bluuebunny/crossref_metadata_2025_split huggingface dataset:
Localisation of facts compensator using reptile search algorithm for enhancing power system security under multi‐contingency conditions
Authors: Punam Sumit Ramswami , Kumar Sunil 
Published: October 2023

AbstractDue to the improvement in power demand usage, the system causes more stress. Suitable placement of FACTS is introduced to improve power flows, stability, and power system security. An advanced optimal method has been introduced to resolve optimal power flow under various conditions. Five varied FACTS devices were positioned inside the power system to improve system safety at the least cost. Selecting the suitable location and size of FACTS device enabling high security and less cost was proposed using RSA. The objective functions were mitigated based on the RSA's inspiration to improve system security. The placement of the FACTS compensator was based on objective functions like LOSI, voltage deviation, real power loss, investment cost, sensitivity index, fuel costs, and constraints. The proposed model was validated under three conditions, namely generator outage, line outage, and both outage in IEEE 118 and IEEE 30 bus‐systems. In the IEEE 30 bus system, TCSC provides better security of 1.4 severity at normal conditions and 1.3 severity in contingency conditions. In the IEEE 118 bus system, UPFC has less severity of 2.4 at normal conditions, and STATCOM has the least severity of 3 at contingency conditions. The proposed model provides enhanced security in all circumstances and reduces overall costs.
Ideological alignment, public sector size and tax morale: empirical evidence from OECD economies
Authors: Rodriguez-Justicia David , Theilen Bernd 
Published: October 2023

AbstractThis paper analyzes the extent to which ideological differences between citizens and their governments, in conjunction with the observed size of the public sector, influence taxpayers’ intrinsic motivations to pay taxes. We utilize data from the World Values Survey and the European Values Study, encompassing 23 OECD economies from 1995 to 2018. Our findings indicate that the tax morale of citizens who hold right-wing ideological views in relation to their government significantly decreases when the public sector surpasses the OECD average. Conversely, citizens who hold left-wing ideological views in relation to their government exhibit a higher intrinsic motivation to pay taxes, particularly when the public sector is considered undersized.
On the weighting of homo economicus and homo virtus in human behaviour
Authors: Parada-Contzen Marcela , Parada-Daza José Rigoberto 
Published: October 2023

AbstractIn economic sciences, most analyses focus on the economic person construct. However, this that formalization does not capture the complex nature of human behaviour. This paper estimates the weight that economic and noneconomic dimensions of human behaviour have on wellbeing. A utility function is considered that models behaviour from a complex standpoint, where the motivations of the economic individual (homo economicus) are analysed in a broad perspective by integrating emotional wellbeing and human virtues into the model (homo virtus). Three empirical measures of wellbeing are used: The Well-Being Index developed by the Boston Consulting Group’s Sustainable Economic Development Assessment (2008–2018), the Index of Economic Well-Being from the Centre for the Study of Living Standards (1980–2014), and the Happiness Score from the World Happiness Report (2005–2018). Depending on data availability, the model is estimated globally for all countries, OECD countries, European countries, and developing countries using linear regression methods. The results indicate that, on average, the homo virtus dimension of behaviour has a weight of 11% in countries’ wellbeing functions, while the economic perspective has a weight of 89%. Additionally, the results show that richer countries value economic factors more than poorer countries. The analyses also show that the maximum level of emotional satisfaction is higher for European and OECD countries than for developing countries.
Impulse control during the online shopping frenzy in China: the role of consumer inertia
Authors: Shiu Jerry Yuwen , Wei Guohang , Chang Hsiu-Hua 
Published: October 2023

AbstractThis study examined impulse control during an online shopping festival in China. The research model integrated the flow theory into a Stimulus-Organism-Response (S-O-R) framework to explore the direct and indirect impacts of positive eWOM on impulse buying; the mediating role of consumer inertia was assessed as a risk-reduction strategy in impulse-control intervention. The proposed model reasonably well fits the data; all hypotheses are supported. The structural equation modeling results (n = 497) show that positive eWOM contributes to impulse buying because of the online shopping frenzy. Simultaneously, the rise of these stimuli also leads to reflective impulse control, and the three types of consumer inertia can explain the transition from positive eWOM to impulse buying. Furthermore, the affective and cognitive inertia enhance the dependence on the behavioral type after continuous stimulation with positive reinforcement. The findings imply consumer inertia helps secure decision quality and alleviates impulse behavior. In addition, the behavioral inertia generated by sustained affective and cognitive stimulation is essential to subsequent impulse purchases associated with evolving brand loyalty. The study consequently offers some suggestions for marketers to promote repeat purchases as well as potential lines of inquiry for further research.
Investigating charge traps in MoTe2 field-effect transistors: SiO2 insulator traps and MoTe2 bulk traps
Authors: Kim Giheon , Dang Dang Xuan , Gul Hamza Zad , Ji Hyunjin , Kim Eun Kyu , Lim Seong Chu 
Published: October 2023

