Natural Framework Research Articles

Exploring the causal paths and longitudinal impact of telework on health: the TeleHealth survey–data linkage study protocol

BackgroundThe transition from traditional office work to telework has accelerated significantly since the late 20th century, especially in light of the COVID-19 pandemic. Despite its widespread adoption, the long-term health...

Situation Networks: The emotions and activities that are central to nature-connectedness experiences

Situation Networks: The emotions and activities that are central to nature-connectedness experiences

Graph-structured variable selection with Gaussian Markov random field horseshoe prior

A graph structure is commonly used to characterize the dependence between variables, which may be induced by time, space, biological networks or other factors. Incorporating this dependence structure into the variable selection procedure can improve the identification of relevant variables, especially those with subtle effects. The Bayesian approach provides a natural framework to integrate this information through the prior distributions. In this work, we propose combining two priors that have been well studied separately-the Gaussian Markov random field prior and the horseshoe prior-to perform selection on graph-structured variables. Local shrinkage parameters that capture the dependence between connected covariates are specified to encourage similar amount of shrinkage for their regression coefficients, while a standard horseshoe prior is used for non-connected variables. After evaluating the performance of the method on different simulated scenarios, we present three applications: one in quantitative trait loci mapping with block sequential structure, one in near-infrared spectroscopy with sequential non-disjoint dependence and another in gene expression study with a general dependence structure.

The revival of U(1)Le-Lμ: a natural solution for (g-2)μ with a sub-GeV dark matter

The experiments searching for a hidden gauge sector with a leptophilic neutral gauge boson have already ruled out U(1)Le-Lμ as a feasible extension of the Standard Model (SM) gauge group (GSM) for explaining the observed discrepancy in (g-2)μ. The paper proposes a simple extension of the minimal particle content of GSM⊗U(1)Le-Lμ with a TeV-scale scalar leptoquark S1. Due to the non-trivial transformation of S1 under U(1)Le-Lμ, the model generates additional one-loop contributions to (g-2)μ, reviving the considered gauge extension within the experimentally allowed regions of the parameter space. The model can also accommodate a viable Dark Matter (DM) candidate χ – a vector-like SM-singlet fermion in the sub-GeV mass regime. The theory provides a natural framework to test the proposed DM phenomenology through electron excitation signals. Moreover, the DM-specific observables and (g-2)μ being connected through the New Physics (NP) parameters, the future beam dump experiments hunting for light, feebly interacting particles and the DM direct detection experiments are complementary to each other for constraining/falsifying the model.

Hamilton–Jacobi method in non-minimal coupling inflation: metric vs. Palatini

The Hamilton–Jacobi approach offers a natural framework for analyzing inflationary dynamics, relying on the specified Hubble parameter rather than the potential, particularly in extended gravity theories. In this study, we apply this method to investigate inflation with non-minimal coupling, comparing the metric and Palatini formulations. Using a power-law Hubble parameter in the Jordan frame, we find that both formalisms satisfy the attractor condition, with a slight suppression in the Palatini case. Our results align closely with the latest observational data, demonstrating the Palatini formalism’s heightened sensitivity to coupling constants and model parameters. Furthermore, we show that within the model’s viable parameter space, the reheating process can achieve sufficiently high temperatures to support successful leptogenesis.

Regularity Results for Hybrid Proportional Operators on Hölder Spaces

Recently, a new type of derivative has been introduced, known as Caputo proportional derivatives. These are motivated by the applications of such derivatives (which are a generalization of Caputo’s standard fractional derivative) and the need to incorporate such calculus into the research on operators. The investigation therefore focuses on the equivalence of differential and integral problems for proportional calculus problems. The operators are always studied in the appropriate function spaces. Furthermore, the investigation extends these results to encompass the more general notion of Hilfer hybrid derivatives. The primary aim of this study is to preserve the maximal regularity of solutions for this class of problems. To this end, we consider such operators not only in spaces of absolutely continuous functions, but also in particular in little Hölder spaces. It is widely acknowledged that these spaces offer a natural framework for the study of classical Riemann–Liouville integral operators as inverse operators with derivatives of fractional order. This paper presents a comprehensive study of this problem for proportional derivatives and demonstrates the application of the obtained results to Langevin-type boundary problems.

