General Concept Research Articles

School District Discourse Coalitions and Equity-Focused Policy Innovations in Oakland, California 2020–2021

Purpose: To better understand how district leaders at urban school districts innovate and design policies, this study investigated how actors in one district, Oakland Unified School District, discussed ways to improve their education system at school board meetings during 2020 and 2021. Approach: The research used a case study approach to develop a process of inquiry, drawing on theoretical literature on innovation in social fields, empirical work on urban education reforms, and methodological considerations from critical social analysis techniques. It used a variety of qualitative methods to identify critical policy issues and policy innovations, and examined the way actors in two coalitions, each with a different approach to change, used discourse to convey their ideologies about racial equity and justice. Findings: District actors collaboratively designed and passed five innovative policies: school safety, remote learning, climate change, Black reparations, and mental health. While one group, the incumbent discourse coalition, focused attention on fiscal stability and closing underperforming schools, another, the challenger discourse coalition, conveyed strategies on improving schools and surrounding communities through new education policies. Though unexpected crises expedited innovation, the perspectives of the dominant culture were most privileged in the final designs of resolutions. Implications: By using broad theories and generalizable concepts this study shows how discourse in policymaking can be examined in other settings and illustrates ways similar school districts may innovate in their systems.

An Analytical Study of the Concepts of ʿĀmm (Generalization) and Khāṣ (Specification): Their Characteristics and Implications for Qur'anic Exegesis in Nigeria

The noble Qur’an is a book of universal guidance for all people, at all times and places, until the Day of Judgement. The purpose of revealing the Qur’an is to show people the correct path in their relationship with their Creator and with one another. Allah (S.W.T.) revealed this noble book to His beloved Prophet Muhammad (SAW) to explain and exemplify its contents for the Ummah to follow. Among the Prophet’s practical explanations are the concepts of generalization (ʿĀmm) and specification (Khāṣ) regarding certain rules. Therefore, this paper attempts to illustrate examples of generalization and specification in the verses of the Glorious Qur’an and their implications for Qur’anic exegesis. Content analysis was used to generate the examples. The paper emphasizes the importance of understanding these two concepts for scholars of tafsir. Conclusion and recommendations are provided.

On the generalization of PINNs outside the training domain and the hyperparameters influencing it

AbstractGeneralization is a key property of machine learning models to perform accurately on unseen data. Conversely, in the field of scientific machine learning (SciML), generalization entails not only predictive accuracy but also the capacity of the model to encapsulate underlying physical principles. In this paper, we delve into the concept of generalization for Physics-informed neural networks (PINNs) by investigating the consistency of the predictions of a PINN outside of its training domain. Through the lenses of a novel metric and statistical analysis, we study the scenarios in which a PINN can provide consistent predictions outside the region considered for training and hereinafter assess whether the algorithmic setup of the model can influence its potential for generalizing. Our results highlight why overparametrization is not a crucial component in SciML while encouraging overfitting on the training data. Despite being counterintuitive, the outcome of our analysis serves as a guideline for training PINNs for engineering applications.

Centering cognitive neuroscience on task demands and generalization.

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Utilization of geospatial distribution in the measurement of study cohort representativeness

ObjectiveThe ability to apply results from a study to a broader population remains a primary objective in translational science. Distinct from intrinsic elements of scientific rigor, the extrinsic concept of generalization requires there be alignment between a study cohort and population in which results are expected to be applied. Widespread efforts have been made to quantify representativeness of study cohorts. These techniques, however, often consider the study and target cohorts as monolithic collections that can be directly compared. Overlooking known impacts to health from socio-demographic and environmental factors tied to individual’s geographical location, and potentially obfuscating misalignment in underrepresented population subgroups. This manuscript introduces several measures to account for geographic information in the assessment of cohort representation. MethodsMetrics were defined across two themes: First, measures of recruitment, to assess if a study cohort is drawn at an expected rate and in an expected geographical pattern with respect to individuals in a reference cohort. Second, measures of individual characteristics, to assess if the individuals in the study cohort accurately reflect the sociodemographic, clinical, and geographic diversity observed across a reference cohort while accounting for the geospatial proximity of individuals. ResultsAs an empirical demonstration, methods are applied to an active clinical study examining asthma in Black/African American patients at a US Midwestern pediatric hospital. Results illustrate how areas of over- and under-recruitment can be identified and contextualized in light of study recruitment patterns at an individual-level, highlighting the ability to identify a subset of features for which the study cohort closely resembled the broader population. In addition they provide an opportunity to dive deeper into misalignments, to identify study cohort members that are in some way distinct from the communities for which they are expected to represent. ConclusionTogether, these metrics provide a comprehensive spatial assessment of a study cohort with respect to a broader target population. Such an approach offers researchers a toolset by which to target expected generalization of results derived from a given study.

