bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Analysis of Residential Care: A Five-Country Multiple Case-Design Study
Authors: James Sigrid , Wilczek Lucas , Kilian Juri , Timonen-Kallio Eeva , Bravo Amaia , del Valle Jorge F. , Formenti Laura , Petrauskiene Alina , Pivoriene Jolanta , Rigamonti Alessandra 
Published: December 2021

Abstract
Background
Despite a global policy push toward the advancement of family- and community-based care, residential care for children and youth remains a relevant and highly utilized out-of-home care option in many countries, fulfilling functions of care and accommodation as well as education and treatment.

Objective
As part of a larger project involving five European countries (Finland, Germany, Italy, Lithuania, and Spain), the objective was “to map” the context and content of residential care in each country, thereby building a foundation for meaningful comparisons and deepened understanding of each system’s inherent logic. Within the context of global deinstitutionalization efforts, the study also aimed to understand factors that hinder or enhance the transformation of residential care.

Method
Using an embedded multiple-case design, data was gathered by each country on its residential care macro context as well as salient variables related to three units of analysis–residential care system/program features, residential care training and personnel, characteristics of youth. Cross-case synthesis was used to summarize and compare cases across relevant dimensions.

Results
The analysis highlighted areas of overlap and singularity, particularly with regard to utilization rates, concepts and methods, workforce professionalization, and characteristics of youth.

Conclusions
Findings provide a more nuanced understanding of how residential care continues to be viewed and utilized in some countries, challenging the ‘residential-care-as-a-last-resort-only’ rhetoric that is currently dominating the discourse on residential care. It further provides an understanding of historical and sociocultural factors that need to be considered when trying to transform services for children, youth, and their families.
Accessing Self-Control
Authors: Koi Polaris 
Published: December 2021

AbstractSelf-control is that which is enacted to align our behaviour with intentions, motives, or better judgment in the face of conflicting impulses of motives. In this paper, I ask, what explains interpersonal differences in self-control? After defending a functionalist conception of self-control, I argue that differences in self-control are analogous to differences in mobility: they are modulated by inherent traits and environmental supports and constraints in interaction. This joint effect of individual (neuro)biology and environmental factors is best understood in terms of access to self-control behaviours. I sketch an account of access as including the three criteria of means, awareness, and non-excessive effort. I further demonstrate that people with disorders such as ADHD have limited access to self-control behaviours and stand therefore at a disadvantage with regard to self-control.
Honest signaling in mouse lemur vocalizations?
Authors: Fichtel Claudia , Kappeler Peter M. , Perret Martine , Huchard Elise , Henry Pierre-Yves 
Published: December 2021

AbstractAnimal vocalizations may provide information about a sender’s condition or motivational state and, hence, mediate social interactions. In this study, we examined whether vocalizations of gray mouse lemurs (Microcebus murinus) emitted in aggressive contexts (grunts, tsaks) co-vary with physical condition, which would underly and indicate honest signaling. We recorded calls from captive individuals that were subjected to a caloric restricted (CR) or ad libitum (AL) diet, assuming that individuals on an ad libitum dietary regime were in better condition. We analyzed 828 grunts produced by seven CR and nine AL individuals and 270 tsaks by eight CR and five AL individuals. Grunts consisted of two separate elements, with the 1st element having more energy in higher frequencies than the 2nd element. Body mass correlated negatively with acoustic features of grunts, and heavier individuals produced lower-frequency grunts. Acoustic features of grunts did not differ between sexes. Acoustic features of tsaks were predicted by neither body mass nor sex. However, tsaks produced by AL individuals were noisier than those of CR individuals. Hence, manipulation of body condition via dietary regimes affected acoustic features of calls given during aggression in different ways: acoustic features of grunts varied according to the rule of acoustic allometry, and can be considered as honest signals. Acoustic features of tsaks, however, varied according to motivational structural rules. Longitudinal studies are now indicated to examine whether intra-individual changes in body mass are also reflected in the acoustic structure of calls, allowing callers to signal more flexible variation in condition.
Immigrant Legal Status Disparities in Health Among First- and One-point-five-Generation Latinx Immigrants in California
Authors: Hamilton Erin R. , Patler Caitlin , Savinar Robin 
Published: December 2021

