bluuebunny/crossref_metadata_2025_split huggingface dataset:
A large frozen debris avalanche entraining warming permafrost ground—the June 2021 Assapaat landslide, West Greenland
Authors: Svennevig Kristian , Hermanns Reginald L. , Keiding Marie , Binder Daniel , Citterio Michele , Dahl-Jensen Trine , Mertl Stefan , Sørensen Erik Vest , Voss Peter H. 
Published: July 2022

Abstract
A large landslide (frozen debris avalanche) occurred at Assapaat on the south coast of the Nuussuaq Peninsula in Central West Greenland on June 13, 2021, at 04:04 local time. We present a compilation of available data from field observations, photos, remote sensing, and seismic monitoring to describe the event. Analysis of these data in combination with an analysis of pre- and post-failure digital elevation models results in the first description of this type of landslide. The frozen debris avalanche initiated as a 6.9 * 106 m3 failure of permafrozen talus slope and underlying colluvium and till at 600–880 m elevation. It entrained a large volume of permafrozen colluvium along its 2.4 km path in two subsequent entrainment phases accumulating a total volume between 18.3 * 106 and 25.9 * 106 m3. About 3.9 * 106 m3 is estimated to have entered the Vaigat strait; however, no tsunami was reported, or is evident in the field. This is probably because the second stage of entrainment along with a flattening of slope angle reduced the mobility of the frozen debris avalanche. We hypothesise that the initial talus slope failure is dynamically conditioned by warming of the ice matrix that binds the permafrozen talus slope. When the slope ice temperature rises to a critical level, its shear resistance is reduced, resulting in an unstable talus slope prone to failure. Likewise, we attribute the large-scale entrainment to increasing slope temperature and take the frozen debris avalanche as a strong sign that the permafrost in this region is increasingly at a critical state. Global warming is enhanced in the Arctic and frequent landslide events in the past decade in Western Greenland let us hypothesise that continued warming will lead to an increase in the frequency and magnitude of these types of landslides. Essential data for critical arctic slopes such as precipitation, snowmelt, and ground and surface temperature are still missing to further test this hypothesis. It is thus strongly required that research funds are made available to better predict the change of landslide threat in the Arctic.
Hostility measure for multi-level study of data complexity
Authors: Lancho Carmen , Martín De Diego Isaac , Cuesta Marina , Aceña Víctor , Moguerza Javier M. 
Published: July 2022

AbstractComplexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. The proposal is built by estimating the novel notion of hostility: the difficulty of correctly classifying a point, a class, or a whole dataset given their corresponding neighborhoods. The proposed measure is estimated at the instance level by applying the k-means algorithm in a recursive and hierarchical way, which allows to analyze how points from different classes are naturally grouped together across partitions. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. The validity of the proposal is evaluated through a variety of experiments dealing with the three perspectives and the corresponding comparative with the state-of-the-art measures. Throughout the experiments, the hostility measure has shown promising results and to be competitive, stable, and robust.
Optical Investigation of the Influence of In-cylinder Flow and Mixture Inhomogeneity on Cyclic Variability in a Direct-Injection Spark Ignition Engine
Authors: Laichter Judith , Kaiser Sebastian A. 
Published: July 2022

AbstractIn this study, a single-cylinder direct-injection spark-ignition research engine with full optical access was used to investigate the influence of the flow field and fuel/air mixing on cyclic variability, in particular in the early flame propagation. The engine was operated under lean-burn conditions at 1500 rpm. Two different injection strategies were compared, port-fuel injection (PFI) and direct injection (DI), the latter with early and late injection split about 2:1 in fuel mass. High-speed particle image velocimetry captured the flow in the tumble plane in the compression stroke. The velocity fields and the movement of the tumble vortex are analyzed. Simultaneously, a second camera detected the chemiluminescence of the flame, and the projected area of the line-of-sight-integrated flame luminosity was extracted through morphological image processing. By combining pressure-based combustion analysis and high-speed optical diagnostics, the early flame propagation and the flow field are correlated. In separate experiments the equivalence ratio was imaged for the DI at selected crank angles and correlated with CA10 to learn about the influence of mixture inhomogeneity on early flame propagation. With PFI, the flow near the spark plug just before ignition is closely related to the subsequent speed of combustion. The combustion-relevant flow features can be traced back in time to about –90 °CA. In contrast, the chosen DI scheme results in a decorrelation of flow and flame, and the equivalence ratio distribution at ignition becomes more important. For both flow and mixture fields, regions of high correlation with early-combustion metrics are typically associated with gradients in the multi-cycle average fields.
The role of obesity and bariatric surgery-induced weight loss in breast cancer
Authors: Bohm Margaret S. , Sipe Laura M. , Pye Madeline E. , Davis Matthew J. , Pierre Joseph F. , Makowski Liza 
Published: July 2022

