Overcoming bias in the comparison of human language and animal communication.

Human language is a powerful communicative and cognitive tool. Scholars have long sought to characterize its uniqueness, but each time a property is proposed to set human language apart (e.g., reference, syntax), some (attenuated) version of that property is found in animals. Recently, the uniqueness argument has shifted from linguistic rules to cognitive capacities underlying them. Scholars argue that human language is unique because it relies on ostension and inference, while animal communication depends on simple associations and largely hardwired signals. Such characterizations are often borne out in published data, but these empirical findings are driven by radical differences in the ways animal and human communication are studied. The field of animal communication has been dramatically shaped by the "code model," which imagines communication as involving information packets that are encoded, transmitted, decoded, and interpreted. This framework standardized methods for studying meaning in animal signals, but it does not allow for the nuance, ambiguity, or contextual variation seen in humans. The code model is insidious. It is rarely referenced directly, but it significantly shapes how we study animals. To compare animal communication and human language, we must acknowledge biases resulting from the different theoretical models used. By incorporating new approaches that break away from searching for codes, we may find that animal communication and human language are characterized by differences of degree rather than kind.

Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.

Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early studies on neonates suggest that individualized FC signatures are absent. We posit that individual uniqueness is present in neonatal FC data and that conventional linear models fail to capture the rapid developmental trajectories characteristic of newborn brains. To explore this hypothesis, we employed a deep generative model, known as a variational autoencoder (VAE), leveraging two extensive public datasets: one comprising resting-state functional MRI (rs-fMRI) scans from 100 adults and the other from 464 neonates. VAE models trained on rs-fMRI from both adults and newborns produced superior age prediction performance (with r between predicted- and actual age ∼ 0.7) and individual identification accuracy (∼45 %) compared to models trained solely on adult or neonatal data. The VAE model also showed significantly higher individual identification accuracy than linear models (=10∼30 %). Importantly, the VAE differentiated connections reflecting age-related changes from those indicative of individual uniqueness, a distinction not possible with linear models. Moreover, we derived 20 latent variables, each corresponding to distinct patterns of cortical functional network (CFNs). These CFNs varied in their representation of brain maturation and individual signatures; notably, certain CFNs that failed to capture neurodevelopmental traits, in fact, exhibited individual signatures. CFNs associated with neonatal neurodevelopment predominantly encompassed unimodal regions such as visual and sensorimotor areas, whereas those linked to individual uniqueness spanned multimodal and transmodal brain regions. The VAE's capacity to extract features from rs-fMRI data beyond the capabilities of linear models positions it as a valuable tool for delineating cognitive traits inherent in rs-fMRI and exploring individualized imaging phenotypes.

Analysis of Hybrid NAR-RBFs Networks for complex non-linear Covid-19 model with fractional operators.

The Hybrid NAR-RBFs Networks for COVID-19 fractional order model is examined in this scientific study. Hybrid NAR-RBFs Networks for COVID-19, that is more infectious which is appearing in numerous areas as people strive to stop the COVID-19 pandemic. It is crucial to figure out how to create strategies that would stop the spread of COVID-19 with a different age groups. We used the epidemic scenario in the Hybrid NAR-RBFs Networks as a case study in order to replicate the propagation of the modified COVID-19. In this research work, existence and stability are verified for COVID-19 as well as proved unique solutions by applying some results of fixed point theory. The developed approach to investigate the impact of Hybrid NAR-RBFs Networks due to COVID-19 at different age groups is relatively advanced. Also obtain solutions for a proposed model by utilizing Atanga Toufik technique and fractal fractional which are the advanced techniques for such type of infectious problems for continuous monitoring of spread of COVID-19 in different age groups. Comparisons has been made to check the efficiency of techniques as well as for finding the reliable solutions to understand the dynamical behavior of Hybrid NAR-RBFs Networks for non-linear COVID-19. Finally, the parameters are evaluated to see the impact of illness and present numerical simulations using Matlab to see actual behavior of this infectious disease for Hybrid NAR-RBFs Networks of COVID-19 for different age groups.

