bluuebunny/crossref_metadata_2025_split huggingface dataset:
Charge‐transfer‐regulated bimetal ferrocene‐based organic frameworks for promoting electrocatalytic oxygen evolution
Authors: Hu Jinsong , Xu Qiaoling , Wang Xiaoyu , Huang Xinhua , Zhou Chunhui , Ye Ying , Zhang Lei , Pang Huan 
Published: January 2023

AbstractThe ferrocene (Fc)‐based metal–organic frameworks (MOFs) are regarded as compelling platforms for the construction of efficient and robust oxygen evolution reaction (OER) electrocatalysts due to their superior conductivity and flexible electronic structure. Herein, density functional theory simulations were addressed to predict the electronic structure regulations of CoFc‐MOF by nickel doping, which demonstrated that the well‐proposed CoNiFc‐MOFs delivered a small energy barrier, promoted conductivity, and well‐regulated d‐band center. Inspired by these, a series of sea‐urchin‐like CoNiFc‐MOFs were successfully synthesized via a facile solvothermal method. Moreover, the synchrotron X‐ray and X‐ray photoelectron spectroscopy measurements manifested that the introduction of nickel could tailor the electronic structure of the catalyst and induce the directional transfer of electrons, thus optimizing the rate‐determining step of *O → *OOH during the OER process and yielding decent overpotentials of 209 and 252 mV at 10 and 200 mA cm−2, respectively, with a small Tafel slope of 39 mV dec−1. This work presents a new paradigm for developing highly efficient and durable MOF‐based electrocatalysts for OER.
Anisotropic Crystal Growth of Layered Vanadates with Bulky Interlayer Cations
Authors: Ban Takayuki , Konishi Karin , Mizuno Motoki , Takai‐Yamashita Chika , Ohya Yutaka 
Published: January 2023

AbstractThe crystallization behavior of layered tetramethylammonium octavanadate [N(CH3)4]V8O20 including both V4+ and V5+ during hydrothermal synthesis is examined. According to the Bravais–Friedel–Donnay–Harker law, layered metalate crystals with bulky interlayer cations tend to have shapes with large surfaces parallel to the metalate layers; however, small rodlike crystals elongated along the direction normal to the vanadate layers are produced at the early stage of crystallization. As the crystallization proceeds, the crystal shapes are changed to large belt‐like and platelike ones with the largest surfaces parallel to the vanadate layers. If sols containing only the small rodlike crystals formed at the early stage of crystallization are prepared, they will be useful for the fabrication of thin films with the vanadate layers vertically aligned normal to the substrate surface. Furthermore, ultrasonic treatment of the aqueous suspensions of the [N(CH3)4]V8O20 crystals leads to their exfoliation to a thickness of several tens of nanometers. For the relation between crystal shapes and crystallographic directions, the small crystals obtained using ultrasonic treatment are different from the ones formed at the early stage of crystallization. Probably, the morphology of the small rodlike crystals formed at the early stage of crystallization is attributed to the metastable growth form.
An arbitrary Lagrangian–Eulerian discontinuous Galerkin method for two‐dimensional compressible flows on adaptive quadrilateral meshes
Authors: Zhao Xiaolong , Huang Chaobao , Yu Xijun , Zou Shijun , Qing Fang 
Published: January 2023

AbstractIn this article, a discontinuous Galerkin (DG) scheme on the adaptive quadrilateral meshes is proposed to simulate two‐dimensional compressible flows in the direct arbitrary Lagrangian–Eulerian (ALE) framework. In our scheme, the Euler equations are discretized in the reference element with the help of a bilinear map. A kind of Taylor expansion basis functions in the reference element is used to construct the interpolation polynomials of variables. We describe the property that the material derivatives of the basis functions used in the DG discretization are equal to zero, with which the scheme is simplified. Furthermore, the mesh velocity in our ALE framework is obtained by implementing the approach of mesh movement based on the variational principle from [Adaptive mesh methods for one‐ and two‐dimensional hyperbolic conservation laws. SIAM J Numer Anal. 2003;41:487–515]. This approach of mesh movement automatically concentrates the mesh nodes near the regions with large gradient values of the variables and can greatly improve the resolution of the solution near these regions. In addition, a WENO (weighted essentially non‐oscillatory) reconstruction helps our scheme remove the numerical oscillations. Some numerical examples are presented to demonstrate the accuracy, high resolution, and robustness of our scheme.
Agronomic assessment of two populations of intermediate wheatgrass—Kernza® (Thinopyrum intermedium) in temperate South America
Authors: Locatelli Andres , Gutierrez Lucia , Duchene Olivier , Speranza Pablo R. , Picasso Risso Valentin D. 
Published: January 2023

