Indicators for the optimization of sustainable urban energy systems based on energy system modeling

Background Urban energy systems are responsible for 75% of the world’s energy consumption and for 70% of the worldwide greenhouse gas emissions. Energy system models are used to optimize, benchmark and compare such energy systems with the help of energy sustainability indicators. We discuss several indicators for their basic suitability and their response to changing boundary conditions, system structures and reference values. The most suitable parameters are applied to four different supply scenarios of a real-world urban energy system. Results There is a number of energy sustainability indicators, but not all of them are suitable for the use in urban energy system optimization models. Shortcomings originate from the omission of upstream energy supply chains (secondary energy efficiency), from limited capabilities to compare small energy systems (energy productivity), from excessive accounting expense (regeneration rate), from unsuitable accounting methods (primary energy efficiency), from a questionable impact of some indicators on the overall system sustainability (self-sufficiency), from the lack of detailed information content (share of renewables), and more. On the other hand, indicators of absolute greenhouse gas emissions, energy costs, and final energy demand are well suitable for the use in optimization models. However, each of these indicators only represents partial aspects of energy sustainability; the use of only one indicator in the optimization process increases the risk that other important aspects will deteriorate significantly, eventually leading to suboptimal or even unrealistic scenarios in practice. Therefore, multi-criteria approaches should be used to enable a more holistic optimization and planning of sustainable urban energy systems. Conclusion We recommend multi-criteria optimization approaches using the indicators of absolute greenhouse gas emissions, absolute energy costs, and absolute energy demand. For benchmarking and comparison purposes, specific indicators should be used and therefore related to the final energy demand, respectively, the number of inhabitants. Our example scenarios demonstrate modeling strategies to optimize sustainability of urban energy systems.

A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis

Accurate and efficient detection of attention-deficit/hyperactivity disorder (ADHD) is critical to ensure proper treatment for affected individuals. Current clinical examinations, however, are inefficient and prone to misdiagnosis, as they rely on qualitative observations of perceived behavior. We propose a robust machine learning based framework that analyzes pupil-size dynamics as an objective biomarker for the automated detection of ADHD. Our framework integrates a comprehensive pupillometric feature engineering and visualization pipeline with state-of-the-art binary classification algorithms and univariate feature selection. The support vector machine classifier achieved an average 85.6% area under the receiver operating characteristic (AUROC), 77.3% sensitivity, and 75.3% specificity using ten-fold nested cross-validation (CV) on a declassified dataset of 50 patients. 218 of the 783 engineered features, including fourier transform metrics, absolute energy, consecutive quantile changes, approximate entropy, aggregated linear trends, as well as pupil-size dilation velocity, were found to be statistically significant differentiators (p < 0.05), and provide novel behavioral insights into associations between pupil-size dynamics and the presence of ADHD. Despite a limited sample size, the strong AUROC values highlight the robustness of the binary classifiers in detecting ADHD—as such, with additional data, sensitivity and specificity metrics can be substantially augmented. This study is the first to apply machine learning based methods for the detection of ADHD using solely pupillometrics, and highlights its strength as a potential discriminative biomarker, paving the path for the development of novel diagnostic applications to aid in the detection of ADHD using oculometric paradigms and machine learning.

Scientific and Methodological Bases of Exergetic Analysis of the Processes of Heat Treatment of Concrete Products in Heat Technology Installations

Having proven its effectiveness in finding the best options for energy supply and energy consumption the exergetic method of thermodynamic analysis of complex heat and power systems has been widely recognized in recent years. However, its application is hindered by the lack of appropriate scientific and methodological heat technology support, especially if their application involves not only transformation of energy, but also transformation of substances. Heat treatment of concrete and reinforced concrete products belongs to such technologies. This article presents new scientific results related to the development of exergetic balances of the processes of preparation of concrete mixture in a mixer and heat treatment of a concrete product in a heat-technological installation. For each of these cases, the analysis of exergetic flows was carried out, the structure of the exergy of the concrete mixture and the hardening concrete was determined. Based on the analysis of the literature data on the chemical composition of cement clinkers, cements, and hydration products, new dependences have been proposed for calculating the exergy of the concrete mixture flow and the exergy of concrete under its heat treatment, including all their components, viz. thermomechanical, reaction, and concentration constituents. Absolute energy indicators have been developed. The calculation of the mentioned values was performed on a specific example with the use of the developed scientific and methodological support. In the second part of this paper, the results of the study related to the determination of relative exergetic indicators that allow evaluating the energy efficiency of the processes of heat treatment of concrete products in heat technology installations will be published. The results obtained in this paper can be used for the selection of energy-saving modes of heat-technological equipment intended for industrial heat treatment of concrete products.

