First-order Level Research Articles

Investigating preservice secondary mathematics teachers' skills in posing realistic mathematics tasks

Mathematics tasks situated in realistic situations play a central role in developing and applying mathematical knowledge. However, mathematics educators face difficulties in designing realistic mathematics tasks. This study was conducted to gain deeper insights into the characteristics of mathematical problems that underprepared preservice secondary mathematics teachers pose. This study examined 32 preservice secondary mathematics teachers' problem-posing skills based on three problem-posing situations: structured problem-posing, semi-structured problem-posing, and free problem-posing. As the context of the participants, they did not have learning experiences in designing realistic mathematics tasks. In evaluating the preservice teachers' works, the researchers used several evaluation criteria, namely the compatibility of the problem with the mathematical principles, plausibility and sufficiency of information, the problem text, level of context authenticity, cognitive demand, and the correctness of the solution. The results revealed that most preservice teachers posed problems with the first-order level of context use and low cognitive demand. Additionally, many of them encountered difficulties when attempting to solve the problem. The findings of this study were expected to serve as a basis for developing a curriculum for pedagogic and mathematics courses in teacher education.

A Radiomics Model for the Differentiation of Intracranial Solitary Fibrous Tumor/Hemangiopericytoma and Meningioma Based on Multiparametric Magnetic Resonance Imaging.

Although the imaging findings of intracranial solitary fibrous tumor (SFT)/hemangiopericytoma (HPC) and meningioma are similar, their treatment and prognosis are quite different. Accurate preoperative identification of these two types of tumors is crucial for individualized treatment. The aim of this study was to develop a radiomics model for the differentiation of intracranial SFT/HPC and meningioma based on multiparametric magnetic resonance imaging (mpMRI). A total of 99 patients from July 2012 to July 2018 with histologically and immunohistochemically confirmed SFT/HPC (n = 40) or meningiomas (n = 59) were retrospectively analyzed. A total of 1118 features were extracted based on its image shape, intensity and texture features. The logistic regression (LR) and multi-layer artificial neural network (ANN) classifiers were used to classify SFT/HPC and meningioma. The predictive performance was calculated using receiver operating characteristic curves (ROC). We found significant difference in terms of sex between the SFT/HPC and meningioma group (χ2= 4.829, P < 0.05), but no significant difference was found in age (P > 0.05). The most significant radiomics features included five shape and four first-order level features. For the LR classifier, the prediction accuracy of SFT/HPC was 71.0% and meningioma was 78.7%. For the ANN classifier, the prediction accuracy of SFT/HPC was 83.9% and meningioma was 80.9%. Both of the two classifiers achieved a high accuracy rate, but ANN was better. Radiomics features, especially when combined with an ANN classifier, can provide satisfactory performance in distinguishing SFT/HPC and meningioma.

Systemic sustainability assessment: Analyzing environmental and social impacts of actions on sustainable development

An improved sustainability performance of one system does not automatically lead to an improved sustainability performance of larger systems (society and nature). A reliable sustainability assessment must be carried out to improve environmental and social sustainability performance for whole systems. It is necessary to show how a system of interest at any scale should (and can) improve sustainability performance. An examination of the literature on sustainability assessment highlights a lack of a systems perspective in most sustainability assessment frameworks. The aim of this paper is, therefore, to provide a framework that enables an assessment of environmental and social sustainability performance, taking systemic aspects into account. This assessment framework is based on a definition of first- and second-order sustainability performance. The former focuses on narrow efficiency issues, while second-order sustainability performance focuses on systemic effectiveness and covers sustainability impacts on society and nature. Improving the first-order level (narrow perspective) will not automatically lead to an improved second-order sustainability performance (systemic perspective). Thus, systemic effectiveness is not automatically increased in the case of first-order sustainability performance. Therefore, three essential dimensions have been identified and combined in an assessment framework, i.e., the dimension of scale, the decision horizon, and sustainability principles. The conceptualized assessment framework allows to analyze whether an action of a system of interest (e.g., corporate action, any process, project, or policy) contributes to global sustainable development.

Report on “Axiomatizing Conditional Normative Reasoning”

This is a report on the project “Axiomatizing Conditional Normative Reasoning” (ANCoR, M 3240-N) funded by the Austrian Science Fund (FWF). The project aims to deepen our understanding of conditional normative reasoning by providing an axiomatic study of it at the propositional but also first-order level. The focus is on a particular framework, the so-called preference-based logic for conditional obligation, whose main strength has to do with the treatment of contrary-to-duty reasoning and reasoning about exceptions. The project considers not only the meta-theory of this family of logics but also its mechanization.

