Functional Framework Research Articles

The sustainable state: a meta-governance framework

ABSTRACT While there are established concepts of the green or environmental state, no clear idea exists of what constitutes a sustainable state and by what standards to evaluate such a state. This article aims to clarify the notion of the sustainable state in terms of a framework of sustainability-oriented meta-governance functions. To this end, a comprehensive idea of sustainability is linked with a meta-governance perspective on the contemporary state. Seven features of sustainability transformation are identified that challenge state governing and that the sustainable state must address in terms of seven meta-governance functions. This systematically derived framework expands on notions of the green or environmental state and sheds new light on the state’s role in governing sustainability transformation. The framework can serve as a basis for empirically assessing the extent to which states possess sustainability-oriented meta-governance capacities and for practically shaping state transformations toward sustainability.

In Support of Multidimensional Frailty: A Structural Equation Model from the Canadian Positive Brain Health Now Cohort.

The objective of this study was to estimate the structure and relationships between four h ypothesized frailty dimensions (physical, emotional, cognitive, and social) and the extent to which personal and HIV-related factors and comorbidity associate with these frailty dimensions. This is a secondary analysis of an existing dataset arising from Positive Brain Health Now study (n = 856) in people aging with HIV (mean age: 52.3 ± 8.1 years). Structural equation modeling (SEM) models were applied to two cross-sections of the data: one at study entry and one at second visit, 9-month apart. Multidimensional frailty was modeled based on the combined Wilson-Cleary and International Classification of Functioning, Disability and Health framework. Four dimensions were operationalized with patient-reported and self-report measures from standardized questionnaires. The SEM model from the first visit was replicated using data from the second visit, testing measurement invariance. The proposed model showed acceptable fit at both visits (including no violation of measurement invariance). The final model for the first visit showed that sex, body mass index, HIV diagnosis pre-1997, current or nadir CD4 counts, and comorbidity did not associate with any frailty dimension; however, age (β range: 0.12-0.25), symptoms (β range: -0.35 to -0.58), and measured cognition (β range: 0.10-0.24) directly associated with all frailty dimensions. The model remained stable across the two visits. This study contributes evidence for operationalizing multidimensional frailty. Evidence-based interventions are available for many of the measures considered here, offering opportunities to improve the lives of people with frailty in the context of HIV.

Investigating cognitive impairment, biopsychosocial barriers, and predictors of return to daily life among older stroke survivors.

The aim was to investigate the associations between cognitive impairment and biopsychosocial factors among older stroke survivors and predictors of poststroke return to daily life. This cross-sectional study involved 117 stroke survivors (61% men) with an average age of 77 years (range 65-91). The participants completed two questionnaires (Riksstroke and Short Form 36 questionnaires). The Montreal Cognitive Assessment (MoCA) was used to assess cognitive abilities. The International Classification of Functioning, Disability, and Health (ICF) framework guided the selection of biopsychosocial variables. We used Spearman's correlation coefficient and multiple logistic regression in the analyses. The average MoCA score was 21.7 points (range: 4-30, SD 5.6). The need for assistance from relatives and professionals, need for help with dressing and household chores, reliance on others for mobility, and reading and balance problems were correlated with more severe cognitive impairment (r = 0.20-0.33). Cognitive impairment, fatigue, and balance issues predicted an unfavorable return to daily life (odds ratio: 6.2-6.8). The study indicated that cognitive impairment is associated with difficulties in all ICF domains. Cognitive impairment, fatigue, and balance issues are associated with an unsuccessful return to daily life. Prioritizing these factors and screening for cognitive impairment with objective assessment tools may improve rehabilitation outcomes and enhance overall quality of life poststroke.

Functional Covalent Organic Frameworks' Microspheres Synthesized by Self-Limited Dynamic Linker Exchange for Stationary Phases.

Synthesizing uniform functional covalent organic framework (COF) microspheres is the prerequisite of applying COFs as novel stationary phases for liquid chromatography. However, the synthesis of functionalized COF microspheres is challenging due to the difficulty in maintaining microspheric morphology when conferring functions. Here, a facile and universal "self-limited dynamic linker exchange" strategy is developed to achieve surface functionalization of uniform COF microspheres. Six different types of COF microspheres are constructed, showing the universality and superiority of the strategy. The library of COF microspheres' stationary phases can be further enriched on demand by varying different functional building blocks. The "self-limited dynamic linker exchange" is attributed to the result of a delicate balance of reaction thermodynamics and molecular diffusion energy barrier. As a demonstration, the chiral functional COF microspheres are used as stationary phases of chiral chromatography and realized effective enantioseparation.