AbstractBackgroundKernza® intermediate wheatgrass is a perennial grain and forage crop that can provide several ecosystem services. Major research efforts focused on Kernza have taken place in high latitudes. The goal of this study was to evaluate, for the first time, the agronomic performance of Kernza in a low‐latitude region with mild winters.MethodsA KS‐cycle 4 Kernza population (A) was planted in spring in Wisconsin, USA, and selected in one cycle for lower vernalization requirements, obtaining a new population (B). These two populations, at three nitrogen (N) fertilization rates, were evaluated in a full factorial, completely randomized field experiment in Uruguay over 2 years.ResultsThe populations were similar in grain yields and flowering time in the 1st year, but population B had 63% lower grain yield in the 2nd year and 20% lower forage yield throughout the experiment. Increasing the N rate to 160 kg ha−1 led to a 63% increase in grain yield and 28% increase in forage yield across populations. Forage yields and nutritive values were similar to those reported in the northern hemisphere. However, grain yields for both the 1st (316 kg ha−1) and 2nd year (41 kg ha−1) were lower due to reduced flowering and weed competition.ConclusionsExpansion of Kernza to lower‐latitude regions will require further breeding to improve reproductive performance.
Effects of scenario‐based attribution on collective emotions and stigma toward persons with COVID‐19: A cross‐sectional survey
Authors: Boo Hye In , Choi Yun‐Kyeung 
Published: January 2023

AbstractBackground and AimsDuring this COVID‐19 pandemic, many people experience and share emotions such as fear, anxiety, sadness, anger, and disgust, which can be regarded as collective emotions. This study investigated the effects of scenario‐based attribution for serious diseases on collective emotions and social stigma.MethodsParticipants were 297 healthy adults who met two conditions: (1) not having tested positive for COVID‐19 (including their family members or close friends) and no experience of self‐quarantine; and (2) not having been diagnosed with lung cancer, and not having family members or close friends diagnosed with it. Three hundred participants were recruited, through a company conducting online surveys. A total of 297 data sets were analyzed, excluding data supplied by three participants who might have responded unreliably to the filler question. Scenarios were recorded according to attribution type (internal vs. external) and disease (COVID‐19 vs. lung cancer). A 2 × 2 factorial design was used, whereby participants were randomly assigned to one of four conditions.ResultsThe COVID‐19 condition showed higher scores on the perceived risk and fear of the disease compared to the lung cancer one. The COVID‐19/internal attribution condition showed the highest scores for fear and anger toward scenario characters, and the lung cancer/external attribution condition showed higher sympathy scores than other conditions. Although attribution to COVID‐19 was not directly related to social stigma, it could evoke negative emotions toward infected people.ConclusionThe findings suggest that attributions of serious diseases such as COVID‐19 to infected persons can influence collective emotions and the level of social stigma associated with the disease. Attention to the collective emotions and stigma associated with disease is a key component for communities and countries to recover from and respond to its impacts.
Prognosis of COVID‐19 patients using lab tests: A data mining approach
Authors: Khounraz Fariba , Khodadoost Mahmood , Gholamzadeh Saeid , Pourhamidi Rashed , Baniasadi Tayebeh , Jafarbigloo Aida , Mohammadi Gohar , Ahmadi Mahnaz , Ayyoubzadeh Seyed Mohammad 
Published: January 2023