Fault-Tolerant Quantum Algorithm for Symmetry-Adapted Perturbation Theory

The efficient computation of observables beyond the total energy is a key challenge and opportunity for fault-tolerant quantum computing approaches in quantum chemistry. Here, we consider the symmetry-adapted perturbation-theory (SAPT) components of the interaction energy as a prototypical example of such an observable. We provide a guide for calculating this observable on a fault-tolerant quantum computer while optimizing the required computational resources. Specifically, we present a quantum algorithm that estimates interaction energies at the first-order SAPT level with a Heisenberg-limited scaling. To this end, we exploit a high-order tensor-factorization and block-encoding technique that efficiently represents each SAPT observable. To quantify the computational cost of our methodology, we provide resource estimates in terms of the required number of logical qubits and Toffoli gates to execute our algorithm for a range of benchmark molecules, also taking into account the cost of the eigenstate preparation and the cost of block encoding the SAPT observables. Finally, we perform the resource estimation for a heme and artemisinin complex as a representative large-scale system encountered in drug design, highlighting the performance of our algorithm in this new benchmark study and discussing possible bottlenecks that may be improved in future work. Published by the American Physical Society 2024

Ecological assessment and development of analytical methods for concurrent quantification of valsartan and sacubitril: whiteness comparative study based on relative scoring.

Sustainable analytical chemistry is gaining great interest in global environmental pollution control. In addition, valsartan (VAS) and sacubitril (SAB) have been recently approved by the FDA as a fixed-dose combination "LCZ696". It showed efficacy and safety enough to extend its application from heart failure to hypertension control. VAS/SAB dual therapy is considered expensive; however, its prescription has increased significantly worldwide. This prescription increased the demand for developing sustainable analytical methods that simultaneously analyze VAS and SAB. Highly sensitive and selective spectrofluorimetric methods have been developed for this purpose. A synchronous spectrofluorimetric technique was applied. In one method, it was followed by spectral derivatization at the first-order level. The signals were recorded at 230 and 211 nm for VAS and SAB, respectively. Synchronous spectrofluorimetry was coupled to a dual-wavelength mathematical approach in the second method. Signals were derived by subtracting synchronous responses at 241 nm, 226 nm, and 239 nm from the response at 208 nm for VAS and SAB, respectively. Method validation was carried out following ICH guidelines. VAS showed linear calibration curves spanning the range of 60-200 and 80-600 ng mL-1 for the derivative and dual wavelength-assisted approaches, respectively. SAB achieved linear responses in the range of 17-190 and 30-350 ng mL-1 for the first and second methods, respectively. The green profile of the proposed methods was confirmed using the analytical eco-scale (AES), green analytical procedure index (GAPI), and analytical greenness metric (AGREE) tools. The proposed hybrid methods proved highly sustainable through the whiteness RGB 12 algorithm evaluation approach. Whiteness was comparatively assessed for the proposed and reported methods based on relative scoring depending on the parameters of each method. Despite this scoring approach being accurate as a relative score for comparative purposes, it gave rise to underestimated absolute scores. Therefore, to obtain a proper conclusion from the comparative whiteness study, all the methods were ranked according to their whiteness score, illustrating the excellent whiteness ranks of the proposed methods. Upon complete comparison with the reported methods, the suggested ones showed several advantages concerning analytical performance and the greenness level. The proven affordability and simplicity encourage their wide industrial application in developing countries.

Aligning numeracy task design with SDG goals: Nutrition facts as a context for prospective mathematics teachers' problem posing

Nutrition facts are the details on food packaging that describe its nutritional value, including serving size, calories, macronutrients like carbohydrates, protein, and fat, and micronutrients such as vitamins and minerals. These facts have mathematical concepts that can be utilized as a context for prospective teachers to create mathematical problems. By leveraging this real-world data, educators can contribute to multiple Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), by promoting nutritional awareness, and SDG 4 (Quality Education) by enhancing student engagement and understanding through relatable examples. This study examines the profile of mathematical tasks created by prospective mathematics teachers using nutrition facts as a context that meets numeracy task criteria. Data were collected from 62 mathematical tasks created by 31 prospective teachers attending a realistic mathematics education course on numeracy based on a problem-posing task at a public university in Surabaya, Indonesia. The posed tasks were categorized into solvable or unsolvable tasks and the domains of the level of context use and the level of cognitive processes. Results revealed that the level of context use in the posed tasks varied from zero to second order. Surprisingly, most of the tasks were in the first-order level context. Most posed tasks reached the application level, with only a few identified as reasoning tasks. Interestingly, some tasks coded as second-order context were classified as reasoning tasks. The study provides implications for designing numeracy tasks using nutrition facts and interventions in teacher education related to numeracy task design.