Competition Nutrition Practices of Elite Male Professional Rugby Union Players

Thirty-four elite male professional rugby union players from the New Zealand Super Rugby championship completed dietary intakes via the Snap-N-Send method during a seven-day competition week. Mean seven-day absolute energy intake was significantly higher for forwards (4606 ± 719 kcal·day−1) compared to backs (3761 ± 618 kcal·day−1; p < 0.01; d = 1.26). Forwards demonstrated significantly higher mean seven-day absolute macronutrient intakes compared to backs (p < 0.03; d = 0.86–1.58), but no significant differences were observed for mean seven-day relative carbohydrate (3.5 ± 0.8 vs. 3.7 ± 0.7 g·kg·day−1), protein (2.5 ± 0.4 vs. 2.4 ± 0.5 g·kg·day−1), and fat (1.8 ± 0.4 vs. 1.8 ± 0.5 g·kg·day−1) intakes. Both forwards and backs reported their highest energy (5223 ± 864 vs. 4694 ± 784 kcal·day−1) and carbohydrate (4.4 ± 1.2 vs. 5.1 ± 1.0 g·kg·day−1) intakes on game day, with ≈62% of total calories being consumed prior to kick-off. Mean pre-game meal composition for all players was 1.4 ± 0.5 g·kg−1 carbohydrate, 0.8 ± 0.2 g·kg−1 protein, and 0.5 ± 0.2 g·kg−1 fat. Players fell short of daily sports nutrition guidelines for carbohydrate and appeared to “eat to intensity” by increasing or decreasing energy and carbohydrate intake based on the training load. Despite recommendations and continued education, many rugby players select what would be considered a “lower” carbohydrate intake. Although these intakes appear adequate to be a professional RU player, further research is required to determine optimal dietary intakes.

Quantification of seizure termination patterns reveals limited pathways to seizure end

Seizures result from a variety of pathologies and exhibit great diversity in their dynamics. Although many studies have examined the dynamics of seizure initiation, few have investigated the mechanisms leading to seizure termination. We examined intracranial recordings from patients with intractable focal epilepsy to differentiate seizure termination patterns and investigate whether these termination patterns are indicative of different underlying mechanisms. Seizures (n=710) were recorded intracranially from 104 patients and visually classified as focal or secondarily generalized. Only two patterns emerged from this analysis: (a) those that end simultaneously across the brain (synchronous termination), and (b) those whose ictal activity terminates in some regions but continues in others (asynchronous termination). Finally, seizures ended with either an intermittent bursting pattern (burst suppression pattern), or continuous activity (continuous bursting). These findings allowed for a classification and quantification of the burst suppression ratio, absolute energy and network connectivity of all seizures and comparison across different seizure termination patterns. We found that different termination patterns can manifest within a single patient, even in seizures originating from the same onset locations. Most seizures terminate with patterns of burst suppression regardless of generalization but that seizure that secondarily generalize show burst suppression patterns in 90% of cases, while only 60% of focal seizures exhibit burst suppression. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous bursting were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous bursting compared to seizures with burst suppression. Our study demonstrates that there are a limited number of seizure termination patterns, suggesting that, unlike seizure initiation, the number of mechanisms underlying seizure termination is constrained. The study of termination patterns may provide useful clues about how these seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.

