bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moore on Degrees of Responsibility
Authors: Kaiserman Alex 
Published: July 2023

AbstractIn his latest book Mechanical Choices, Michael Moore provides an explication and defence of the idea that responsibility comes in degrees. His account takes as its point of departure the view that free action and free will consist in the holding of certain counterfactuals. In this paper, I argue that Moore’s view faces several familiar counterexamples, all of which serve to motivate Harry Frankfurt’s classic insight that whether and to what extent one is responsible for one’s action has more to do with what actually caused that action than with what one could or couldn’t have done instead. I then go on to sketch an alternative approach to degrees of responsibility that takes seriously this insight. I’ll argue that Moore ought to be sympathetic to this approach, inasmuch as it combines two familiar Moorean ideas: the idea that causal contribution comes in degrees, and the idea that acting freely is compatible with, and indeed entails, the fact that one’s action was caused by prior states of affairs.
Ideas from the Frontline: Improvement Opportunities in Federally Qualified Health Centers
Authors: Jung Olivia S. , Begum Fahima , Dorbu Andrea , Singer Sara J. , Satterstrom Patricia 
Published: July 2023

AbstractBackgroundEngaging frontline clinicians and staff in quality improvement is a promising bottom-up approach to transforming primary care practices. This may be especially true in federally qualified health centers (FQHCs) and similar safety-net settings where large-scale, top-down transformation efforts are often associated with declining worker morale and increasing burnout. Innovation contests, which decentralize problem-solving, can be used to involve frontline workers in idea generation and selection.ObjectiveWe aimed to describe the ideas that frontline clinicians and staff suggested via organizational innovation contests in a national sample of 54 FQHCs.InterventionsInnovation contests solicited ideas for improving care from all frontline workers—regardless of professional expertise, job title, and organizational tenure and excluding those in senior management—and offered opportunities to vote on ideas.ParticipantsA total of 1,417 frontline workers across all participating FQHCs generated 2,271 improvement opportunities.ApproachesWe performed a content analysis and organized the ideas into codes (e.g., standardization, workplace perks, new service, staff relationships, community development) and categories (e.g., operations, employees, patients).Key ResultsIdeas from frontline workers in participating FQHCs called attention to standardization (n = 386, 17%), staffing (n = 244, 11%), patient experience (n = 223, 10%), staff training (n = 145, 6%), workplace perks (n = 142, 6%), compensation (n = 101, 5%), new service (n = 92, 4%), management-staff relationships (n = 82, 4%), and others. Voting results suggested that staffing resources, standardization, and patient communication were key issues among workers.ConclusionsInnovation contests generated numerous ideas for improvement from the frontline. It is likely that the issues described in this study have become even more salient today, as the COVID-19 pandemic has had devastating impacts on work environments and health/social needs of patients living in low-resourced communities. Continued work is needed to promote learning and information exchange about opportunities to improve and transform practices between policymakers, managers, and providers and staff at the frontlines.
Cybersuicide: Online-Assisted Suicide
Authors: Moir Courtney-Louise , Tzani Calli , Ioannou Maria , Lester David , Synnott John , Williams Thomas James Vaughan 
Published: July 2023

AbstractSuicide is a major public health problem around the world, with an estimated 800,000 deaths from suicide each year. Research suggests that the ever-growing use of the Internet is contributing to and facilitating suicide. A thematic analysis was conducted on the final communications of three individuals aged between 18 and 21 to obtain an understanding of why people commit suicide online, how the Internet is involved, and how individuals who are online can influence a person’s choice of life or death. The analysis found that all three suicides were encouraged and baited by their audience, often as the audience doubted the legitimacy of the events, which resulted in pride issues for the victim. The issue of excitement from the audience was also present in all three cases, suggesting that online communications feel more like entertainment than real life. Other themes include social disorders, hesitation, manipulation, and attention seeking behaviour related to low self-esteem. The finding showed that those who die online by suicide have moments of hesitation, which are relevant for suicide-prevention strategies. The limitations of this study in terms of generalisability are discussed, and recommendations for the future of public awareness projects and research are given.
CXCR5 Regulates Neuronal Polarity Development and Migration in the Embryonic Stage via F-Actin Homeostasis and Results in Epilepsy-Related Behavior
Authors: Zhang Zhijuan , Zhang Hui , Antonic-Baker Ana , Kwan Patrick , Yan Yin , Ma Yuanlin 
Published: July 2023

