bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact Assessment of Eco-Friendly Cooling System Implementation on Sea Transportation: A GTAP-E-Power Model Application
Authors: Huang Michael C. , Iwaki Yoko , Liou Ming-Huan 
Published: May 2023

AbstractClimate change and global warming have significantly threatened food security and the global supply chain. As the trade volume of bulk commodity grains has been growing steadily, ensuring quality while minimizing losses during long-distance shipping between warm and cold seawater has become a critical issue. An evidence-based approach to provide quantified implications is needed to illustrate a roadmap toward a net-zero-carbon society. This study applied the GTAP-E-Power model to examine the economic and welfare impacts of eco-friendly cooling system implementation on sea transportation in Japan, Australia, and New Zealand. By creating scenarios of Japan’s technology change calibrated from SPIAS-e and capital-use subsidy on sectors of electronics, solar power, and sea transportation as cooling system implementation, the simulation results showed a GDP growth of 0.09% in Japan and 0.11% in Australia and New Zealand. Moreover, Japan's welfare could improve by USD 4,219 million, while greenhouse gas emissions might be reduced to 8.4 million tons, equivalent to 0.9% of the total emission of Japan’s sea transportation sector.
Datafizierte Schulaufsicht?! Zur Erfassung des komplexen Zusammenspiels von wirkmächtigen Dateninfrastrukturen und vielfältigen Datenpraktiken
Authors: Hartong Sigrid , Dabisch Vito 
Published: May 2023

ZusammenfassungZiel dieses Kapitels ist es, die wachsende Datafizierung von Schulaufsicht aus einercritical data studiesPerspektive heraus zu betrachten und ein entsprechend differenziertes Verständnis für die Wirkmächtigkeit von Dateninfrastrukturen zu entwickeln. Mit dem Blick auf Schulaufsichtspersonen wird eine Personengruppe in den Fokus gerückt, die im Rahmen datenbasierterEducational Governanceeine zentrale, aber auch ambivalente Rolle spielt – nämlich „zwischen“ Kontrolle und Beratung, Pädagogik und Verwaltung sowie hierarchisch regulierter und autonom professionalisierter Steuerung. Dennoch ist die Schulaufsicht gerade in der Datafizierungsforschung bislang tendenziell unterbelichtet geblieben. Diese Lücke adressierend, führten wir im Kontext von DATAFIED sowohl eine umfangreiche Dokumentenanalyse als auch eine Interviewstudie durch, um die Dateninfrastrukturen, in denen sich Schulaufsichtspersonen bewegen, ebenso wie damit zusammenhängenden vielfältigen und spannungsreichen Datenpraktiken systematischer zu erfassen und zu reflektieren.
Zur Erfassung und Modellierung der „Hinterbühne“ von Datenflüssen: Das Beispiel Unterrichtsausfall
Authors: Jarke Juliane , Hartong Sigrid , Raabe Tjark , Dabisch Vito , Breiter Andreas , Lange Angelina , Zakharova Irina 
Published: May 2023

ZusammenfassungDieses Kapitel beschäftigt sich mit Datenflüssen und spezifischer der Frage, wie Daten innerhalb von Dateninfrastrukturen „beweglich“ gemacht werden. Während Datenflüsse im Rahmen datenbasierter Steuerung bzw. Schulentwicklung oftmals funktional betrachtet und auch visuell dargestellt werden – also etwa bezüglich einer möglichst „reibungsfreien“ Datenweitergabe – liegt der Fokus dieses Kapitels im Sinne einer critical data studies Perspektive vor allem auf den für Datafizierung hochgradig konstitutiven Reibungen, Brüchen, Spannungen sowie auf der aktiven „Herstellung“ von Datenbewegung als Festschreibung, aber auch als Verhandlung bestimmter datafizierter Sichtbarkeiten. Gleichzeitig stellt sich die Frage, ob und wie die Darstellung einer derartigen Perspektive modelliert und damit visualisiert werden kann. Beides diskutieren wir am Beispiel von Unterrichtsausfalldaten, deren Produktion, Verarbeitung und Weitergabe uns im DATAFIED-Verbund als hochpolitischer, und als ebenso komplexer und ambivalenter Prozess begegnet ist.
Adaptive Lernsoftware oder adaptierende Lehrkräfte? Das Ringen um Handlungsspielräume
Authors: Macgilchrist Felicitas , Jornitz Sieglinde , Mayer Ben , Troeger Jasmin 
Published: May 2023