Energy efficiency in Norwegian news media

This article investigates how energy efficiency features in Norwegian news media discourse. Based on an analysis of 309 news articles, we explore the objectification of energy efficiency and its rhetorical connections to energy savings and reductions. Energy efficiency is surrounded by positive overtones and used flexibly to include different meanings as well as effects. As a discursive object, the term wields significant rhetorical and legitimizing power, producing consensus across conflicting narratives and controversies in what we call the “discourse-as-usual”. We argue that energy efficiency shares characteristics with boundary objects, conveying an interpretive flexibility to bridge otherwise incommensurable perspectives on the need to decrease or increase absolute energy consumption. However, there are a few instances where controversy turns toward energy efficiency itself, revealing different views on absolute limits to energy consumption. By scrutinizing one of these glitches in consensus, we examine the normal through the anomaly to pinpoint the moral prerogative of energy efficiency in the discourse-as-usual. By black-boxing the complex relationship between efficiency and reductions, the term allows for avoiding the question of absolute limits to energy consumption in news media debates. Rather than translate between climate change and economic stability and growth narratives, we assert that energy efficiency as a discursive object conceals opposition between them. We discuss this concealment as a form of system dependency, as it is by black-boxing the effects of energy efficiency that it can unite adversaries and ensure ongoing activity.

Identification of pathogenic missense mutations using protein stability predictors

Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 13 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, significantly outperforms all other predictors in the identification of disease variants. Moreover, we demonstrate that employing predicted absolute energy change scores improves performance of nearly all predictors in distinguishing pathogenic from benign variants. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

Band Positioning in Boron Nitride and Metal-Free Photocatalysts via Photoelectron- and UV-Vis Diffuse Reflectance Spectroscopy

<p>New photocatalysts, particularly porous ones such as porous boron nitride, have emerged that exhibit complex structures and for which, there is limited knowledge of the electronic structure. Gaining insight into their complete band structure on the absolute energy scale will help assessing their suitability for a given photocatalytic reaction. To address this, we rationalise key concepts of band positioning alignment for both porous and non-porous semiconductors on the absolute energy scale. The approach employs a range of techniques generally accessible to many research groups. It involves a combination of spectroscopic techniques, namely X-ray photoelectron spectroscopy to determine the work function and valence band offset, and UV-Vis diffuse reflectance spectroscopy to measure the band gap. We apply this to present the complete band structure of boron nitride, in both porous and non-porous forms. We validate our methodology by comparing the experimentally obtained band structure for graphitic carbon nitride and amorphous boron, both amorphous semiconductors with a known band structure. We show how this can help predict possible photocatalytic reactions and demonstrate this in the context of CO2 photoreduction. With porous materials, such as porous BN, garnering increasing interest for photocatalytic applications, shedding light on their band structures could pave the way towards a methodical tuning and optimization of the photochemistry of these materials. </p>

Band Positioning in Boron Nitride and Metal-Free Photocatalysts via Photoelectron- and UV-Vis Diffuse Reflectance Spectroscopy

<p>New photocatalysts, particularly porous ones such as porous boron nitride, have emerged that exhibit complex structures and for which, there is limited knowledge of the electronic structure. Gaining insight into their complete band structure on the absolute energy scale will help assessing their suitability for a given photocatalytic reaction. To address this, we rationalise key concepts of band positioning alignment for both porous and non-porous semiconductors on the absolute energy scale. The approach employs a range of techniques generally accessible to many research groups. It involves a combination of spectroscopic techniques, namely X-ray photoelectron spectroscopy to determine the work function and valence band offset, and UV-Vis diffuse reflectance spectroscopy to measure the band gap. We apply this to present the complete band structure of boron nitride, in both porous and non-porous forms. We validate our methodology by comparing the experimentally obtained band structure for graphitic carbon nitride and amorphous boron, both amorphous semiconductors with a known band structure. We show how this can help predict possible photocatalytic reactions and demonstrate this in the context of CO2 photoreduction. With porous materials, such as porous BN, garnering increasing interest for photocatalytic applications, shedding light on their band structures could pave the way towards a methodical tuning and optimization of the photochemistry of these materials. </p>