bluuebunny/crossref_metadata_2025_split huggingface dataset:
Challenges in modeling and predicting floods and droughts: A review
Authors: Brunner Manuela I. , Slater Louise , Tallaksen Lena M. , Clark Martyn 
Published: March 2021

AbstractPredictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted at minimizing negative societal and economic impacts. Forecasts at daily and seasonal scale are vital for early warning, estimation of event frequency for hydraulic design, and long‐term projections for developing adaptation strategies to future conditions. All three types of predictions—forecasts, frequency estimates, and projections—typically treat droughts and floods independently, even though both types of extremes can be studied using related approaches and have similar challenges. In this review, we (a) identify challenges common to drought and flood prediction and their joint assessment and (b) discuss tractable approaches to tackle these challenges. We group challenges related to flood and drought prediction into four interrelated categories: data, process understanding, modeling and prediction, and human–water interactions. Data‐related challenges include data availability and event definition. Process‐related challenges include the multivariate and spatial characteristics of extremes, non‐stationarities, and future changes in extremes. Modeling challenges arise in frequency analysis, stochastic, hydrological, earth system, and hydraulic modeling. Challenges with respect to human–water interactions lie in establishing links to impacts, representing human–water interactions, and science communication. We discuss potential ways of tackling these challenges including exploiting new data sources, studying droughts and floods in a joint framework, studying societal influences and compounding drivers, developing continuous stochastic models or non‐stationary models, and obtaining stakeholder feedback. Tackling one or several of these challenges will improve flood and drought predictions and help to minimize the negative impacts of extreme events.This article is categorized under:
Science of Water > Science of Water
On the Generalized Hyers–Ulam Stability of a Functional Equation and Its Consequences
Authors: Ciepliński Krzysztof 
Published: March 2021

AbstractThe aim of this note is to show the generalized Hyers–Ulam stability of a functional equation in four variables. In order to do this, the fixed point method is applied. As corollaries from our main result, some outcomes on the stability of some known equations will be also derived.
Laser-based powder bed fusion of niobium with different build-up rates
Authors: Griemsmann Tjorben , Abel Arvid , Hoff Christian , Hermsdorf Jörg , Weinmann Markus , Kaierle Stefan 
Published: March 2021

AbstractNiobium is an important material for high temperature applications, in space, in superconductors or in chemical process constructions. Laser-based powder bed fusion of niobium (PBF-LB/M/Nb) offers new opportunities in design, though it is still an expensive technique. The build-up rate is an important factor for economical manufacturing using PBF-LB/M/Nb. It is largely influenced by variation of process parameters, affecting the heat flow during the manufacturing process. In this work, an empirical model for PBF-LB/M/Nb is developed. Based on this model, manufacturing parameter sets using different volume build-up rates are predicted and confirmed. They enable the manufacture of parts with homogeneous and crack-free microstructure with more than 99.9% relative density. Tensile and hardness tests of specimens, which were manufactured using different parameter sets, are performed to determine the effects of the build-up rate—and thus the heat flow during manufacturing—on different mechanical properties. The ultimate tensile strength and yield strength of as-manufactured specimens reach values up to 525 MPa and 324 MPa, respectively, while the elongation at break ranges between approximately 8 and 16%. The Vickers hardness of all specimens was in the range of 149 ± 8 HV0.1. In addition, the microstructure of the manufactured samples is investigated by means of light as well as scanning electron microscopy.
Does Overfilling Smooth Inflatable Saline-Filled Breast Implants Decrease the Deflation Rate? Experience with 4761 Augmentation Mammaplasty Patients
Authors: Eisenberg Ted 
Published: March 2021

Abstract
Background
Research suggests that overfilling saline inflatable breast implants may decrease their deflation rates. To date, there has been no large-scale study comparing breast implants filled within vs. beyond the manufacturer’s recommended fill volumes.

Methods
A retrospective comparative study was conducted for 4761 women who underwent aesthetic augmentation mammaplasty by the author. Patients were divided into two groups: Group 1 includes 2960 patients who had breast augmentation between 2002 and 2009 with implants filled within the manufacturer’s recommended fill volumes. Group 2 includes 1801 patients who had breast augmentation between 2011 and 2018. Their implants were overfilled with an average volume between 42 and 50 cc, or 10–13%, respectively.
All surgeries were performed with Mentor Style 1600 saline breast implants (smooth, round moderate profile) through an inframammary incision; implants were placed in the subpectoral (dual) plane. Also evaluated was the yearly increase in deflation rates.

