bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synergistic effects of cold rolling and age hardening on the hardness and tensile characteristics of AA6061 hybrid composites
Authors: Sadanand Ramakrishna Vikas , Sharma Sathyashankara , Prabhu P R 
Published: March 2024

Abstract
The present study involves the fabrication of aluminium alloy 6061 matrix hybrid composites with varying weight fractions of silica sand and copper particles by employing the conventional stir casting method. The combined influence of age hardening (AH) and low temperature thermomechanical treatment (LTMT) on the hardness and tensile properties of AA6061 hybrid composites was investigated. The uniform dispersion of the particles in the matrix was confirmed by microstructure analysis and the improvement of Brinell hardness values. The composites exhibited higher tensile strength and hardness than the base alloy. Both AH and LTMT enhanced the properties of the hybrid composites and a comparison between them revealed the best results for LTMT hybrid composites. The LTMT hybrid composite with 3 wt% silica sand and 3 wt% copper (3S3C) subjected to 12% rolling deformation and aged at 100 °C had the highest hardness and tensile strength of 144.26 HV and 290 MPa respectively. The hardness and tensile strength of AA6061-3S3C hybrid composite subjected to LTMT in peak aged condition showed an improvement of 125 and 97% respectively when compared with those of AA6061 alloy. Fracture surface analysis of the thermomechanical treated composites in peak aged condition showed a mixed mode of failure dominant with the ductile fracture.
Generation of stable Gaussian cluster states in optomechanical systems with multifrequency drives
Authors: Yazdi Nahid , Zippilli Stefano , Vitali David 
Published: March 2024

Abstract
We show how to dissipatively stabilize the quantum state of N mechanical resonators in an optomechanical system, where the resonators interact by radiation pressure with N optical modes, which are driven by properly selected multifrequency drives. We analyze the performance of this approach for the stationary preparation of Gaussian cluster states.
Open code and data are not enough: understandability as design goal for energy system models
Authors: Pfenninger Stefan 
Published: March 2024

Abstract
Energy system models do not represent natural processes but are assumption-laden representations of complex engineered systems, making validation practically impossible. Post-normal science argues that in such cases, it is important to communicate embedded values and uncertainties, rather than establishing whether a model is ‘true’ or ‘correct’. Here, we examine how open energy modelling can achieve this aim by thinking about what ‘a model’ is and how it can be broken up into manageable parts. Collaboration on such building blocks—whether they are primarily code or primarily data—could become a bigger focus area for the energy modelling community. This collaboration may also include harmonisation and intercomparison of building blocks, rather than full models themselves. The aim is understandability, which will make life easier for modellers themselves (by making it easier to develop and apply problem-specific models) as well as for users far away from the modelling process (by making it easier to understand what is qualitatively happening in a model—without putting undue burden on the modellers to document every detail).
Training quantum Boltzmann machines with the β-variational quantum eigensolver
Authors: Huijgen Onno , Coopmans Luuk , Najafi Peyman , Benedetti Marcello , Kappen Hilbert J 
Published: March 2024

Abstract
The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy from the model to the target state. This requires QBM expectation values which are computationally intractable for large models in general. It is therefore important to develop heuristic training methods that work well in practice. In this work, we study a heuristic method characterized by a nested loop: the inner loop trains the β-variational quantum eigensolver (β-VQE) by Liu et al (2021 Mach. Learn.: Sci. Technol.
2 025011) to approximate the QBM expectation values; the outer loop trains the QBM to minimize the relative entropy to the target. We show that low-rank representations obtained by β-VQE provide an efficient way to learn low-rank target states, such as classical data and low-temperature quantum tomography. We test the method on both classical and quantum target data with numerical simulations of up to 10 qubits. For the cases considered here, the obtained QBMs can model the target to high fidelity. We implement a trained model on a physical quantum device. The approach offers a valuable route towards variationally training QBMs on near-term quantum devices.
A method for quantifying the generalization capabilities of generative models for solving Ising models
Authors: Ma Qunlong , Ma Zhi , Gao Ming 
Published: March 2024