On S-2-Prime Ideals of Commutative Rings

Prime ideals and their generalizations are crucial in numerous research areas, particularly in commutative algebra. The concept of generalization of prime ideals begins with the study of weakly prime ideals. Since then, subsequent works aimed at expanding this concept into more generalized forms. Among these, S-prime ideals and 2-prime ideals have reaped attention recently. This paper aims to characterize S-2-prime ideals, which serve as a generalization encompassing both 2-prime ideals and S-prime ideals. To accomplish this objective, we construct an ideal which distinct from a multiplicatively closed subset with the help of commutative rings. We investigate the localization and the S-2-prime avoidance lemma in commutative rings. Furthermore, we explore the properties of this class of ideals in trivial ring extensions and amalgamated algebras along an ideal. We delve into S-properties for compactly packedness, compactly 2-packedness and coprimely packedness in trivial ring extentions. Moreover, this notion of ideals helps us to indicate that many results stated in S-prime ideals and 2-prime ideals can be readily expanded to the framework of S-2-prime ideals. Supporting examples also highlight a significant distinction between S-2-prime ideals and stated ideals.

Guiding Principles for the Use of Feedback in Educational and Psychosocial Interventions

<p style="text-align: justify;">Psychosocial and educational intervention approaches employ diverse treatment frameworks, most of which involve delivering some form of feedback to participants about their behavior. General conceptions of feedback are well-known to underlie mainstream therapeutic and educational approaches. Recently emerging ‘smart’ approaches also rely on feedback principles. However, little scholarship is available to stitch together evolving strands of feedback principles and no literature characterizes explicitly the diverse landscape of feedback practices employed in education or intervention science. This paper reviews intrinsic conceptions of feedback along with diverse cases of its use in intervention and education. Based on our consideration, we present a typology of feedback modalities that we hope will enrich the efforts of interventionists and educators to design treatment and educational frameworks incorporating feedback.</p>

Generalizability and robustness evaluation of attribute-based zero-shot learning

In the field of deep learning, large quantities of data are typically required to effectively train models. This challenge has given rise to techniques like zero-shot learning (ZSL), which trains models on a set of “seen” classes and evaluates them on a set of “unseen” classes. Although ZSL has shown considerable potential, particularly with the employment of generative methods, its generalizability to real-world scenarios remains uncertain.The hypothesis of this work is that the performance of ZSL models is systematically influenced by the chosen “splits”; in particular, the statistical properties of the classes and attributes used in training. In this paper, we test this hypothesis by introducing the concepts of generalizability and robustness in attribute-based ZSL and carry out a variety of experiments to stress-test ZSL models against different splits. Our aim is to lay the groundwork for future research on ZSL models’ generalizability, robustness, and practical applications.We evaluate the accuracy of state-of-the-art models on benchmark datasets and identify consistent trends in generalizability and robustness. We analyze how these properties vary based on the dataset type, differentiating between coarse- and fine-grained datasets, and our findings indicate significant room for improvement in both generalizability and robustness. Furthermore, our results demonstrate the effectiveness of dimensionality reduction techniques in improving the performance of state-of-the-art models in fine-grained datasets.

Putting Hybrid Nanomaterials to Work for Biomedical Applications.

Hybrid nanomaterials have found use in many biomedical applications. This article provides a comprehensive review of the principles, techniques, and recent advancements in the design and fabrication of hybrid nanomaterials for biomedicine. We begin with an introduction to the general concept of material hybridization, followed by a discussion of how this approach leads to materials with additional functionality and enhanced performance. We then highlight hybrid nanomaterials in the forms of nanostructures, nanocomposites, metal-organic frameworks, and biohybrids, including their fabrication methods. We also showcase the use of hybrid nanomaterials to advance biomedical engineering in the context of nanomedicine, regenerative medicine, diagnostics, theranostics, and biomanufacturing. Finally, we offer perspectives on challenges and opportunities.

Putting Hybrid Nanomaterials to Work for Biomedical Applications

AbstractHybrid nanomaterials have found use in many biomedical applications. This article provides a comprehensive review of the principles, techniques, and recent advancements in the design and fabrication of hybrid nanomaterials for biomedicine. We begin with an introduction to the general concept of material hybridization, followed by a discussion of how this approach leads to materials with additional functionality and enhanced performance. We then highlight hybrid nanomaterials in the forms of nanostructures, nanocomposites, metal–organic frameworks, and biohybrids, including their fabrication methods. We also showcase the use of hybrid nanomaterials to advance biomedical engineering in the context of nanomedicine, regenerative medicine, diagnostics, theranostics, and biomanufacturing. Finally, we offer perspectives on challenges and opportunities.