ZusammenfassungViele Hoffnungen, aber auch Kritik richten sich auf und an neue kommerzielle Akteure im Schulsystem. Was Anbieter von Software für den Unterricht konzipiert haben, tritt oftmals in eine Spannung zu dem, wie Lehrkräfte die Software einsetzen wollen. Der Beitrag lässt nun die Anbieter einer bestimmten Software, hier: System|X und Lehrkräfte, die diese in ihrem Unterricht verwenden, in der Analyse aufeinandertreffen. Durch die Relationierung von Aussagen zu den drei Aspekten der Leistungsdifferenzierung, Fehlertoleranz und Belohnung zeigt sich ein Ringen um den jeweiligen Handlungsspielraum. In den Interviews deutet sich an, wie die Ziele der Anbieter durch den Einsatz im kontextspezifischen Unterricht weitgehend adaptiert werden. Diese Adaption geschieht durch die Orientierung der Anbieter an der eigenständigen Nutzung durch Schüler*innen, die aber herausgefordert wird durch die Einbettung der Lernsoftware in das soziale, materielle, körperliche, sprachliche und symbolische Gefüge des Unterrichts. Lehrkräfte und Anbieter ringen um die Möglichkeit, im Unterricht vermitteln zu können. Insgesamt identifiziert der Beitrag eine „vermittelte Vermittlung“, in der die Software vermittelt, wie die Lehrkräfte vermitteln können und die Lehrkräfte vermitteln, wie die Software vermitteln kann. Sowohl Lernsoftware als auch Lehrkräfte zeigen sich in einem bestimmten Sinne als adaptiv bzw. adaptierend. Schule und Unterricht werden, so die Implikation, neu konfiguriert, wenn diese vermittelte Vermittlung zu neuen Entscheidungen in der Alltagspraxis führt.
Pandemiebedingte Schulschließungen und die Nutzung digitaler Technologien. Welchen Einblick Twitter- und Interviewanalysen geben können
Authors: Mayer Ben , Jornitz Sieglinde , Zakharova Irina , Brick Yan , Jarke Juliane 
Published: May 2023

ZusammenfassungUm besser zu verstehen, vor welchen Fragen und Aufgaben Lehrer*innen in Deutschland in den Schuljahren 2019/2020 und 2020/2021 hinsichtlich des Einsatzes von digitalen Medien standen und mit welchen digitalen Medien Schulen versucht haben, Unterricht auf Distanz zu ermöglichen, stellen wir im Folgenden die Auswertung zweier Datenkorpora vor, die im Rahmen des Verbundprojektes DATAFIED erhoben wurden. Während Twitter-Tweets mit dem Hashtag #twitterlehrerzimmer und #twlz den einen Datenkorpus bilden, umfasst der andere Datenkorpus fünfzehn online geführte Interviews mit sieben Lehrer*innen aus drei Projektschulen. Die Analyse bezieht sich auf drei relevante Zeiträume im Verlauf der pandemiebedingten Schulschließungen: (1) das Frühjahr 2020 kurz nach den ersten Schulschließungen, (2) den Sommer 2020 kurz vor den Sommerferien und (3) um das Frühjahr 2021, also ein Jahr nach den ersten landesweiten Schulschließungen. Die jeweiligen thematischen Schwerpunkte bzw. Interessen variieren und lassen sich für die aufgeführten Zeiträume aufzeigen. Doch ob diese Interessen der Community anders oder gleich jenen der Lehrer*innen in der Praxis der Projektschulen ist, wollen wir versuchen, mit der Zusammenschau beider Datenkorpora zu beantworten.
Water Losses
Authors: Ong Corinne , Tortajada Cecilia , Arora Ojasvee 
Published: May 2023

AbstractThe volume of water losses depends largely on the approach to active leakage control of the water networks. Losses comprise physical losses (leakages on transmission and/or distribution networks, at utility’s storage tanks, and on service connections), commercial losses (unauthorised consumption and metering inaccuracies and data handling errors) and unbilled authorised consumption (unbilled metered and unmetered consumption). Reduction of real water losses may be achieved through leak detection and pipe repair and replacement, district metered areas (DMAs), and pressure management. Water loss management for reducing commercial losses may be achieved through water meters and customer reporting’s of illegal connections. All ASEAN countries implement water loss management through a combination of such measures.
Non-price Mechanisms
Authors: Ong Corinne , Tortajada Cecilia , Arora Ojasvee 
Published: May 2023