AbstractFor Ising models with complex energy landscapes, whether the ground state can be found by neural networks depends heavily on the Hamming distance between the training datasets and the ground state. Despite the fact that various recently proposed generative models have shown good performance in solving Ising models, there is no adequate discussion on how to quantify their generalization capabilities. Here we design a Hamming distance regularizer in the framework of a class of generative models, variational autoregressive networks (VANs), to quantify the generalization capabilities of various network architectures combined with VAN. The regularizer can control the size of the overlaps between the ground state and the training datasets generated by networks, which, together with the success rates of finding the ground state, form a quantitative metric to quantify their generalization capabilities. We conduct numerical experiments on several prototypical network architectures combined with VAN, including feed-forward neural networks, recurrent neural networks, and graph neural networks, to quantify their generalization capabilities when solving Ising models. Moreover, considering the fact that the quantification of the generalization capabilities of networks on small-scale problems can be used to predict their relative performance on large-scale problems, our method is of great significance for assisting in the Neural Architecture Search field of searching for the optimal network architectures when solving large-scale Ising models.
The Self: A Very Short Introduction
Authors: Schechtman Marya 
Published: March 2024

Abstract
The Self: A Very Short Introduction uses insights from philosophy, psychology, psychiatry, and popular thought to consider some of the most compelling and puzzling questions about the self, including questions about what kind of object a self is if it is an object at all, what it means to be oneself and why it is important, what kinds of changes the self can and cannot survive, whether a self can be separated from its body, whether more than one self can exist in a single body, and what role engagement with the environment and with other selves plays in constituting and maintaining the self. These investigations yield a complex, multi-dimensional picture of the self as a subject and agent embedded in and interacting with a natural and social world.
Divided and distressed selves
Authors: Schechtman Marya 
Published: March 2024

Abstract
This chapter considers what can be learned by reflection on a handful of psychiatric and neurological cases in which selfhood seems to be compromised. Cases include “split brain” cases (corpus callosotomy), dissociative identity disorder, depersonalization and derealization, the phenomenon of inserted thought as a symptom of schizophrenia, and alienation from one’s body, including the phenomenon of hemisphere neglect. Each of these cases involves a form of fragmentation or compartmentalization that challenges the integrity of the self but does not seem to completely undermine it. Lessons learned include the importance of affect in constituting the unity of self-experience, as well as the role of embodiment and agency in a unified self. These lessons point to a new way of thinking about the basic unity of consciousness that is typically taken to constitute a self, as well as suggesting that this unity may be less complete than we usually take it to be even in typical cases.
Embodied, social selves
Authors: Schechtman Marya 
Published: March 2024

Abstract
This chapter offers sustained examination of some of the major arguments for the claim that selves are inherently dependent on embodiment and/or embeddedness in a social context. Topics include theories that place the origins of selfhood in immunological response or neurological self-modeling, as well as phenomenological arguments that the experiencing subject emerges only in conjunction with consciousness of an experienced environment. Various positions on the social constitution of the self are also explored, including performative views of the self and accounts that see individual selves as abstractions from a broader community, without which they could not exist. The connections between the many different insights into the self generated in this and earlier chapters are discussed, revealing multiple ways in which subjective experience, embodiment, and social context work together to constitute and maintain the self. This suggests the value of a multi-dimensional approach that sees the self as a dynamic subject and agent, living a life filled with experiences, choices, emotions, activities, and relationships.
Achieving Joint Attention and Understanding of Task Responsibilities in Synchronous Hybrid L2 Classroom Group Work
Authors: Jakonen Teppo , Jauni Heidi , Sert Olcay 
Published: March 2024