AbstractBackgroundThe rapid prevalence of coronavirus disease 2019 (COVID‐19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID‐19 patients using data mining techniques.MethodsIn this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other.ResultsBased on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models.ConclusionData mining methods have the potential to be used for predicting outcomes of COVID‐19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID‐19 patients.
Histopathological carcinoma classification using parallel, cross‐concatenated and grouped convolutions deep neural network
Authors: Kadirappa Ravindranath , Subbian Deivalakshmi , Ramasamy Pandeeswari , Ko Seok‐Bum 
Published: January 2023

AbstractCancer is more alarming in modern days due to its identification at later stages. Among cancers, lung, liver and colon cancers are the leading cause of untimely death. Manual cancer identification from histopathological images is time‐consuming and labour‐intensive. Thereby, computer‐aided decision support systems are desired. A deep learning model is proposed in this paper to accurately identify cancer. Convolutional neural networks have shown great ability to identify the significant patterns for cancer classification. The proposed Parallel, Cross Concatenated and Grouped Convolutions Deep Neural Network (PC2GCDN2) has been developed to obtain accurate patterns for classification. To prove the robustness of the model, it is evaluated on the KMC and TCGA‐LIHC liver dataset, LC25000 dataset for lung and colon cancer classification. The proposed PC2GCDN2 model outperforms states‐of‐the‐art methods. The model provides 5.5% improved accuracy compared to the LiverNet proposed by Aatresh et. al on the KMC dataset. On the LC25000 dataset, 2% improvement is observed compared to existing models. Performance evaluation metrics like Sensitivity, Specificity, Recall, F1‐Score and Intersection‐Over‐Union are used to evaluate the performance. To the best of our knowledge, PC2GCDN2 can be considered as gold standard for multiple histopathology image classification. PC2GCDN is able to classify the KMC and TCGA‐LIHC liver dataset with 96.4% and 98.6% accuracy, respectively, which are the best results obtained till now. The performance has been superior on LC25000 dataset with 99.5% and 100% classification accuracy on lung and colon dataset, by utilizing less than 0.5 million parameters.
Influence of international ownership on the performance of local social enterprises: Evidence from the global microfinance industry
Authors: Djan Kwame Ohene , Nyarko Samuel Anokye , Mersland Roy , Beisland Leif Atle , Nakato Linda 
Published: January 2023

AbstractInternational ownership positively impacts the social performance of social enterprises at the expense of financial performance. International owners mostly use their resources and controlling rights to improve social performance at the expense of financial performance. Current ownership theories do not address how the multidimensional utility function might affect the governance and financing of a social enterprise. There is a need to develop theories for firms with conflicting objectives.
Development of a New Care Pathway for Depression and Anxiety in Adult‐Onset Isolated Dystonia
Authors: Martino Davide , Nosratmirshekarlou Elaheh , Cothros Nicholas , Medina Escobar Alex , Goodarzi Zahra 
Published: January 2023

ABSTRACTBackgroundRecently, we identified barriers and facilitators to the screening and treatment of depressive and anxiety symptoms in adult‐onset isolated dystonia (AOID). These symptoms are common, functionally impairing, and often underdetected and undertreated.ObjectivesTo develop a care pathway for mood symptoms in AOID.MethodsWe used a multistep modified Delphi approach to seek consensus among healthcare professionals with experience of AOID on the screening, diagnosis, and treatment of mood symptoms. A combination of face‐to‐face meetings and online surveys was performed from 2019 to 2020. We created the survey and then reviewed with stakeholders before 2 rounds of Delphi surveys, all of which was finally reviewed in a consensus meeting. A purposive sample of 41 expert stakeholders from 4 Canadian provinces, including neurologists, nurses, psychiatrists, psychologists, and family physicians, was identified by the research team.ResultsThe Delphi process led to consensus on 12 statements that operationalized a pathway of care to screen for and manage depression and anxiety in people with AOID. Key actions of the pathway included yearly screening with self‐rated instruments, multidisciplinary involvement in management involving local networks of providers coordinated by movement disorders neurologists, and access to educational resources. The Delphi panel indicated the 2 core steps as the documentation of the most recent screening outcome and the documentation of a management plan for patients who were positive at the last screening.ConclusionsThis new care pathway represents a potentially useful intervention that can be used to build an integrated model of care for AOID.
Aerial Application of Organic Pellets Eliminates Lake Trout Recruitment from a Primary Spawning Reef in Yellowstone Lake
Authors: Koel Todd M. , Doepke Philip D. , MacDonald Drew J. , Thomas Nathan A. , Vender Cody W. , Glassic Hayley C. , Poole Alex S. , Guy Christopher S. , Zale Alexander V. 
Published: January 2023