Results
Group I had 119 deflations, representing a deflation rate of 4.02%. Group 2 had 33 deflations, providing a deflation rate of 1.83%. The author found a protective effect in overfilling the device (p < 0.00001 and Z-score of 4.17). Fold failure was the major reason for deflation in both groups.
The implants filled within the manufacturer’s recommended volume had a yearly increase in deflation rate of 0.41%, not 1% as is commonly reported. The overfilled implants did not have a yearly increase in deflation rate.

Conclusion
Overfilling Mentor Style 1600 saline breast implants (smooth, round moderate profile) 10–13% significantly reduced the deflation rate.

Level of Evidence III
This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
Using virtual 3D-models in surgical planning: workflow of an immersive virtual reality application in liver surgery
Authors: Boedecker Christian , Huettl Florentine , Saalfeld Patrick , Paschold Markus , Kneist Werner , Baumgart Janine , Preim Bernhard , Hansen Christian , Lang Hauke , Huber Tobias 
Published: March 2021

Abstract
Purpose
Three-dimensional (3D) surgical planning is widely accepted in liver surgery. Currently, the 3D reconstructions are usually presented as 3D PDF data on regular monitors. 3D-printed liver models are sometimes used for education and planning.

Methods
We developed an immersive virtual reality (VR) application that enables the presentation of preoperative 3D models. The 3D reconstructions are exported as STL files and easily imported into the application, which creates the virtual model automatically. The presentation is possible in “OpenVR”-ready VR headsets. To interact with the 3D liver model, VR controllers are used. Scaling is possible, as well as changing the opacity from invisible over transparent to fully opaque. In addition, the surgeon can draw potential resection lines on the surface of the liver. All these functions can be used in a single or multi-user mode.

Results
Five highly experienced HPB surgeons of our department evaluated the VR application after using it for the very first time and considered it helpful according to the “System Usability Scale” (SUS) with a score of 76.6%. Especially with the subitem “necessary learning effort,” it was shown that the application is easy to use.

Conclusion
We introduce an immersive, interactive presentation of medical volume data for preoperative 3D liver surgery planning. The application is easy to use and may have advantages over 3D PDF and 3D print in preoperative liver surgery planning. Prospective trials are needed to evaluate the optimal presentation mode of 3D liver models.
Endocannabinoid and dopaminergic system: the pas de deux underlying human motivation and behaviors
Authors: Laksmidewi A. A. A. Putri , Soejitno Andreas 
Published: March 2021

AbstractEndocannabinoid system (ECS) has been identified ever since cannabinoid, an active substance of Cannabis, was known to interact with endogenous cannabinoid (endocannabinoid/eCB) receptors. It later turned out that eCB was more intricate than previously thought. It has a pervasive role and exerts a multitude of cellular signaling mechanisms, regulating various physiological neurotransmission pathways in the human brain, including the dopaminergic (DA) system. eCB roles toward DA system were robust, clearly delineated, and reproducible with respect to physiological as well as pathological neurochemical and neurobehavioral manifestations of DA system, particularly those involving the nigrostriatal and mesocorticolimbic pathways. The eCB–DA system regulates the basics in the Maslow’s pyramid of hierarchy of needs required for individual survival such as food and sexual activity for reproductive purpose to those of higher needs in the pyramid, including self-actualization behaviors leading to achievement and reward (e.g., academic- and/or work-related performance and achievements). It is, thus, interesting to specifically discuss the eCB–DA system, not only on the molecular level, but also its tremendous potential to be developed as a future therapeutic strategy for various neuropsychiatric problems, including obesity, drug addiction and withdrawal, pathological hypersexuality, or low motivation behaviors.
Restoring mechanism of mechanical properties of recycled polyethylene pellet moldings by a repelletizing treatment using a twin-screw extruder
Authors: Okubo H. , Yao S. 
Published: March 2021