Solvability analysis for the Boussinesq model of heat transfer under the nonlinear Robin boundary condition for the temperature.

We consider the new boundary value problem for the generalized Boussinesq model of heat transfer under the inhomogeneous Dirichlet boundary condition for the velocity and under mixed boundary conditions for the temperature. It is assumed that the viscosity, thermal conductivity and buoyancy force in the model equations, as well as the heat exchange boundary coefficient, depend on the temperature. The mathematical apparatus for studying the inhomogeneous boundary value problem under study based on the variational method is being developed. Using this apparatus, we prove the main theorem on the global existence of a weak solution of the mentioned boundary value problem and establish sufficient conditions for the problem data ensuring the local uniqueness of the weak solution that has the additional property of smoothness with respect to temperature. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

New results on bifurcation for fractional-order octonion-valued neural networks involving delays.

This work chiefly explores fractional-order octonion-valued neural networks involving delays. We decompose the considered fractional-order delayed octonion-valued neural networks into equivalent real-valued systems via Cayley-Dickson construction. By virtue of Lipschitz condition, we prove that the solution of the considered fractional-order delayed octonion-valued neural networks exists and is unique. By constructing a fairish function, we confirm that the solution of the involved fractional-order delayed octonion-valued neural networks is bounded. Applying the stability theory and basic bifurcation knowledge of fractional order differential equations, we set up a sufficient condition remaining the stability behaviour and the appearance of Hopf bifurcation for the addressed fractional-order delayed octonion-valued neural networks. To illustrate the justifiability of the derived theoretical results clearly, we give the related simulation results to support these facts. Simultaneously, the bifurcation plots are also displayed. The established theoretical results in this work have important guiding significance in devising and improving neural networks.

A nonautonomous model for the interaction between a size-structured consumer and an unstructured resource.

In this paper, we propose and analyze a nonautonomous model that describes the dynamics of a size-structured consumer interacting with an unstructured resource. We prove the existence and uniqueness of the solution of the model using the monotone method based on a comparison principle. We derive conditions on the model parameters that result in persistence and extinction of the population via the upper-lower solution technique. We verify and complement the theoretical results through numerical simulations.

Fourier uniqueness in even dimensions.

In recent work, methods from the theory of modular forms were used to obtain Fourier uniqueness results in several key dimensions ([Formula: see text]), in which a function could be uniquely reconstructed from the values of it and its Fourier transform on a discrete set, with the striking application of resolving the sphere packing problem in dimensions [Formula: see text] and [Formula: see text] In this short note, we present an alternative approach to such results, viable in even dimensions, based instead on the uniqueness theory for the Klein-Gordon equation. Since the existing method for the Klein-Gordon uniqueness theory is based on the study of iterations of Gauss-type maps, this suggests a connection between the latter and methods involving modular forms. The derivation of Fourier uniqueness from the Klein-Gordon theory supplies conditions on the given test function for Fourier interpolation, which are hoped to be optimal or close to optimal.

Roles of astrocytes and prions in Alzheimer's disease: insights from mathematical modeling.

We present a mathematical model that explores the progression of Alzheimer's disease, with a particular focus on the involvement of disease-related proteins and astrocytes. Our model consists of a coupled system of differential equations that delineates the dynamics of amyloid beta plaques, amyloid beta protein, tau protein, and astrocytes. Amyloid beta plaques can be considered fibrils that depend on both the plaque size and time. We change our mathematical model to a temporal system by applying an integration operation with respect to the plaque size. Theoretical analysis including existence, uniqueness, positivity, and boundedness is performed in our model. We extend our mathematical model by adding two populations, namely prion protein and amyloid beta-prion complex. We characterize the system dynamics by locating biologically feasible steady states and their local stability analysis for both models. The characterization of the proposed model can help inform in advancing our understanding of the development of Alzheimer's disease as well as its complicated dynamics. We investigate the global stability analysis around the interior equilibrium point by constructing a suitable Lyapunov function. We validate our theoretical analysis with the aid of extensive numerical illustrations.

