bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning
Authors: Mamalakis Michail , Banerjee Abhirup , Ray Surajit , Wilkie Craig , Clayton Richard H. , Swift Andrew J. , Panoutsos George , Vorselaars Bart 
Published: August 2024

AbstractThe development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems.
Challenges with segmenting intraoperative ultrasound for brain tumours
Authors: Weld Alistair , Dixon Luke , Anichini Giulio , Patel Neekhil , Nimer Amr , Dyck Michael , O’Neill Kevin , Lim Adrian , Giannarou Stamatia , Camp Sophie 
Published: August 2024

Abstract
Objective - Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods - 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist’s, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons

$$[IoU:0.789, \ DSC:0.876, \ HD:103.227]$$

[
I
o
U
:
0.789
,

D
S
C
:
0.876
,

H
D
:
103.227
]

and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators

$$[IoU:0.723, \ DSC:0.813, \ HD:115.675]$$

[
I
o
U
:
0.723
,

D
S
C
:
0.813
,

H
D
:
115.675
]

. After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by

$$[IoU:37\%, \ DSC:41\%, \ HD:54\%]$$

[
I
o
U
:
37
%
,

D
S
C
:
41
%
,

H
D
:
54
%
]

. Conclusion - This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncological intraoperative brain ultrasound. We then show that bounding box annotation could serve as a useful complementary approach for both clinical and technical reasons.
Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm
Authors: Wang Zhihui , Peng Hui , Wan Jie , Song Anping 
Published: August 2024

AbstractThe aim of this study is to establish a deep learning (DL) model to predict the pathological type of gastric adenocarcinoma cancer based on whole-slide images(WSIs). We downloaded 356 histopathological images of gastric adenocarcinoma (STAD) patients from The Cancer Genome Atlas database and randomly divided them into the training set, validation set and test set (8:1:1). Additionally, 80 H&E-stained WSIs of STAD were collected for external validation. The CLAM tool was used to cut the WSIs and further construct the model by DL algorithm, achieving an accuracy of over 90% in identifying and predicting histopathological subtypes. External validation results demonstrated the model had a certain generalization ability. Moreover, DL features were extracted from the model to further investigate the differences in immune infiltration and patient prognosis between the two subtypes. The DL model can accurately predict the pathological classification of STAD patients, and provide certain reference value for clinical diagnosis. The nomogram combining DL-signature, gene-signature and clinical features can be used as a prognostic classifier for clinical decision-making and treatment.
Empfehlungen der DGINA und DIVI zur Struktur und Ausstattung von Notaufnahmen 2024
Authors: Brod Torben , Bernhard Michael , Blaschke Sabine , Dodt Christoph , Dormann Patrick , Drynda Susanne , Dubb Rolf , Gries André , Hoffmann Florian , Janssens Uwe , Kaltwasser Arnold , Markewitz Andreas , Möckel Martin , Pedersen Vera , Pin Martin , Walcher Felix , Wrede Christian 
Published: August 2024