AbstractRestrictive US immigration laws and law enforcement undermine immigrant health by generating fear and stress, disrupting families and communities, and eroding social and economic wellbeing. The inequality and stress created by immigration law and law enforcement may also generate disparities in health among immigrants with different legal statuses. However, existing research does not find consistent evidence of immigrant legal status disparities in health, possibly because it does not disaggregate immigrants by generation, defined by age at migration. Immigration and life course theory suggest that the health consequences of non-citizen status may be greater among 1.5-generation immigrants, who grew up in the same society that denies them formal membership, than among the 1st generation, who immigrated as adolescents or adults. In this study, we examine whether there are legal status disparities in health within and between the 1st generation and the 1.5 generation of 23,288 Latinx immigrant adults interviewed in the 2005–2017 waves of the California Health Interview Survey. We find evidence of legal status disparities in heart disease within the 1st generation and for high blood pressure and diabetes within the 1.5 generation. Non-citizens have higher rates of poor self-rated health and distress within both generations. Socioeconomic disadvantage and limited access to care largely account for the worse health of legally disadvantaged 1st- and 1.5-generation Latinx adults in California.
A nomogram for bladder pain syndrome/interstitial cystitis based on netrin-1
Authors: Ang Xiaojie , Jiang Yufeng , Cai Zongqiang , Zhou Qi , Li Miao , Zhang Bin , Chen Weiguo , Chen Li-Hua , Zhang Xi 
Published: December 2021

Abstract
Purpose
This study aimed to combine plasma netrin-1 and clinical parameters to construct a diagnostic model for bladder pain syndrome/interstitial cystitis (BPS/IC).

Methods
We analyzed the independent diagnostic value of netrin-1 and the correlation with clinical symptom scores of BPS/IC. Clinical parameters were selected using LASSO regression, and a multivariate logistic regression model based on netrin-1 was established, and then a nomogram of BPS/IC prevalence was constructed. The nomogram was evaluated using calibration curves, the C-index, and decision curve analysis (DCA). Finally, the model was validated using an internal validation method.

Results
The area under the curve for the ability of netrin-1 to independently predict BPS/IC diagnosis was 0.858 (p < 0.001), with a sensitivity of 85% and specificity of 82%. The predicted nomogram included three variables: age, CD3 + /CD4 + T lymphocyte ratio, and netrin-1. The C-index of this nomogram was 0.882, and the predicted values were highly consistent with the actual results in the calibration curve. In addition, the internally validated C-index of 0.870 confirms the high reliability of the model. DCA results show that the net patient benefit of the netrin-1 combined with other clinical parameters was higher than that of the single netrin-1 model.

Conclusion
Netrin-1 can be used as a diagnostic marker for BPS/IC and is associated with pain. The nomogram constructed by combining netrin-1 and clinical parameters was able to predict BPS/IC with great accuracy. In addition, Netrin-1 may also serve as a novel therapeutic target for BPS/IC.
Test zur Einschätzung wissenschaftlicher Widersprüche (TEWI): Messung wissenschaftsbezogenen Reflexionsvermögens in der Sekundarstufe II
Authors: Kramer Jochen , Thomm Eva , Oschatz-Grant Kerstin , Wagner Wolfgang , Bromme Rainer 
Published: December 2021