Abstract
This article addresses the need to better understand interactional asymmetries, challenges, and solutions in implementing synchronous hybrid language teaching. We investigate video-recorded peer interactions in a higher education language teaching context in which a student uses a telepresence robot, a remotely moveable videoconferencing tool, to participate in small-group task work in L2 English together with students who are physically located in the language classroom. Drawing on multimodal conversation analysis, we examine how the geographically dispersed peer group achieves, maintains, and repairs their joint attention on task-relevant learning materials as they are accomplishing a task, and how this kind of referential interactional work enables their co-operation as a group. Based on the analysis, we argue that in synchronous hybrid learning there is a need to reflexively adjust interactional practices to secure an intersubjective understanding of learning tasks and their progressivity. The findings also suggest that sensory and interactional asymmetries should be taken into account when developing and implementing synchronous hybrid learning environments that aim at equality of opportunities regardless of the participation mode.
Utility of a Short-Form Phonemic Fluency Task
Authors: Kaufman Jack R , Fatima Hudaisa , Lacritz Laura H , Cullum C Munro 
Published: March 2024

Abstract

Objective
to establish a proof-of-concept and ascertain the reliability of an abbreviated 30-second (30s) phonemic fluency measure as a cognitive screening tool in older adults.

Methods
in all, 201 English-speaking individuals with normal cognition (NC; n = 119) or cognitive impairment (CI; mild CI or dementia; n = 82) were administered a standard 60s phonemic fluency task (FAS/CFL) with discrete 30s intervals denoted.

Results
for all letters, 30s trial scores significantly predicted 60s scores for the same letter, R2 = .7–.9, F(1, 200) = 850–915, p &lt; .001. As with 60s total scores, 30s cumulative scores (for all three trials) were significantly different between NC and CI groups (p &lt; .001). Receiver operating characteristic analyses showed that 30s total scores distinguished NC and CI groups as effectively (AUC = .675) as 60s total scores (AUC = .658).

Conclusions
these findings support the utility and reliability of a short-form phonemic fluency paradigm, as 30s performance reliably predicted 60s/trial totals and was equally accurate in distinguishing impaired/non-impaired groups.
GPCR-IPL score: multilevel featurization of GPCR–ligand interaction patterns and prediction of ligand functions from selectivity to biased activation
Authors: Kumar Surendra , Teli Mahesh K , Kim Mi-hyun 
Published: March 2024

Abstract
G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure–activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR–ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR–ligand complexes. As a result, we developed GPCR–ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out ‘residue level’ (for flexible transmembrane helices of GPCRs), (2) zoomed-in ‘pocket level’ (for sophisticated mode of action) and (3) ‘atom level’ (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.
AleRax: a tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss
Authors: Morel Benoit , Williams Tom A , Stamatakis Alexandros , Szöllősi Gergely J 
Published: March 2024

Abstract

Motivation
Genomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree.

Results
We present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic—reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as duplication, transfer, and loss probabilities for genome-scale datasets with hundreds of taxa.

Availability and implementation
GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz.
Novel photoactive material and fabrication techniques for solar cells application: nanocellulose-based graphene oxide CdS composite
Authors: Shoyiga Hassan Oriyomi , Akpasi Stephen Okiemute , Akpan Joseph , Amune Ubani Oluwaseun , Kiambi Sammy Lewis 
Published: March 2024

AbstractIn recent times, solar energy has become one of the largest available sources of renewable energy at our disposal. However, the design of highly efficient solar cells is increasingly becoming crucial as there has been a surge for economically viable alternative energy sources with the lowest cost. Significant advances have been made through different routes to make photovoltaic (PV)/solar technologies economically viable, eco-friendly and consequently scalable. As a result, cellulose nanomaterials have become one of the emerging technologies in this regard because of the advantages of high-value bio-based nanostructured materials, such as their abundance and sustainability. Nanocellulose-based photoactive nanocomposite materials can be made by integrating conducting photoactive and electroconductive materials with hydrophilic biocompatible cellulose. Inorganic nanoparticles, such as graphene/reduced graphene oxide cadmium sulphide quantum dots, amongst others, can be introduced into the nanocellulose matrix and can be applied either as charge transporters or photoactive materials in different types of solar cells. Thus, in this review, we highlight the optoelectronic properties of different photoactive materials, particularly nanocellulose-based graphene nanocomposites; their efficiencies and drawbacks were X-rayed. The effect of doping each PV material on the PV performance is also discussed. It is anticipated that the novel material would result in a reduction in the cost of solar cells, jointly enhancing their efficacy in generating environmentally friendly electricity. Since the fabrication techniques and equipment play a crucial role in the development of solar cells, the fabrication techniques of bulk-heterojunction (BHJ) cells containing a nanocellulose-based graphene composite and case studies of already fabricated BHJ PV cells with nanocellulose-based graphene composite are discussed.
Dynamic identification of important nodes in complex networks by considering local and global characteristics
Authors: Cao Mengchuan , Wu Dan , Du Pengxuan , Zhang Ting , Ahmadi Sina 
Published: March 2024

