proposed, including those by Paul and Anand, Yi et al., Lv et al., and Hui et al. These models are often limited by their specificity to sub-processes or subsystems. This results in limitations in their applicability to other manufacturing machines or inaccuracies in energy consumption predictions. The simulation accuracy ACC is mostly in the range of 90% with the limitation of small sample sizes. Moreover, nearly, all these models rely heavily on process time information, making the accuracy of their simulations largely dependent on the quality of the underlying time model.
In the following study, two manufacturing machines of the PBF-LB/M process are analyzed and compared with other studies. The aim is to analyze the power and resource consumption to use these data to build an improved energy model with a high accuracy, which can be used as an additional parameter in the adapted design methodology. Furthermore, potential savings are derived from the load curves.
Many borate crystals feature nonlinear optical properties that allow for efficient frequency conversion of common lasers down into the ultraviolet spectrum. Twinning may degrade crystal quality and affect nonlinear optical properties, in particular if crystals are composed of twin domains with opposing polarities. Here, we use measurements of optical activity to demonstrate the existence of inversion twins within single crystals of YAl3(BO3)4 (YAB) and K2Al2B2O7 (KABO). We determine the optical rotatory dispersion of YAB and KABO throughout the visible spectrum using a spectrophotometer with rotatable polarizers. Space-resolved measurements of the optical rotation can be related to the twin structure and give estimates on the extent of twinning. The reported dispersion relations for the rotatory power of YAB and KABO may be used to assess crystal quality and to select twin-free specimens.
Social media data are transforming sustainability science. However, challenges from restrictions in data accessibility and ethical concerns regarding potential data misuse have threatened this nascent field. Here, we review the literature on the use of social media data in environmental and sustainability research. We find that they can play a novel and irreplaceable role in achieving the UN Sustainable Development Goals by allowing a nuanced understanding of human-nature interactions at scale, observing the dynamics of social-ecological change, and investigating the co-construction of nature values. We reveal threats to data access and highlight scientific responsibility to address trade-offs between research transparency and privacy protection, while promoting inclusivity. This contributes to a wider societal debate of social media data for sustainability science and for the common good.