bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robust effects of In, Fe, and Co additions on microstructures, thermal, and mechanical properties of hypoeutectic Sn–Zn-based lead-free solder alloy
Authors: El-Taher A. M. , Mansour S. A. , Lotfy I. H. 
Published: February 2023

AbstractThis study examines the influence of separate and dual minor alloying additions of indium (In) and ferrous/cobalt (Fe/Co) on the microstructure, thermal, and mechanical properties of hypoeutectic Sn–7wt.% Zn lead-free solder alloy. The results showed that the addition of In resulted in significant refinement in the microstructure and formation of new intermetallic compounds (IMCs) such as In–Sn phases which capable of extensively removing the unfavorable needle-like α-Zn phase. However, the additions of Fe/Co resulted in the creation of new coarsening flower-shaped IMCs identified as Zn–Co, Fe–Sn, and Co–Sn–Zn distributed uniformly which may have a marked effect on the mechanical and thermal properties of Sn–Zn solder alloy. Thermal analysis by a differential scanning calorimeter indicates slightly reduction in the onset temperature, melting temperature, and undercooling of the Sn–7wt.% Zn by the addition of In, while the pasty range enlarged as compared to Fe/Co additions. The tensile tests indicate that the Sn-Zn-In solder alloy exhibited a superior balance of ultimate tensile strength, yield strength, Young’s modulus, and elongation of 51.5 MPa, 44.4 MPa, 22.2GPa, and 44.5%, respectively, which are better than both those of the Sn–Zn and Sn–Zn–Fe/Co solder alloys. This can be attributed to the synergistic strengthening mechanism of refinement in the microstructure and precipitation of fine secondary particles.
The status and trends of mitochondrial dynamics research: A global bibliometric and visualized analysis
Authors: Guo Zijian , Wang Zehua , Gao Zhenzhong , Feng Tengda , Gao Yingjie , Yin Zhiwen , Tian Zui , Liu Yang , Mao Xingjia , Xiang Chuan 
Published: February 2023

Abstract
Background
Mitochondria are remarkably dynamic organelles encapsulated by bilayer membranes. The dynamic properties of mitochondria are critical for energy production.

Aims
The aim of our study is to investigate the global status and trends of mitochondrial dynamics research and predict popular topics and directions in the field.

Methods
Publications related to the studies of mitochondrial dynamics from 2002 to 2021 were retrieved from Web of Science database. A total of 4,576 publications were included. Bibliometric analysis was conducted by visualization of similarities viewer and GraphPadPrism 5 software.

Results
There is an increasing trend of mitochondrial dynamics research during the last 20 years. The cumulative number of publications about mitochondrial dynamics research followed the logistic growth model $$\mathrm{f}(x)=\mathrm{a}/\left[1+{e}^{(b-cx)}\right]$$

f

(
x
)

=
a
/

1
+

e

(
b
-
c
x
)

. The USA made the highest contributions to the global research. The journal Biochimica et Biophysica Acta (BBA)—Molecular Cell Research had the largest publication numbers. Case Western Reserve University is the most contributive institution. The main research orientation and funding agency were cell biology and HHS. All keywords related studies could be divided into three clusters: “Related disease research”, “Mechanism research” and “Cell metabolism research”.

Conclusions
Attention should be drawn to the latest popular research and more efforts will be put into mechanistic research, which may inspire new clinical treatments for the associated diseases.
BV equivalence with boundary
Authors: Simão F. M. Castela , Cattaneo A. S. , Schiavina M. 
Published: February 2023

AbstractAn extension of the notion of classical equivalence of equivalence in the Batalin–Vilkovisky (BV) and Batalin–Fradkin–Vilkovisky (BFV) frameworks for local Lagrangian field theory on manifolds possibly with boundary is discussed. Equivalence is phrased in both a strict and a lax sense, distinguished by the compatibility between the BV data for a field theory and its boundary BFV data, necessary for quantisation. In this context, the first- and second-order formulations of nonabelian Yang–Mills and of classical mechanics on curved backgrounds, all of which admit a strict BV–BFV description, are shown to be pairwise equivalent as strict BV–BFV theories. This in particular implies that their BV complexes are quasi-isomorphic. Furthermore, Jacobi theory and one-dimensional gravity coupled with scalar matter are compared as classically equivalent reparametrisation-invariant versions of classical mechanics, but such that only the latter admits a strict BV–BFV formulation. They are shown to be equivalent as lax BV–BFV theories and to have isomorphic BV cohomologies. This shows that strict BV–BFV equivalence is a strictly finer notion of equivalence of theories.
A regularity result for the bound states of N-body Schrödinger operators: blow-ups and Lie manifolds
Authors: Ammann Bernd , Mougel Jérémy , Nistor Victor 
Published: February 2023

