bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of foam generation and stabilization in the presence of crude oil using a microfluidics system
Authors: AlYousef Zuhair , Gizzatov Ayrat , AlMatouq Hana , Jian Guoqing 
Published: January 2023

AbstractThe use of foams is a promising technique to overcome gas mobility challenges in petroleum reservoirs. Foam reduces the gas mobility by increasing the gas apparent viscosity and reducing its relative permeability. A major challenge facing foam application in reservoirs is its long-term stability. Foam effectiveness and stability depends on several factors and will typically diminish over time due to degradation as well as the foam-rock-oil interactions. In this study, the effect of crude oil on CO2-foam stability and mobility will be investigated using in-house build microfluidics system developed for rapid prescreening of chemical formulations. Two-phase flow emulsification test (oil-surfactant solutions) and dynamic foam tests (in the absence and presence of crude oil) were conducted to perform a comparative assessment for different surfactant solutions. A microfluidics device was used to evaluate the foam strength in the presence and absence of crude oil. The assessment was conducted using five surfactant formulations and different oil fractions. The role of foam quality (volume of gas/total volume) on foam stability was also addressed in this study. The mobility reduction factor (MRF) for CO2-foam was measured in the absence and presence of crude oil using high salinity water and at elevated temperatures. The results indicated that foam stability has an inverse relationship with the amount of crude oil. Crude oil has a detrimental effect on foams, and foam stability decreased as the amount of crude oil was increased. Depending on the surfactant type, the existence of crude oil in porous media, even at very low concentrations of 5% can significantly impact the foam stability and strength. The oil can act as an antifoaming agent. It enters the thin aqueous film and destabilizes it. This resulted in a lower foam viscosity and less stable foams. Thus, the CO2 MRF dropped significantly in the presence of higher oil fractions. This study also demonstrated that in-house assembled microfluidics system allows for a rapid and cost-efficient screening of formulations.
New polychlorinated bibenzyls from Rhododendron minutiflorum
Authors: Zhu Yang-Li , Deng Li , Tang Yu , Fan Xian-Zhe , Han Yang , Pan Mei , Zhang Li-Jun , Liao Hai-Bing 
Published: January 2023

AbstractFive new polychlorinated bibenzyls (1–5) along with 3 known compounds (6–8) were isolated from the stems and leaves of Rhododendronminutiflorum. The chemical structures of all the isolates were determined by spectroscopic methods, and compounds 1 and 2 were further verified by single-crystal X-ray diffraction analyses. Compounds 1–5 were halogenated compounds which bear three to five chlorine atoms in their chemical structures. Biologically, compounds 2, 5 and 6 showed varying degrees of toxicity toward the Asian citrus psyllid (Diaphorinacitri) with LD50 values 27.15, 17.02 and 16.20 mg/L, respectively. These values were comparable to the positive control matrine (LD50 = 11.86 mg/L), which were calculated using observations on day 6. Meanwhile, compound 4 had α-glucosidase inhibitory activity with IC50 value of 17.87 ± 0.74 μM.
Graphical Abstract
Multiple sparse detection-based evolutionary algorithm for large-scale sparse multiobjective optimization problems
Authors: Ren Jin , Qiu Feiyue , Hu Huizhen 
Published: January 2023

AbstractSparse multiobjective optimization problems are common in practical applications. Such problems are characterized by large-scale decision variables and sparse optimal solutions. General large-scale multiobjective optimization problems (LSMOPs) have been extensively studied for many years. They can be well solved by many excellent custom algorithms. However, when these algorithms are used to deal with sparse LSMOPs, they often encounter difficulties because the sparse nature of the problem is not considered. Therefore, aiming at sparse LSMOPs, an algorithm based on multiple sparse detection is proposed in this paper. The algorithm applies an adaptive sparse genetic operator that can generate sparse solutions by detecting the sparsity of individuals. To improve the deficiency of sparse detection caused by local detection, an enhanced sparse detection (ESD) strategy is proposed in this paper. The strategy uses binary coefficient vectors to integrate the masks of nondominated solutions. Essentially, the mask is globally and deeply optimized by coefficient vectors to enhance the sparsity of the solutions. In addition, the algorithm adopts an improved weighted optimization strategy to fully optimize the key nonzero variables to balance exploration and optimization. Finally, the proposed algorithm is named MOEA-ESD and is compared to the current state-of-the-art algorithm to verify its effectiveness.
Predicting melt track geometry and part density in laser powder bed fusion of metals using machine learning
Authors: Kuehne Maxim , Bartsch Katharina , Bossen Bastian , Emmelmann Claus 
Published: January 2023

