bluuebunny/crossref_metadata_2025 huggingface dataset:
Psychosocial Perspective on Problem Gambling: The role of Social Relationships, Resilience, and COVID-19 Worry
Authors: Sirola Anu , Nyrhinen Jussi , Wilska Terhi-Anna
Published: January 2023

The COVID-19 pandemic has amplified several psychosocial risks and problem behaviors among vulnerable individuals. Given that gambling has high addictive potential, it is important to consider the underlying mechanisms of problem gambling. This study examined psychosocial factors associated with pandemic-time problem gambling.

Cross-sectional data were gathered via an online survey of 18–75-year-old Finnish, Swedish, and British respondents (n = 2,022) who reported having gambled at least occasionally during the pandemic. Measures included problem gambling, loneliness, COVID-19 worry, social support, and psychological resilience. Control variables included gender, age, and education. Structural equation modeling was used as an analytical technique.

Loneliness was found to be associated with problem gambling. While COVID-19 worry was not directly associated with problem gambling, it predicted higher loneliness, which in turn was associated with problem gambling. Evidence was not found regarding the protective role of resilience or social support in problem gambling. However, social support was found to be associated with higher problem gambling severity. Male gender and younger age were associated with problem gambling.

The results bring insight into underlying vulnerabilities regarding problem gambling during the pandemic. More focus should be placed on the quality and sources of social support, as well as on how psychosocial risk and protective factors might work differently among different populations of gamblers.
Exact Discrete Solutions of Boundary Control Problems for the 1D Heat Equation
Authors: Lang Jens , Schmitt Bernhard A.
Published: January 2023

Method-of-lines discretizations are demanding test problems for stiff integration methods. However, for PDE problems with known analytic solution, the presence of space discretization errors or the need to use codes to compute reference solutions may limit the validity of numerical test results. To overcome these drawbacks, we present in this short note a simple test problem with boundary control, a situation where one-step methods may suffer from order reduction. We derive exact formulas for the solution of an optimal boundary control problem governed by a one-dimensional discrete heat equation and an objective function that measures the distance of the final state from the target and the control costs. This analytical setting is used to compare the numerically observed convergence orders for selected implicit Runge–Kutta and Peer two-step methods of classical order four, which are suitable for optimal control problems.
A Class of Implicative Expansions of Belnap-Dunn Logic in which Boolean Negation is Definable
Authors: Robles Gemma , Méndez José M.
Published: January 2023

Belnap and Dunn’s well-known 4-valued logic FDE is an interesting and useful non-classical logic. FDE is defined by using conjunction, disjunction and negation as the sole propositional connectives. Then the question of expanding FDE with an implication connective is of course of great interest. In this sense, some implicative expansions of FDE have been proposed in the literature, among which Brady’s logic BN4 seems to be the preferred option of relevant logicians. The aim of this paper is to define a class of implicative expansions of FDE in whose elements Boolean negation is definable, whence strong logics such as the paraconsistent and paracomplete logic PŁ4 and BN4 itself are definable, in addition to classical propositional logic.
Reaching Latinx Communities with Algorithmic Optimization for SARS-CoV-2 Testing Locations
Authors: Searcy Jacob A. , Cioffi Camille C. , Tavalire Hannah F. , Budd Elizabeth L. , Cresko William A. , DeGarmo David S. , Leve Leslie D.
Published: January 2023

The COVID-19 pandemic has disproportionately affected communities of color, including Latinx communities. Oregon Saludable: Juntos Podemos (OSJP) is a randomized clinical trial aimed at reducing this disparity by both increasing access to testing for SARS-CoV-2, the virus that causes COVID-19, for Oregon Latinx community members and studying the effectiveness of health and behavioral health interventions on turnout and health outcomes. OSJP established SARS-CoV-2 testing events at sites across Oregon. A critical early question was how to locate these sites to best serve Latinx community members. To propose sites in each participating county, we implemented an algorithmic approach solving a facilities location problem. This algorithm was based on minimizing driving time from Latinx population centers to SARS-CoV-2 testing locations. OSJP staff presented these proposed testing locations to community partners as a starting place for identifying final testing sites. Due to differences in geography, population distributions, and potential site accessibility, the study sites exhibited variation in how well the algorithmic optimization objectives could be satisfied. From this variation, we inferred the effects of the drive time optimization metric on the likelihood of Latinx community members utilizing SARS-CoV-2 testing services. After controlling for potential confounders, we found that minimizing the drive time optimization metric was strongly correlated with increased turnout among Latinx community members. This paper presents the algorithm and data sources used for site proposals and discusses challenges and opportunities for community-based health promotion research when translating algorithm proposals into action across a range of health outcomes.
The drivers of anguillid eel movement in lentic water bodies: a systematic map
Authors: Williamson Michael J. , Jacoby David M. P. , Piper Adam T.
Published: January 2023