AbstractEpilepsy is a common, chronic neurological disorder that has been associated with impaired neurodevelopment and immunity. The chemokine receptor CXCR5 is involved in seizures via an unknown mechanism. Here, we first determined the expression pattern and distribution of the CXCR5 gene in the mouse brain during different stages of development and the brain tissue of patients with epilepsy. Subsequently, we found that the knockdown of CXCR5 increased the susceptibility of mice to pentylenetetrazol- and kainic acid-induced seizures, whereas CXCR5 overexpression had the opposite effect. CXCR5 knockdown in mouse embryos via viral vector electrotransfer negatively influenced the motility and multipolar-to-bipolar transition of migratory neurons. Using a human-derived induced an in vitro multipotential stem cell neurodevelopmental model, we determined that CXCR5 regulates neuronal migration and polarization by stabilizing the actin cytoskeleton during various stages of neurodevelopment. Electrophysiological experiments demonstrated that the knockdown of CXCR5 induced neuronal hyperexcitability, resulting in an increased number of seizures. Finally, our results suggested that CXCR5 deficiency triggers seizure-related electrical activity through a previously unknown mechanism, namely, the disruption of neuronal polarity.
A discussion of stability analysis for systems of differential equations with multiple and distributed delays
Authors: Dassios Ioannis , Liu Muyang , Milano Federico 
Published: July 2023

AbstractIn this article, we consider a class of systems of multiple delay differential equations (MDDEs). We first define a characteristic matrix equation that can be used to analyze the stability of the equilibrium of a system of MDDEs. Then we construct a matrix based on the coefficients of the characteristic matrix equation and use the spectrum of this matrix to derive necessary and sufficient conditions for the system to be stable. Next we discuss a comparison of the stability equivalency between a system of delay differential equations (DDEs) to the system of MDDEs and relate our results to distributed delay systems (DDSs). Numerical examples are given to justify our theory.
Novel Imaging Technologies for Accurate Assessment of Cardiac Allograft Performance
Authors: Higuita Manuela Lopera , Jain Rohil , Osho Asishana A. , Rabi S. Alireza , Pruett Timothy L. , 3rd Pierson Richard N. , Iaizzo Paul A. , Tessier Shannon N. 
Published: July 2023

Abstract
Purpose of the Review
The current lack of objective and quantitative assessment techniques to determine cardiac graft relative viability results in risk-averse decision-making, which negatively impact the utilization of cardiac grafts. The purpose of this review is to highlight the current deficiencies in cardiac allograft assessment before focusing on novel cardiac assessment techniques that exploit conventional and emerging imaging modalities, including ultrasound, magnetic resonance, and spectroscopy.

Recent Findings
Extensive work is ongoing by the scientific community to identify improved objective metrics and tools for cardiac graft assessment, with the goal to safely increasing the number and proportion of hearts accepted for transplantation.

Summary
This review briefly discusses the in situ and ex vivo tools currently available for clinical organ assessment, before focusing on the individual capabilities of ultrasound, magnetic resonance, and spectroscopy to provide insightful, non-invasive information regarding cardiac graft functional and metabolic status that may be used to predict outcome after transplantation.
Operational Challenges and Economics in Future Voltage Control Services
Authors: Davi-Arderius Daniel , Troncia Matteo , Peiró Juan Julián 
Published: July 2023

Abstract
Purpose of Review
Decarbonizing the power system entails the need to update voltage control strategies, traditionally based on synchronous generators, and energy flows from transmission to distribution grid level. We analyze the voltage control strategies implemented up to now, considering both the technical and economic views.

Recent Findings
We study how the transmission and distribution grid operators in Spain, Croatia, and Thailand improved their voltage control strategies to exploit potentials from power electronics from wind and photovoltaic generation. Moreover, we analyze economic fundamentals and market design issues related with the implementation of these new strategies, essential to set efficient economic incentives for their successful implementation.

Summary
We discuss recent innovative projects and solutions implemented in some countries that show promising and relevant potential from the implemented renewable-based voltage control strategies. However, we highlight that there are very few empirical analyses in real conditions, which are essential to implement improved and efficient voltage control strategies.
Influence of train speed and its mitigation measures in the short- and long-term performance of a ballastless transition zone
Authors: Ramos Ana , Calçada Rui , Correia António Gomes 
Published: July 2023