AbstractLaser powder bed fusion of metals (PBF-LB/M) is a process widely used in additive manufacturing (AM). It is highly sensitive to its process parameters directly determining the quality of the components. Hence, optimal parameters are needed to ensure the highest part quality. However, current approaches such as experimental investigation and the numerical simulation of the process are time-consuming and costly, requiring more efficient ways for parameter optimization. In this work, the use of machine learning (ML) for parameter search is investigated based on the influence of laser power and speed on simulated melt pool dimensions and experimentally determined part density. In total, four machine learning algorithms are considered. The models are trained to predict the melt pool size and part density based on the process parameters. The accuracy is evaluated based on the deviation of the prediction from the actual value. The models are implemented in python using the scikit-learn library. The results show that ML models provide generalized predictions with small errors for both the melt pool dimensions and the part density, demonstrating the potential of ML in AM. The main limitation is data collection, which is still done experimentally or simulatively. However, the results show that ML provides an opportunity for more efficient parameter optimization in PBF-LB/M.
A marine bird (sulidae, Aves) from the Langhian (middle Miocene) of Penedo beach (Setúbal Peninsula—SW Portugal) and its paleoenvironmental context
Authors: Figueiredo Silvério , de Carvalho Carlos Neto , Cachão Mário , Fonseca Alexandre 
Published: January 2023

AbstractThe fossil remains of birds from the Miocene of Portugal are scarce, encompassing a total of twelve specimens, from nine paleontological outcrops located in Leiria (central Portugal), southern sector of Setúbal Peninsula and along the lower Tagus Basin. This study focuses on a new specimen found in the Praia do Penedo Norte (Sesimbra) coastal cliff corresponding to a coracoid bone, attributed toMorussp., a sulid bird, biostratigraphically framed by calcareous nannofossils within the middle Miocene (Langhian).
GENEREIT: generating multi-talented reinforcement learning agents
Authors: Lazaridis Aristotelis , Vlahavas Ioannis 
Published: January 2023

AbstractCreating an intelligent system that is able to generalize and reach human or above-human performance in a variety of tasks will be part of the crowning achievement of Artificial General Intelligence. However, even though many steps have been taken towards this direction, they have critical shortcomings that prevent the research community from drawing a clear path towards that goal, such as limited learning capacity of a model, sample-inefficiency or low overall performance. In this paper, we propose GENEREIT, a meta-Reinforcement Learning model in which a single Deep Reinforcement Learning agent (meta-learner) is able to produce high-performance agents (inner-learners) for solving different environments under a single training session, in a sample-efficient way, as shown by primary results in a set of various toy-like environments. This is partially due to the fixed subset selection strategy implementation that allows the meta-learner to focus on tuning specific traits of the generated agents rather than tuning them completely. This, combined with our system’s modular design for introducing higher levels in the meta-learning hierarchy, can also be potentially immune to catastrophic forgetting and provide ample learning capacity.
High-Order Method with Moving Frames to Compute the Covariant Derivatives of Vectors on General 2D Curved Surfaces
Authors: Chun Sehun 
Published: January 2023