Global Existence and Weak-Strong Uniqueness for Chemotaxis Compressible Navier-Stokes Equations Modeling Vascular Network Formation.

A model of vascular network formation is analyzed in a bounded domain, consisting of the compressible Navier-Stokes equations for the density of the endothelial cells and their velocity, coupled to a reaction-diffusion equation for the concentration of the chemoattractant, which triggers the migration of the endothelial cells and the blood vessel formation. The coupling of the equations is realized by the chemotaxis force in the momentum balance equation. The global existence of finite energy weak solutions is shown for adiabatic pressure coefficients γ>8/5. The solutions satisfy a relative energy inequality, which allows for the proof of the weak-strong uniqueness property.

Deficiency and overexpression of Rtl1 in the mouse cause distinct muscle abnormalities related to Temple and Kagami-Ogata syndromes.

Temple and Kagami-Ogata syndromes are genomic imprinting diseases caused by maternal and paternal duplication of human chromosome 14, respectively. They exhibit different postnatal muscle-related symptoms as well as prenatal placental problems. Using the mouse models for these syndromes, it has been demonstrated that retrotransposon gag like 1 [Rtl1, also known as paternally expressed 11 (Peg11)] located in the mouse orthologous imprinted region is responsible for the prenatal placental problems because it is an essential placental gene for maintenance of fetal capillary network during gestation. However, the causative imprinted gene for the postnatal muscle-related symptoms remains unknown. Here, we demonstrate that Rtl1 also plays an important role in fetal/neonatal skeletal muscle development: its deletion and overproduction in mice lead to neonatal lethality associated with severe but distinct skeletal muscle defects, similar to those of Temple and Kagami-Ogata syndromes, respectively. Thus, it is strongly suggested that RTL1 is the major gene responsible for the muscle defects in addition to the placental defects in these two genomic imprinting diseases. This is the first example of an LTR retrotransposon-derived gene specific to eutherians contributing to eutherian skeletal muscle development.

On the decomposition of WKL!!

From the conceptual viewpoint, many mathematical propositions implicitly contain at least two kinds of principle. One is a logical principle such as the law-of-excluded-middle or De Morgan's law. Another is a function-existence principle. For both conceptual and practical reasons, it is an interesting enterprise to calibrate how amount of logical and function-existence principles are implicit in mathematical theorems and axioms. This is the topic of constructive reverse mathematics, which specifies necessary and sufficient axioms to prove each mathematical proposition constructively. In this paper, we decompose weak König's lemma with a uniqueness hypothesis [Formula: see text] by Moschovakis, into logical and function-existence principles in a recent framework of constructive reverse mathematics. This article is part of the theme issue 'Modern perspectives in Proof Theory'.

Preference for branded fresh produce and consumers' need for uniqueness: A mixed-methods investigation of consumer choice and thought process.

Branding is an important tool to increase a product's value. Fresh fruits and vegetables are largely unbranded. This study aimed to 1) investigate consumer preferences for brands (including brand transfers) of fresh produce, compared with other extrinsic attributes, 2) identify consumer segments and describe them with the psychological scale "food and beverage need for uniqueness" (FBNFU), and 3) explore consumer thought processing of brands during choice. We applied a concurrent mixed-methods approach: a discrete choice experiment and latent class analysis (quantitative) and a think-aloud interview (qualitative). Results showed that brands are not generally the most important attribute for choice. Experimental brand transfers received the lowest preference rating (i.e., part-worth utilities). However, for certain consumer segments, branding is most important. The consumer segments with the strongest preference for brands showed the highest level of FBNFU. Consumers' thought process during choice typically starts with brand recognition, and associations follow. Consumers with negative or no associations, or who were unfamiliar with the brand, disregarded the brand as the choice process progressed. Our results provide insights into the value of fresh-produce brands for consumers. Practitioners should target the FBNFU mindset and maximize brand awareness when promoting branded fresh produce.

Tourists' need for uniqueness and ethnic food purchase intention: A moderated serial mediation model.