ZusammenfassungWissenschaftspropädeutik gehört zu den drei zentralen Bildungszielen der gymnasialen Oberstufe. Aufgrund eines Mangels an geeigneten Untersuchungsinstrumenten, gibt es jedoch kaum Studien zur Erreichung dieses Bildungsziels oder seines Einflusses auf den Werdegang junger Menschen. Im vorliegenden Beitrag werden die theoretischen Ansätze zur Wissenschaftspropädeutik kurz erläutert, bestehende Strukturmodelle vorgestellt und als zentrale Elemente die fächerübergreifende Denkweise und die kritisch-reflexive Perspektive auf Wissenschaft herausgearbeitet. Wissenschaftsbezogene Reflexionskompetenz im Umgang mit Widersprüchen in der Wissenschaft wird als eine Kernkomponente von Wissenschaftspropädeutik skizziert. Sie bildet auch den Ausgangspunkt für die Entwicklung des Tests zur Einschätzung wissenschaftlicher Widersprüche (TEWI) für das nationale Bildungspanel (NEPS). Im empirischen Teil werden Befunde zur Entwicklung und Pilotierung des TEWI sowie eine Studie zur konvergenten Konstruktvalidierung (N = 394) des TEWI vorgestellt. Die Ergebnisse weisen den TEWI als reliables Instrument mit zufriedenstellenden Skalierungseigenschaften aus.
Rigid-Body Analysis of a Beveled Shape Structure in Regular Waves Using the Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) Method
Authors: Hikmatullah Sahib Siti Ayishah Thaminah , Ramli Muhammad Zahir , Azman Muhammad Afiq , Abd Rahman Muhammad Mazmirul , Miskon Mohd Fuad , Ariffin Effi Helmy , Jeofry Muhammad Hafeez , Yunus Kamaruzzaman 
Published: December 2021

AbstractIn many cases of wave structure interactions, three-dimensional models are used to demonstrate real-life complex environments in large domain scales. In the seakeeping context, predicting the motion responses in the interaction of a long body resembling a ship structure with regular waves is crucial and can be challenging. In this work, regular waves interacting with a rigid floating structure were simulated using the open-source code based on the weakly compressible smoothed particle hydrodynamics (WCSPH) method, and optimal parameters were suggested for different wave environments. Vertical displacements were computed, and their response amplitude operators (RAOs) were found to be in good agreement with experimental, numerical, and analytical results. Discrepancies of numerical and experimental RAOs tended to increase at low wave frequencies, particularly at amidships and near the bow. In addition, the instantaneous wave contours of the surrounding model were examined to reveal the effects of localized waves along the structure and wave dissipation. The results indicated that the motion response from the WCSPH responds well at the highest frequency range (ω > 5.235 rad/s).
Artificial Intelligence in Current Diabetes Management and Prediction
Authors: Nomura Akihiro , Noguchi Masahiro , Kometani Mitsuhiro , Furukawa Kenji , Yoneda Takashi 
Published: December 2021

Abstract
Purpose of Review
Artificial intelligence (AI) can make advanced inferences based on a large amount of data. The mainstream technologies of the AI boom in 2021 are machine learning (ML) and deep learning, which have made significant progress due to the increase in computational resources accompanied by the dramatic improvement in computer performance. In this review, we introduce AI/ML-based medical devices and prediction models regarding diabetes.

Recent Findings
In the field of diabetes, several AI-/ML-based medical devices and regarding automatic retinal screening, clinical diagnosis support, and patient self-management tool have already been approved by the US Food and Drug Administration. As for new-onset diabetes prediction using ML methods, its performance is not superior to conventional risk stratification models that use statistical approaches so far.

Summary
Despite the current situation, it is expected that the predictive performance of AI will soon be maximized by a large amount of organized data and abundant computational resources, which will contribute to a dramatic improvement in the accuracy of disease prediction models for diabetes.
Autoimmune Inflammation and Insulin Resistance: Hallmarks So Far and Yet So Close to Explain Diabetes Endotypes
Authors: Petrelli Alessandra , Giovenzana Anna , Insalaco Vittoria , Phillips Brett E. , Pietropaolo Massimo , Giannoukakis Nick 
Published: December 2021

Abstract
Purpose of Review
Diabetes mellitus can be categorized into two major variants, type 1 and type 2. A number of traits such as clinical phenotype, age at disease onset, genetic background, and underlying pathogenesis distinguish the two forms.