AbstractThe covariant derivative is a generalization of differentiating vectors. The Euclidean derivative is a special case of the covariant derivative in Euclidean space. The covariant derivative gathers broad attention, particularly when computing vector derivatives on curved surfaces and volumes in various applications. Covariant derivatives have been computed using the metric tensor from the analytically known curved axes. However, deriving the global axis for the domain has been mathematically and computationally challenging for an arbitrary two-dimensional (2D) surface. Consequently, computing the covariant derivative has been difficult or even impossible. A novel high-order numerical scheme is proposed for computing the covariant derivative on any 2D curved surface. A set of orthonormal vectors, known as moving frames, expand vectors to compute accurately covariant derivatives on 2D curved surfaces. The proposed scheme does not require the construction of curved axes for the metric tensor or the Christoffel symbols. The connectivity given by the Christoffel symbols is equivalently provided by the attitude matrix of orthonormal moving frames. Consequently, the proposed scheme can be extended to the general 2D curved surface. As an application, the Helmholtz‐Hodge decomposition is considered for a realistic atrium and a bunny.
Growth of RB Population in the Conversion Phase of Chlamydia Life Cycle
Authors: Wan Frederic Y. M. 
Published: January 2023

AbstractUpon infecting a host cell, the reticulate body (RB) form of the Chlamydia bacteria simply proliferates by binary fission for an extended period. Available data show only RB units in the infected cells 20 hours post infection (hpi), spanning nearly half way through the development cycle. With data collected every 4 hpi, conversion to the elementary body (EB) form begins abruptly at a rapid rate sometime around 24 hpi. By modeling proliferation and conversion as simple birth and death processes, it has been shown that the optimal strategy for maximizing the total (mean) EB population at host cell lysis time is a bang-bang control qualitatively replicating the observed conversion activities. However, the simple birth and death model for the RB proliferation and conversion to EB deviates in a significant way from the available data on the evolution of the RB population after the onset of RB-to-EB conversion. By working with a more refined model that takes into account a small size threshold eligibility requirement for conversion noted in the available data, we succeed in removing the deficiency of the previous models on the evolution of the RB population without affecting the optimal bang-bang conversion strategy.
Generalism in nature: a community ecology perspective
Authors: Loxdale Hugh D. , Harvey Jeffrey A. 
Published: January 2023

AbstractLife on Earth is complex and generally abounds in food webs with other living organisms in terms of an ecological community. Besides such complexity, and the fact that populations of most living organisms have never been studied in terms of their molecular ecology, it is best to tread carefully when describing a given species as a ‘generalist’, more especially in terms of dietary and habitat breadth. We very much doubt that population homogeneity ever exists—because populations are always undergoing molecular-genetic changes, sometimes rapid, in response to various ecological challenges (e.g. climate, intra- and interspecific competition). In any case, a population may already have begun to undergo cryptic speciation. Such entities can occupy different habitats or exhibit different dietary breadths as a result of various ecological interactions formed over different spatial scales. These scales include everything from local (including islands) to geographic. The fossil evidence reveals that specialisations have existed over vast swathes of time. Besides, as is well documented, evolution only occurs as a result of adaptations leading to specialisation, and ultimately, specialist entitles, i.e. species and lower levels of ecological-evolutionary divergence. Here, focusing on diet, we posit that the terms mono-, oligo-and polyphagous are more accurate in relation to the dietary breadth of animals, with omnivory adopted in the case of organisms with very different food items. Thus, we strongly urge that the dubious and unscientific term ‘generalism’ be dropped in favour of these more precise and scientifically accurate terms directly relating to levels of phagy.
Weed genomics: yielding insights into the genetics of weedy traits for crop improvement
Authors: Huang Yujie , Wu Dongya , Huang Zhaofeng , Li Xiangyu , Merotto Aldo , Bai Lianyang , Fan Longjiang 
Published: January 2023

AbstractWeeds cause tremendous economic and ecological damage worldwide. The number of genomes established for weed species has sharply increased during the recent decade, with some 26 weed species having been sequenced and de novo genomes assembled. These genomes range from 270 Mb (Barbarea vulgaris) to almost 4.4 Gb (Aegilops tauschii). Importantly, chromosome-level assemblies are now available for 17 of these 26 species, and genomic investigations on weed populations have been conducted in at least 12 species. The resulting genomic data have greatly facilitated studies of weed management and biology, especially origin and evolution. Available weed genomes have indeed revealed valuable weed-derived genetic materials for crop improvement. In this review, we summarize the recent progress made in weed genomics and provide a perspective for further exploitation in this emerging field.
The Pre-implementation Process of Adapting a Culturally Informed Stress Reduction Intervention for Native American Head Start Teachers
Authors: Wilson Deborah H. , Nelson Katie E. , Gresh Ashley , Ricker Adriann , Littlepage Shea , Krienke Lydia Koh , Brockie Teresa N. 
Published: January 2023

