Density Constraints Research Articles

Casimir wormholes inspired by electric charge in Einstein–Gauss–Bonnet gravity

This investigation assesses the feasibility of a traversable wormhole by examining the energy densities associated with charged Casimir phenomena. We focus on the influence of the electromagnetic field created by an electric charge as well as the negative energy density arising from the Casimir source. We have developed different shape functions by defining energy densities from this combination. This paper explores various configurations of Casimir energy densities, specifically those occurring between parallel plates, cylinders and spheres positioned at specified distances from each other. Furthermore, the impact of the generalized uncertainty principle correction is also examined. The behavior of wormhole conditions is evaluated based on the Gauss–Bonnet coupled parameter (μ) and electric charge (Q) through the electromagnetic energy density constraint. This is attributed to the fact that the electromagnetic field satisfies the characteristic ρ = −p r . Subsequently, we examine the active gravitational mass of the generated wormhole geometries and explore the behavior of μ and Q concerning active mass. The embedding representations for all formulated shape functions are examined. Investigations of the complexity factor of the charged Casimir wormhole have demonstrated that the values of the complexity factor consistently fall within a particular range in all scenarios. Finally, using the generalized Tolman–Oppenheimer–Volkoff equation, we examine the stability of the resulting charged Casimir wormhole solutions.

Spatiotemporal Modeling of Mitochondrial Network Architecture

In many cell types, mitochondria undergo extensive fusion and fission to form dynamic, responsive network structures that contribute to a number of homeostatic, metabolic, and signaling functions. The relationship between the dynamic interactions of individual mitochondrial units and the cell-scale network architecture remains an open area of study. In this work, we use coarse-grained simulations and approximate analytic models to establish how the network morphology is governed by local mechanical and kinetic parameters. The transition between fragmented structures and extensive networks is controlled by local fusion-to-fission ratios, network density, and geometric constraints. Similar fusion rate constants are found to account for the very different structures formed by mammalian networks (poised at the percolation transition) and well-connected budding yeast networks. Over a broad parameter range, the simulated network structures can be described by effective mean-field association constants that exhibit a nonlinear dependence on the microscopic nonequilibrium fusion, fission, and transport rates. Intermediate fusion rate constants are shown to result in the highest rates of network remodeling, with mammalian mitochondrial networks situated in a regime of high turnover. This spatially resolved modeling and simulation framework helps elucidate the emergence of cellular scale network structures, and allows for the quantitative extraction of microscopic kinetic parameters from past and future experimental data. Published by the American Physical Society 2024

Revisiting the decoupling limit of the Georgi-Machacek model with a scalar singlet

We study the connection between collider and dark matter phenomenology in the singlet extension of the Georgi-Machacek model. In this framework, the singlet scalar serves as a suitable thermal dark matter (DM) candidate. Our focus lies on the region vχ< 1 GeV, where vχ is the common vacuum expectation value of the neutral components of the scalar triplets of the model. Setting bounds on the model parameters from theoretical, electroweak precision and LHC experimental constraints, we find that the BSM Higgs sector is highly constrained. Allowed values for the masses of the custodial fiveplets, triplets and singlet are restricted to the range 140 GeV <MH50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {M}_{H_5^0} $$\\end{document}< 350 GeV, 150 GeV <MH30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {M}_{H_3^0} $$\\end{document}< 270 GeV and 145 GeV < MH< 300 GeV. The extended scalar sector provides new channels for DM annihilation into BSM scalars that allow to satisfy the observed relic density constraint while being consistent with direct DM detection limits. The allowed region of the parameter space of the model can be explored in the upcoming DM detection experiments, both direct and indirect. In particular, the possible high values of BR(H50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {H}_5^0 $$\\end{document} → γγ) can lead to an indirect DM signal within the reach of CTA. The same feature also provides the possibility of exploring the model at the High-Luminosity run of the LHC. In a simple cut-based analysis, we find that a signal of about 4σ significance can be achieved in final states with at least two photons for one of our benchmark points.

Dark matter from anomaly cancellation at the LHC

We discuss a class of theories that predict a fermionic dark matter candidate from gauge anomaly cancellation. As an explicit example, we study the predictions in theories where the global symmetry associated with baryon number is promoted to a local gauge symmetry. In this context, the symmetry-breaking scale has to be below the multi-TeV scale in order to be in agreement with the cosmological constraints on the dark matter relic density. The new physical “Cucuyo” Higgs boson in the theory has very interesting properties, decaying mainly into two photons in the low mass region, and mainly into dark matter in the intermediate mass region. We study the most important signatures at the Large Hadron Collider, evaluating the experimental bounds. We discuss the correlation between the dark matter relic density, direct detection, and collider constraints. We find that these theories are still viable and are susceptible to being probed in current, and future high-luminosity, running. Published by the American Physical Society 2024

Entropy optimization of MHD second-grade nanofluid thermal transmission along stretched sheet with variable density and thermal-concentration slip effects