Abstract
Two-dimensional material-based field-effect transistors are promising for future use in electronic and optoelectronic applications. However, trap states existing in the transistors are known to hinder device performance. They capture/release carriers in the channel and lead to hysteresis in the transfer characteristics. In this work, we fabricated MoTe2 field-effect transistors on two different gate dielectrics, SiO2 and h-BN, and investigated temperature-dependent charge trapping behavior on the hysteresis in their transfer curves. We observed that devices with SiO2 back-gate dielectric are affected by both SiO2 insulator traps and MoTe2 intrinsic bulk traps, with the latter becoming prominent at temperatures above 310 K. Conversely, devices with h-BN back-gate dielectric, which host a negligible number of insulator traps, primarily exhibit MoTe2 bulk traps at high temperatures, enabling us to estimate the trap energy level at 389 meV below the conduction band edge. A similar energy level of 396 meV below the conduction band edge was observed from the emission current transient measurement. From a previous computational study, we expect these trap states to be the tellurium vacancy. Our results suggest that charge traps in MoTe2 field-effect transistors can be reduced by careful selection of gate insulators, thus providing guidelines for device fabrication.
Slow-roll inflation in
modified gravity*
Authors: Bhat Aaqid , Mandal Sanjay , Sahoo P.K. 
Published: October 2023

Abstract
In this study, we explore the concept of cosmological inflation within the framework of the

theory of gravity, where f is a general function of the torsion scalar T and the trace

of the energy-momentum tensor. It is assumed that the conditions of slow-roll inflation are applicable in

gravity. To determine different observables related to inflation, such as the tensor-to-scalar ratio r, scalar spectral index

, spectral index

, and tensor spectral index

, the Hubble slow-roll parameters are utilized for a particular model of

. Lastly, an assessment is carried out to determine the feasibility of the models by conducting a numerical analysis of the parameters. The findings indicate that it is feasible to achieve compatibility with the observational measurements of slow-roll parameters by utilizing different values of the free parameters.
Magnetic and quadrupole moments of the
,
, and
states in the diquark-antidiquark picture
Authors: Özdem Ulaş 
Published: October 2023

Abstract
The magnetic and quadrupole moments of the

,

, and

states are calculated within the QCD light-cone sum rules. The compact diquark-antidiquark interpolating currents and the distribution amplitudes of the on-shell photon are used to extract the magnetic and quadrupole moments of these states. The magnetic moments are acquired as

,

, and

for the

,

, and

states, respectively. The magnetic moments evaluated for the

,

, and

states are sufficiently large to be experimentally measurable. The magnetic moment is an excellent platform for studying the internal structure of hadrons governed by the quark-gluon dynamics of QCD because it is the leading-order response of a bound system to a weak external magnetic field. The quadrupole moment results are

,

, and

for the

,

, and

states, respectively. We obtain a non-zero, but small, value for the quadrupole moments of the

states, which indicates a non-spherical charge distribution. The nature and internal structure of these states can be elucidated by comparing future experimental data on the magnetic and quadrupole moments of the

,

, and

states with the results of the present study.
Data-based program management of system-wide IV smart pump integration
Authors: Giuliano Karen K , Mahuren Rebecca S , Balyeat Jacob 
Published: October 2023

Abstract

Purpose
Smart pump bidirectional interoperability offers automated infusion programming and documentation that can improve patient safety and workflow efficiency. This technology has been poorly implemented across US hospitals, and there is little guidance on the tracking or monitoring of interoperability systems. The purpose of this report is to describe the successful implementation of intravenous (IV) smart pump interoperability in a large health system.

Summary
Bidirectional IV smart pump interoperability and compliance monitoring were implemented across a large Midwestern health system using ICU Medical’s Plum 360 and LifeCare PCA devices and Smith Medical’s MedFusion 4000 Syringe Pump devices. The hospital system’s experience in implementing and monitoring IV smart pump compliance using automated reports and a dedicated medication safety integration nurse is described. Compliance trends suggest that the implementation of IV smart pump interoperability has achieved a reduction in programming outside of the dose error reduction system, manual overrides, and IV medication administration error rates.

Conclusion
The monitoring of smart pump compliance has had demonstrated benefits in investigating usability concerns, recognizing system errors, and identifying increased needs for nurse training. This program can serve as an example for other healthcare systems adopting IV smart pump interoperability.
The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data
Authors: Willie Elijah , Yang Pengyi , Patrick Ellis 
Published: October 2023

Abstract

Motivation
The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research.

Results
In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis.

Availability and implementation
The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.
Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor
Authors: Gan Yanglan , Huang Xingyu , Guo Wenjing , Yan Cairong , Zou Guobing 
Published: October 2023

Abstract

Motivation
Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems.