AbstractTo develop material-recycling processes for used plastic products, it is necessary to design new material-recycling techniques to optimize the mechanical properties of the recycled plastics since the mechanical properties of recycled plastic products are normally deteriorated. In this study, we suggest one of the approaches that is a repelletizing treatment for improving the mechanical properties of recycled-polyethylene moldings using a twin-screw extruder. The results of the tensile test revealed the significant effect of the repelletizing treatment on the tensile performance of the recycled-polyethylene moldings. The tensile performance of the repelletized recycled-polyethylene moldings was significantly higher than that of the original recycled-polyethylene moldings under specific repelletizing conditions. The analysis of the polymeric structures revealed the significant effect of the repelletizing treatment on the secondary and higher-order structures such as the lamellar shape, aggregation of the crystal domains, amorphousness, and polyethylene chain conformation in the recycled-polyethylene moldings. The repelletizing treatment increased the tensile performances by optimizing the internal structure of the recycled-polyethylene moldings. The results confirmed the efficacy of the repelletizing treatment to optimize the mechanical performance of the recycled-polymer materials, thereby facilitating the reuse of waste plastics as value-added materials for various industrial applications.
On the stochastic vehicle routing problem with time windows, correlated travel times, and time dependency
Authors: Bomboi Federica , Buchheim Christoph , Pruente Jonas 
Published: March 2021

AbstractMost state-of-the-art algorithms for the Vehicle Routing Problem, such as Branch-and-Price algorithms or meta heuristics, rely on a fast feasibility test for a given route. We devise the first approach to approximately check feasibility in the Stochastic Vehicle Routing Problem with time windows, where travel times are correlated and depend on the time of the day. Assuming jointly normally distributed travel times, we use a chance constraint approach to model feasibility, where two different application scenarios are considered, depending on whether missing a customer makes the rest of the route infeasible or not. The former case may arise, e.g., in drayage applications or in the pickup-and-delivery VRP. In addition, we present an adaptive sampling algorithm that is tailored for our setting and is much faster than standard sampling techniques. We use a case study for both scenarios, based on instances with realistic travel times, to illustrate that taking correlations and time dependencies into account significantly improves the quality of the solutions, i.e., the precision of the feasibility decision. In particular, the nonconsideration of correlations often leads to solutions containing infeasible routes.
Multiple b-values improve discrimination of cortical gray matter regions using diffusion MRI: an experimental validation with a data-driven approach
Authors: Ganepola Tara , Lee Yoojin , Alexander Daniel C. , Sereno Martin I. , Nagy Zoltan 
Published: March 2021

Abstract
Objective
To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach.

Methods
Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm2 along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm2) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs.

Results
Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates.

Conclusion
Acquisitions with varying b-values are more suitable for discriminating cortical areas.
Role of environmental filtering and seed source availability in natural regeneration processes following large-scale disturbances in mountain forests
Authors: Szwagrzyk Jerzy , Gazda Anna , Zwijacz-Kozica Tomasz , Zięba Antoni , Ciesielska Barbara , Szewczyk Janusz , Foremnik Kacper , Muter Elżbieta , Bodziarczyk Jan 
Published: March 2021

AbstractThe relative importance of environmental factors and species pool in natural regeneration processes is still intensively disputed. Is environmental filtering especially important for species with higher requirements for temperature and soil fertility? Is the seed source limitation especially pronounced among tree species with lower dispersal abilities? Trees, seedlings and saplings measurements were conducted in 600 plots of 0.05 ha distributed in a regular grid in the Tatra Mountains (elevation range 817–1797 m a.s.l.). Boosted regression trees were used to analyse the relationships between the numbers of young trees, factors acting as environmental filters and the potential seed source availability. The most important factor affecting the distribution of young trees of most species was elevation; bedrock type was the second most important factor for Acer pseudoplatanus, while potential solar radiation was important for Sorbus aucuparia. The relationship between the presence of mature trees and abundance of young ones was strong in Fagus sylvatica, moderately strong in A. pseudoplatanus, and weak in Picea abies, Abies alba and S. aucuparia. The role of environmental factors in shaping the abundance of young trees varies strongly among species, while the seed source availability is very important for large-seeded trees.
A Random Walk on the Indecomposable Summands of Tensor Products of Modular Representations of SL2 $\left ({\mathbb {F}_p}\right )$
Authors: McDowell Eoghan 
Published: March 2021

AbstractIn this paper we introduce a novel family of Markov chains on the simple representations of SL2$\left ({\mathbb {F}_p}\right )$

F

p

in defining characteristic, defined by tensoring with a fixed simple module and choosing an indecomposable non-projective summand. We show these chains are reversible and find their connected components and their stationary distributions. We draw connections between the properties of the chain and the representation theory of SL2$\left ({\mathbb {F}_p}\right )$

F

p

, emphasising symmetries of the tensor product. We also provide an elementary proof of the decomposition of tensor products of simple SL2$\left ({\mathbb {F}_p}\right )$

