bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonlinear ultrasound in liquid containing multiple coated microbubbles: effect of buckling and rupture of viscoelastic shell on ultrasound propagation
Authors: Nguyen Quoc Nam , Kanagawa Tetsuya 
Published: April 2023

AbstractWith promising applications in medical diagnosis and therapy, the behavior of shell-encapsula-ted ultrasound contrast agents (UCAs) has attracted considerable attention. Currently, second-generation contrast agents stabilized by a phospholipid membrane are widely used and studies have focused on the dynamics of single phospholipid shell-encapsulated microbubbles. To improve the safety and the efficiency of the methods using the propagation or targeted ultrasound, a better understanding of the propagation of ultrasound in liquids containing multiple encapsulated microbubbles is required. By incorporating the Marmottant–Gompertz model into the multiple scale analysis of two-phase model, this study derived a Korteweg–de Vries–Burgers equation as a weakly nonlinear wave equation for one-dimensional ultrasound in bubbly liquids. It was found that the wave propagation characteristics changed with the initial surface tension, highlighting two notable features of the phospholipid shell: buckling and rupture. These results may provide insights into the suitable state of microbubbles, and better control of ultrasound for medical applications, particularly those that require high precision.
Mortality Modeling of Partially Observed Cohorts Using Administrative Death Records
Authors: Goldstein Joshua R. , Osborne Maria , Atherwood Serge , Breen Casey F. 
Published: April 2023

AbstractNew advances in data linkage provide mortality researchers with access to administrative datasets with millions of mortality records and rich demographic covariates. Although these new datasets allow for high-resolution mortality research, administrative mortality records often have technical limitations, such as limited mortality coverage windows and incomplete observation of survivors. We describe a method for fitting truncated distributions that can be used for estimating mortality differentials in administrative data. We apply this method to the CenSoc datasets, which link the United States 1940 Census records to Social Security administrative mortality records. Our approach may be useful in other contexts where administrative data on deaths are available. As a companion to the paper, we release the R packagegompertztrunc, which implements the methods introduced in this paper.
From Contact Relations to Modal Operators, and Back
Authors: Gruszczyński Rafał , Menchón Paula 
Published: April 2023

AbstractOne of the standard axioms for Boolean contact algebras says that if a regionxis in contact with the join ofyandz, thenxis in contact with at least one of the two regions. Our intention is to examine a stronger version of this axiom according to which ifxis in contact with the supremum of some familySof regions, then there is a yinSthat is in contact withx. We study a modal possibility operator which is definable in complete algebras in the presence of the aforementioned axiom, and we prove that the class of complete algebras satisfying the axiom is closely related to the class of modal KTB-algebras. We also demonstrate that in the class of complete extensional contact algebras the axiom is equivalent to the statement:every region is isolated. Finally, we present an interpretation of the modal operator in the class of the so-calledresolution contact algebras.
Some Observations on the FGH Theorem
Authors: Kurahashi Taishi 
Published: April 2023

AbstractWe investigate the Friedman–Goldfarb–Harrington theorem from two perspectives. Firstly, in the frameworks of classical and modal propositional logics, we study the forms of sentences whose existence is guaranteed by the FGH theorem. Secondly, we prove some variations of the FGH theorem with respect to Rosser provability predicates.
An Automated Design Flow for Adaptive Neural Network Hardware Accelerators
Authors: Ratto Francesco , Máinez Ángela Porras , Sau Carlo , Meloni Paolo , Deriu Gianfranco , Delucchi Stefano , Massa Massimo , Raffo Luigi , Palumbo Francesca 
Published: April 2023