Discovery of sugar-based natural framework as phytopathogenic virus capsid protein inhibitors using a state-of-the-art multiple screening strategy.

The prompt and efficient identification of targeted inhibitors against unscrupulous pathogenic viruses holds promise for preventing epidemic disease outbreaks. Herein, a comprehensive multichannel screening method (multiple docking cross-validation, molecular dynamics simulation, and density functional theory calculation) integrated with bioactivity identification is rationally established using sugar-based natural ligand libraries to target tobacco mosaic virus (TMV) capsid proteins. Encouragingly, compounds A0 (Kd=0.14μM) and A4 (Kd=1.43μM) were evaluated to have excellent binding capacities to TMV capsid protein, evidently exceeding that of viricide Ningnanmycin (Kd=3.47μM) by 24.8 and 2.4-folds. Moreover, A0 and A4 significantly down-regulated the expression of capsid proteins at the transcriptional level, effectively blocking the biosynthesis and assembly of TMV in tobacco. Additionally, bioactivity evaluation illustrated that the anti-TMV curative effects of A0 (EC50=310.9μg/mL) and A4 (EC50=371.2μg/mL) were comparable to Ningnanmycin (EC50=343.8μg/mL). Considering the availability, cost and synthesis difficulty of precursors, the more affordable A4 is reckoned to be a promising candidate for capsid protein inhibitors and warrants further exploration in follow-up studies. Current findings highlight that this state-of-the-art virtual strategy, integrated with bioactivity validation, facilitates the discovery of targeted candidates to combat pathogenic viruses.

On particular integrability for (co)symplectic and (co)contact Hamiltonian systems

Abstract As a generalization and extension of our previous paper [Escobar-Ruiz
and Azuaje, J. Phys. A: Math. Theor. 57, 105202 (2024)], in this work, the
notions of particular integral and particular integrability in classical mechanics are
extended to the formalisms of cosymplectic, contact and cocontact geometries. This
represents a natural framework for studying dissipative systems, enabling a reduction
of the equations of motion and, in certain cases, allowing explicit solutions to be
found within a subset of the overall dynamics where integrability conditions are met.
Specifically, for Hamiltonian systems on cosymplectic, contact and cocontact manifolds,
it is demonstrated that the existence of a particular integral allows us to find certain
integral curves from a reduced, lower dimensional, set of Hamilton’s equations. In
the case of particular integrability, these trajectories can be obtained by quadratures.
Notably, for dissipative systems described by contact geometry, a particular integral
can be viewed as a generalization of the important concept of dissipated quantity as
well.

Dynamics of architectural and urban development of the Southern Baikal region

The present comprehensive study investigates the architectural and urban development of the Southern Baikal region, focusing on the defining conceptual role of the natural framework and historically established transit trade routes. The study is aimed at identifying traditional and innovative opportunities for transforming and optimizing residential areas, as well as at determining their formative trends under unique natural and climatic conditions. The paper is based on the analysis of specific urban planning factors within a multi-component discrete dynamic system of the territory, including the interaction between human activities and natural-climatic conditions throughout the historical development of the Slyudyansky District in Irkutsk Oblast. The study examines the experience of developing foreign coastal territories. The experience of Baikal territories has been studied and differentiated in the following areas: typology of natural-planning framework, typology of transport framework, typological aspects of existing development, trends in business programs, tendencies in the transformation of coastal territories. The factors of territorial-spatial system organization are determined by historically established transit trade routes in the structure of the natural framework of the Baikal region. The relevance of contemporary approaches to land use under current socio-economic conditions and the preservation and reclamation of natural conditions serve as the foundation for enhancing the quality of architecture of prospective developments and improving the structural stability under seismic conditions.