Anguillid eels are near globally distributed catadromous fishes with marine spawning areas and inshore and inland growth areas in both lotic (rivers, estuaries) and lentic (lakes, ponds, lagoons) water bodies. As predators, anguillid eels play an important ecological role in both marine and freshwater systems, and several species are harvested commercially for food. However, some of the more widely distributed species have undergone severe declines in recruitment and their population status is now of significant concern. Given the multiple and lengthy migrations undertaken by anguillid eels, understanding of the drivers of movement is fundamental for species conservation and management. Yet, despite the importance of lentic systems to their ecology, most studies on anguillid eel movement have been conducted in lotic systems. Given that key influences on eel migration in lotic water bodies, such as fluctuations in flow and water temperature, may be minimised in lentic environments, the transferability of findings between lotic and lentic systems cannot be assumed. A systematic map was constructed to synthesise current knowledge on the extrinsic and intrinsic drivers of anguillid eel movement in lentic systems. The current state of knowledge of the drivers of eel movement in lentic systems is presented and compared to the relatively well-understood drivers of movement in lotic systems. We also discuss current knowledge gaps and limitations, and identify key future research requirements to inform the management and conservation of anguillid eels in understudied lentic systems.

Graphical abstract
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps
Authors: Mercea Vanessa , Paraschiv Alin Razvan , Lacatus Daniela Adriana , Marginean Anca , Besliu-Ionescu Diana
Published: January 2023

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of machine learning. Detecting sunquakes is a daunting task for human operators, and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine-learning representation methods for sunquake detection using autoencoders, contrastive learning, object detection and recurrent techniques, which we enhance by introducing several custom, domain-specific data augmentation transformations. We address the main challenges of the automated sunquake-detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage, and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.
The Daniel K. Inouye Solar Telescope (DKIST) Cryogenic Near-Infrared Spectro-Polarimeter
Authors: Fehlmann André , Kuhn Jeffrey R. , Schad Thomas A. , Scholl Isabelle F. , Williams Rebecca , Agdinaoay Rodell , Berst D. Christopher , Craig Simon C. , Giebink Cynthia , Goodrich Bret , Hnat Kirby , James Don , Lockhart Charles , Mickey Donald L. , Oswald Daniel , Puentes Myles M. , Schickling Richard , de Vanssay Jean-Benoit , Warmbier Eric A.
Published: January 2023

The Cryogenic Near-Infrared Spectro-Polarimeter (Cryo-NIRSP) is a combination slit-based spectrograph and context-imaging instrument designed and built for the National Science Foundation’s Daniel K. Inouye Solar Telescope (DKIST). It is optimized for polarimetric observations of spectral band-passes between 1 and 5 microns, which makes it uniquely suited for polarimetric observations of the solar corona, while also enabling access to other important infrared diagnostics such as the CO fundamental band at 4.6 microns. Integration, testing, and commissioning occurred between 2019 and 2020, and it is now conducting science experiments as part of DKIST early operations. In this article, we discuss its science mission, the requirements driving the design, its fabrication and installation, current capabilities, and the first science data.
The indirect association between moral disengagement and bystander behaviors in school bullying through motivation: Structural equation modelling and mediation analysis
Authors: Thornberg Robert , Jungert Tomas , Hong Jun Sung
Published: January 2023