Compliance checking on first-order knowledge with conflicting and compensatory norms: a comparison among currently available technologies

This paper analyses and compares some of the automated reasoners that have been used in recent research for compliance checking. Although the list of the considered reasoners is not exhaustive, we believe that our analysis is representative enough to take stock of the current state of the art in the topic. We are interested here in formalizations at the first-order level. Past literature on normative reasoning mostly focuses on the propositional level. However, the propositional level is of little usefulness for concrete LegalTech applications, in which compliance checking must be enforced on (large) sets of individuals. Furthermore, we are interested in technologies that are freely available and that can be further investigated and compared by the scientific community. In other words, this paper does not consider technologies only employed in industry and/or whose source code is non-accessible. This paper formalizes a selected use case in the considered reasoners and compares the implementations, also in terms of simulations with respect to shared synthetic datasets. The comparison will highlight that lot of further research still needs to be done to integrate the benefits featured by the different reasoners into a single standardized first-order framework, suitable for LegalTech applications. All source codes are freely available at https://github.com/liviorobaldo/compliancecheckers, together with instructions to locally reproduce the simulations.

The rule of unreason: Analyzing (anti‐)democratic regression

The rule of unreason: Analyzing (anti‐)democratic regression

Knowledge Management and Dynamic Capabilities: A Digital Embracing of Interrelated Processes

Exploration and exploitation are the main processes that work concurrently to achieve an organisation's evolution and success. Knowledge Management (KM) and Dynamic Capabilities (DCs) are significant fields that share these processes. The application of exploration and exploitation processes is still implicit in organisations, and it is not formally represented in technical configuration levels. Mapping these processes to Information technology (IT) creates an advanced robust translation of their use. It also identifies a tangible role for KM and DCs. The idea of deconstructing dynamic capabilities into three levels has been adopted in this mapping. Levels are high-order learning capabilities with the highest exploration learning activities, first-order ordinary capabilities with the highest exploitation learning activities, and lower-order functional capabilities, intermediate between both levels. The digital embracing of the exploration and exploitation model has been proposed to translate all levels of capabilities into different digital configurations. Automated functions of search/browse, create/add, modify/update, remove/delete and read/revise are utilised to present these digital configurations in this model. Search/browse and create/add reflect the higher-order level. Modify/update and remove/delete mirror the intermediate level. Read/revise matches the first-order level. These IT configurations are simple and would be sufficient to assess exploration and exploitation processes. It can also support a new vision for measuring organisational ambidexterity. A near-future case study of an educational institution will be accomplished to evaluate this model. Two main domains, which involve internal systems (strategies, policies, and regulations) and educational materials, will be assessed with their e-resources and the use of the proposed digital functions that represent exploration and exploitation. A quantitative approach using an online survey will be used in this evaluation. The survey will be distributed to academics with administrative positions in all institution departments. The findings of this evaluation would expect to assess the extent of using online search in finding and creating new e-resources which present the two nominated evaluation domains. It would also appraise updating, deleting, and reading available e-resources according to these domains' new requirements or traditional proceeded routines. &#x0D;

Benchmark calculations of the 3 D Rydberg spectrum of beryllium

High-accuracy calculations are performed for the four lowest states of the beryllium atom. All-electron explicitly correlated Gaussian (ECG) functions are employed to expand the functions and the non-relativistic internal Hamiltonian used in the calculations, which is obtained by rigorously separating out the centre-of-mass motion from the laboratory-frame Hamiltonian, explicitly depends on the finite nuclear mass of Be. The nonrelativistic wave functions of the considered states of Be are generated variationally with the nonlinear parameters of the Gaussians optimised using a procedure that employs the energy gradient determined with respect to these parameters. The nonrelativistic wave functions are used to calculate the leading relativistic corrections employing the perturbation theory at the first-order level. Only corrections that do not produce fine/hyperfine splitting of the energy levels are considered. The corrections are added to the nonrelativistic energies and the results are used to calculate the so-called ‘centre of gravity’ transition energies with respect to the Be ground state. A comparison with high-quality experimental results shows agreement to within about 0.6 cm.