AbstractWe prove regularity estimates in weighted Sobolev spaces for the $$L^2$$

L
2

-eigenfunctions of Schrödinger-type operators whose potentials have inverse square singularities and uniform radial limits at infinity. In particular, the usual N-body Hamiltonians with Coulomb-type singular potentials are covered by our result: in that case, the weight is "Equation missing", where "Equation missing" is the usual Euclidean distance to the union "Equation missing" of the set of collision planes $${\mathcal {F}}$$
F
. The proof is based on blow-ups of manifolds with corners and Lie manifolds. More precisely, we start with the radial compactification $${\overline{X}}$$

X
¯

of the underlying space X and we first blow up the spheres $${\mathbb {S}}_Y \subset {\mathbb {S}}_X$$

S
Y

⊂

S
X

at infinity of the collision planes $$Y \in {\mathcal {F}}$$

Y
∈
F

to obtain the Georgescu–Vasy compactification. Then, we blow up the collision planes $${\mathcal {F}}$$
F
. We carefully investigate how the Lie manifold structure and the associated data (metric, Sobolev spaces, differential operators) change with each blow-up. Our method applies also to higher-order differential operators, to certain classes of pseudodifferential operators, and to matrices of scalar operators.
Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial intelligence-based approach
Authors: Sekaran Karthik , Alsamman Alsamman M. , George Priya Doss C. , Zayed Hatem 
Published: February 2023

Abstract
The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces the adulthood of elderly individuals. The pathogenesis of the condition is primarily unascertained, turning the treatment efficacy more arduous. Therefore, understanding the genetic etiology of AD is essential to identifying targeted therapeutics. This study aimed to use machine-learning techniques of expressed genes in patients with AD to identify potential biomarkers that can be used for future therapy. The dataset is accessed from the Gene Expression Omnibus (GEO) database (Accession Number: GSE36980). The subgroups (AD blood samples from frontal, hippocampal, and temporal regions) are individually investigated against non-AD models. Prioritized gene cluster analyses are conducted with the STRING database. The candidate gene biomarkers were trained with various supervised machine-learning (ML) classification algorithms. The interpretation of the model prediction is perpetrated with explainable artificial intelligence (AI) techniques. This experiment revealed 34, 60, and 28 genes as target biomarkers of AD mapped from the frontal, hippocampal, and temporal regions. It is identified ORAI2 as a shared biomarker in all three areas strongly associated with AD's progression. The pathway analysis showed that STIM1 and TRPC3 are strongly associated with ORAI2. We found three hub genes, TPI1, STIM1, and TRPC3, in the network of the ORAI2 gene that might be involved in the molecular pathogenesis of AD. Naive Bayes classified the samples of different groups by fivefold cross-validation with 100% accuracy. AI and ML are promising tools in identifying disease-associated genes that will advance the field of targeted therapeutics against genetic diseases.
Facilitation of phosphorus acquisition by Banksia attenuata allows Adenanthos cygnorum (Proteaceae) to extend its range into severely phosphorus-impoverished habitats
Authors: Shen Qi , Ranathunge Kosala , Zhong Hongtao , Finnegan Patrick M. , Lambers Hans 
Published: February 2023

Abstract
Background and aims
In extremely low-phosphorus (P) environments, most Proteaceae exude carboxylates from cluster roots. These carboxylates mobilise inorganic P which leads to a relatively high leaf manganese concentration ([Mn]). However, we found that Adenanthos cygnorum (Proteaceae) in a low-P habitat did not invariably have a high leaf [Mn] in south-western Australia. We aimed to explore how A. cygnorum acquires P in severely P-impoverished habitats.

Methods
We determined soil P concentrations and leaf [Mn] of A. cygnorum growing within 1 m and more than 10 m away from other large Proteaceae. We also grew plants in a glasshouse to determine its root carboxylate exudation and rhizosheath phosphatase activity.

Results
Adenanthos cygnorum did not produce functional cluster roots. It depended on carboxylates released by a P-mobilising neighbour, Banksia attenuata (Proteaceae), to acquire P when growing in severely P-impoverished soil (< 8 mg P kg− 1 dry soil). In slightly less P-impoverished soil (> 11 mg P kg− 1 dry soil), phosphatases released by A. cygnorum hydrolysed sufficient organic P that was relatively mobile.