ZusammenfassungDie Notaufnahmen deutscher Krankenhäuser sind ein zentraler Anlaufpunkt für über 20 Millionen Notfallpatienten pro Jahr. Um eine hochwertige Patientenversorgung sicherstellen zu können, sind personelle, apparative und infrastrukturelle Voraussetzungen erforderlich, die nur bedingt im Beschluss des Gemeinsamen Bundesausschusses (G-BA) zu einem gestuften System von Notfallstrukturen an Krankenhäusern von 2018 festgeschrieben sind. Darüber hinaus gibt es bislang in Deutschland keine einheitlichen Empfehlungen zur erforderlichen Ausstattung von Notaufnahmen. Auf Grundlage existierender Publikationen nationaler und internationaler Fachgesellschaften haben die Deutsche Gesellschaft für Interdisziplinäre Notfall- und Akutmedizin (DGINA) und die Deutsche Interdisziplinäre Vereinigung für Intensiv- und Notfallmedizin (DIVI) Empfehlungen zur Struktur und Ausstattung von Notaufnahmen erarbeitet. Diese umfassen insbesondere Empfehlungen zur ärztlichen und nicht-ärztlichen Personalbesetzung, apparativen und baulichen Ausstattung sowie den für eine effiziente Notfallversorgung erforderlichen Prozessen und Schnittstellen. Weiterhin werden die Aspekte Forschung und Lehre, Qualitätsmanagement und Public Health Surveillance in der klinischen Akut- und Notfallmedizin betrachtet. Mit der Umsetzung dieser Strukturempfehlungen wird eine weitere Professionalisierung der klinischen Akut- und Notfallmedizin in Deutschland angestrebt.
Variation in Zero Plane Displacement and Roughness Length for Momentum Revisited
Authors: Kunadi Ashvath Singh , Silberstein Richard P. , Thompson Sally E. 
Published: August 2024

AbstractZero plane displacement height ($$d_0$$

d
0

) and momentum roughness length ($$z_{0m}$$

z

0
m

), describe the aerodynamic characteristics of a vegetated surface. Usually, $$d_0$$

d
0

and $$z_{0m}$$

z

0
m

are assumed to be constant functions of the physical characteristics of the surface. Prior evidence collected from the literature and our examination of flux tower data show that $$d_0$$

d
0

and $$z_{0m}$$

z

0
m

vary in time at sites with tree and shrub canopies, but not grasslands. The conventional explanations of these variations are based on linear functions of wind velocity and friction velocity, with little theoretical basis. This study explains the variation in aerodynamic parameters by matching four analytical canopy velocity models to a logarithmic above-canopy velocity profile at canopy height. $$d_0$$

d
0

and $$z_{0m}$$

z

0
m

come out as functions of 2 non-dimensional terms, the canopy momentum absorption capacity (parameter) and a (measurable) Péclet number. To test the theories of variation, we analysed the velocity profiles from Ozflux and Ameriflux sites. None of the theories could recreate $$d_0$$

d
0

and $$z_{0m}$$

z

0
m

at half-hourly intervals. However, the canopy velocity models were able better to recreate the distribution of the variations in $$d_0$$

d
0

and $$z_{0m}$$

z

0
m

. Additionally, the estimates of canopy momentum absorption capacity varied consistently with phenological changes in the canopies, whereas, the fitting parameters of the linear regression of using wind speed and friction velocity did not exhibit physically interpretable variations. The canopy velocity models may offer better predictions with an accurate estimation of the canopy height, a horizontally homogeneous and rigid canopy, and incorporation of the roughness sublayer.
International Cooperation and Kantian Moral Behaviour: Complements or Substitutes?
Authors: Ulph Alistair , Ulph David 
Published: August 2024

AbstractFaced with a global emissions problem such as climate change, we know that if countries’ emissions decisions are made in an independent and self-interested fashion the outcome can be very far from optimal. One proposed solution is to have countries enter international environmental agreements (IEAs) whereby individual countries’ emissions decisions are taken in the interests of all the participating countries and so reflect a degree of altruism. However, if the decision to co-operate is made in a self-interested fashion the standard non-cooperative model of IEAs yields the pessimistic conclusion that the more serious the environmental problem the smaller will be the equilibrium membership of an IEA. Our paper examines the implications for emissions, IEA membership and welfare of assuming that countries make both emissions and IEA membership decisions in the alternative moral fashion of acting as imperfect Kantians as defined by Alger and Weibull (Econometrica 81:2269–2302, 2013). We show that (i) the first-best can be achieved when countries either act as Perfect Kantians or by fully cooperating; (ii) in a more imperfect setting, these two forms of moral behaviour are complementary approaches to improving welfare outcomes in the sense that the greater the weights on Kantian behaviour the larger is the equilibrium coalition; (iii) the weights on Kantian behaviour that will induce full cooperation and hence the first-best are significantly less than 1; (iv) for given Kantian weights, our model generates higher equilibrium IEA membership, lower emissions and higher welfare than in the related paper by Eichner and Pethig (International environmental agreements when countries behave morally) which, we argue, does not fully capture the benefits of membership decisions.
Design and Installation of a Local Monitoring System to Validate Debris Flow Methodology for Risk Mitigation
Authors: Moraci Nicola , Ciurleo Mariantonietta , Mandaglio Maria Clorinda , Pisano Marilene 
Published: August 2024