Possibility operators over n-valued Gödel logic

In the area of fuzzy logic, expansions of these logics by $\Delta$ operator have been intensively studied; the interest of $\Delta$ operator is due to the fact that it presents a fuzzy behavior, the associated systems were studied in propositional and first-order level. On the other hand, the possibility operators that define Łukasiewicz-Moisil algebras have been studied over different classes of algebras; these operators are known as Moisil's operators in the literature. One of these operators coincides with $\Delta$, showing there are other operators with fuzzy behavior. In this paper, we present the study of Moisil's operators over an extension of a fuzzy logic; namely, n-valued Gödel logic, thus opening the possibility to explore more fuzzy operators.

Radiomics with 3-dimensional magnetic resonance fingerprinting: influence of dictionary design on repeatability and reproducibility of radiomic features

ObjectivesWe aimed to investigate the influence of magnetic resonance fingerprinting (MRF) dictionary design on radiomic features using in vivo human brain scans.MethodsScan-rescans of three-dimensional MRF and conventional T1-weighted imaging were performed on 21 healthy volunteers (9 males and 12 females; mean age, 41.3 ± 14.6 years; age range, 22–72 years). Five patients with multiple sclerosis (3 males and 2 females; mean age, 41.2 ± 7.3 years; age range, 32–53 years) were also included. MRF data were reconstructed using various dictionaries with different step sizes. First- and second-order radiomic features were extracted from each dataset. Intra-dictionary repeatability and inter-dictionary reproducibility were evaluated using intraclass correlation coefficients (ICCs). Features with ICCs > 0.90 were considered acceptable. Relative changes were calculated to assess inter-dictionary biases.ResultsThe overall scan-rescan ICCs of MRF-based radiomics ranged from 0.86 to 0.95, depending on dictionary step size. No significant differences were observed in the overall scan-rescan repeatability of MRF-based radiomic features and conventional T1-weighted imaging (p = 1.00). Intra-dictionary repeatability was insensitive to dictionary step size differences. MRF-based radiomic features varied among dictionaries (overall ICC for inter-dictionary reproducibility, 0.62–0.99), especially when step sizes were large. First-order and gray level co-occurrence matrix features were the most reproducible feature classes among different step size dictionaries. T1 map-derived radiomic features provided higher repeatability and reproducibility among dictionaries than those obtained with T2 maps.ConclusionMRF-based radiomic features are highly repeatable in various dictionary step sizes. Caution is warranted when performing MRF-based radiomics using datasets containing maps generated from different dictionaries.Key Points• MRF-based radiomic features are highly repeatable in various dictionary step sizes.• Use of different MRF dictionaries may result in variable radiomic features, even when the same MRF acquisition data are used.• Caution is needed when performing radiomic analysis using data reconstructed from different dictionaries.

A comparison of in-service teachers’ conceptions of barriers to mobile technology-integrated instruction and technology-integrated instruction

Researchers have identified three orders of barriers to probe teachers’ conceptions of technology-integrated instruction, namely extrinsic obstacles, intrinsic obstacles and the lack of design thinking. However, few studies have investigated barriers to teachers’ mobile technology-integrated instruction and compared them with the barriers to their technology-integrated instruction. This study recruited 20 in-service teachers with experience in adopting mobile learning (in practice) to compare their conceptions of the two kinds of barriers. Firstly, we utilised the phenomenographic method and subsequently transformed qualitative interview data into quantitative data. The research results found an additional crucial barrier, namely classroom management. Thus, this study revealed four classifications of barriers to (mobile) technology-integrated instruction. Moreover, teachers with less experience in adopting mobile learning confronted more obstructions to mobile technology-integrated instruction than to technology-integrated instruction at the first-order level (i.e., extrinsic obstacles). On the contrary, teachers with more experience in adopting mobile learning encountered more barriers related to design thinking upon the educational integration of mobile technology over conventional technology. This study provides the field with new insights into understanding teachers’ conceptions of barriers to the adoption of emerging technology-integrated instruction and sheds light for researchers and policymakers who are working on teacher professional development. Implications for practice or policy: This research reveals a framework of the four orders of barriers for providing an understanding of teachers’ barriers to advanced technology-integrated instruction as the technology grows. More courses involving design thinking for mobile learning should be provided as part of teacher training. Future research should carefully explore the effective solutions for eliminating the barriers to mobile technology-integrated instruction.