Although the need for uniqueness has been extensively investigated by consumer researchers, food and tourism researchers have been silent on this promising research topic. This study examined food neophilia and ethnic food involvement as potential mediators of the association between tourists' need for uniqueness (TNFU) and ethnic food purchase intention. Additionally, it was tested whether independent self-construal moderates this serial mediation effect. Results showed that the relationship between tourists' need for uniqueness and ethnic food purchase intention was serially and positively mediated by food neophilia and ethnic food involvement. Furthermore, the moderated serial mediation effect was stronger at higher values of independent self-construal.

Potential hotspots of amphibian roadkill risk in Spain.

We test a forecasting strategy to identify potential hotspots of amphibian roadkill, combining the spatial distribution of amphibians, their relative risk of collision with vehicles and data on road density in Spain. We extracted a large dataset from studies reporting road casualties of 39 European amphibian species and then estimated the 'relative roadkill risk' of species as the frequency of occurrence of casualties for each amphibian and standardized by the range of distribution of the species in Europe. Using a map with the spatial distribution of Spanish amphibians at a spatial resolution of 10 × 10 Km squares, we estimated the 'cumulative relative risk of roadkill' for each amphibian assemblage as the sum of risk estimates previously calculated for each species. We also calculated the total length of roads in each square (road density). Finally, combining all layers of information, we elaborated a forecasting map highlighting the potential amphibian roadkill risk across Spain. Our findings are relevant to suggest areas that should be focused on at more detailed spatial scales. Additionally, we found that the frequency of roadkill was unrelated to the evolutionary distinctiveness score and conservation status of amphibian species, while was positively correlated with their distribution range.

Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission.

To achieve the aim of immediately halting spread of COVID-19 it is essential to know the dynamic behavior of the virus of intensive level of replication. Simply analyzing experimental data to learn about this disease consumes a lot of effort and cost. Mathematical models may be able to assist in this regard. Through integrating the mathematical frameworks with the accessible disease data it will be useful and outlay to comprehend the primary components involved in the spreading of COVID-19. There are so many techniques to formulate the impact of disease on the population mathematically, including deterministic modeling, stochastic modeling or fractional order modeling etc. Fractional derivative modeling is one of the essential techniques for analyzing real-world issues and making accurate assessments of situations. In this paper, a fractional order epidemic model that represents the transmission of COVID-19 using seven compartments of population susceptible, exposed, infective, recovered, the quarantine population, recovered-exposed, and dead population is provided. The fractional order derivative is considered in the Caputo sense. In order to determine the epidemic forecast and persistence, we calculate the reproduction number R 0 . Applying fixed point theory, the existence and uniqueness of the solutions of fractional order derivative have been studied . Moreover, we implement the generalized Adams-Bashforth-Moulton method to get an approximate solution of the fractional-order COVID-19 model. Finally, numerical result and an outstanding graphic simulation are presented.

Pandemic portfolio choice.

COVID-19 has taught us that a pandemic can significantly increase biometric risk and at the same time trigger crashes of the stock market. Taking these potential co-movements of financial and non-financial risks into account, we study the portfolio problem of an agent who is aware that a future pandemic can affect her health and personal finances. The corresponding stochastic dynamic optimization problem is complex: It is characterized by a system of Hamilton-Jacobi-Bellman equations which are coupled with optimality conditions that are only given implicitly. We prove that the agent's value function and optimal policies are determined by the unique global solution to a system of non-linear ordinary differential equations. We show that the optimal portfolio strategy is significantly affected by the mere threat of a potential pandemic.

The No-Cloning Life: Uniqueness and Complementarity in Quantum and Quantum-like Theories.