AbstractLandslides pose significant threats to communities and infrastructure worldwide, necessitating the development of effective hazard and exposure reduction strategies to mitigate the potential risks. In particular, debris flows can be highly destructive landslides. The paper deals with an integrated monitoring system designed both to further validate an existing debris flow inception and propagation methodology developed by the authors and to fine-tune the landslide inception and warning model to be used in an early warning system. The design of the integrated monitoring system, the definition of the acquisition time of instruments, and the processing of the measured data were based on studies performed in the study area of Favazzina (Italy), which is affected by debris flows that are very difficult to monitor. The proposed approach used to design the integrated monitoring system may serve as a useful methodological tool to be adopted in similar geological and geotechnical contexts within the framework of risk mitigation strategies.
Teachers as Creators of Digital Multimedia Learning Materials: Are they Aligned with Multimedia Learning Principles
Authors: Désiron Juliette C. , Schmitz Maria-Luisa , Petko Dominik 
Published: August 2024

AbstractDigital technologies have facilitated access to instructional multimedia materials, and a large body of literature has shown that these technologies can support students’ learning. However, recent research also shows that many teachers have misconceptions about learning from multimedia materials. Further, numerous studies point out that numerous factors account for the successful use of technologies in class. Based on data from a large-scale study with upper secondary school teachers (n = 2247), we analyzed what predicted teachers’ creation of digital multimedia, to determine whether it was solely technology related factors derived from the will-skill-tool pedagogy model, or also factors related to teachers’ alignment with well-established design and processing principles of multimedia learning. Overall, a third of the respondents were fully aligned with the multimedia principles, and another third with all but the coherence principle. We investigated how this alignment affects the creation of multimedia learning material. Multilevel linear modeling analysis (MLM) indicates that of all multimedia principles, only the dual coding assumption has a significant and positive impact on teachers’ frequency of multimedia document creation. In addition, teachers’ positive beliefs about technology use in class, their self-reported technological knowledge (skill) and their self-reported knowledge in teaching with digital technologies significantly and positively predict how often they create digital multimedia documents. These results stress that, for now, teachers rely more on their technological beliefs and self-reported knowledge to integrate digital tools in their lesson than on their alignment with multimedia design guidelines. As previous studies show that available digital instructional multimedia do not always follow multimedia learning principles, this could lead teachers to create or use inappropriate digital multimedia materials.
Housing the poor? Accessibility and exclusion in the local housing systems of Vienna and Milan
Authors: Wolfgring Constanze , Peverini Marco 
Published: August 2024

AbstractDespite featuring very different housing systems, both in Vienna and Milan a growing number of people struggle to access affordable and decent housing. Even though social housing policies are in place in both contexts, for many the unregulated and often unaffordable private housing market is the only option. Building upon Antonio Tosi’s work, we centre our analysis on the poor and compare how two very different local housing systems create the conditions for their inclusion or exclusion. Through a comparative analysis of the Viennese and Milanese local housing regimes and adopting a mixed methodology, we discuss how both generate conditions of exclusion and who the ones excluded are, which local policies address the poor and how appropriate these are for mitigating housing exclusion. Finally, we indicate some directions for policies aimed at tackling the intersections of poverty and housing exclusion, advocating for a maximization of the ‘sociality’ of housing policies, involving a shift in prioritisation from the middle classes to the most vulnerable groups and the loosening of certain formal access requirements to housing or welfare services that constitute barriers to these groups, where possible.
Do 5G cell phone towers decrease house prices? Evidence from Warsaw
Authors: Marona Bartłomiej , Gaca Radosław , Głuszak Michał 
Published: August 2024