AbstractWater users’ consumption behaviour may be influenced by non-economic tools to reduce their water usage and/or shape water-use practices and habits. Such measures include, but are not limited to, education and awareness-raising campaigns, water restrictions, water-saving devices and labelling schemes, water efficiency audits, and consumer outreach through utility bills. Water service providers are gradually incorporating these as part of their WDM strategies. In ASEAN, non-price mechanisms are widely implemented through various measures with demonstrated effectiveness in outcomes.
Hypergraph Computation Paradigms
Authors: Dai Qionghai , Gao Yue 
Published: May 2023

AbstractThis chapter introduces three hypergraph computation paradigms, including intra-hypergraph computation, inter-hypergraph computation, and hypergraph structure computation. Intra-hypergraph computation representation aims to conduct representation learning of a hypergraph, where each subject is represented by a hypergraph of its components. Inter-hypergraph computation is to conduct representation learning of vertices in the hypergraph, where each subject is a vertex in the hypergraph. Hypergraph structure computation is to conduct hypergraph structure prediction, which aims to find the connections among vertices. This chapter is a general introduction of hypergraph computation paradigms to show how to formulate the task in the hypergraph computation framework.
Typical Hypergraph Computation Tasks
Authors: Dai Qionghai , Gao Yue 
Published: May 2023

AbstractAfter hypergraph structure generation for the data, the next step is how to conduct data analysis on the hypergraph. In this chapter, we introduce four typical hypergraph computation tasks, including label propagation, data clustering, imbalance learning, and link prediction. The first typical task is label propagation, which is to predict the labels for the vertices, i.e., assigning a label to each unlabeled vertex in the hypergraph, based on the labeled information. In general cases, label propagation is to propagate the label information from labeled vertices to unlabeled vertices through structural information of the hyperedges. In this part, we discuss the hypergraph cut on hypergraphs and random walk interpretation of label propagation on hypergraphs. The second typical task is data clustering, which is formulated as dividing the vertices into several parts in a hypergraph. In this part, we introduce a hypergraph Laplacian smoothing filter and an embedded model for hypergraph clustering tasks. The third typical task is cost-sensitive learning, which targets on learning with different mis-classification costs. The fourth typical task is link prediction, which aims to discover missing relations or predict new coming hyperedges based on the observed hypergraph.
Hypergraph Structure Evolution
Authors: Dai Qionghai , Gao Yue 
Published: May 2023

AbstractIn practice, noise exists in the process of data collection and hypergraph construction. Therefore, missing, abundant, and noisy connections may be introduced into the generated hypergraph structure, which may lead to inaccurate inference on hypergraph. Another issue comes from the increasing data stream, which is also very common in many applications. It is important to consider the structure evolution methods on the hypergraph, which optimize the hypergraph structure accordingly. Early hypergraph computation methods mainly rely on static hypergraph structure, which may suffer from the limitation of the static mechanism when confronting random and increasing data scenarios. In this chapter, we introduce dynamic hypergraph structure evolution methods, including both hypergraph component optimization and hypergraph structure optimization. Finally, we briefly introduce the incremental learning method on growing data.
Neural Networks on Hypergraph
Authors: Dai Qionghai , Gao Yue 
Published: May 2023

AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods. For the spectral-based methods, the convolution operation is formulated in the spectral domain of graph, and we introduce the typical spectral-based methods, including hypergraph neural networks (HGNN), hypergraph convolution with attention (Hyper-Atten), and hyperbolic hypergraph neural network (HHGNN), which extend hypergraph computation to hyperbolic spaces beyond the Euclidean space. For the spatial-based methods, the convolution operation is defined in groups of spatially close vertices. We then present spatial-based hypergraph neural networks of the general hypergraph neural networks (HGNN+) and the dynamic hypergraph neural networks (DHGNN). Additionally, there are several convolution methods that attempt to reduce the hypergraph structure to the graph structure, so that the existing graph convolution methods can be directly deployed. Lastly, we analyze the association and comparison between hypergraph and graph in the two areas described above (spectral-based, spatial-based), further demonstrating the ability and advantages of hypergraph on constructing and computing higher-order correlations in the data.
Large Scale Hypergraph Computation
Authors: Dai Qionghai , Gao Yue 
Published: May 2023