Guided by the social-cognitive theory and self-determination theory, this study examined whether moral disengagement is indirectly associated with pro-bullying, passive bystanding, and defending, mediated by autonomous motivation, introjected motivation, extrinsic motivation, and amotivation to defend victims of bullying among early adolescents. Participants were 901 upper elementary students from 43 school classes at 15 public schools in Sweden who completed a questionnaire in their classrooms. The results showed that students who were less inclined to morally disengage in peer bullying tended to be more autonomously motivated to take the victim’s side, which in turn was associated with greater defending and fewer pro-bullying behaviors. Introjected motivation to defend negatively mediated the association between moral disengagement and defending, and positively mediated moral disengagement’s associations with passive bystanding and pro-bullying behavior. Extrinsic motivation to defend mediated moral disengagement’s associations with passive bystanding and pro-bullying behavior. Finally, students who were more prone to morally disengage in peer bullying tended to be more amotivated to take the victim’s side, which in turn was associated with greater pro-bullying behavior and less defending.
What is a related work? A typology of relationships in research literature
Authors: Doroudi Shayan
Published: January 2023

An important part of research is situating one’s work in a body of existing literature, thereby connecting to existing ideas. Despite this, the various kinds of relationships that might exist among academic literature do not appear to have been formally studied. Here I present a graphical representation of academic work in terms of entities and relations, drawing on structure-mapping theory (used in the study of analogies). I then use this representation to present a typology of operations that could relate two pieces of academic work. I illustrate the various types of relationships with examples from medicine, physics, psychology, history and philosophy of science, machine learning, education, and neuroscience. The resulting typology not only gives insights into the relationships that might exist between static publications, but also the rich process whereby an ongoing research project evolves through interactions with the research literature.
Combining phytoremediation with bioenergy production: developing a multi-criteria decision matrix for plant species selection
Authors: Amabogha Obed Nadari , Garelick Hemda , Jones Huw , Purchase Diane
Published: January 2023

The use of plants to extract metal contaminants from soils has been proposed as a cost-effective means of remediation, and utilizing energy crops for this phytoextraction process is a useful way of attaining added value from the process. To simultaneously attain both these objectives successfully, selection of an appropriate plant species is crucial to satisfy a number of imporTant criteria including translocation index, metal and drought tolerance, fast growth rate, high lignocellulosic content, good biomass production, adequate calorific value, second generation attribute, and a good rooting system. In this study, we proposed a multi-criteria decision analysis (MCDA) to aid decision-making on plant species based on information generated from a systematic review survey. Eight speciesHelianthus annuus(sunflower),Brassica juncea(Indian mustard),Glycine max(soybean),Salixspp. (willow),Populusspp. (poplar),Panicum virgatum(switchgrass),Typha latifolia(cattails), andMiscanthus sinensis(silvergrass) were examined based on the amount of hits on a number of scientific search databases. The data was normalized by estimating their min–max values and their suitability. These criteria/indicators were weighted based on stipulated research objectives/priorities to form the basis of a final overall utility scoring. Using the MCDA, sunflower and silvergrass emerged as the top two candidates for both phytoremediation and bioenergy production. The multi-criteria matrix scores assist the process of making decisions because they compile plant species options quantitatively for all relevant criteria and key performance indicators (KPIs) and its weighing process helps incorporate stakeholder priorities to the selection process.
The identity turn in science education research: a critical review of methodologies in a consolidating field
Authors: Danielsson Anna T. , King Heather , Godec Spela , Nyström Anne-Sofie
Published: January 2023

This manuscript reflects on the affordances and limitations of methodological approaches commonly adopted by science education researchers examining learner identities. Our aims are to unpack the relative strengths and weaknesses of such approaches and note their respective prevalence. In so doing, we identify and critique studies which we consider exemplify the different approaches and, in turn, note the direction of fruitful developments and the nature of key challenges. From our review of the field, we suggest that three discrete methodological approaches can be identified: macro-studies within a psychological tradition; macro-studies within a sociological tradition; and micro-studies within an interpretive tradition. Our review comprised a critical analysis of papers included in the Web of Science databases published between 1998 and 2018. A total of 198 papers examining aspects of learner identity relating to science were identified. Of these, the majority (146) were categorised as micro-studies within an interpretive tradition. We discuss the implications of methodological choices for the advancement of understanding and further note ambiguities in the field particularly in relation to the ways in which learner identity research is conceived. We also raise questions for the field relating to the ways in which findings may be scaled, and how the field might develop to allow stronger theoretical and conceptual coherence.
A whale of a time: engaging in a war of values for youth activism in science education
Authors: Gisewhite Rachel A.
Published: January 2023