Abstract
Obesity is a complex metabolic condition considered a worldwide public health crisis, and a deeper mechanistic understanding of obesity-associated diseases is urgently needed. Obesity comorbidities include many associated cancers and are estimated to account for 20% of female cancer deaths in the USA. Breast cancer, in particular, is associated with obesity and is the focus of this review. The exact causal links between obesity and breast cancer remain unclear. Still, interactions have emerged between body mass index, tumor molecular subtype, genetic background, and environmental factors that strongly suggest obesity influences the risk and progression of certain breast cancers. Supportive preclinical research uses various diet-induced obesity models to demonstrate that weight loss, via dietary interventions or changes in energy expenditure, reduces the onset or progression of breast cancers. Ongoing and future studies are now aimed at elucidating the underpinning mechanisms behind weight-loss-driven observations to improve therapy and outcomes in patients with breast cancer and reduce risk. This review aims to summarize the rapidly emerging literature on obesity and weight loss strategies with a focused discussion of bariatric surgery in both clinical and preclinical studies detailing the complex interactions between metabolism, immune response, and immunotherapy in the setting of obesity and breast cancer.
Non-Mendelian transmission of accessory chromosomes in fungi
Authors: Komluski Jovan , Stukenbrock Eva H. , Habig Michael 
Published: July 2022

AbstractNon-Mendelian transmission has been reported for various genetic elements, ranging from small transposons to entire chromosomes. One prime example of such a transmission pattern are B chromosomes in plants and animals. Accessory chromosomes in fungi are similar to B chromosomes in showing presence/absence polymorphism and being non-essential. How these chromosomes are transmitted during meiosis is however poorly understood—despite their often high impact on the fitness of the host. For several fungal organisms, a non-Mendelian transmission or a mechanistically unique meiotic drive of accessory chromosomes have been reported. In this review, we provide an overview of the possible mechanisms that can cause the non-Mendelian transmission or meiotic drives of fungal accessory chromosomes. We compare processes responsible for the non-Mendelian transmission of accessory chromosomes for different fungal eukaryotes and discuss the structural traits of fungal accessory chromosomes affecting their meiotic transmission. We conclude that research on fungal accessory chromosomes, due to their small size, ease of sequencing, and epigenetic profiling, can complement the study of B chromosomes in deciphering factors that influence and regulate the non-Mendelian transmission of entire chromosomes.
Relaxed dissipativity assumptions and a simplified algorithm for multiobjective MPC
Authors: Eichfelder Gabriele , Grüne Lars , Krügel Lisa , Schießl Jonas 
Published: July 2022

AbstractWe consider nonlinear model predictive control (MPC) with multiple competing cost functions. In each step of the scheme, a multiobjective optimal control problem with a nonlinear system and terminal conditions is solved. We propose an algorithm and give performance guarantees for the resulting MPC closed loop system. Thereby, we significantly simplify the assumptions made in the literature so far by assuming strict dissipativity and the existence of a compatible terminal cost for one of the competing objective functions only. We give conditions which ensure asymptotic stability of the closed loop and, what is more, obtain performance estimates for all cost criteria. Numerical simulations on various instances illustrate our findings. The proposed algorithm requires the selection of an efficient solution in each iteration, thus we examine several selection rules and their impact on the results. and we also examine numerically how different selection rules impact the results
Intergenerational Transmission of Social Anxiety in Childhood Through Fear of Negative Child Evaluation and Parenting
Authors: de Vente Wieke , Majdandžić Mirjana , Bögels Susan M. 
Published: July 2022

Abstract
Background
Parents’ fear of negative evaluation (FNE) has been proposed to play a role in the intergenerational transmission of social anxiety. We investigated whether (1) parents’ own FNE, a core belief of social anxiety, extends to their child’s environment resulting in fear of negative child evaluation (FNCE); and (2) parents’ FNCE mediates the association between parents’ social anxiety, parenting behavior and children’s social anxiety.

Methods
In this cross-sectional study, fathers (n = 84) and mothers (n = 92) from 99 Caucasian families completed questionnaires on social anxiety, FNE, FNCE, and parenting (intrusiveness, negative parenting and encouragement of social daring) when their child was 7.5 years old. Child social anxiety was measured by questionnaires (child and parent report).

Results
For both parents, FNE mediated the association between their social anxiety and FNCE. Furthermore, FNCE mediated the relation between both parents’ social anxiety and intrusiveness and the relation between mothers’ social anxiety and negative parenting. In addition, FNCE mediated the relation between fathers’ social anxiety and children’s social anxiety (parents’ report only) directly and through intrusive parenting.