On the Ubiquity of Symmetry in Logical Geometry

The research framework of Logical Geometry investigates two major sets of logical rela- tions holding between formulas in a logical fragment or between concepts in a lexical field. On the one hand, there is the classical set of Aristotelian relations of contradiction, (sub)contrariety and subalternation/implication. On the other hand, there is the set of Duality relations of internal, ex- ternal and dual negation. Networks of logical relations are then given a visual representation by means of logical diagrams, the most well-known among which is no doubt the so-called Square of Oppositions. In this paper we investigate the omnipresent role of symmetry in Logical Geometry. This role can, first of all, be considered both from a logical/semantic perspective and from a geo- metrical/visual perspective. Secondly, symmetry turns up both on the first-order level of individual logical relations/ diagrams and on the second-order level of sets of logical relations/diagrams. This yields the four steps in our analysis: (i) symmetry within logical relations: logical &amp; first order, (ii) symmetry between logical relations: logical &amp; second-order, (iii) symmetry within logical dia- grams: geometrical &amp; first-order, and (iv) symmetry between logical diagrams: geometrical &amp; sec- ond-order.

Machine Learning–based Differentiation of Benign and Premalignant Colorectal Polyps Detected with CT Colonography in an Asymptomatic Screening Population: A Proof-of-Concept Study

Background CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps. Purpose To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (n = 14), gray level histogram statistics (n = 18), and image texture (n = 68) were extracted from segmentations after applying 22 image filters, resulting in 1906 feature-filter combinations. Based on these features, a random forest classification algorithm was trained to predict the individual polyp character. Diagnostic performance was validated in an external test set. Results The random forest model was fitted using a training set consisting of 107 colorectal polyps in 63 patients (mean age, 63 years ± 8 [standard deviation]; 40 men) comprising 169 segmentations on CT colonographic images. The external test set included 77 polyps in 59 patients comprising 118 segmentations. Random forest analysis yielded an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.85, 0.96), a sensitivity of 82% (65 of 79) (95% CI: 74%, 91%), and a specificity of 85% (33 of 39) (95% CI: 72%, 95%) in the external test set. In two subgroup analyses of the external test set, the area under the receiver operating characteristic curve was 0.87 in the size category of 6-9 mm and 0.90 in the size category of 10 mm or larger. The most important image feature for decision making (relative importance of 3.7%) was quantifying first-order gray level histogram statistics. Conclusion In this proof-of-concept study, machine learning-based image analysis enabled noninvasive differentiation of benign and premalignant colorectal polyps with CT colonography. © RSNA, 2021 Online supplemental material is available for this article.

Precise Determination of Astronomical Azimuth by the Hour Angle Method of Multiple Meridian Stars

The method for precisely determining the astronomical azimuth using the local hour angle (LHA) of Polaris is only applicable in northern middle latitudes. In addition, the meridian star pair method is limited by the conditions of star selection and matching. A new hour angle method for multiple meridian stars is proposed, which is suitable for any part of the global region. When multiple south and north stars have been observed, the influence of longitude error and timing error on the azimuth is modified by linear regression. A precise astronomical position is not required, and there is no limit to the specific position of the meridian stars. Therefore, both the number of observable stars and work efficiency are greatly increased. Our experiment indicates that the results of the new method can reach the first-order accuracy level; i.e., the root mean square error (RMSE) is less than 0.5″, even when the amount of observed data is reduced to half that of the Polaris method.

A comprehensive analysis of the psychometric properties of the contingencies of self-worth scale (CSWS)

The Contingencies of Self-Worth Scale (CSWS) is a widely used personality self-report questionnaire developed for measuring the domains in which self-esteem is sustained by successes and achievements as well as threatened by obstacles and failures. Two studies (Nstudy1 = 453, Nstudy2 = 293) aimed to further refine our knowledge of its psychometric properties. Results attested that, at the first-order level, the originally hypothesized seven-factor model proved to be the best-fitting one, but the inclusion of a method factor significantly improved the fit to the data. At the second-order level, the model with two higher-order variables representing private sphere and public sphere of CSW fit better than alternative models. Finally, there was evidence that first- and second-order domains had a good degree of construct and discriminant validity. Overall, these studies provided a step forward in refining the psychometric structure of the CSWS.

Modeling higher order adaptivity of a network by multilevel network reification

AbstractIn network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

The Grounds of Political Legitimacy

AbstractThe debate over rival conceptions of political legitimacy tends to focus on first-order considerations—for example, on the relative importance of procedural and substantive values. In this essay, I argue that there is an important, but often overlooked, distinction among rival conceptions of political legitimacy that originates at the meta-normative level. This distinction, which cuts across the distinctions drawn at the first-order level, concerns the source of the normativity of political legitimacy, or, as I refer to it here, the grounds of political legitimacy. If we focus on the grounds of political legitimacy, there are three main conceptions of political legitimacy: will-based, belief-based, and fact-based conceptions. I present an objection to each of those main conceptions and defend a hybrid account of the grounds of political legitimacy.