Bijective upsampling and learned embedding for point clouds correspondences

In this paper, we present a novel pipeline to compute and refine a data-driven solution for estimating the correspondence between 3D point clouds. Our method is compatible with the functional map framework, so it relies on a functional representation of the correspondence, but, differently from other similar approaches, this method is specifically designed to exploit this functional scenario for point cloud matching. Our new method merges a data-driven approach to compute functional basis and descriptors on the shape’s surface and a new refinement method designed for the learned basis. This refinement algorithm arises from a different way of converting functional operators into point-to-point correspondence, which we prove to promote bijectivity between maps, exploiting a theoretical result. Iterating this procedure and performing basis upsampling in the same way as other similar methods, ours increases the accuracy of the correspondence, leading to more bijective correspondences. Different from other approaches, our method allows us to train a functional basis, considering the refinement stage. Combining our new pipeline with an improved feature extractor, our solution outperforms previous methods in various evaluations and settings. We test our method over different datasets, comprising near-isometric and non-isometric pairs.

Neuropsychological Diagnosis and Assessment of Alexia: A Mixed-Methods Study.

The neuropsychological diagnosis and assessment of alexia remain formidable due to its multifaceted presentations and the intricate neural underpinnings involved. The current study employed a mixed-method design, incorporating cluster and thematic analyses, to illuminate the complexities of alexia assessment. We used the Web of Science and Scopus to retrieve articles spanning from 1985 to February 2024. Our selection was based on identified keywords in relation to the assessment and diagnosis of alexia. The analysis of 449 articles using CiteSpace (Version 6.3.R1) and VOSviewer (Version 1.6.19) software identified ten key clusters such as 'pure alexia' and 'posterior cortical atrophy', highlighting the breadth of research within this field. The thematic analysis of the most cited and recent studies led to eight essential categories. These categories were synthesized into a conceptual model that illustrates the interaction between neural, cognitive, and diagnostic aspects, in accordance with the International Classification of Functioning, Disability, and Health (ICFDH) framework. This model emphasizes the need for comprehensive diagnostic approaches extending beyond traditional reading assessments to include specific tasks like character identification, broader visual processing, and numerical tasks. Future diagnostic models should incorporate a diverse array of alexia types and support the creation of advanced assessment tools, ultimately improving clinical practice and research.

Armed Conflict in the Democratic Republic of Congo: A Moral-Rational Review and Need for a Functional Explanatory Framework in Understanding of African Armed Conflicts

Armed Conflict in the Democratic Republic of Congo: A Moral-Rational Review and Need for a Functional Explanatory Framework in Understanding of African Armed Conflicts

Classical density functional theory in three dimensions with GPU-accelerated automatic differentiation: Computational performance analysis using the example of adsorption in covalent-organic frameworks

We show how classical density functional theory can greatly benefit from algorithmic advances in machine learning, especially neural networks. By exploiting GPU-accelerated backward automatic differentiation, we overcome the often cumbersome and error-prone implementation of functional derivatives for classical density functional theory computations. This provides an efficient and straightforward solution for computing functional derivatives, opening up a wide range of applications. We show the gain in computational performance by using backward automatic differentiation to compute the functional derivatives on GPUs, and exemplify the use of this easy-to-implement and highly extensible classical density functional theory framework to predict the adsorption isotherms of a methane/ethane mixture described by a Helmholtz energy functional based on the PC-SAFT equation of state in the covalent-organic framework 2,3-DhaTph. Together with this manuscript, we provide the full classical density functional theory code as supplementary material.