Exposure and experience with ethical dilemmas and controversial socioscientific issues provide a link to students’ lives or a pathway for sympathy/empathy and care, where youth use emotion to engage with the scenario and develop critical thinking skills to respond to ethical issues. For this theoretical paper, I focus on how informal science can be used in science classes to provide such exposure and experience, creating spaces for students to foster erotic relationships with the nature-Other and their local environment. More specifically, this paper aims to discuss how educators can use these informal science experiences, and in this case—those involving marine mammals, to find value for natural phenomena through erotic generosity and phenomenological experiences within the environment and use their knowledge and power to act responsibly.
Systematic Fitting and Comparison of Hyperelastic Continuum Models for Elastomers
Authors: Ricker Alexander , Wriggers Peter
Published: January 2023

Hyperelasticity is a common modeling approach to reproduce the nonlinear mechanical behavior of rubber materials at finite deformations. It is not only employed for stand-alone, purely elastic models but also within more sophisticated frameworks like viscoelasticity or Mullins-type softening. The choice of an appropriate strain energy function and identification of its parameters is of particular importance for reliable simulations of rubber products. The present manuscript provides an overview of suitable hyperelastic models to reproduce the isochoric as well as volumetric behavior of nine widely used rubber compounds. This necessitates firstly a discussion on the careful preparation of the experimental data. More specific, procedures are proposed to properly treat the preload in tensile and compression tests as well as to proof the consistency of experimental data from multiple experiments. Moreover, feasible formulations of the cost function for the parameter identification in terms of the stress measure, error type as well as order of the residual norm are studied and their effect on the fitting results is illustrated. After these preliminaries, invariant-based strain energy functions with decoupled dependencies on all three principal invariants are employed to identify promising models for each compound. Especially, appropriate parameter constraints are discussed and the role of the second invariant is analyzed. Thus, this contribution may serve as a guideline for the process of experimental characterization, data processing, model selection and parameter identification for existing as well as new materials.
Deterministic and Probabilistic Health Risk Assessment of Toxic Metals in the Daily Diets of Residents in Industrial Regions of Northern Ningxia, China
Authors: Wang Yan , Cao Deyan , Qin Jiaqi , Zhao Siyuan , Lin Jianzai , Zhang Xi , Wang Junji , Zhu Meilin
Published: January 2023

This study was designed to investigate the toxic metal (aluminum (Al), arsenic (As), chromium (Cr), cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn)) concentrations in drinking water and different foodstuffs meat (pork, beef, and mutton), cereals (rice, flour, corn, millet), beans (cowpeas, tofu), potatoes (potato, sweet potato), solanaceous fruits (pepper, eggplant, bitter gourd, cucumber), vegetables (cabbage, cauliflower, spinach), and fruits (apples, watermelons, pears, grapes)) and then estimate the potential health risks of toxic metal consumption to local residents in industrial regions of northern Ningxia, China. As in drinking water, Cr in meat, Pb in cereals, Pb in beans, As and Pb in potatoes, Pb in solanaceous fruits, Cr and Ni in vegetables, and Ni and Pb in fruits were the most contaminated heavy metals in the corresponding food with over-standard rates of 16.7%, 12.5%, 5.1%, 60%, 50%, 50%, 38.2%, 44.4%, 44.4%, 31.8%, and 31.8%, respectively.