AbstractImage and video processing are one of the main driving application fields for the latest technology advancement of computing platforms, especially considering the adoption of neural networks for classification purposes. With the advent of Cyber Physical Systems, the design of devices for efficiently executing such applications became more challenging, due to the increase of the requirements to be considered, of the functionalities to be supported, as well as to the demand for adaptivity and connectivity. Heterogeneous computing and design automation are then turning into essential. The former guarantees a variegated set of features under strict constraints (e.g., by adopting hardware acceleration), and the latter limits development time and cost (e.g., by exploiting model-based design). In this context, the literature is still lacking adequate tooling for the design and management of neural network hardware accelerators, which can be adaptable and customizable at runtime according to the user needs. In this work, a novel almost automated toolchain based on the Open Neural Network eXchange format is presented, allowing the user to shape adaptivity right on the network model and to deploy it on a runtime reconfigurable accelerator. As a proof of concept, a Convolutional Neural Network for human/animal classification is adopted to derive a Field Programmable Gate Array accelerator capable of trading execution time for power by changing the resources involved in the computation. The resulting accelerator, when necessary, can consume 30% less power on each layer, taking about overall 8% more time to classify an image.
Genetic breaks caused by ancient forest fragmentation: phylogeography of Staudtia kamerunensis (Myristicaceae) reveals distinct clusters in the Congo Basin
Authors: Vanden Abeele Samuel , Matvijev Katarina , Hardy Olivier J. , Assumani Dieu-Merci , Angoboy Ilondea Bhély , Beeckman Hans , Bouka Gael U. D. , Boupoya Clay Archange , Deklerck Victor , Flot Jean-François , Gillet Jean-François , Kamdem Narcisse Guy , Lisingo Janvier , Monthe Franck , Sonké Bonaventure , Janssens Steven B. 
Published: April 2023

AbstractDocumenting species and population diversity is becoming increasingly important as the destruction and degradation of natural ecosystems are leading to a worldwide biodiversity loss. Despite the rapid development of genetic tools, many species remain undocumented and little is known about the diversity of individuals and populations, especially for tropical African plants. In this study, we aim to identify putative hidden species and/or differentiated populations in the tropical African treeStaudtia kamerunensisWarb. (Myristicaceae), a widespread species characterized by a high morphological diversity and a complex taxonomical history. Historical herbarium vouchers were sampled and leaf or cambium samples were collected in the field, dried in silica gel, and subsequently genotyped at 14 microsatellite loci (SSRs), as well as sequenced for two nuclear genes (At103,Agt1) and one plastid region (psbA-trnH). These genetic data were then analyzed using Bayesian clustering, population genetics, and the construction of haplowebs to assess genetic clustering patterns, the distribution of genetic diversity, and genetic differentiation among populations. Multiple genetically differentiated clusters were observed in parapatry throughout Central Africa. Genetic diversity was high and similar among these clusters, apart from the most differentiated populations in southeast Democratic Republic of the Congo (DR Congo), which showed lower levels of genetic diversity. The genetic breaks detected betweenS. kamerunensispopulations are likely not indicative of hidden species but rather result from ancient rainforest fragmentation during cold and dry periods in the Pliocene and/or Pleistocene. The strong genetic divergence between populations in southeast DR Congo could be the result of an ongoing speciation linked to ecological niche differentiation.
Therapeutenwechsel in der Sozialtherapie
Authors: Heintzsch Ronja , Weckmann Anette Claudia 
Published: April 2023

ZusammenfassungDie therapeutische Allianz gilt mithin als wichtigster Wirkfaktor für den Erfolg von Psychotherapie. Kommt es zu (geplanten oder ungeplanten) Therapeut*innenwechseln innerhalb der Behandlung, muss eine therapeutische Beziehung oft mühsam neu aufgebaut werden. Auch in der Sozialtherapie können solche Wechsel stattfinden, indes ist jedoch wenig über die Gründe und Häufigkeiten selbiger bekannt. In einer qualitativen Interviewbefragung mit 19 Expert*innen psychologischer Fachdienste aus sozialtherapeutischen Anstalten in 5 Bundesländern wurden Gründe für Therapeut*innenwechsel erfragt. Als häufigste Gründe für Wechsel ergaben sich hierbei allgemeine Interaktionsprobleme, Personalwechsel, Elternzeit sowie Stationswechsel des Inhaftierten oder Behandelnden. Dabei berichtete die Mehrzahl der Interviewten, dass Wechsel gar nicht oder nur in Ausnahmefällen von Inhaftierten initiiert werden konnten. Wechsel wurden zudem vor allem bei Inhaftierten mit Bindungsstörungen vermieden. Möglichkeiten zur Steigerung von Kontinuität innerhalb der Sozialtherapie sowie Grenzen der Entscheidungsfreiheit von Inhaftierten werden abschließend diskutiert.
Resonance assignments of the microtubule-binding domain of the microtubule-associated protein 7 (MAP7)
Authors: Adler Agnes , Kjaer Lenette F. , Beugelink J. Wouter , Baldus Marc , van Ingen Hugo 
Published: April 2023