F

p

-representations.
A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction
Authors: Bollwein Ferdinand , Westphal Stephan 
Published: March 2021

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.
Inelastic displacement ratios for non-structural components in steel framed structures under forward-directivity near-fault strong-ground motion
Authors: Bravo-Haro M. A. , Virreira J. R. , Elghazouli A. Y. 
Published: March 2021

AbstractThis paper describes a detailed numerical investigation into the inelastic displacement ratios of non-structural components mounted within multi-storey steel framed buildings and subjected to ground motions with forward-directivity features which are typical of near-fault events. The study is carried out using detailed multi-degree-of-freedom models of 54 primary steel buildings with different structural characteristics. In conjunction with this, 80 secondary non-structural elements are modelled as single-degree-of-freedom systems and placed at every floor within the primary framed structures, then subsequently analysed through extensive dynamic analysis. The influence of ground motions with forward-directivity effects on the mean response of the inelastic displacement ratios of non-structural components are compared to the results obtained from a reference set of strong-ground motion records representing far-field events. It is shown that the mean demand under near-fault records can be over twice as large as that due to far-fault counterparts, particularly for non-structural components with periods of vibration lower than the fundamental period of the primary building. Based on the results, a prediction model for estimating the inelastic displacement ratios of non-structural components is calibrated for far-field records and near-fault records with directivity features. The model is valid for a wide range of secondary non-structural periods and primary building fundamental periods, as well as for various levels of inelasticity induced within the secondary non-structural elements.
Multi-label learning with missing and completely unobserved labels
Authors: Huang Jun , Xu Linchuan , Qian Kun , Wang Jing , Yamanishi Kenji 
Published: March 2021

AbstractMulti-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved for the training data. We refer to the problem asmulti-label learning with missing and completely unobserved labels, and argue that it is necessary to discover these completely unobserved labels in order to mine useful knowledge and make a deeper understanding of what is behind the data. In this paper, we propose a new approach named MCUL to solve multi-label learning with Missing and Completely Unobserved Labels. We try to discover the unobserved labels of a multi-label data set with a clustering based regularization term and describe the semantic meanings of them based on the label-specific features learned by MCUL, and overcome the problem of missing labels by exploiting label correlations. The proposed method MCUL can predict both the observed and newly discovered labels simultaneously for unseen data examples. Experimental results validated over ten benchmark datasets demonstrate that the proposed method can outperform other state-of-the-art approaches on observed labels and obtain an acceptable performance on the new discovered labels as well.
Which Factors Influence Teacher Report of Adaptive Functioning in Autistic Children?
Authors: Moore Heather L. , Rogan Leanne , Taylor Lauren J. , Charman Tony , Le Couteur Ann , Green Jonathan , Grahame Victoria ,   the PACT-G Consortium, Aldred Catherine , Balabanovska Matea , Beach Hilary , Bennett Claire , Carruthers Sophie , Crook Imogen , Danvers Hannah , Dartnall Kate , Ellis Ceri , Emsley Richard , Foote Hannah , Graham Jessica , Howlin Patricia , Humphrey Neil , James Kirsty , Jamieson Sarah , Kappa Anna , Knight Anna , Leadbitter Kathy , Lowe Jo , Langhorne Sophie , Madeley Ruth , Maskell Deborah , Mitchell Olivia , McConachie Helen , Monteiro Francisca , Brooks Cat Papastavrou , Parr Jeremy , Pearson Amelia , Pickles Andrew , Rose Jessica , Slater Lisa , Slonims Vicky , Taylor Carol , Vosper Susanna , Wilson Helen 
Published: March 2021

AbstractA wealth of parent-report research shows adaptive functioning difficulties in autistic children, with parent-report influenced by a number of child factors. Adaptive functioning in autistic children is known to vary across settings; however, no research has yet explored factors influencing education professional-report. This study investigated the rate and profile of impairment, and child factors influencing education professional-reported adaptive skills in 248 autistic children. Twelve children were < 3 years (min age for available normative data on the adaptive function measure), so were removed from the analyses. Results replicated parent-literature; adaptive skills were negatively associated with age and informant-reported autism severity, and positively associated with nonverbal ability and expressive language. Adaptive functioning is important for real-world outcomes, e.g. educational attainment, independence, and support needs. Improving our understanding of adaptive functioning in the education context may support opportunities for shared learning and enhance personalised support .