Abstract
Invasive Lake Trout Salvelinus namaycush in the Yellowstone Lake ecosystem have been gillnetted since 1995 to suppress the population and allow for recovery of native Yellowstone Cutthroat Trout Oncorhynchus clarkii bouvieri. Although gillnetting is effective (Lake Trout population growth rate λ ≤ 0.6 during 2012–2022), the effort only targets free-swimming, age-2 and older Lake Trout. We developed a complementary suppression method using organic (soy and wheat) pellets to cause Lake Trout embryo mortality and reduce recruitment from spawning areas. The entire Carrington Island spawning reef (0.5 ha) was aerially treated with 3.56 and 3.00 kg/m2 of pellets in 2019 and 2020, respectively. Pellet decomposition caused dissolved oxygen concentrations to decline to lethal levels at 20 cm depth in the substrate, and pellets mostly dissipated from the reef within 12 d. Lake Trout fry trap CPUE was reduced to zero after ice-off each spring after the treatments. Prior to the treatments, 71 fry were captured during 58 trap-nights of effort in 2017–2019. After the treatments, no fry were captured during 273 trap-nights in 2020 and 2021. Lake Trout CPUE in large-mesh gill nets set near Carrington Island in September did not decline during 2017–2021 and fry were again trapped on the reef in spring 2022, suggesting that adults were not deterred from spawning there in the years after the pellet treatments. Complementary methods that increase mortality of prerecruits may allow for a reduction in gill-netting effort and the long-term costs of maintaining Lake Trout population suppression in Yellowstone Lake. Treatment of spawning areas may improve suppression efficiency for Lake Trout and invasive fish populations elsewhere because entire cohorts are targeted while immobile and temporarily concentrated in relatively small areas.
The duality of drought: Pelagic- and benthic-spawning stream fishes show opposing responses to drought in the southern Great Plains
Authors: Nguyen Erin , Mayes Kevin B. , Smith Ryan , Trungale Joe , Perkin Joshuah S. 
Published: January 2023

Abstract

Objective
Streamflow is a primary determinant of fish assemblage structure in riverine systems, but alteration of natural flow regimes can result in fish assemblage shifts through the process of environmental filtering. Because natural drought conditions reduce and homogenize streamflow in a manner comparable to projections for climate change in some regions, drought could serve as a proxy for expected future flow conditions.

Methods
We investigated the effects of drought as a temporally dynamic environmental filter of the occurrence of two guilds of fishes, benthic spawners that deposit adhesive ova along the benthic zone of rivers and pelagic spawners that release semibuoyant, nonadhesive ova into the pelagic zone of rivers. We developed species-specific random forest models to estimate annual probability of occurrence for three benthic-spawning and four pelagic-spawning minnow species at three sites in the upper Brazos River, Texas, for the period 1950–2018. We then used a generalized additive mixed-effects model to assess the relationship between drought intensity and likelihood of occurrence to test whether reproductive modes differed in response to drought (hypothesis 1) and whether response to drought was spatially (hypothesis 2) or temporally (hypothesis 3) variable.

Result
We found support for hypothesis 1 as two of four pelagic-spawning species (Shoal Chub Macrhybopsis hyostoma, Smalleye Shiner Notropis buccula) declined and two of three benthic-spawning species increased as drought intensified, support for hypothesis 2 as responses to drought varied by gauge location, and no support for hypothesis 3 as drought response was consistent for periods 1950–1979 and 1980–2018.