The results of the deterministic assessment of health risks showed that the total noncarcinogenic risk value of dietary intake of toxic metals by the local population was 5.6106, indicating that toxic metals pose a high noncarcinogenic risk. The order of the non-carcinogenic risk is HIcereal (1.2104) > HIsolanaceous fruit (0.9134) > HIVegetables (0.8726) > HIFruit (0.8170) > HIMeat (0.7269) > HIDrinking water (0.6139) > HIBeans (0.2991) > HIPotatoes (0.1573). The total carcinogenic health risk from exposure to toxic metals through dietary intake was 9.98 × 10−4, indicating that the total cancer risk value of residents is beyond the acceptable range (10−4) under the current daily dietary exposure and implies a high risk of cancer. The order of the carcinogenic risk is RDrinking water (2.34 × 10−4) > RMeat (2.11 × 10−4) > Rsolanaceous fruit (1.89 × 10−4) > RFruit (1.88 × 10−4) > Rcereal (1.36 × 10−4) > RPotatoes (2.44 × 10−5) > RVegetables (1.51 × 10−5) > RBeans (0). The probabilistic assessment results showed that 98.83% of the population is exposed to severe noncarcinogenic risk and 87.02% is exposed to unacceptable carcinogenic risk. The sensitivity analysis showed that drinking water, local cereals, vegetables, and fruits were the major contributors to health risks. Our results indicated that the daily dietary exposure of residents in industrial regions of northern Ningxia poses a serious threat to human health, and it is suggested that relevant departments should strengthen monitoring and control of the current situation of toxic metal pollution in the environment and continue to pay attention and take measures to reduce the exposure of toxic metals in the diets of residents in this area.

Graphical Abstract
AI as a challenge for legal regulation – the scope of application of the artificial intelligence act proposal
Authors: Ruschemeier Hannah
Published: January 2023

The proposal for the Artificial Intelligence Act is the first comprehensive attempt to legally regulate AI. Not merely because of this pioneering role, the draft has been the subject of controversial debates about whether it uses the right regulatory technique, regarding its scope of application and whether it has sufficient protective effect. Moreover, systematic questions arise as to how the regulation of constantly evolving, dynamic technologies can succeed using the means of the law. The choice of the designation as Artificial Intelligence Act leads to legal-theoretical questions of concept formation as a legal method and legislative technique. This article examines the difficulties of regulating the concept of AI using the scope of the Artificial Intelligence Act as an example.
Phenotypic and Genetic Factors Associated with Absence of Cardiomyopathy Symptoms in PLN:c.40_42delAGA Carriers
Authors: Lopera-Maya Esteban A. , Li Shuang , de Brouwer Remco , Nolte Ilja M. , van Breen Justin ,   The Netherlands A. C. M./P. L. N. Registry, Bosman Laurens P. , Verstraelen Tom E. , van Lint Freya H. M. , Cox Moniek G. P. J. , Groeneweg Judith A. , Mast Thomas P. , van der Zwaag Paul A. , Volders Paul G. A. , Evertz Reinder , Wong Lisa , de Groot Natasja M. S. , Zeppenfeld Katja , van der Heijden Jeroen F. , van den Berg Maarten P. , Wilde Arthur A. M. , Asselbergs Folkert W. , Hauer Richard N. W. , te Riele Anneline S. J. M. , van Tintelen J. Peter ,   Lifelines Cohort Study, Aguirre-Gamboa Raul , Deelen Patrick , Franke Lude , Kuivenhoven Jan A. , Maya Esteban A. Lopera , Nolte Ilja M. , Sanna Serena , Snieder Harold , Swertz Morris A. , Visscher Peter M. , Vonk Judith M. , Wijmenga Cisca , Jongbloed Jan D. H. , Swertz Morris A. , Snieder Harold , Franke Lude , Wijmenga Cisca , de Boer Rudolf A. , Deelen Patrick , van der Zwaag Paul A. , Sanna Serena
Published: January 2023

The c.40_42delAGA variant in the phospholamban gene (PLN) has been associated with dilated and arrhythmogenic cardiomyopathy, with up to 70% of carriers experiencing a major cardiac event by age 70. However, there are carriers who remain asymptomatic at older ages. To understand the mechanisms behind this incomplete penetrance, we evaluated potential phenotypic and genetic modifiers in 74 PLN:c.40_42delAGA carriers identified in 36,339 participants of the Lifelines population cohort. Asymptomatic carriers (N = 48) showed shorter QRS duration (− 5.73 ms, q value = 0.001) compared to asymptomatic non-carriers, an effect we could replicate in two different independent cohorts. Furthermore, symptomatic carriers showed a higher correlation (rPearson = 0.17) between polygenic predisposition to higher QRS (PGSQRS) and QRS (p value = 1.98 × 10–8), suggesting that the effect of the genetic variation on cardiac rhythm might be increased in symptomatic carriers. Our results allow for improved clinical interpretation for asymptomatic carriers, while our approach could guide future studies on genetic diseases with incomplete penetrance.