AbstractThis study investigates the impact of telecommunication infrastructure on residential property prices in Poland. This study contributes to the discussion of economic externalities related to new public infrastructure within the urban landscape. We use hedonic regression, matching techniques, and a difference-in-differences estimator to assess the impact of base transceiver stations on apartment prices in Warsaw, the capital of Poland, which covered market data on the sales of residential premises located in Miasteczko Wilanów estate in Warsaw from 2016 to 2021. In the analysed period, 1,825 residential sales were recorded and used for econometric modelling. The results do not confirm the influence of the vicinity of the wireless communication technology infrastructure on residential real estate prices in the studied local market. In particular, we did not observe a detrimental effect of cell phone towers on housing prices. Additionally, we investigate whether residential sale prices in proximity to BTS changed significantly after the introduction of the 5G standard. This particular issue has not been addressed in the economic literature. We found that the sale prices of apartments located in Warsaw were not statistically affected by the introduction of a fifth-generation technology standard for broadband cellular networks. Our research contributes to a better understanding of stigmatisation effects related to telecommunication infrastructure, and in particular, the links between the presence of cell phone towers and residential property values in the neighbourhood. The results may be of interest to all potential agents involved in neighbourhood conflicts arising from investments in cell phone towers and the development of new communication infrastructure in urban landscapes.
Describing group evolution in temporal data using multi-faceted events
Authors: Failla Andrea , Cazabet Rémy , Rossetti Giulio , Citraro Salvatore 
Published: August 2024

AbstractGroups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.
Recovery of cranial nerve neuropathies after LINAC-based stereotactic radiosurgery for benign cavernous sinus meningioma
Authors: Kaisman-Elbaz Tehila , Blumenfeld Philip , Wygoda Marc , Feldman John , Shoshan Yigal 
Published: August 2024

Abstract
Purpose
Cranial Nerve Neuropathies (CNNs) often accompany Cavernous Sinus Meningioma (CSM), for which Stereotactic Radiosurgery (SRS) or fractionated stereotactic radiotherapy (FSR) are established treatments. This study assesses CNNs recovery in CSM patients treated with LINAC, offering insight into treatment effectiveness.

Methods
This study was conducted on 128 patients with CSM treated with LINAC-based SRS/FSR between 2005 and 2020 at a single institution. 46 patients presented with CNNs. The study analyzed patients’ demographics, clinical parameters, SRS/FSR treatment characteristics, post-treatment CNNs recovery duration, status, and radiological control on their last follow-up.

Results
The median follow-up duration was 53.4 months. Patients were treated with SRS (n = 25) or FSR (n = 21). The mean pretreatment tumor volume was 9.5 cc decreasing to a mean end-of-follow-up tumor volume was 5.1 cc. Radiological tumor control was achieved in all cases. CNN recovery was observed in 80.4% of patients, with specific nerve recoveries documented as follows: extra-ocular nerves (43.2%), trigeminal nerve (32.4%), and optic nerve (10.8%). A higher CNNs recovery rate was associated with a smaller pre-treatment tumor volume (p < 0.001), and the median time-to-improvement was 3.7 months. Patients with tumor volumes exceeding 6.8 cc and those treated with FSR exhibited prolonged time-to-improvement (P < 0.03 and P < 0.04 respectively).

Conclusions
This study suggests that SRS/FSR for CSM provides good and sustainable CNNs recovery outcomes with excellent long-term radiological control. A higher CNNs recovery rate was associated with a smaller pre-treatment tumor volume. while shorter time-to-improvement was identified in patients treated with SRS compared to FSR, particularly in those with small pre-treatment tumor volume.