AbstractThe ballastless track is nowadays the most popular railway system due to the required low number of maintenance operations and costs, despite the high investment. The gradual change from ballasted to ballastless tracks has been occurring in Asia, but also in Europe, increasing the number of transition zones. The transition zones are a special area of the railway networks where there is an accelerated process of track degradation, which is a major concern of the railway infrastructure managers. Thus, the accurate prediction of the short- and long-term performance of ballastless tracks in transition zones is an important topic in the current paradigm of building/rehabilitating high-speed lines. This work purposes the development of an advanced 3D model to study the global performance of a ballastless track in an embankment–tunnel transition zone considering the influence of the train speed (220, 360, 500, and 600 km/h). Moreover, a mitigation measure is also adopted to reduce the stress and displacements levels of the track in the transition. A resilient mat placed in the tunnel and embankment aims to soften the transition. The behaviour of the track with the resilient mat is evaluated considering the influence of the train speed, with special attention regarding the critical speed. The used methodology is a novel and hybrid approach that allows including short-term and long-term performance, through the development of a powerful 3D model combined with the implementation of a calibrated empirical permanent deformation model.
Hierarchical graph neural network with subgraph perturbations for key gene cluster discovery in cancer staging
Authors: Hou Wenju , Wang Yan , Zhao Ziqi , Cong Yizhi , Pang Wei , Tian Yuan 
Published: July 2023

AbstractAnalyzing highly individual-specific genomic data to understand genetic interactions in cancer development is still challenging, with significant implications for the discovery of individual biomarkers as well as personalized medicine. With the rapid development of deep learning, graph neural networks (GNNs) have been employed to analyze a wide range of biomolecular networks. However, many neural networks are limited to black box models, which are only capable of making predictions, and they are often challenged to provide reliable biological and clinical insights. In this research, for sample-specific networks, a novel end-to-end hierarchical graph neural network with interpretable modules is proposed, which learns structural features at multiple scales and incorporates a soft mask layer in extracting subgraphs that contribute to classification. The perturbations caused by the input graphs' deductions are used to evaluate key gene clusters, and the samples are then grouped into classes to produce both sample- and stage-level explanations. Experiments on four gene expression datasets from The Cancer Genome Atlas (TCGA) show that the proposed model not only rivals the advanced GNN methods in cancer staging but also identifies key gene clusters that have a great impact on classification confidence, providing potential targets for personalized medicine.
Feature gene selection based on fuzzy neighborhood joint entropy
Authors: Wang Yan , Sun Minjie , Long Linbo , Liu Jinhui , Ren Yifan 
Published: July 2023

AbstractThis paper addresses the feature selection problem for gene expression profiles. Feature selection based on fuzzy neighborhood rough sets is very important in gene expression profiles. The process of extracting key features in gene expression profiles may have two disadvantages: (1) it may generate many redundant features, thus reducing the classification accuracy, and (2) it may ignore some information. To address the above problems, this paper proposes a fuzzy neighborhood joint entropy model based on feature selection; this model adopts the nonnegative principle of fuzzy neighborhood joint entropy to evaluate the importance of a candidate feature gene. Based on the fuzzy neighborhood joint entropy model, this paper proposes a new feature gene selection algorithm: the fuzzy neighborhood joint entropy (FNJE) algorithm. In the model, first, fuzzy neighborhood particles and fuzzy decision-making are combined with the uncertainty measure of joint entropy to construct a fuzzy neighborhood joint entropy model. Second, the importance degree of a feature is introduced as the measurement standard of candidate feature genes to evaluate the importance of each feature. Third, the algorithm uses the importance of candidate features in selecting features to reduce the redundancy of the selected features and improve the classification accuracy. In this paper, based on the UCI and gene datasets, we conduct a series of experiments. The experimental results show that the proposed algorithm can select fewer feature genes and achieve higher classification accuracy. Compared with the other four algorithms, our proposed algorithm can improve the accuracy by 0.4$$\%$$
%
$$-$$
-
10.42$$\%$$
%
and 1.16$$\%$$
%
$$-$$
-
15.18$$\%$$
%
and can reach maximum accuracy values of 90.52$$\%$$
%
and 87.02$$\%$$
%
with the linear-SVM and KNN classifiers, respectively.
Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system
Authors: Zhou Xiaoping , Chen Xinyue , Tong Le , Wang Yang 
Published: July 2023