AbstractHead Start is a federally funded program for children (3–5 years) from low-income families. In the Fort Peck Native American Reservation, tribal Head Start teachers have reported high stress in supporting children experiencing adverse childhood experiences. Thus, we adapted the Little Holy One intervention (ClinicalTrials.gov: NCT04201184) for the teachers’ context and culture to enhance psychological health and well-being. Within a participatory framework, the eight-step ADAPT-ITT methodology was used to guide the adaptation process: assessment; decision; adaptation; production; topical experts; integration; training; and testing. For Step 1, we purposive sampled 27 teachers, ancillary staff, and parents to understand teachers’ stress, support mechanisms, and interest in an intervention via focus groups (n = 9) and individual interviews (n = 18). Qualitative data underscored teachers’ experiences of stress, depression, and need for support (Step 1). Iterative feedback from a tribal advisory board and Little Holy One designers rendered selection of five lessons (Step 2, 5), which were adapted for the teachers via theater testing (Step 3, 4). Community capacity assessment revealed their ability to implement the intervention (Step 6). Testing of this adapted intervention in a feasibility trial (steps 7, 8) will be reported in a future publication. A rigorous systematic process within a participatory framework allowed intervention adaption based on community input. Leveraging “culture as treatment” may be useful for enhancing psychological health outcomes for Native Americans who historically underutilize existing psychological services.
A quantitative study of teachers’ beliefs and practices regarding fair classroom differentiation
Authors: van Vijfeijken Marijke , van Schilt-Mol Tamara , Scholte Ron H. J. , Denessen Eddie 
Published: January 2023

AbstractTeachers must make choices about distributing resources and how to differentiate in the classroom. These choices are morally significant because they affect pupils’ learning opportunities. This article reports the findings of an exploratory study of primary school teachers’ differentiation beliefs and practices, which are assessed using the principles of distributive justice (equity, equality and need). A survey was completed by 294 primary school teachers. Latent profile analysis (LPA), a person-centered approach, was employed to explore the typology of teachers’ distributive justice preferences based on their differentiation beliefs. In addition, Wald chi-square analyses were conducted to identify the differences among the profiles in terms of the application of different differentiation practices. Using LPA, we distinguished four different teacher profiles regarding their beliefs about the distribution of educational resources, such as attention and support, and educational outcomes via classroom differentiation. Most teachers predominantly supported the principle of equity alongside the principle of equality in terms of equal resources to all pupils. However, teachers’ beliefs regarding differentiation had weak correlations with their differentiation practices, thus indicating that the differentiation practices used did not reflect teachers’ beliefs. We suggest that teachers should use the principles of distributive justice to reflect on ethical differentiation dilemmas.
Optimal income taxes
Authors: Lüthy Herbert , Chovanec Michal 
Published: January 2023

AbstractWe define a new mathematical function that is an adaptation of a welfare function consistent with the premises of classical welfare. This function is innovative in that it enables us to specifically identify income tax functions that optimally balance the objectives of justice and efficiency in an economy. The new welfare function combines gross national product as an expression of economic performance with the Gini coefficient as a measure of its distributive justice. Afterwards we concentrate on the application of the new welfare function to the design of tax regimes and discuss the question of how to optimize income taxes. On the basis of our data, we discover interesting solutions. To find these, we compute optimal values of the welfare function under constraints of negative tax incentives; in the present case, tax avoidance.
Apropos of “Speciesist bias in AI: how AI applications perpetuate discrimination and unfair outcomes against animals”
Authors: Arandjelović Ognjen 
Published: January 2023