Conclusion
The reliance on facilitation of P acquisition in A cygnorum depended strongly on location. We demonstrated the exudation of phosphatases, which mobilise inorganic P; this P was adequate for growth when there was sufficient organic P in soil. Facilitation of P acquisition by B. attenuata allowed A. cygnorum to extend its range into severely P-impoverished habitats where it cannot exist without facilitation. This knowledge provides a better understanding of the diversity of P-acquisition strategies in severely P-impoverished environments.
The impact of voter turnout on referendum outcomes: evidence from Ireland
Authors: Munley Vincent , Garcia-Rodriguez Abian , Redmond Paul 
Published: February 2023

AbstractWe estimate the causal effect of voter turnout on referendum outcomes in Ireland using a newly created dataset that links 25 years of constituency-level referendum results to a variety of demographic and economic characteristics, as well as daily rainfall amounts. Our instrumental variables methodology uses rainfall as an instrument for voter turnout to overcome issues of endogeneity. By exploiting Ireland’s extensive experience with referendums, we are the first to explore the heterogeneous effects of turnout by referendum type (social issues versus regime-related issues). We find that a one-percentage-point increase in turnout is associated with a 1.6-percentage-point increase in support for progressively liberal social policies, which include issues such as same-sex marriage and abortion. For regime-related issues, such as EU economic treaties, we find no statistically significant effect of voter turnout on referendum outcomes. Our finding that turnout benefits socially progressive policies is consistent with a related strand of literature for general elections that indicates higher turnout benefits left-wing political parties.
Abnormal nodes sensing model in regional wireless networks based on convolutional neural network
Authors: Xu Xingkun , Lin Jerry Chun-Wei 
Published: February 2023

AbstractThere are some problems in abnormal node sensing in regional wireless networks, such as low sensing accuracy and poor judgment results of abnormal states of sensing nodes. Therefore, this paper develops a method for abnormal node sensing in regional wireless networks based on convolutional neural network. In addition, we will analyze the structure of regional wireless network nodes and determine the distribution mode of wireless network nodes. The regional wireless network node data are extracted and the pivot quantity and two-dimensional Gaussian distribution state are constructed using the median to build the regional wireless network node deployment model according to the confidence interval of the data characteristics; analyze the basic principle of convolution neural network, determine the operation mode of convolution kernel, classify the regional wireless network node data using Bayesian network, set a safety distance to determine the abnormal node of the regional wireless network, train the determined abnormal data as the input data of convolutional neural network and input it into the constructed perception model of the abnormal node of the regional wireless network, the loss function is set to continuously update the iterative results to realize the perception of abnormal node in the regional wireless network. The simulation results show that the sensing range of this method is relatively consistent with the range set by the sample, and the sensing accuracy reaches more than 95%, and the abnormal state error of abnormal nodes in the evaluation sample area is always less than 2%, which verifies that this method improves the sensing accuracy, reduces the error, and has higher application value.
The state of art, opportunities and challenges of blockchain in the insurance industry: a systematic literature review
Authors: Dominguez Anguiano Teresa , Parte Laura 
Published: February 2023

AbstractBlockchain technologies are quickly changing the competitive business landscape. However, key stakeholders are still sceptical about how, when, and what blockchain might bring to their businesses. This study aims to analyse how blockchain might reshape the insurance industry from an economic and business perspective, as well as to identify which are the challenges and enablers that specifically affect blockchain adoption within this industry, through a Systematic Literature Review (SRL). We also classify existing challenges into five strategic areas: Social, Technological, Environmental, Economic, and Political. Additionally, we provide several recommendations to the manager on identifying the existing hurdles and smoothing transformation.
Data-driven information for action
Authors: Wulff Kristin , Finnestrand Hanne 
Published: February 2023

AbstractBecause of the increase in data and the possibilities created by machine learning, organizations are now looking to become more data-driven. In sociotechnical systems design there has been a focus on designing information for action to support decentralized organizations. The purpose of this article, published in Gruppe. Interaktion. Organisation. is to discuss how data may be gathered and used in organizations striving to become data-driven.Explorations are based on interviews with experts (leaders and designers) in 13 organizations working on becoming more data-driven.This study points to 4 findings: first, if someone is expected to record data that informs other people’s actions can lead to data quality issues, which can be mitigated by providing transparency or supporting a joint information for action as an organizational design choice. Second, as organizations are becoming more data-driven, many tasks performed in the organization become design-related. This influences the type of data recorded and used for action. Third, more of the people in the organizations engage in designing the information for action for themselves and others, which means that they might need reskilling. Fourth, the boundaries of what can be considered information for action and for whom should by explored and reflected upon by the people involved in the (re)design.This means that, as organizations strive to become data-driven, the sociotechnical principle of information flow becomes a central challenge. To ensure quality organizations, there is a need to upskill or reskill employees so that they are able to design and use data for action.
Pre-training in Medical Data: A Survey
Authors: Qiu Yixuan , Lin Feng , Chen Weitong , Xu Miao 
Published: February 2023