Conclusion
These findings offer insight into the future of riverine fish assemblages as climate change is expected to exacerbate regional drought conditions. Management of pelagic-spawning fishes during extreme drought in the southern Great Plains may require strategies such as (1) rescues of fish from drying reaches, (2) captive holding and propagation, and (3) assisted recolonization following subsidence of drought conditions.
Natal Origin Differentiation Using Eye Lens Stable Isotope Analysis
Authors: Rosinski Caroline L. , Glaid Jeff , Hahn Matt , Fetzer William W. 
Published: January 2023

Abstract
Natal origin determination can be critical to understanding the movement, recruitment, and stock discrimination of fish populations. We investigated the utility of eye lens stable isotope analysis as a natural marker to determine natal origins, specifically the differentiation of stocked versus naturally reproduced individuals. We compared isotopic values for stocked and naturally reproduced Rainbow Trout Oncorhynchus mykiss in the North Platte River (Wyoming, USA) and found that the metabolically inert core of the eye lens retained hatchery feed values as the fish grew poststocking. The stable isotope values of eye lens cores from stocked Rainbow Trout were &gt;15‰ higher for δ34S and &gt;4‰ higher for δ13C than naturally reproduced fish. The values for δ15N proved to be a less useful indicator for natal origin than sulfur and carbon. These high carbon and sulfur isotopic values were retained in the core, despite subsequent lens layers showing a dietary shift to natural prey items poststocking. Rainbow Trout formed over 10 layers at age 0, decreasing to an estimated one to two layers during subsequent years. Eye lens stable isotope analysis represents an additional tool for researchers and managers to use natural isotopic markers to accurately assess natal origins and hatchery contributions to fish populations.
A non‐uniform rational B‐splines enhanced finite element formulation based on the scaled boundary parameterization for the analysis of heterogeneous solids
Authors: Reichel Rainer , Klinkel Sven 
Published: January 2023

AbstractThe contribution is concerned with a finite element formulation for the nonlinear analysis of heterogeneous solids in boundary representation. It results in an element with an arbitrary number of curved boundary edges. The curved edges can be parametrized by, for example, non‐uniform rational B‐splines (NURBS). The presented element formulation is based on the scaling concept, which is adopted from the so‐called scaled boundary finite element method (SBFEM). In contrast to SBFEM, the proposed method uses a numerical approximation for the displacement response in scaling direction. This enables the analysis of geometrically and physically nonlinear problems in solid mechanics. The interpolation at the boundary in circumferential direction is independent of interpolation in scaling direction. Thus, different basis functions can be used for each direction, for example, NURBS basis functions in circumferential and Lagrange basis functions in radial direction. It allows the construction of polygonal elements with an arbitrary number of curved sides, which are described by either whole NURBS curves or NURBS curves' segments. The advantage of the presented element formulation is the flexibility in mesh generation. For example, using Quadtree algorithms, a fast and reliable mesh generation can be achieved. Furthermore, in connection with trimming algorithms, the element formulation allows a precise representation of the geometry even with coarse meshes. Some benchmark tests are presented to evaluate the accuracy of the proposed numerical method against analytical solutions, and a comparison to standard element formulations is given as well.
Linking discrete and continuum diffusion models: Well‐posedness and stable finite element discretizations
Authors: Schenk Christina , Portillo David , Romero Ignacio 
Published: January 2023

AbstractIn the context of mathematical modeling, it is sometimes convenient to integrate models of different nature. These types of combinations, however, might entail difficulties even when individual models are well‐understood, particularly in relation to the well‐posedness of the ensemble. In this article, we focus on combining two classes of dissimilar diffusive models: the first one defined over a continuum and the second one based on discrete equations that connect average values of the solution over disjoint subdomains. For stationary problems, we show unconditional stability of the linked problems and then the stability and convergence of its discretized counterpart when mixed finite elements are used to approximate the model on the continuum. The theoretical results are highlighted with numerical examples illustrating the effects of linking diffusive models. As a side result, we show that the methods introduced in this article can be used to infer the solution of diffusive problems with incomplete data.