Artificial Intelligence and Cardiovascular Diseases

Abstract: Artificial intelligence (AI) has reshaped significant aspects of our lives, including its role in healthcare. : AI is a machine-based system that can make predictions, recommendations, and decisions influencing real or virtual environments of a given set of human-defined objectives. It is designed to operate with varying levels of autonomy. : Since cardiovascular medicine is rapidly progressing and new technologies are introduced to cardiovascular tools, AI has become valuable in cardiovascular medicine. This narrative review will discuss the general concept of AI and its role in diagnosing cardiovascular diseases, including ECG, echocardiography, cardiac CT, nuclear cardiology, cardiac MRI, cardiac catheterization, electrophysiology, heart failure, clinical decision support system, and face recognition.

Social life results in social stress protection: a novel concept to explain individual life-history patterns in social insects.

Resistance to and avoidance of stress slow aging and confer increased longevity in numerous organisms. Honey bees and other superorganismal social insects have two main advantages over solitary species to avoid or resist stress: individuals can directly help each other by resource or information transfer, and they can cooperatively control their environment. These benefits have been recognised in the context of pathogen and parasite stress as the concept of social immunity, which has been extensively studied. However, we argue that social immunity is only a special case of a general concept that we define here as social stress protection to include group-level defences against all biotic and abiotic stressors. We reason that social stress protection may have allowed the evolution of reduced individual-level defences and individual life-history optimization, including the exceptional aging plasticity of many social insects. We describe major categories of stress and how a colonial lifestyle may protect social insects, particularly against temporary peaks of extreme stress. We use the honey bee (Apis mellifera L.) to illustrate how patterns of life expectancy may be explained by social stress protection and how modern beekeeping practices can disrupt social stress protection. We conclude that the broad concept of social stress protection requires rigorous empirical testing because it may have implications for our general understanding of social evolution and specifically for improving honey bee health.

Investigation of the Impact of Temporal Dose Delivery Patterns of Ion Irradiation with the Local Effect Model.

We present an extension of the Local Effect Model (LEM) to include time-dose relationships for predicting effects of protracted and split-dose ion irradiation at arbitrary LET. With this kinetic extension, the spatial and temporal induction and processing of DNA double strand breaks (DSB) in cellular nuclei can be simulated for a wide range of ion radiation qualities, doses and dose rates. The key concept of the extension is based on the joint spatial and temporal coexistence of initial DSB, leading to the formation of clustered DNA damage on the µm scale (as defined e.g., by the size scale of Mbp chromatin loops), which is considered to have an increased cellular lethality as compared to isolated, single DSB. By simulating the time dependent induction and repair of DSB and scoring of isolated and clustered DSB upon irradiation, the impact of dose rate and split dose on the cell survival probability can be computed. In a first part of this work, we systematically analyze the predicted impact of protraction in dependence of factors like dose, LET, ion species and radiosensitivity as characterized by the photon LQ-parameters. We establish links to common concepts that describe dose rate effects for low LET radiation. We also compare the model predictions to experimental data and find agreement with the general trends observed in the experiments. The relevant concepts of our approach are compared to other models suitable for predicting time effects. We investigate an apparent analogy between spatial and temporal concentration of radiation delivery, both leading to increased effectiveness, and discuss similarities and differences between the general dependencies of these clustering effects on their impacting factors. Finally, we conclude that the findings give additional support for the general concept of the LEM, i.e. the characterization of high LET radiation effects based on the distinction of just two classes of DSB (isolated DSB and clustered DSB).

Serious Games in Higher Education in the Transforming Process to Education 4.0—Systematized Review

The digital transformation associated with the Fourth Industrial Revolution is having an impact on the way we teach. Under the term Education 4.0, new teaching methods, new technologies, as well as a student-centered approach, are expected to be used in teaching. One established method of teaching is the use of Serious Games, as it has various positive effects in terms of motivation and engagement. This paper deals with the question of how the transformation towards Education 4.0 influences the way Serious Games are designed and used in the context of higher education. To this end, a systematized literature review was conducted. Out of 550 publications, 28 were included. This revealed works on the general conception as well as studies on Serious Games in various areas of university teaching. The results show different concepts of Serious Games, with the structure often being adapted to learning content and not to students. In terms of technical implementation, Serious Games are mostly web- or desktop-applications instead of new technologies such as VR. As a result, new concepts seem necessary to adapt Serious Games to Education 4.0. In the future, we want to design Serious Games that respond flexibly to students’ needs and make it easy to integrate new technologies.

What Does It Mean to Be Human Today?