This article considers a rarely discussed aspect, the no-cloning principle or postulate, recast as the uniqueness postulate, of the mathematical modeling known as quantum-like, Q-L, modeling (vs. classical-like, C-L, modeling, based in the mathematics adopted from classical physics) and the corresponding Q-L theories beyond physics. The principle is a transfer of the no-cloning principle (arising from the no-cloning theorem) in quantum mechanics (QM) to Q-L theories. My interest in this principle, to be related to several other key features of QM and Q-L theories, such as the irreducible role of observation, complementarity, and probabilistic causality, is connected to a more general question: What are the ontological and epistemological reasons for using Q-L models vs. C-L ones? I shall argue that adopting the uniqueness postulate is justified in Q-L theories and adds an important new motivation for doing so and a new venue for considering this question. In order to properly ground this argument, the article also offers a discussion along similar lines of QM, providing a new angle on Bohr's concept of complementarity via the uniqueness postulate.

Senses along Which the Entropy Sq Is Unique.

The Boltzmann-Gibbs-von Neumann-Shannon additive entropy SBG=-k∑ipilnpi as well as its continuous and quantum counterparts, constitute the grounding concept on which the BG statistical mechanics is constructed. This magnificent theory has produced, and will most probably keep producing in the future, successes in vast classes of classical and quantum systems. However, recent decades have seen a proliferation of natural, artificial and social complex systems which defy its bases and make it inapplicable. This paradigmatic theory has been generalized in 1988 into the nonextensive statistical mechanics-as currently referred to-grounded on the nonadditive entropy Sq=k1-∑ipiqq-1 as well as its corresponding continuous and quantum counterparts. In the literature, there exist nowadays over fifty mathematically well defined entropic functionals. Sq plays a special role among them. Indeed, it constitutes the pillar of a great variety of theoretical, experimental, observational and computational validations in the area of complexity-plectics, as Murray Gell-Mann used to call it. Then, a question emerges naturally, namely In what senses is entropy Sq unique? The present effort is dedicated to a-surely non exhaustive-mathematical answer to this basic question.

Piecewise Business Bubble System under Classical and Nonsingular Kernel of Mittag-Leffler Law.

This study aims to investigate the dynamics of three agents in the emerging business bubble model based on the Mittag-Leffler law pertaining to the piecewise classical derivative and non-singular kernel. By generalizing the business bubble dynamics in terms of fractional operators and the piecewise concept, this study presents a new perspective to the field. The entire set of intervals is partitioned into two piecewise intervals to analyse the classical order and conformable order derivatives of an Atangana-Baleanu operator. The subinterval analysis is critical for removing discontinuities in each sub-partition. The existence and uniqueness of the solution based on a piecewise global derivative are tested for the considered model. The approximate root of the system is determined using the piecewise numerically iterative technique of the Newton polynomial. Under the classical order and non-singular law, the approximate root scheme is applied to the piecewise derivative. The curve representation for the piece-wise globalised system is tested by applying the data for the classical and different conformable orders. This establishes the entire density of each compartment and shows a continuous spectrum instead of discrete dynamics. The concept of this study can also be applied to investigate crossover behaviours or abrupt changes in the dynamics of the values of each market.

Concert experiences in virtual reality environments.

Spurred by recent advances in digital technologies, virtual concerts have become established modes for event attendance and represent a rapidly growing segment of the music industry. Yet, up to now, general experience of virtual concert attendees remains largely underexplored. Here, we focus on a subcategory in this domain: music concerts in virtual reality (VR). Our approach is situated within the theoretical framework of embodied music cognition and entailed investigation through a survey study. Responses of seventy-four VR concert attendees were collected, consisting of demographics, motivations, experiences, and future perspectives. In contrast to previous research, which generally identified social connectedness as a main motivator for concert attendance, our sample regarded it as one of the least important incentives. On the other hand, in line with previous studies, 'seeing specific artists perform' and 'uniqueness of the experience', were pivotal. The latter was mostly fueled by the possibility to experience/interact with visuals and environments considered as unconceivable in the real world. Furthermore, 70% of our sample regarded VR concerts as 'the future of the music industry', mainly relating to the accessibility of such events. Positive evaluations of VR concert experiences, as well as future perspectives regarding the medium, were significantly influenced by the level of experienced immersivity. To our knowledge, this is the first study to provide such an account. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-023-00814-y.