Graphical abstract
Oncoplastic versus conventional breast-conserving surgery in breast cancer: a pooled analysis of 6941 female patients
Authors: Hasan Mohammed Tarek , Hamouda Mohamed , khashab Mohammad. K. El , Elsnhory Ahmed Bostamy , Elghamry Abdullah Mohamed , Hassan Obada Atef , Fayoud Aya Mamdouh , Hafez Abdelrahman H. , Al-kafarna Mohammed , Hagrass Abdulrahman Ibrahim , Rabea Randa Kamal , Gbreel Mohamed Ibrahim
Published: January 2023

Background

Breast cancer is the most prevalent cancer in women. In the past few years, surgical interventions for breast cancer have experienced massive changes from radical excision to conserving approaches. In this study, we aim to compare the two breast surgery interventions, including conventional breast-conserving surgery (CBCS) versus oncoplastic breast-conserving surgery (OPBCS).

Methods

We searched on PubMed, Web of Science (WOS), Scopus, Embase, and Cochrane till 2 October 2021. All relevant randomized controlled trials (RCTs) and observational studies were included. The data were extracted and pooled using Review Manager software (RevMan 5.4).

Results

The pooled meta-analysis of the included studies showed that OPBCS was significantly superior to CBCS in most of the outcomes. Re-excision significantly favoured CBCS (RR = 0.49, 95% CI [0.37, 0.63], P < 0.00001). However, local recurrence (RR = 0.55, 95% CI [0.27, 1.09], P = 0.09), close surgical margins (RR = 0.37, 95% CI [0.14, 1.00], P = 0.05) and end up to the risk of mastectomy (RR = 0.73, 95% CI [0.54, 97], P = 0.06) showed no significant difference between both techniques. Notably, while performing a sensitivity analysis, other outcomes as local recurrence, significantly showed favourable results towards OPBCS. In terms of safety outcomes, there was no significant difference between OPBCS and CBCS.

Conclusion

We recommend the oncoplastic approach rather than the conventional one in females with breast cancer. Re-excision rates showed better results following OPBCS.
A resilient public health in 2030: #buthow?
Authors: Hagenaars Luc , Waterlander Wilma , den Hertog Karen , Stronks Karien
Published: January 2023

Game changers in public health are traditionally seen as specific in(ter)ventions, but contemporary public health challenges warrant acknowledging complexity instead. Our game changer is a compass to deal with this complexity. In our vision of 2030, system beliefs, goals, structures and events line up to create a society that balances health, climate, social cohesion and economy. To reach this desired system, a resilient public health sector actively interacts with public discourse, political windows of opportunity are seized for institutionalizing health for all policies, and research is intertwined with the policy process, without the merging of the two.
Adaptive spatial discretization using reinforcement learning
Authors: Butt Jemil Avers , Wieser Andreas
Published: January 2023

A well-known challenge for deformation monitoring is the spatial discretization, i.e. the choice of monitoring points at which measurements are to be taken. Well-chosen monitoring points employ prior knowledge to yield a significant amount of information about a certain aspect of the monitored object. However, the choice of such a set of points is typically made to be practically expedient or left to the measurement instrument itself. We aim to derive adaptive discretization strategies that implicitly incorporate domain knowledge about the monitored object via a cycle of interaction and learning. In those strategies, previous measurements impact the locations of subsequent ones. We formulate the choice of monitoring points as a decision theoretical problem and review the framework of reinforcement learning which formalizes the problem of deriving optimal sequential decisions under uncertainty. Iterative algorithms produce solution schemes for this optimal control task. We benchmark the performance of reinforcement learning and compare its results to random, pseudorandom, and numerically designed discretization strategies on several geodetically motivated examples. Advantages, disadvantages, and practical feasibility of the approach are evaluated and reveal a significant boost in efficiency of the data collection scheme compared to classical approaches.
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

The 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

Five 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

Sparse 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

Laser 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

The 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

Creating 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

The 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

Upon 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

Life 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

Weeds 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

Head 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

Teachers 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.