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Fueling a greener tomorrow: The impact of energy diversification on green growth

AbstractMotivated by the necessity of attaining carbon neutrality and striking a balance between clean and conventional energy sources, the emphasis is on how urgent it is to combat climate change and make the switch to sustainable energy systems. In this context, the current study aims to examine the effects of energy diversification on green growth, considering such complementary factors as green technology, human capital, remittance inflows, foreign direct investment inflows, trade openness, and gross fixed capital formation. An empirical analysis is conducted by framing a green growth function in the panel data framework which is analyzed using the dynamic standard correlated model for the BRICS nations (Brazil, Russia, India, China, and South Africa) for the period between 1995 and 2020. The results show that (i) energy diversification exerts a dampening effect on the trajectory of green growth, (ii) the process of green growth was affected due to green technology and foreign direct investment inflows, (iii) underscores the pivotal role of human capital and gross fixed capital formation in bolstering the green growth trajectory, (iv) despite their potential relevance, remittance inflows and trade openness exhibit negligible impact within the framework of the green growth function, thus underscoring their limited contribution to the overarching sustainable development. The practical policy recommendations and invaluable insights provided by these empirical findings are instrumental in fostering green growth among the BRICS countries. Moreover, it contributes to the discourse on sustainable development by providing a solid foothold for informing the development of relevant policies in similar situations around the world.

Neurodynamics of Relational Aesthetic Engagement in Creative Arts Therapies

Aesthetic experiences, emerging saliently in the arts, play a pivotal role in transformative learning and creative processes that elicit physiological, affective, and cognitive responses associated with mental health indices. Interactions between subjects and aesthetic objects (e.g., visual artwork, music, and moving bodies) often entail elements of surprise and uncertainty that drive the inference of the hidden causes in the subject’s internal and external environment. These generate dynamics that align with the action-oriented Predictive Processing framework of brain function. Creative Arts Therapies (CATs) harness these dynamics by cultivating relational engagement using the arts modalities, prompting affective and cognitive processing. In this manuscript, we offer a review and conceptual analysis of recent empirical findings and theoretical premises that underpin aesthetic experiences and their relation to the psychotherapeutic use of the arts with a broad spectrum of populations and mental health conditions. We present a neuroscience-based approach to aesthetic intra- and inter-personal experiences, integrating therapeutic change factors of externalization-concretization, embodiment, and symbolization with functional network configurations, and interpersonal brain-to-brain coupling, to support predictive processing, learning, and creativity. Present and future interdisciplinary collaborations are underlined to elucidate the neurodynamic mechanisms driving psychological transformations, bridging neuroaesthetics and CATs.

Blow-up of solutions to the wave equations with memory terms in Schwarzschild spacetime

The main goal of this work is to discuss formation of singularities of solutions to the Cauchy problems for single wave equation and coupled system of wave equations with nonlinear memory terms in Schwarzschild spacetime. For the initial value problem of nonlinear wave equations, blow-up results of sign changing solutions are derived by employing the suitable test function technique which is related to the Riemann-Liouville fractional derivative and contradiction argument. Additionally, upper bound lifespan estimates of solutions to the small initial value problem of wave equations are established by imposing certain conditions on the initial values. It is worth to mention that crucial difficulty is the setting of test function framework, which has not been applied to the Schwarzschild spacetime in the previous literatures. To the best of our knowledge, the results in Theorems 1.1-1.4 are new.

The (Dis)unity of psychological (social) bias

ABSTRACT This paper explores the complex nature of social biases, arguing for a functional framework that recognizes their unity and diversity. The functional approach posits that all biases share a common functional role in overcoming underdetermination. This framework, I argue, provides a comprehensive understanding of how all psychological biases, including social biases, are unified. I then turn to the question of disunity, demonstrating how psychological social biases differ systematically in the mental states and processes that constitute them. These differences indicate that biases at various levels of the cognitive architecture require distinct treatment along at least two dimensions: epistemic evaluation and mitigation strategies. By examining social biases through this dual lens of unity and diversity, we can more effectively identify when and how to intervene on problematic biases. Ultimately, this approach provides a nuanced understanding of the nature of social bias, offering practical guidance for addressing existing biases and proactively managing emerging biases in both human and artificial minds.

Insight into compression-rebound behavior of lignocellulose-derived aerogel through finite element modeling based dynamic impact simulation

In this study, lignocellulose-derived aerogel (LA) with compression-rebound was synthesized from liquefied wood through sol-gel polymerization, aging and freeze-drying process. The results presented a gradual increase in the compressive strength, flexural strength, and maximum decomposition temperature of the LA sample with prolonged aging time from 3 to 10 h, demonstrating excellent elasticity and thermal stability. After more than 10 h, the bending strength began to decrease, indicating that the material’s rigidity was greater than its elastic toughness at this time. A nonlinear viscoelastic constitutive model based on the Ogden function and parallel rheological framework showed very high correlation with experimental measurement results, and also demonstrated admirable mechanical properties. These predictions can provide an effective approach for the application of LA in buffer packaging.