AbstractAs introduced in the previous chapters, the complexity of hypergraph computation is relatively high. In practical applications, the hypergraph may not be in a small scale, where we often encounter the scenario that the size of the hypergraph is very large. Therefore, hypergraph computation confronts the complexity issues in many applications. Therefore, how to handle large scale data is an important task. In this chapter, we discuss the computation methods for large scale hypergraphs and their applications. Two types of hypergraph computation methods are provided to handle large scale data, namely the factorization-based hypergraph reduction method and hierarchical hypergraph learning method. In the factorization-based hypergraph reduction method, the large scale hypergraph incidence matrix is reduced to two low-dimensional matrices. The computing procedures are conducted with the reduced matrices. This method can support the hypergraph computation with more than 10,000 vertices and hyperedges. On the other hand, the hierarchical hypergraph learning method splits all samples as some sub-hypergraphs and merges the results obtained from each sub-hypergraph computation. This method can support hypergraph computation with millions of vertices and hyperedges.
A note on measures related to compactness and the Banach–Saks property in $$l_{1}$$
Authors: Kryczka Andrzej 
Published: May 2023

AbstractUsing the Bessaga–Pełczyński selection principle, we give an alternative and concise proof of the results obtained by Tu (Arch Math, 117:315–322, 2021) that several quantities defined for bounded subsets of Banach spaces and related to compactness, weak compactness, and the Banach–Saks property coincide in $$l_{1}$$

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Concomitant trauma of brain and upper cervical spine: lessons in injury patterns and outcomes
Authors: Marchesini Nicolò , Demetriades Andreas K. , Peul Wilco C. , Tommasi Nicola , Zanatta Paolo , Pinna Giampietro , Sala Francesco 
Published: May 2023

Abstract
Purpose
The literature on concomitant traumatic brain injury (TBI) and traumatic spinal injury is sparse and a few, if any, studies focus on concomitant TBI and associated upper cervical injury. The objective of this study was to fill this gap and to define demographics, patterns of injury, and clinical data of this specific population.

Methods
Records of patients admitted at a single trauma centre with the main diagnosis of TBI and concomitant C0–C1–C2 injury (upper cervical spine) were identified and reviewed. Demographics, clinical, and radiological variables were analyzed and compared to those of patients with TBI and: (i) C3–C7 injury (lower cervical spine); (ii) any other part of the spine other than C1–C2 injury (non-upper cervical); (iii) T1–L5 injury (thoracolumbar).

Results
1545 patients were admitted with TBI and an associated C1–C2 injury was found in 22 (1.4%). The mean age was 64 years, and 54.5% were females. Females had a higher rate of concomitant upper cervical injury (p = 0.046 vs non-upper cervical; p = 0.050 vs thoracolumbar). Patients with an upper cervical injury were significantly older (p = 0.034 vs lower cervical; p = 0.030 vs non-upper cervical). Patients older than 55 years old had higher odds of an upper cervical injury when compared to the other groups (OR = 2.75). The main mechanism of trauma was road accidents (RAs) (10/22; 45.5%) All pedestrian injuries occurred in the upper cervical injured group (p = 0.015). ICU length of stay was longer for patients with an upper cervical injury (p = 0.018). Four patients died in the upper cervical injury group (18.2%), and no death occurred in other comparator groups (p = 0.003).

Conclusions
The rate of concomitant cranial and upper cervical spine injury was 1.4%. Risk factors were female gender, age ≥ 55, and pedestrians. RAs were the most common mechanism of injury. There was an association between the upper cervical injury group and longer ICU stay as well as higher mortality rates. Increased understanding of the pattern of concomitant craniospinal injury can help guide comprehensive diagnosis, avoid missed injuries, and appropriate treatment.
Adaptive responses of yeast strains tolerant to acidic pH, acetate, and supraoptimal temperature
Authors: Salas-Navarrete Prisciluis Caheri , Rosas-Santiago Paul , Suárez-Rodríguez Ramón , Martínez Alfredo , Caspeta Luis 
Published: May 2023