Results
Here, we propose DGSSynADR, a new deep learning method based on global structured features of drugs and targets for predicting synergistic anticancer drug combinations. DGSSynADR constructs a heterogeneous graph by integrating the drug–drug, drug–target, protein–protein interactions and multi-omics data, utilizes a low-rank global attention (LRGA) model to perform global weighted aggregation of graph nodes and learn the global structured features of drugs and targets, and then feeds the embedded features into a bilinear predictor to predict the synergy scores of drug combinations in different cancer cell lines. Specifically, LRGA network brings better model generalization ability, and effectively reduces the complexity of graph computation. The bilinear predictor facilitates the dimension transformation of the features and fuses the feature representation of the two drugs to improve the prediction performance. The loss function Smooth L1 effectively avoids gradient explosion, contributing to better model convergence. To validate the performance of DGSSynADR, we compare it with seven competitive methods. The comparison results demonstrate that DGSSynADR achieves better performance. Meanwhile, the prediction of DGSSynADR is validated by previous findings in case studies. Furthermore, detailed ablation studies indicate that the one-hot coding drug feature, LRGA model and bilinear predictor play a key role in improving the prediction performance.

Availability and implementation
DGSSynADR is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/DGSSynADR.
What Challenges Emerge from Cases Where Children Are Made Subject to a ‘Care Order at Home’?
Authors: Murphy Ciarán , Maguinness Noreen , Stathopoulos Eoanna 
Published: October 2023

Abstract
A continued and unprecedented demand for England’s child protection services has coincided with increasing numbers of children being made subject to Care Orders within the Family Court. There is a growing understanding of the associated challenges—not least in terms of cost and placement availability. However, there has been limited discussion of the difficulties associated with children being made subject to a Care Order, but who remain living at home with their parents. This article reports on an iterative mixed method study which sought to explore child protection social workers’ experiences of ‘Care Orders at home’. It discusses the prevalence of ‘Care Orders at home’ within Northwest England and identifies specific challenges in the context of ‘managing child protection partners’ expectations’; apparent ‘confusion and disempowerment of the child and family’ and ‘an increased sense of risk’ with respect to several of the key stakeholders involved. Implications that emerge from the study are that a ‘Care Order at home’ might, in some circumstances, be considered as a ‘contradictory’ and therefore ‘confusing’ concept, and that it can offer less protection to a child than a care plan for ‘No Order’ running concurrently to a detailed child protection plan.
Machine learning nominates the inositol pathway and novel genes in Parkinson’s disease
Authors: Yu Eric , Larivière Roxanne , Thomas Rhalena A , Liu Lang , Senkevich Konstantin , Rahayel Shady , Trempe Jean-François , Fon Edward A , Gan-Or Ziv 
Published: October 2023

Abstract
There are 78 loci associated with Parkinson’s disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson’s disease locus and identify variants and pathways potentially involved in Parkinson’s disease. We trained a machine learning model to predict Parkinson’s disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson’s disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson’s disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson’s disease.
Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning
Authors: Lee Jeyeon , Burkett Brian J , Min Hoon-Ki , Senjem Matthew L , Dicks Ellen , Corriveau-Lecavalier Nick , Mester Carly T , Wiste Heather J , Lundt Emily S , Murray Melissa E , Nguyen Aivi T , Reichard Ross R , Botha Hugo , Graff-Radford Jonathan , Barnard Leland R , Gunter Jeffrey L , Schwarz Christopher G , Kantarci Kejal , Knopman David S , Boeve Bradley F , Lowe Val J , Petersen Ronald C , Jack Clifford R , Jones David T 
Published: October 2023

Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
Cerebral dominance representation of directed connectivity within and between left–right hemispheres and frontal-posterior lobes in mild cognitive impairment
Authors: Wang Luchun , Liang Xixi , Wang Jing , Zhang Ying , Fan Zili , Sun Tingting , Yu Xin , Wu Dan , Wang Huali 
Published: October 2023

Abstract
Electroencephalography can assess connectivity between brain hemispheres, potentially influencing cognitive functions. Much of the existing electroencephalography research primarily focuses on undirected connectivity, leaving uncertainties about directed connectivity alterations between left–right brain hemispheres or frontal-posterior lobes in mild cognitive impairment. We analyzed resting-state electroencephalography data from 34 mild cognitive impairment individuals and 23 normal controls using directed transfer function and graph theory for directed network analysis. Concerning the dominance within left–right hemispheres or frontal-posterior lobes, the mild cognitive impairment group exhibited decreased connectivity within the frontal compared with posterior brain regions in the delta and theta bands. Regarding the dominance between the brain hemispheres or lobes, the mild cognitive impairment group showed reduced connectivity from the posterior to the frontal regions versus the reverse direction in the same bands. Among all participants, the intra-lobe frontal-posterior dominance correlated positively with executive function in the delta and alpha bands. Inter-lobe dominance between frontal and posterior regions also positively correlated with executive function, attention, and language in the delta band. Additionally, interhemispheric dominance between the left and right hemispheres positively correlated with attention in delta and theta bands. These findings suggest altered cerebral dominance in mild cognitive impairment, potentially serving as electrophysiological markers for neurocognitive disorders.