AbstractThe present comment concerns a recent AI & Ethics article which purports to report evidence of speciesist bias in various popular computer vision (CV) and natural language processing (NLP) machine learning models described in the literature. I examine the authors’ analysis and show it, ironically, to be prejudicial, often being founded on poorly conceived assumptions and suffering from fallacious and insufficiently rigorous reasoning, its appeal in large part relying on the extant consensus in the community.
The ethical agency of AI developers
Authors: Griffin Tricia A. , Green Brian Patrick , Welie Jos V. M. 
Published: January 2023

AbstractPublic and academic discourse about the ethics of artificial intelligence, machine learning, and data science has largely focused on the algorithms and the companies deploying them. Little attention has been paid to the ethical agency of the developers. This study is the first of its kind that centers developers in the ethical environment. Semi-structured interviews with 40 developers about the ethics of being a developer revealed more than 20 themes, 3 of which are the subject of this paper: ethics in the occupational ecosystem, developer ethical agency, and the characteristics of an ethical developer. These themes reveal significant gaps between how developers perceive themselves and the reality of their work experiences. Their ethical agency is likewise variable. They have some authority to intervene for ethical reasons in systems they work on, but they often do not realize just how many ethical decisions they make. Nonetheless, this study reveals a growing ethical wisdom in this community, one that needs to be surfaced and nurtured by engaging with developers.
Bacteriuria in older adults triggers confusion in healthcare providers: A mindful pause to treat the worry
Authors: Amenta Eva M. , Jump Robin L.P. , Trautner Barbara W. 
Published: January 2023

Abstract
The evidence base for refraining from screening for or treating asymptomatic bacteriuria (ASB) in older adults is strong, but both practices remain prevalent. Clinical confusion over how to respond to a change from baseline, when to order a urinalysis and urine culture, and what to do with a positive urine culture fuels unnecessary antibiotic use for ASB. If the provider can take a mindful pause to apply evidenced-based assessment tools, the resulting increased clarity in how to manage the situation can reduce overtreatment of ASB.
Going local: Evaluating guideline adherence and appropriateness of antibiotic prescribing in patients with febrile neutropenia at an academic teaching hospital
Authors: Liu Rachel , Gitman Melissa R. , Morris Andrew M. , So Miranda 
Published: January 2023

Abstract

Background:
Febrile neutropenia (FN) is a medical emergency with significant morbidity and mortality for oncology patients, requiring comprehensive workup and timely antibiotic administration. We evaluated concordance with locally developed FN guidelines and outcomes of cancer patients admitted to general internal medicine at an academic teaching hospital.

Methods:
We conducted a retrospective observational cohort study of patients admitted between July 1, 2016, and June 30, 2017, for FN. Patients were classified as having low-risk or high-risk FN according to their malignancy and chemotherapy. Primary outcome was the proportion of patients receiving guideline-concordant antibiotics within 48 hours of admission to general internal medicine. Secondary outcomes were the proportion of patients in whom empirical antibiotics were active against pathogens isolated, rate of antibiotic-associated adverse events, and in-hospital mortality. We used logistic regression to model relationship between FN risk and guideline-concordant antibiotics.

Results:
Among 100 patients included, 34 (34%) were low-risk FN and 66 (66%) were high-risk. Proportion of guideline-concordant empirical antibiotics was significantly lower among low-risk FN patients than high-risk patients: 12 (35%) of 34 versus 47 (71%) of 66 (P = .001). Empirical antibiotics were active against 17 (94%) of 18 isolated pathogens. The mortality rate was 3%, and 16% of patients experienced antibiotic-associated adverse events. Hematological malignancy and infectious diseases–trained physician involvement were associated with guideline-concordant prescribing, with adjusted odds ratios of 3.76 (95% CI, 1.46–9.70; P = .006) and 3.71 (95% CI, 1.49–9.23; P = .005), respectively.

Conclusions:
Guideline concordance was low compared to published reports. Factors influencing appropriate antimicrobial prescribing in patients with FN warrant further exploration.