AbstractThe microtubule-associated protein 7 (MAP7) is a protein involved in cargo transport along microtubules (MTs) by interacting with kinesin-1 through the C-terminal kinesin-binding domain. Moreover, the protein is reported to stabilize MT, thereby playing a key role in axonal branch development. An important element for this latter function is the 112 amino-acid long N-terminal microtubule-binding domain (MTBD) of MAP7. Here we report NMR backbone and side-chain assignments that suggest a primarily alpha-helical secondary fold of this MTBD in solution. The MTBD contains a central long α-helical segment that includes a short four-residue ‘hinge’ sequence with decreased helicity and increased flexibility. Our data represent a first step towards analysing the complex interaction of MAP7 with MTs at an atomic level via NMR spectroscopy.
Correction to: Layered Networks, Equilibrium Dynamics, and Stable Coalitions
Authors: Fu Jing , Page Frank , Zigrand Jean-Pierre 
Published: April 2023

AbstractAn important aspect of network dynamics that has been missing from our understanding of network dynamics in various applied settings is the influence of strategic behavior in determining equilibrium network dynamics. Our main objective hears to say what we can regarding the emergence of stable club networks—and therefore, stable coalition structures—based on the stability properties of strategically determined equilibrium network formation dynamics. Because club networks are layered networks, our work here can be thought of as a first work on the strategic dynamics of layered networks. In addition to constructing a discounted stochastic game model (i.e., a DSG model) of club network formation, (1) we show that our DSG of network formation possesses a stationary Markov perfect equilibrium in players’ membership-action strategies; (2) we identify the assumptions on primitives which ensure that the induced equilibrium Markov process of layered club network formation satisfies the Tweedie Stability Conditions (Tweedie in Stoch Processes Their Appl 92:345–354) and (3) we show that, as a consequence, the equilibrium Markov network formation process generates a unique decomposition of the set of state-network pairs into a transient set together with finitely many basins of attraction. Moreover, we show that if there is a basin containing a vio set (a visited infinitely often set) of club networks sufficiently close together, then the coalition structures across club networks in the vio set will be the same (i.e., closeness across networks in a vio set leads to invariance in coalition structure across networks in a vio set).
Non-linear Young equations in the plane and pathwise regularization by noise for the stochastic wave equation
Authors: Bechtold Florian , Harang Fabian A. , Rana Nimit 
Published: April 2023

AbstractWe study pathwise regularization by noise for equations on the plane in the spirit of the framework outlined by Catellier and Gubinelli (Stoch Process Appl 126(8):2323–2366, 2016). To this end, we extend the notion of non-linear Young equations to a two dimensional domain and prove existence and uniqueness of such equations. This concept is then used in order to prove regularization by noise for stochastic equations on the plane. The statement of regularization by noise is formulated in terms of the regularity of the local time associated to the perturbing stochastic field. For this, we provide two quantified example: a fractional Brownian sheet and the sum of two one-parameter fractional Brownian motions. As a further illustration of our regularization results, we also prove well-posedness of a 1D non-linear wave equation with a noisy boundary given by fractional Brownian motions. A discussion of open problems and further investigations is provided.
Feature selection using non-dominant features-guided search for gene expression profile data
Authors: Pan Xiaoying , Sun Jun , Yu Huimin , Xue Yufeng 
Published: April 2023