Recent Findings
Recent evidence indicates that type 1 diabetes can be accompanied by insulin resistance and type 2 diabetes exhibits self-reactivity. These two previously unknown conditions can influence the progression and outcome of the disease. Unlike most conventional considerations, diabetes appears to consist of a spectrum of intermediate phenotypes that includes monogenic and polygenic loci linked to inflammatory processes including autoimmunity, beta cell impairment, and insulin resistance.

Summary
Here we discuss why a shift of the classical bi-modal view of diabetes (autoimmune vs. non-autoimmune) is necessary in favor of a model of an immunological continuum of endotypes lying between the two extreme “insulin-resistant” and “autoimmune beta cell targeting,” shaped by environmental and genetic factors which contribute to determine specific immune-conditioned outcomes.
Older People in Germany During the COVID-19 Pandemic:The Least, the More, and the Most Affected
Authors: Horn Vincent , Semmler Malte , Schweppe Cornelia 
Published: December 2021

AbstractOlder people have been identified as a particularly vulnerable group during the COVID-19 pandemic. However, the question of how older people actually fared during the COVID-19 pandemic has only been sporadically addressed. This article aims to partly fill this gap by classifying subgroups of older people using Latent Class Analysis. Indicators used are: risk perception, safety behavior, and well-being. To predict subgroup membership, age, gender, living arrangement, children, chronic illness, conflict, socioeconomic status, and migration history are controlled for. The data analyzed stem from a phone survey among 491 older people (75–100 years) in Germany conducted in September/October 2020. Results show that three subgroups of older people – the least, the more and the most affected – can be formed based on their risk perception, safety behavior, and well-being, indicating the usefulness of these three constructs for identifying and studying older people particularly affected by the COVID-19 pandemic and the measures taken to contain it.
The application of probabilistic linguistic CODAS method based on new score function in multi-criteria decision-making
Authors: Chen Liuxin , Gou Xiaoling 
Published: December 2021

AbstractProbabilistic linguistic term sets (PLTSs) play an important role in multi-criteria decision-making(MCDM) problems because it can not only describe objects with several possible linguistic terms, but also represent the proportion of each linguistic term, which can effectively avoid the distortion of decision information to a greater extent and ensure the credibility of decision results. First, to compare PLTS more simply and reasonably, we define a new score function that takes into account partial deviations. Then considering the superiority of the classic combinative distance-based assessment (CODAS) method in the complete representation of information, it is extended to the probabilistic linguistic environment. Subsequently, we improved the classic CODAS method and proposed the PL-CODAS method. Finally, we apply the PL-CODAS method to a cases of venture investors choosing emerging companies, and we compare the proposed method with PL-TOPSIS method, PL-TODIM method and PL-MABAC method to verify its applicability and effectiveness.
P-adic L-functions in universal deformation families
Authors: Loeffler David 
Published: December 2021

AbstractWe construct examples of p-adic L-functions over universal deformation spaces for $${{\,\mathrm{GL}\,}}_2$$

GL

2

. We formulate a conjecture predicting that the natural parameter spaces for p-adic L-functions and Euler systems are not the usual eigenvarieties (parametrising nearly-ordinary families of automorphic representations), but other, larger spaces depending on a choice of a parabolic subgroup, which we call ‘big parabolic eigenvarieties’.
Scalable and distributed strategies for socially distanced human mobility
Authors: Roy Satyaki , Ghosh Preetam 
Published: December 2021