Optimal Control of Heat Equation by Coupling FVM and FEM Codes

In this paper, the optimal control theory is applied to a temperature optimization problem by coupling finite element and finite volume codes. The optimality system is split into the state and adjoint system. The direct problem is solved by the widely adopted finite volume OpenFOAM code and the adjoint-control equation using a variational formulation of the problem with the in-house finite element FEMuS code. The variational formulation of the problem is the natural framework for accurately capturing the control correction while OpenFOAM guarantees the accuracy of the state solution. This coupling is facilitated through the open-source MED and MEDCoupling libraries of the SALOME platform. The code coupling is implemented with the MED libraries and additional routines added in the FEMuS and OpenFOAM codes. We demonstrate the accuracy, robustness, and performance of the proposed approach with examples targeting different objectives using distributed and boundary controls in each case.

Using the natural capital framework to integrate biodiversity into sustainable, efficient and equitable environmental-economic decision-making.

One of Georgina Mace's many transformational research contributions was to provide a universally applicable framework for incorporating any or all elements and connections of the natural environment within conventional economic decision-making. We apply this natural capital framework to consider the overall effects of a suite of land-use policy options intended to promote the conservation and renewal of biodiversity. Options considered include sharing, sparing, three-compartment sparing, rewilding and organic farming regimes. Each is assessed in terms of its impact on both domestic and global biodiversity. Reinforced by an empirical application considering land use in Great Britain, we show that while policy has prioritized sharing options, evidence supports land sparing and three-compartment approaches as more efficient, sustainable and equitable alternatives.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

Black holes, cavities, and blinking islands

Placing a black hole in a cavity provides a natural framework for exploring gravitational scales, thermodynamic instabilities, and effective gravity theories. In this paper, we examine the evolution of entanglement entropy and entanglement islands in a two-sided extension of the Schwarzschild black hole in a cavity. By introducing a reflecting boundary in the eternal black hole exteriors, we regulate the infrared modes of Hawking radiation, finding that the entanglement entropy eventually saturates at a constant value. This value can be lower than the black hole’s thermodynamic entropy, thus avoiding the Page formulation of the information paradox. Regarding entanglement islands, we identify a universal effect induced by the boundary, which we term the “blinking island”—where the entanglement island temporarily disappears, resulting in a short-time information paradox. Published by the American Physical Society 2025

The problem of translatability of sad-related adjectives in Spanish and English

The objective of this study is to address translatability issues of the three sad-related adjectives in Spanish and English through the lens of Natural Semantic Metalanguage framework: a) triste/sad; b) abatido/dejected, depressed, and c) apesadumbrado/distressed, afflicted. Systematic corpus inquiry and semantic analysis was the methodology used to elaborate semantic explications of these adjectives in Spanish, which were contrasted to their English counterparts (Wierzbicka, 1972, 1992b). The findings point to the complexity of direct translation of these adjectives due to the following reasons. Firstly, in comparison to their English semi-equivalents, the explications of the Spanish adjectives proposed here are more meaning-specific and recognize more than one meaning for the same adjective (the cases of triste and abatido). The second problem derives from the combinatory with the two allolexes of to be in Spanish (ser, be permanently, and estar, be temporally). While in Spanish there is a distinction between feeling, attitude, and character trait for triste (sad), in English this distinction has not been described for sad. Finally, the explanations proposed here for the Spanish adjectives are richer in semantic features than the explanations of their English semi-equivalents. Although this prevents a direct comparison between both languages, the analysis led to some suggestions to lexicographers related to the definitions of emotions in bilingual dictionaries.