AbstractTo achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging technology. The RIS-assisted mmWave massive MIMO vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of RIS-assisted mmWave V2X communications is though not exempt of challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted mmWave massive MIMO system to improving the safety and traffic efficiency of cooperative automated driving. The received signal of the vehicle is decomposed into four-dimensional tensors and study the channel, frequency and time attention (CFTA) algorithm using the low order tensor characteristics of mmWave channels. This algorithm can extract the frequency and time characteristics of the channel, and obtain the millimeter wave characteristic channel between the corresponding base station (BS) and the vehicles. The problem of jointly optimization of beamforming matrix at the BS and the phase shift matrix on the RIS is constructed and solved using deep reinforcement learning (DRL). Unlike traditional alternating optimization methods, the integration of DRL techniques into the optimal design of the system enables the observation of immediate rewards, learning from the environment and improving it to obtain optimal solutions to high-dimensional jointly beamforming optimization problems ultimately. The simulation results show that the proposed algorithm has meaningful performance compared with other methods.
Dual-drive collaboration surrogate-assisted evolutionary algorithm by coupling feature reduction and reconstruction
Authors: Yu Haibo , Gong Yiyun , Kang Li , Sun Chaoli , Zeng Jianchao 
Published: July 2023

AbstractSurrogate-assisted evolutionary algorithm (SAEA) prevails in the optimization of computationally expensive problems. However, existing SAEAs confront low efficiency in the resolution of high-dimensional problems characterized by multiple local optima and multivariate coupling. To this end, this paper offers a dual-drive collaboration surrogate-assisted evolutionary algorithm (DDCSAEA) by coupling feature reduction and reconstruction, which coordinates two unsupervised feature learning techniques, i.e., principal component analysis and autoencoder, in tandem. DDCSAEA creates a low-dimensional solution space by downscaling the target high-dimensional space via principal component analysis and collects promising candidates in the reduced space by collaborating a surrogate-assisted evolutionary sampling with differential mutation. An autoencoder is used to perform the feature reconstruction on the collected candidates for infill-sampling in the target high-dimensional space to sequentially refine the neighborhood landscapes of the optimal solution. Experimental results reveal that DDCSAEA has stronger convergence performance and optimization efficiency against eight state-of-the-art SAEAs on high-dimensional benchmark problems within 200 dimensions.
A quantitative comparison of devices for in vivo biomechanical characterization of human skin
Authors: Junker Håvar J. , Thumm Bettina , Halvachizadeh Sascha , Mazza Edoardo 
Published: July 2023

AbstractNon-invasive skin characterization devices are emerging as a valuable tool in clinical skin research. In recent years, the range of available experimental techniques and methods used to determine the biomechanical properties of skin has increased considerably. Although a substantial amount of work has been devoted to assessing the working principle of macroscopic skin characterization devices individually, a rationalization and comparison between them is still lacking. This motivated the present study, which aimed to characterize and compare three commonly used working principles: suction, dynamic shear loading, and indentation. A synthetic model system with tunable mechanical properties was used to assess the three devices, and the results rationalized based on corresponding finite element models. In vivo measurements were performed on healthy volunteers to investigate the capability of differentiating the biomechanical properties of skin at different body locations, and to assess the intra- and inter-rater reliability of each device. The present comparative analysis indicates that the analyzed functional principles perceive the stiffness of human skin differently, with relevant implications for the interpretation of the respective measurement results.
Ants/Nest-Associated Fungi and Their Specialized Metabolites: Taxonomy, Chemistry, and Bioactivity
Authors: Aguilar-Colorado Ángel Sahid , Rivera-Chávez José 
Published: July 2023

AbstractMicroscopic fungi occupy a vast number of habitats, are taxonomically diverse, degrade complex substrates, and have stood out for their capacity to biosynthesize a plethora of specialized metabolites. Such molecules are structurally diverse, and many have applications in fundamental and applied sciences, for example, in medicine, material sciences, food chemistry, textile and pharmaceutical industries, and agronomy, among other fields. However, despite the tremendous biotechnological value of fungi, these organisms are understudied, limiting the knowledge to their taxonomy, chemistry, and some putative applications. Notably, some specific habitats remain unexplored in terms of their mycobiota. Based on these considerations, this review describes the known fungal diversity associated with ants/nests, their metabolic potential, and the possible applications of their specialized metabolites in drug discovery programs focused on developing treatments for human diseases. According to this revision, fungal diversity has been studied by applying conventional methodologies such as isolation and morphological identification of soil fungi from mounds and nest chambers, and indirectly from ants’ cuticles and glands. The subfamilies, genera, taxonomical information, and geographical origin of ants from which filamentous fungi and yeast are commonly isolated are also described. Furthermore, some important information is presented concerning the difference between the ant-associated mycobiota and that in the surroundings, discarding the extrapolation of the chemical and biological information known for soil fungi. Altogether, this review evidenced the lack of information regarding the chemical composition of ant-associated fungi, encouraging research focused on exploring the chemistry biosynthesized from ants’ mycobiota, as well as the elucidation of their allelopathic potential inside the nests.
Graphical Abstract