With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves "in the image of our machines," and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this self-reification of the human being, the present book defends a humanism of embodiment: our corporeality, vitality, and embodied freedom are the foundations of a self-determined existence, which uses the new technologies only as means instead of submitting to them. The book offers an array of interventions directed against a reductionist naturalism in various areas of science and society. As an alternative, it offers an embodied and enactive account of the human person: we are neither pure minds nor brains, but primarily embodied, living beings in relation with others. This general concept is applied to issues such as artificial intelligence (AI), transhumanism and enhancement, virtual reality, neuroscience, embodied freedom, psychiatry, and finally to the accelerating dynamics of current society which lead to an increasing disembodiment of our everyday life. The book thus applies cutting-edge concepts of embodiment and enactivism to current scientific, technological, and cultural tendencies that will crucially influence our society's development in the twenty-first century.

A new cold war?: The case for a general concept

A new cold war?: The case for a general concept

Assessment of performance rates on the elemental comparison of small and irregular glass fragments using µ-XRF and LIBS

This study describes a systematic assessment of the performance rates when analyzing small and irregular glass fragments using micro-X-ray Fluorescence Spectrometry (µ-XRF) and Laser Induced Breakdown Spectroscopy (LIBS). One hundred glass fragments were collected from the inner and outer panes of a vehicle windshield to assess the false exclusion rates. Additionally, 100 glass fragments originating from different vehicle windshields were used to evaluate the discrimination capabilities. To compare the effects of fragment size on the performance rates, half of the collected fragments were small (longest length between 0.4 mm and < 1 mm, and thickness greater than 0.4 mm for LIBS and 0.1 mm for μ-XRF), and the other half were full-thickness fragments (2 mm and greater). The study shows that precision deteriorates for small/irregular fragments and comparison items must have a similar size, shape, and thickness to minimize error rates. Thus, comparisons between full-thickness and small/irregular fragments should be avoided, regardless of the analytical method. Although this general concept is well known for µ-XRF, this effect was not previously reported as a concern for LIBS. Moreover, this study provides new sampling and comparison recommendations when using modern silicon drift detectors (SDD) and reduced fragment size. Using a 3 s (3 %RSD) comparison interval reduces the false exclusion rates to < 12 % for µ-XRF, and to < 4 % for LIBS when using either a 3 s or 4 s (3 % RSD) criterion. At least 4 known fragments are recommended for full thickness fragments and 6 to 9 known fragments for the small/irregular comparisons.

On epistemic freedom and epistemic injustice

ABSTRACT This article examines the relationship between epistemic freedom, and epistemic injustice and epistemic oppression. I situate epistemic freedom within the larger project of epistemic decolonisation and argue that epistemic freedom is central to both its positive and negative programme. Through exploring the intersections of the notion of epistemic freedom and the scholarship on epistemic injustice and oppression, I argue that one can think of epistemic injustices and oppression as infringements on epistemic freedom. I identify shared themes between the theorisation of epistemic freedom and the literature on epistemic injustice and epistemic oppression, including silencing, active ignorance, and epistemic exploitation. I briefly explore each of these themes, identifying both intersections and divergences. Lastly, I draw on the literature on epistemic injustice and oppression to sharpen Ndlovu-Gatsheni’s (2018. Epistemic Freedom in Africa: Deprovincialization and Decolonization. London: Routledge) general conception of epistemic freedom and develop a tripartite structure for what substantial epistemic freedom entails. I argue that being epistemically free entails being able to choose one’s epistemic endeavours, having the means to pursue them, as well as being able to meaningfully partake in the shared endeavours of the epistemic communities that one belongs to.

Learning from the past: reservoir computing using delayed variables

Reservoir computing is a machine learning method that is closely linked to dynamical systems theory. This connection is highlighted in a brief introduction to the general concept of reservoir computing. We then address a recently suggested approach to improve the performance of reservoir systems by incorporating past values of the input signal or of the reservoir state variables into the readout used to forecast the input or cross-predict other variables of interest. The efficiency of this extension is illustrated by a minimal example in which a three-dimensional reservoir system based on the Lorenz-63 model is used to predict the variables of a chaotic Rössler system.

A study on attention-based deep learning architecture model for image captioning

&lt;span lang="EN-US"&gt;Image captioning has been widely studied due to its ability in a visual scene understanding. Automatic visual scene understanding is useful for remote monitoring system and visually impaired people. Attention-based models, including transformer, are the current state-of-the-art architectures used in developing image captioning model. This study examines the works in the development of image captioning model, especially models that are developed based on attention mechanism. The architecture, the dataset, and the evaluation metrics analysis are done to the collected works. A general flow of image captioning model development is also presented. The literature search process carried out on Google Scholar. There are 36 literatures used in this study, including a specific image captioning development in Indonesian. It is done to take one point of view of image captioning development in a low resource language. Studies using transformer model generally achieves higher evaluation metric scores. In our finding, the highest evaluation scores on the consensus-based image description evaluation (CIDEr) c5 and c40 metrics are 138.5 and 140.5 respectively. This study gives a baseline on future development of image captioning model and brings the general concept of the image captioning development process including a picture of the development in low resource language.&lt;/span&gt;