A SYSTEMATIC REVIEW OF THE GLOBAL AND SOUTH AFRICAN ORANGE INDUSTRY: COMPETITIVE CONDITIONS AND MARKET DYNAMICS

This systematic review analyzes the global and South African orange industries, with a focus on their competitive landscape and market dynamics. Oranges, which were initially cultivated in the Himalayan foothills, have evolved into the most widely produced fruit globally, characterized by significant production and export volumes. Employing a systematic literature review methodology, this study consolidates findings from research conducted between 2000 and 2023, highlighting challenges such as disease outbreaks, climate change, market regulations, and the increasing significance of sustainability in consumer preferences. The analysis identifies North America, particularly the United States, as the leader in the global orange market, while the Asia Pacific region demonstrates a rising demand for orange juice. South Africa, which boasts a rich citrus heritage dating back to 1654, exports the majority of its orange production but confronts challenges related to compliance with international standards and intensifying global competition. The review assesses critical factors influencing the industry, including the utilization of natural resources, regulatory frameworks, and government support. The findings underscore the necessity for innovative strategies and sustainable practices to sustain competitiveness. The study concludes with practical recommendations for stakeholders, advocating for enhanced research and development, improved disease management, and diversification into value-added products to meet evolving market demands. This review offers valuable insights for policymakers, industry participants, and researchers seeking to promote resilience and growth within the orange sector.

Practical Results for General Models of Temporal, Spatial and Semantic Text Processing

Large-scale language models (LLMs) dominate tasks such as natural language processing and computer vision, but using their spatial and temporal predictive power still needs to be improved. The main focus of the task described in this paper is to offer a technical solution that implements the Time Yards Model (TYM), which means putting in evidence time events and their evolution in a novel book or large text in an unsupervised manner. This article has a ground foundation and uses a manually annotated novel to train multiple state-of-the-art machine learning techniques to divide time segments. The aim is to identify specific text chunks based on several features (part-of-speech and part-of-sentence) to branch off the temporal tracks. This manuscript uses TimeML specification language to mark relevant words in time and space to achieve tempo-spatial investigation. Based on the observations, the proposed algorithm extensively analyses various novels presented for comparison, underlining their main strengths and weaknesses. Comprehensive experiments on diversified datasets show that our proposed solution successfully unlocks the potential for spatial and temporal forecasting with reasonable accuracy. Remarkably, our approach has achieved competitive performance compared to relevant papers from this field. Various text-processing methods are described, and different natural language processing frameworks and libraries are compared.

How natural capital accounting frameworks fail ecosystem services

How natural capital accounting frameworks fail ecosystem services

Generalization in multi-objective machine learning

AbstractModern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: We establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: All Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.

Enhancing Gesture-Based Interactions for Individuals with Disabilities through Dual-Attention Wireless Sensing Networks

This paper investigates the application of wireless sensing technology to develop a more natural and intuitive gesture-based interaction framework for individuals with disabilities. The study begins by identifying the primary challenges faced by this demographic when interacting with conventional devices. It then introduces a novel approach for WiFi-enabled gesture recognition and interpretation using dual attention networks. This methodology comprises two essential components: Channel State Information (CSI) preprocessing and a gesture recognition module. The paper details the implementation of these modules and elucidates their roles in enhancing gesture detection accuracy. Furthermore, the discussion extends to the future prospects of wireless sensing technologies, envisioning their integration into smart home systems, public services, and enhanced social interactions. The research underscores the transformative potential of integrating gesture-based interactions with wireless sensing to significantly elevate the quality of life for people with disabilities, suggesting a paradigm shift in how assistive technologies are developed and utilized.

Hierarchical Heuristic Species Delimitation Under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for 1 species versus MSC for 2 species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters (such as population split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here, we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (gdi) and implement them in a Python pipeline called hhsd. We characterize the behavior of the gdi under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as 3 empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to oversplitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

Supervised multiple kernel learning approaches for multi-omics data integration

BackgroundAdvances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.ResultsWe provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches.ConclusionMultiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.