Abstract
By combining centrality measures and community detection, a better insight into the nature of the evolution of important nodes in complex networks is obtained. Meanwhile, the dynamic identification of important nodes in complex networks can be enhanced by considering both local and global characteristics. Local characteristics focus on the immediate connections and interactions of a node within its neighbourhood, while global characteristics take into account the overall structure and dynamics of the entire network. Nodes with high local centrality in dynamic networks may play crucial roles in local information spreading or influence. On the global level, community detection algorithms have a significant impact on the overall network structure and connectivity between important nodes. Hence, integrating both local and global characteristics offers a more comprehensive understanding of how nodes dynamically contribute to the functioning of complex networks. For more comprehensive analysis of complex networks, this article identifies important nodes by considering local and global characteristics (INLGC). For local characteristic, INLGC develops a centrality measure based on network constraint coefficient, which can provide a better understanding of the relationship between neighbouring nodes. For global characteristic, INLGC develops a community detection method to improve the resolution of ranking important nodes. Extensive experiments have been conducted on several real-world datasets and various performance metrics have been evaluated based on the susceptible–infected–recovered model. The simulation results show that INLGC provides more competitive advantages in precision and resolution.
Case report: acute myocarditis complicated with persistent complete heart block: a clinical dilemma when myocardial inflammation remains
Authors: Lima Maria Rita , Custódio Pedro , Tralhão António , Trabulo Marisa 
Published: March 2024

Abstract

Background
Atrioventricular conduction abnormalities due to acute myocarditis are typically transient and do not require ventricular pacing beyond the acute phase of myocardial inflammation. Notwithstanding, selective injury and necrosis of the heart’s conduction system may lead to persistent complete heart block (CHB) requiring device implantation.

Case summary
We report the case of a 23-year-old man with acute lymphocytic myocarditis complicated by cardiogenic shock, cardiac arrest due to ventricular fibrillation, and persistent CHB. Endomyocardial biopsy (EMB) showed signs of subacute myocarditis, with no evidence of granulomas or giant cells, nor criteria for eosinophilic myocarditis. Aetiological work-up found serological evidence of previous Epstein–Barr virus (EBV) infection; Borrelia burgdorferi serology for Lyme disease was negative. The real time–polymerase chain reaction (RT–PCR) of the EMB was positive for the presence of EBV DNA, but in situ hybridization for viral ribosomal RNA (rRNA) was negative. The patient progressed favourably, and left ventricle ejection fraction recovered 2 weeks after initial presentation. However, CHB persisted for more than 3 weeks, and the patient underwent definitive pacemaker implantation with left bundle branch pacing.

Discussion
Persistent CHB after acute myocarditis is generally considered unlikely, but in rare circumstances the damage portended by inflammation may be irreversible. Besides the play of chance, possible mechanisms behind the apparent predilection for the conduction system of the myocardium warrant further research.
Biventricular Takotsubo syndrome complicated with cardiogenic shock and ventricular septal rupture: a case report
Authors: Paiva Mariana Sousa , Maltês Sérgio , Brízido Catarina , Madeira Márcio , Tralhão António 
Published: March 2024

Abstract

Background
Takotsubo syndrome (TTS) mimics acute coronary syndromes but can lead to serious cardiac complications, emphasizing the need for improved understanding and management.

Case summary
We describe a TTS case presented with cardiogenic shock due to ventricular septal rupture (VSR). Successful treatment involved mechanical circulatory support followed by VSR surgical closure.

Discussion
Ventricular septal rupture is the rarest and deadliest complication associated with TTS. Prompt recognition and a multidisciplinary approach are crucial to achieve the best possible outcome.