AbstractGene expression profile data have high-dimensionality with a small number of samples. These data characteristics lead to a long training time and low performance in predictive model construction. To address this issue, the paper proposes a feature selection algorithm using non-dominant feature-guide search. The algorithm adopts a filtering framework based on feature sorting and search strategy to overcome the problems of long training time and poor performance. First, the feature pre-selection is completed according to the calculated feature category correlation. Second, a multi-objective optimization feature selection model is constructed. Non-dominant features are defined according to the Pareto dominance theory. Combined with the bidirectional search strategy, the Pareto dominance features under the current category maximum relevance feature are removed one by one. Finally, the optimal feature subset with maximum correlation and minimum redundancy is obtained. Experimental results on six gene expression data sets show that the algorithm is much better than Fisher score, maximum information coefficient, composition of feature relevancy, mini-batch K-means normalized mutual information feature inclusion, and max-Relevance and Min-Redundancy algorithms. Compared to feature selection method based on maximum information coefficient and approximate Markov blanket, the algorithm not only has high computational efficiency but also can obtain better classification capabilities in a smaller dimension.
Dynamic role discovery and assignment in multi-agent task decomposition
Authors: Xia Yu , Zhu Junwu , Zhu Liucun 
Published: April 2023

AbstractEffective multi-agent teamwork can be facilitated by using personas to decompose goals into lower-level team subtasks through a shared understanding of multi-agent tasks. However, traditional methods for role discovery and assignment are not scalable and fail to adapt to dynamic changes in the environment. To solve this problem, we propose a new framework for learning dynamic role discovery and assignment. We first introduce an action encoder to construct a vector representation for each action based on its characteristics, and define and classify roles from a more comprehensive perspective based on both action differences and action contributions. To rationally assign roles to agents, we propose a representation-based role selection policy based on consideration of role differences and reward horizons, which enables agents to play roles dynamically by dynamically assigning agents with similar abilities to play the same role. Agents playing the same role share their learning of the role, and different roles correspond to different action spaces. We also introduce regularizers to increase the differences between roles and stabilize training by preventing agents from changing roles frequently. Role selection and role policy integrate action representations and role differences in a restricted action space, improving learning efficiency. We conducted experiments in the SMAC benchmark and showed that our method enables effective role discovery and assignment, outperforming the baseline on four of the six scenarios, with an average improvement in win rate of 20$$\%$$
%
, and is effective in hard and super hard maps. We also conduct ablation experiments to demonstrate the importance of each component in our approach.
Hformer: highly efficient vision transformer for low-dose CT denoising
Authors: Zhang Shi-Yu , Wang Zhao-Xuan , Yang Hai-Bo , Chen Yi-Lun , Li Yang , Pan Quan , Wang Hong-Kai , Zhao Cheng-Xin 
Published: April 2023

AbstractIn this paper, we propose Hformer, a novel supervised learning model for low-dose computer tomography (LDCT) denoising. Hformer combines the strengths of convolutional neural networks for local feature extraction and transformer models for global feature capture. The performance of Hformer was verified and evaluated based on the AAPM-Mayo Clinic LDCT Grand Challenge Dataset. Compared with the former representative state-of-the-art (SOTA) model designs under different architectures, Hformer achieved optimal metrics without requiring a large number of learning parameters, with metrics of 33.4405 PSNR, 8.6956 RMSE, and 0.9163 SSIM. The experiments demonstrated designed Hformer is a SOTA model for noise suppression, structure preservation, and lesion detection.
Breathers and rogue waves for semilinear curl-curl wave equations
Authors: Plum Michael , Reichel Wolfgang 
Published: April 2023

AbstractWe consider localized solutions of variants of the semilinear curl-curl wave equation $$s(x) \partial _t^2 U +\nabla \times \nabla \times U + q(x) U \pm V(x) \vert U \vert ^{p-1} U = 0$$

s

(
x
)

∂
t
2

U
+
∇
×
∇
×
U
+
q

(
x
)

U
±
V

(
x
)

|
U
|

p
-
1

U
=
0

for $$(x,t)\in {\mathbb {R}}^3\times {\mathbb {R}}$$

(
x
,
t
)