Quantifying the Impacts of Dry–Wet Combination Events on Vegetation Vulnerability in the Loess Plateau under a Changing Environment

Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.

Toward a systemic functional framework for the multimodal analysis of meaning reconstruction

AbstractIn recent decades, there has been a growing interest in multimodal translation studies. However, there is currently no suitable framework available for analyzing how the original meaning is reconstructed through the interaction of various semiotic resources in the multimodal target text. Under this context, this article proposes a systemic functional framework that integrates the systems of the meaning potential of semiotic resources, including language, sound (human voice), objects, space, and semiotic ensembles of the human body, and a tripartite classification of the relationships between semiotic resources. To exemplify the analytical potential of the framework, this article presents an example of analysis, taking an excerpt from Fences as the object of analysis.

Cr content dependent lattice distortion and solid solution strengthening in additively manufactured CoFeNiCrx complex concentrated alloys – a first principles approach

In the previous study, the authors reported that multicomponent CoFeNiCrx (x = 0, 12, 18, 24 at%) complex concentrated alloys fabricated through laser directed energy deposition experienced a substantial reduction in the saturation magnetization (Ms) and the Curie transition temperature (Tc) with the increase in Cr content. The present investigation additionally explores the effect of Cr concentration on the structural and strengthening behavior in laser directed energy deposition processed CoFeNiCrx alloy system. The novel approach adopted in the present work included the integration of experimental and computational efforts involving laser direct energy deposition (L-DED) based synthesis/fabrication of a series of new CoFeNiCrx alloys through in-situ compositional tuning which is often difficult using conventional techniques and prediction of mechanical properties of these compositionally tuned alloys using a computational methodology based on the density functional theory (DFT). X-ray diffraction and nanoindentation studies revealed that while there was a slight reduction in lattice parameters, the elastic modulus increased by approximately 30 % and the yield strength increased by 25 % with increasing Cr content. The density functional theory framework was employed to interpret the influence of Cr atoms on local lattice distortions and observed yield strength. The calculated local lattice distortion, in terms of first nearest neighbors, significantly increased with Cr concentration, which is attributed to the enlargement of Cr atomic radius in the alloyed multielement environment in comparison to its radius in the elemental form. This distortion at the local level caused solid solution strengthening. The theoretically predicted solid solution strengthening values using the Varvenne model are consistent with experimental results.

Taming Negative Ion Resonances Using Nonlocal Exchange-Correlation Functionals.

The characterization of negative ion resonances poses a fundamental challenge to density functional methods due to the unbound nature of resonances. To overcome this challenge, we propose one-particle nonlocal exchange-correlation (xc) potentials combining the exact-exchange (EXX) and the random phase approximation (RPA) correlation potentials. The negative ion resonances are identified by perturbing the real Hermitian nonlocal xc potentials using complex absorbing local potentials. Our studies show that the nonlocal EXX+RPA potential significantly enhances the description of positions and widths of negative ion resonance states compared to potentials that exclude dynamic polarization in RPA or include only EXX. The use of low-scaling algorithms simplifies the computation of the RPA potential, thereby providing a practical solution for resonance-state characterization within the density functional framework. A theoretical framework and the underlying assumptions required for combining real Hermitian nonlocal xc potentials with complex local potentials are discussed.

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

Functional repeated measures analysis of variance and its application

This paper is inspired by medical studies in which the same patients with multiple sclerosis are examined at several successive visits (doctor’s appointments) and described by fractional anisotropy tract profiles, which can be represented as f unctions. Since the observations for each patient are dependent random processes, they follow a repeated measures design for functional data. To compare the results for different visits, we thus consider functional repeated measures analysis of variance. For this purpose, a pointwise test statistic is constructed by adapting the classical test statistic for one-way repeated measures analysis of variance to the functional data framework. By integrating and taking the supremum of the pointwise test statistic, we create two global test statistics. In addition to verifying the general null hypothesis of the equality of mean functions corresponding to different objects, we also propose a simple method for post hoc analysis. We illustrate the finite sample properties of permutation and bootstrap testing procedures in an extensive simulation study. Finally, we analyze a real data example in detail. All methods are implemented in the R package rmfanova, available on CRAN.