Abstract
Ethanol fermentations can be prematurely halted as Saccharomyces cerevisiae faces adverse conditions, such as acidic pH, presence of acetic acid, and supraoptimal temperatures. The knowledge on yeast responses to these conditions is essential to endowing a tolerant phenotype to another strain by targeted genetic manipulation. In this study, physiological and whole-genome analyses were conducted to obtain insights on molecular responses which potentially render yeast tolerant towards thermoacidic conditions. To this end, we used thermotolerant TTY23, acid tolerant AT22, and thermo-acid tolerant TAT12 strains previously generated by adaptive laboratory evolution (ALE) experiments. The results showed an increase in thermoacidic profiles in the tolerant strains. The whole-genome sequence revealed the importance of genes related to: H+, iron, and glycerol transport (i.e., PMA1, FRE1/2, JEN1, VMA2, VCX1, KHA1, AQY3, and ATO2); transcriptional regulation of stress responses to drugs, reactive oxygen species and heat-shock (i.e., HSF1, SKN7, BAS1, HFI1, and WAR1); and adjustments of fermentative growth and stress responses by glucose signaling pathways (i.e., ACS1, GPA1/2, RAS2, IRA2, and REG1). At 30 °C and pH 5.5, more than a thousand differentially expressed genes (DEGs) were identified in each strain. The integration of results revealed that evolved strains adjust their intracellular pH by H+ and acetic acid transport, modify their metabolism and stress responses via glucose signaling pathways, control of cellular ATP pools by regulating translation and de novo synthesis of nucleotides, and direct the synthesis, folding and rescue of proteins throughout the heat-shock stress response. Moreover, the motifs analysis in mutated transcription factors suggested a significant association of SFP1, YRR1, BAS1, HFI1, HSF1, and SKN7 TFs with DEGs found in thermoacidic tolerant yeast strains.

Key points
• All the evolved strains overexpressed the plasma membrane H+-ATPase PMA1 at optimal conditions
• Tolerant strain TAT12 mutated genes encoding weak acid and heat response TFs HSF1, SKN7, and WAR1
• TFs HSF1 and SKN7 likely controlled the transcription of metabolic genes associated to heat and acid tolerance
A simply calculated nutritional index provides clinical implications in patients undergoing transcatheter aortic valve replacement
Authors: Sudo Mitsumasa , Shamekhi Jasmin , Aksoy Adem , Al-Kassou Baravan , Tanaka Tetsu , Silaschi Miriam , Weber Marcel , Nickenig Georg , Zimmer Sebastian 
Published: May 2023

Abstract
Background
Malnutrition is associated with adverse outcomes in patients with aortic stenosis. The Triglycerides × Total Cholesterol × Body Weight Index (TCBI) is a simple scoring model to evaluate the status of nutrition. However, the prognostic relevance of this index in patients undergoing transcatheter aortic valve replacement (TAVR) is unknown. This study aimed to evaluate the association of the TCBI with clinical outcomes in patients undergoing TAVR.

Methods
A total of 1377 patients undergoing TAVR were evaluated in this study. The TCBI was calculated by the formula; triglyceride (mg/dL) × total cholesterol (mg/dL) × body weight (kg)/1000. The primary outcome was all-cause mortality within 3 years.

Results
Patients with a low TCBI, based on a cut-off value of 985.3, were more likely to have elevated right atrial pressure (p = 0.04), elevated right ventricular pressure (p < 0.01), right ventricular systolic dysfunction (p < 0.01), tricuspid regurgitation ≥ moderate (p < 0.01). Patients with a low TCBI had a higher cumulative 3-year all-cause (42.3% vs. 31.6%, p < 0.01; adjusted HR 1.36, 95% CI 1.05–1.77, p = 0.02) and non-cardiovascular mortality (15.5% vs. 9.1%, p < 0.01; adjusted HR 1.95, 95% CI 1.22–3.13, p < 0.01) compared to those with a high TCBI. Adding a low TCBI to EuroSCORE II improved the predictive value for 3-year all-cause mortality (net reclassification improvement, 0.179, p < 0.01; integrated discrimination improvement, 0.005, p = 0.01).

Conclusion
Patients with a low TCBI were more likely to have right-sided heart overload and exhibited an increased risk of 3-year mortality. The TCBI may provide additional information for risk stratification in patients undergoing TAVR.

Graphical abstract
Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala
Authors: Rolls Edmund T. 
Published: May 2023

AbstractThe orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic system.