Conclusions
Our study suggests that parents’ FNE extends to their children’s environment and provides support for the role of FNCE in various pathways of intergenerational transmission of social anxiety during middle childhood.
ASEAN countries’ environmental policies for the Sustainable Development Goals (SDGs)
Authors: Elder Mark , Ellis Gemma 
Published: July 2022

AbstractThis study’s objective is to examine how ASEAN countries reported their environment-related policies for the Sustainable Development Goals (SDGs) in their Voluntary National Reviews (VNRs), and a dataset of their environmental policies was developed. This is a necessary first step in analyzing the reasons for insufficient progress on the environmental dimension of the SDGs, since policies are key means of implementation. Previous studies of SDG progress and VNRs examined many aspects such as achievement levels, indicators, data, governance, and VNR preparation procedures, but surprisingly, there has been little discussion of countries’ actual policy efforts. Progress on the SDGs’ environmental dimensions is widely considered insufficient, including in Asia. This study showed that insufficient progress on the environmental dimension of the SDGs is not due to a lack of environmental policies or a lower prioritization of policies for environmental SDG targets. ASEAN countries included almost 600 concrete environment-related policies in their VNRs, widely distributed among most SDGs, accounting for about 40 percent of their total reported SDG-related policies. The number of environmental policies was not closely related to GDP, GDP per capita, or VNR date. Many policies appeared substantial, including national action plans, strategies, laws, and regulations, not just small projects or programs. However, some major existing environmental policies, for example on air pollution, were usually not included in the VNRs. Further research is needed to explore other possible factors such as insufficient policy implementation or effectiveness, which this study could not examine. This study’s environmental policy dataset provides the necessary baseline for future research on policy implementation and effectiveness, especially to help future studies identify ASEAN countries’ environmental policies in specific areas such as climate, energy, or water. This will also facilitate comparative analysis.
Correlation between an annotation-free embryo scoring system based on deep learning and live birth/neonatal outcomes after single vitrified-warmed blastocyst transfer: a single-centre, large-cohort retrospective study
Authors: Ueno Satoshi , Berntsen Jørgen , Ito Motoki , Okimura Tadashi , Kato Keiichi 
Published: July 2022

Abstract

Propose
Does an annotation-free embryo scoring system based on deep learning and time-lapse sequence images correlate with live birth (LB) and neonatal outcomes?

Methods
Patients who underwent SVBT cycles (3010 cycles, mean age: 39.3 ± 4.0). Scores were calculated using the iDAScore software module in the Vitrolife Technology Hub (Vitrolife, Gothenburg, Sweden). The correlation between iDAScore, LB rates, and total miscarriage (TM), including 1st- and 2nd-trimester miscarriage, was analysed using a trend test and multivariable logistic regression analysis. Furthermore, the correlation between the iDAScore and neonatal outcomes was analysed.

Results
LB rates decreased as iDAScore decreased (P < 0.05), and a similar inverse trend was observed for the TM rates. Additionally, multivariate logistic regression analysis showed that iDAScore significantly correlated with increased LB (adjusted odds ratio: 1.811, 95% CI: 1.666–1.976, P < 0.05) and decreased TM (adjusted odds ratio: 0.799, 95% CI: 0.706–0.905, P < 0.05). There was no significant correlation between iDAScore and neonatal outcomes, including congenital malformations, sex, gestational age, and birth weight. Multivariate logistic regression analysis, which included maternal and paternal age, maternal body mass index, parity, smoking, and presence or absence of caesarean section as confounding factors, revealed no significant difference in any neonatal characteristics.

Conclusion
Automatic embryo scoring using iDAScore correlates with decreased miscarriage and increased LB and has no correlation with neonatal outcomes.
Type-2 Fuzzy Sets and Newton’s Fuzzy Potential in an Algorithm of Classification Objects of a Conceptual Space
Authors: Jagiełło Adrianna , Lisowski Piotr , Urban Roman 
Published: July 2022

AbstractThis paper deals with Gärdenfors’ theory of conceptual spaces. Let $${\mathcal {S}}$$
S
be a conceptual space consisting of 2-type fuzzy sets equipped with several kinds of metrics. Let a finite set of prototypes $$\tilde{P}_1,\ldots ,\tilde{P}_n\in \mathcal {S}$$

P
~

1

,
…
,

P
~

n

∈
S

be given. Our main result is the construction of a classification algorithm. That is, given an element $${\tilde{A}}\in \mathcal {S},$$

A
~

∈
S
,

our algorithm classifies it into the conceptual field determined by one of the given prototypes $$\tilde{P}_i.$$

P
~

i

.