∈

R

3

×
R

and arbitrary $$p>1$$

p
>
1

. Depending on the coefficients s, q, V we can prove the existence of three types of localized solutions: time-periodic solutions decaying to 0 at spatial infinity, time-periodic solutions tending to a nontrivial profile at spatial infinity (both types are called breathers), and rogue waves which converge to 0 both at spatial and temporal infinity. Our solutions are weak solutions and take the form of gradient fields. Thus they belong to the kernel of the curl-operator so that due to the structural assumptions on the coefficients the semilinear wave equation is reduced to an ODE. Since the space dependence in the ODE is just a parametric dependence we can analyze the ODE by phase plane techniques and thus establish the existence of the localized waves described above. Noteworthy side effects of our analysis are the existence of compact support breathers and the fact that one localized wave solution U(x, t) already generates a full continuum of phase-shifted solutions $$U(x,t+b(x))$$

U
(
x
,
t
+
b
(
x
)
)

where the continuous function $$b:{\mathbb {R}}^3\rightarrow {\mathbb {R}}$$

b
:

R

3

→
R

belongs to a suitable admissible family.
Digital twin-enabled smart facility management: A bibliometric review
Authors: Hakimi Obaidullah , Liu Hexu , Abudayyeh Osama 
Published: April 2023

AbstractIn recent years, the architecture, engineering, construction, and facility management (FM) industries have been applying various emerging digital technologies to facilitate the design, construction, and management of infrastructure facilities. Digital twin (DT) has emerged as a solution for enabling real-time data acquisition, transfer, analysis, and utilization for improved decision-making toward smart FM. Substantial research on DT for FM has been undertaken in the past decade. This paper presents a bibliometric analysis of the literature on DT for FM. A total of 248 research articles are obtained from the Scopus and Web of Science databases. VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence, citation, and co-authorship networks; furthermore, the research topics, authors, sources, and countries contributing to the use of DT for FM are identified. The findings show that the current research of DT in FM focuses on building information modeling-based FM, artificial intelligence (AI)-based predictive maintenance, real-time cyber–physical system data integration, and facility lifecycle asset management. Several areas, such as AI-based real-time asset prognostics and health management, virtual-based intelligent infrastructure monitoring, deep learning-aided continuous improvement of the FM systems, semantically rich data interoperability throughout the facility lifecycle, and autonomous control feedback, need to be further studied. This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape, state-of-the-art research trends, and future needs with regard to DT in FM.
Toward combined photobiological–photochemical formation of kerosene-type biofuels: which small 1,3-diene photodimerizes most efficiently?
Authors: Vajravel Sindhujaa , Cid Gomes Leandro , Rana Anup , Ottosson Henrik 
Published: April 2023

AbstractA transition from fossil- to bio-based hydrocarbon fuels is required to reduce greenhouse gas emissions; yet, traditional biomass cultivation for biofuel production competes with food production and impacts negatively on biodiversity. Recently, we reported a proof-of-principle study of a two-step photobiological–photochemical approach to kerosene biofuels in which a volatile hydrocarbon (isoprene) is produced by photosynthetic cyanobacteria, followed by its photochemical dimerization into C10 hydrocarbons. Both steps can utilize solar irradiation. Here, we report the triplet state (T1)-sensitized photodimerization of a broader set of small 1,3-dienes to identify which structural features lead to rapid photodimerization. Neat 1,3-cyclohexadiene gave the highest yield (93%) after 24 h of irradiation at 365 nm, followed by isoprene (66%). The long triplet lifetime of 1,3-cyclohexadiene, which is two orders of magnitude longer than those of acyclic dienes, is key to its high photoreactivity and stem from its planar T1 state structure. In contrast, while isoprene is conformationally flexible, it has both photochemical and photobiological advantages, as it is the most reactive among the volatile 1,3-dienes and it can be produced by cyanobacteria. Finally, we explored the influence of solvent viscosity, diene concentration, and triplet sensitizer loading on the photodimerization, with a focus on conditions that are amenable when the dienes are produced photobiologically. Our findings should be useful for the further development of the two-step photobiological–photochemical approach to kerosene biofuels.
Graphical abstract