AbstractCOVID-19 is a global health crisis that has caused ripples in every aspect of human life. Amid widespread vaccinations testing, manufacture and distribution efforts, nations still rely on human mobility restrictions to mitigate infection and death tolls. New waves of infection in many nations, indecisiveness on the efficacy of existing vaccinations, and emerging strains of the virus call for intelligent mobility policies that utilize contact pattern and epidemiological data to check contagion. Our earlier work leveraged network science principles to design social distancing optimization approaches that show promise in slowing infection spread however, they prove to be computationally prohibitive and require complete knowledge of the social network. In this work, we present scalable and distributed versions of the optimization approaches based on Markov Chain Monte Carlo Gibbs sampling and grid-based spatial parallelization that tackle both the challenges faced by the optimization strategies. We perform extensive simulation experiments to show the ability of the proposed strategies to meet necessary network science measures and yield performance comparable to the optimal counterpart, while exhibiting significant speed-up. We study the scalability of the proposed strategies as well as their performance in realistic scenarios when a fraction of the population temporarily flouts the location recommendations.
Maximal regularity and a singular limit problem for the Patlak–Keller–Segel system in the scaling critical space involving BMO
Authors: Kurokiba Masaki , Ogawa Takayoshi 
Published: December 2021

AbstractWe consider a singular limit problem of the Cauchy problem for the Patlak–Keller–Segel equation in a scaling critical function space. It is shown that a solution to the Patlak–Keller–Segel system in a scaling critical function space involving the class of bounded mean oscillations converges to a solution to the drift-diffusion system of parabolic-elliptic type (simplified Keller–Segel model) strongly as the relaxation time parameter $$\tau \rightarrow \infty $$

τ
→
∞

. For the proof, we show generalized maximal regularity for the heat equation in the homogeneous Besov spaces and the class of bounded mean oscillations and we utilize them systematically as well as the continuous embeddings between the interpolation spaces $$\dot{B}^s_{q,\sigma }({\mathbb {R}}^n)$$

B
˙

q
,
σ

s

(

R

n

)

and $$\dot{F}^s_{q,\sigma }({\mathbb {R}}^n)$$

F
˙

q
,
σ

s

(

R

n

)

for the proof of the singular limit. In particular, end-point maximal regularity in BMO and space time modified class introduced by Koch–Tataru is utilized in our proof.
Multiple Attribute Decision-Making Method Based upon Intuitionistic Fuzzy Partitioned Dual Maclaurin Symmetric Mean Operators
Authors: Wang HongJuan , Liu Yi , Liu Fang , Lin Jun 
Published: December 2021

AbstractWe propound the idea of the partitioned dual Maclaurin symmetric mean (PDMSM) operator stimulated by the partitioned Maclaurin symmetric mean, suppose that we can partition overall attributes into some portions and the attributes are interrelated in the same portion, but the attributes are not interrelated in different portions. We can deal with decision-making issues using PDMSM operator in the intuitionistic fuzzy environment. We also analysis features and peculiar instance of the PDMSM operator. And, we extend the PDMSM operator to introduce the intuitionistic fuzzy partitioned dual Maclaurin symmetric mean operator and the weighted intuitionistic fuzzy partitioned dual Maclaurin symmetric mean operator. Then, we analysis several characteristics and peculiar instances of the developed operators. A new multiple attribute decision-making (MADM) approach grounded on the established weighted intuitionistic fuzzy partitioned dual Maclaurin symmetric mean operator is propounded; the MADM method is to choose the optimal alternative from several alternatives. Finally, we demonstrate the designed method is more general and effective than existing methods through comparative analysis.
A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information
Authors: Ren Chunhua , Sun Linfu 
Published: December 2021

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.
Modeling of Passenger Transport Logistics Based on Intelligent Computational Techniques
Authors: Suraj Zbigniew , Olar Oksana , Bloshko Yurii 
Published: December 2021

AbstractThe paper describes the analysis of the application of classic and optimized triangular norms in the hierarchical structure of weighted fuzzy Petri nets on the example of passenger transport logistics. The research is presented in a scheme with several levels of tasks. Each task includes some knowledge sets which were previously given by the experts and set into tables of “Object–property” type. It leads to the creation of a hierarchical structure of weighted fuzzy Petri net model which includes 125 possible combinations of triples of functions for calculation processing. A classic triple of functions (ZtN, GtN, ZsN) is tested and compared with an optimized triple (ZtN, ZtN, LsN). Additional verification methods are applied to confirm the practical usefulness of these triples. These techniques imply the use of additional triples which can be associated with each other along with practically efficient mathematical calculations.