The construction of our algorithm uses some physical analogies and the Newton potential plays a significant role here. Importantly, the resulting conceptual fields are not convex in the Euclidean sense, which we believe is a reasonable departure from the assumptions of Gardenfors’ original definition of the conceptual space. A partitioning algorithm of the space $$\mathcal {S}$$
S
is also considered in the paper. In the application section, we test our classification algorithm on real data and obtain very satisfactory results. Moreover, the example we consider is another argument against requiring convexity of conceptual fields.
A Numerical Method for Singularly Perturbed Convection-diffusion Problems Posed on Smooth Domains
Authors: Hegarty A. F. , O’Riordan E. 
Published: July 2022

AbstractA finite difference method is constructed to solve singularly perturbed convection-diffusion problems posed on smooth domains. Constraints are imposed on the data so that only regular exponential boundary layers appear in the solution. A domain decomposition method is used, which uses a rectangular grid outside the boundary layer and a Shishkin mesh, aligned to the curvature of the outflow boundary, near the boundary layer. Numerical results are presented to demonstrate the effectiveness of the proposed numerical algorithm.
Performance analysis of near-earth, lunar and interplanetary optical communication links
Authors: Chen Shun-Ping 
Published: July 2022

AbstractThe performance of near-Earth, lunar and interplanetary laser optical communications is investigated by using simulation models which were successfully verified in previous projects. The simulation models consider typical mission design parameters like transmit power and operation wavelength for the optical laser transmitters, different apertures of the optical telescope, varying distances between the Earth ground station and the spacecraft terminals, Geiger-mode avalanche photodiodes (APD) or superconducting nanowire single photon detection (SNSPD) receivers, atmospheric disturbances like scintillation and absorption, background noises for the uplink and downlink and appropriate pulse position modulation (PPM) orders. Typical design parameters are those of published ESA (European Space Agency) and NASA (National Aeronautics and Space Administration) missions. The investigations of laser optical communications for various system parameters and different distances between the Earth ground station and the spacecraft could also help to achieve an overview of free space and deep space optical communications for different orbit constellations for future missions with laser optical communication links.
Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid
Authors: Sharshir Swellam Wafa , Elhelow Ahmed , Kabeel Ahmed , Hassanien Aboul Ella , Kabeel Abd Elnaby , Elhosseini Mostafa 
Published: July 2022

AbstractThis research work intends to enhance the stepped double-slope solar still performance through an experimental assessment of combining linen wicks and cobalt oxide nanoparticles to the stepped double-slope solar still to improve the water evaporation and water production. The results illustrated that the cotton wicks and cobalt oxide (Co3O4) nanofluid with 1wt% increased the hourly freshwater output (HP) and instantaneous thermal efficiency (ITE). On the other hand, this study compares four machine learning methods to create a prediction model of tubular solar still performance. The methods developed and compared are support vector regressor (SVR), decision tree regressor, neural network, and deep neural network based on experimental data. This problem is a multi-output prediction problem which is HP and ITE. The prediction performance for the SVR was the lowest, with 70 (ml/m2 h) mean absolute error (MAE) for HP and 4.5% for ITE. Decision tree regressor has a better prediction for HP with 33 (ml/m2 h) MAE and almost the same MAE for ITE. Neural network has a better prediction for HP with 28 (ml/m2 h) MAE and a bit worse prediction for ITE with 5.7%. The best model used the deep neural network with 1.94 (ml/m2 h) MAE for HP and 0.67% MAE for ITE.
Effects of stand condition and root density on fine-root dynamics across root functional groups in a subtropical montane forest
Authors: Huang Lin , Zhao Rudong , Zhao Xiaoxiang , Tian Qiuxiang , Yue Pengyun , Liu Feng 
Published: July 2022

AbstractFine roots play key roles in belowground C cycling in terrestrial ecosystems. Based on their distinct functions, fine roots are either absorptive fine roots (AFRs) or transport fine roots (TFRs). However, the function-based fine root dynamics of trees and their responses to forest stand properties remain unclear. Here, we studied the dynamics of AFRs and TFRs and their responses to stand conditions and root density in a subtropical montane mixed forest based on a 2-a root window experiment. Mean (± SE) annual production, mortality, and turnover rate of AFRs were 7.87 ± 0.17 m m−2 a−1, 8.13 ± 0.20 m m−2 a−1and 2.96 ± 0.24 a−1, respectively, compared with 7.09 ± 0.17 m m−2 a−1, 4.59 ± 0.17 m m−2 a−1, and 2.01 ± 0.22 a−1, respectively, for TFRs. The production and mortality of fine roots were significantly higher in high root-density sites than in low-root density sites, whereas the turnover of fine roots was faster in the low root-density sites. Furthermore, root density had a larger positive effect than other environmental factors on TFR production but had no obvious impact on AFR production. Tree species diversity had an apparent positive effect on AFR production and was the crucial driver of AFR production, probably due to a complementary effect, but had no evident impact on TFR. Both tree density and tree species diversity were positively correlated with the mortality of AFRs and negatively related to the turnover of TFRs, suggesting that higher root density caused stronger competition for rooting space and that plants tend to reduce maintenance costs by decreasing TFR turnover. These findings illustrated the importance of root functional groups in understanding root dynamics and their responses to changes in environmental conditions.