AbstractMedical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods′ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.
Social Isolation Affects the Mimicry Response in the Use of Smartphones
Authors: Maglieri Veronica , Zanoli Anna , Giunchi Dimitri , Palagi Elisabetta 
Published: February 2023

AbstractHumans are social animals that rely on different ways to interact with each other. The COVID-19 pandemic strongly changed our communication strategies. Because of the importance of direct contact for our species, we predict that immediately after the forced social isolation, people were more prone to engage in direct rather than in virtual interactions, thus showing a lower mimicry response in the use of smartphones. In a non-longitudinal study, we collected behavioral data under naturalistic contexts and directly compared the data of the mimicry response gathered immediately following the Italian lockdown (May–September 2020) with those gathered one year later (May–October 2021). Contrary to our expectations, the mimicry response in the use of smartphones was higher immediately after the lockdown than a year later. Probably the large use of these devices during the lockdown translated into a greater sensitivity to be affected by others’ smartphone manipulation. Indeed, social isolation modified, at least in the short term, the ways we interact with others by making us more prone to engage in “virtual” social interactions. The bright side of the coin unveiled by our findings is that the effect seems to diminish over time. The large behavioral dataset analyzed here (1,608 events; 248 people) also revealed that the mimicry response in the use of smartphones was higher between familiar subjects than between strangers. In this view, mimicry in manipulating smartphones can be considered an example of joint action that fosters behavioral synchrony between individuals that, in the long-term, can translate into the formation of social bonding.
Integrating random forest and synthetic aperture radar improves the estimation and monitoring of woody cover in indigenous forests of South Africa
Authors: Qabaqaba Mcebisi , Naidoo Laven , Tsele Philemon , Ramoelo Abel , Cho Moses Azong 
Published: February 2023

AbstractWoody canopy cover (CC) is important for characterising terrestrial ecosystems and understanding vegetation dynamics. The lack of accurate calibration and validation datasets for reliable modelling of CC in the indigenous forests in South Africa contributes to uncertainties in carbon stock estimates and limits our understanding of how they might influence long-term climate change. The aim of this study was to develop a method for monitoring CC in the Dukuduku indigenous forest in South Africa. Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics of 2008, 2015, and 2018, polarimetric features, and Grey Level Co-occurrence Matrix (GLCMs) were used. Machine learning models Random Forest (RF) vs Support Vector Machines (SVM) were developed and calibrated using Collect Earth Online (CEO) data, a free and open-access land monitoring tool developed by the Food and Agriculture Organisation (FAO). The addition of GLCMs produced the highest accuracy in 2008, R2 (RMSE) = 0.39 (36.04%), and in 2015, R2 (RMSE) = 0.51 (27.82%), and in 2018, only SAR variables gave the highest accuracy R2 (RMSE) = 0.55 (29.50). The best-performing models for 2008, 2015, and 2018 were based on RF. During the ten-year study period, shrubland and wooded grassland had the highest transition, at 6% and 13%, respectively. The observed changes in the different canopies provide valuable insights into the vegetation dynamics of the Dukuduku indigenous forest. The modelling results suggest that the CEO calibration data can be improved by integrating airborne LiDAR data.
Unlocking Teacher Job Satisfaction During the COVID-19 Pandemic: a Multi-criteria Satisfaction Analysis
Authors: Glaveli Niki , Manolitzas Panagiotis , Tsourou Eftychia , Grigoroudis Evangelos 
Published: February 2023

AbstractResearch has so far made a rather limited advancement in identifying the contribution of the aspects of the working environment that matter to teachers’ overall job satisfaction (TJS), as well as in providing evidence-based guidelines for improving their working experience. Addressing these deficiencies, the current work uses data related to school working environment facets, i.e., opportunities for self-fulfillment, work intensity/load, salary/income, leadership and collegial relations, and overall TJS, from a sample of 438 public primary school teachers in Greece and applies a multi-criteria decision analysis method (the multi-criteria satisfaction analysis (MUSA)) to identify the contribution of these facets to overall TJS, underline the strong and weak points of TJS based on their importance for teachers and the school’s performance on them, and provide direct action implications for improving primary TJS. The results reveal that all the examined facets are crucial for TJS. Yet, self-fulfillment is the most important contributor to overall TJS and work intensity/load the least significant one. Also, self-fulfillment is the strongest point of TJS that school leaders and policymakers should continue investing on, whilst salary/income is a risk factor that could easily turn into a threat for TJS into the future.