The goal of present investigation is to explore the influence of exponential variable density and entropy optimization on second-grade nanofluid heating efficiency and mass-concentration transmission along extended surface using external magnetic-field and temperature-concentration slip effects. To enhance the motion of nanoparticles and thermal efficiency, the influence of exponential form of temperature-based density on magnetically charged second-grade nanomaterial is main novelty of this research. For higher temperature difference, the entropy optimization is used. The defined formulation of stream functions and similarities are used to convert leading second-grade nanofluid model into ordinary differential form. The efficient Keller box method and Newton Raphson technique are applied to compute numerical results. The final algebraic equations are solved through global matrix for unknown physical quantities. The consequence of all physical constraints on velocity/U profile, temperature/θ field, concentration/ϕ shapes, skin friction coefficient, Nusselt and Sherwood number are analyzed pictorially and numerically. The following range of parameters 0.1 ≤ ≤ 2.0, 0.0 ≤ ≤ 1.2, 0.1 ≤ ≤ 2.0, 0.07 ≤ ≤ 7.0, 0.01 ≤ ≤ 0.8, 0.01 ≤ ≤ 0.9 is used. It is found that velocity field increases with maximum amplitude as variable density, magnetic force and temperature-slip constraint. It is noted that the slip behavior in temperature field and concentration field are increased with convective boundary conditions. It is depicted that local Nusselt quantity and local Sherwood quantity increases as buoyancy force and Prandtl coefficient increases.

Role of decoupling process on the configurations of compact stars induced by Thomas-Fermi dark matter with null complexity in f(T) gravity

Our goal in this work is to find an anisotropic solution for a self-bound compact object composed of dark matter with a null complexity factor in f(T)-gravity theory. We use a well-known gravitational decoupling via complete geometric deformation (CGD) technique to examine the role of decoupling parameters on the configuration of compact objects. Initially, we derive the null complexity condition for f(T)-gravity decoupled system which leads to a relation between gravitational potentials. Next, we apply the CGD approach to split the decoupled system into two subsystems. The initial system refers to a pure f(T) gravity system consisting of an isotropic fluid distribution, where the isotropy criterion is equivalent to the condition in Einstein's gravity. The solution of the first system is solved through the Vlasenk-Pronin space-time metrics while the second system associated with the deformation function is solved by the density constraints method by mimicking a new source with Thomas-Fermi dark matter density profile that generates the anisotropy in the decoupled system. The physical validity of the anisotropic solution is checked by the graphical analysis of the pressure, density, energy, and stability conditions. We have also shown the effect of torsion and decoupling parameters on the configuration of anisotropic compact objects. The energy exchange (ΔE) of fluid distribution is also discussed. We found that ΔE is positive throughout the stellar configuration, which implies that energy is effectively transmitted to the surrounding environment.

Invariance of Mitochondria and Synapses in the Primary Visual Cortex of Mammals Provides Insight Into Energetics and Function.

The cerebral cortex accounts for substantial energy expenditure, primarily driven by the metabolic demands of synaptic signaling. Mitochondria, the organelles responsible for generating cellular energy, play a crucial role in this process. We investigated ultrastructural characteristics of the primary visual cortex in 18 phylogenetically diverse mammals, spanning a broad range of brain sizes from mouse to elephant. Our findings reveal remarkable uniformity in synapse density, postsynaptic density (PSD) length, and mitochondria density, indicating functional and metabolic constraints that maintain these fundamental features. Notably, we observed an average of 1.9 mitochondria per synapse across mammalian species. When considered together with the trend of decreasing neuron density with larger brain size, we find that brain enlargement in mammals is characterized by increasing proportions of synapses and mitochondria per cortical neuron. These results shed light on the adaptive mechanisms and metabolic dynamics that govern cortical ultrastructure across mammals.

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

On Uniqueness of an Optimal Solution to the Kantorovich Problem With Density Constraints

Abstract We study optimal transportation problems with constraints on densities of transport plans. We obtain a sharp condition for the uniqueness of an optimal solution to the Kantorovich problem with density constraints, namely that the Borel measurable cost function $h(x, y)$ satisfies the following non-degeneracy condition: $h(x, y)$ cannot be expressed as a sum of functions $u(x) + v(y)$ on a set of positive measure.

Peridynamic‐based modeling of elastoplasticity and fracture dynamics

AbstractThis paper introduces a particle‐based framework for simulating the behavior of elastoplastic materials and the formation of fractures, grounded in Peridynamic theory. Traditional approaches, such as the Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH), to modeling elastic materials have primarily relied on discretization techniques and continuous constitutive model. However, accurately capturing fracture and crack development in elastoplastic materials poses significant challenges for these conventional models. Our approach integrates a Peridynamic‐based elastic model with a density constraint, enhancing stability and realism. We adopt the Von Mises yield criterion and a bond stretch criterion to simulate plastic deformation and fracture formation, respectively. The proposed method stabilizes the elastic model through a density‐based position constraint, while plasticity is modeled using the Von Mises yield criterion within the bond of particle paris. Fracturing and the generation of fine fragments are facilitated by the fracture criterion and the application of complementarity operations to the inter‐particle connections. Our experimental results demonstrate the efficacy of our framework in realistically depicting a wide range of material behaviors, including elasticity, plasticity, and fracturing, across various scenarios.

Countability constraints in order-theoretic approaches to computability

AbstractComputability on uncountable sets has no standard formalization, unlike that on countable sets, which is given by Turing machines. Some of the approaches to define computability in these sets rely on order-theoretic structures to translate such notions from Turing machines to uncountable spaces. Since these machines are used as a baseline for computability in these approaches, countability restrictions on the ordered structures are fundamental. Here, we show several relations between the usual countability restrictions in order-theoretic theories of computability and some more common order-theoretic countability constraints, like order density properties and functional characterizations of the order structure in terms of multi-utilities. As a result, we show how computability can be introduced in some order structures via countability order density and multi-utility constraints.

Gravitational wave effects and phenomenology of a two-component dark matter model

We study an extension of the Standard Model (SM) which could have two candidates for dark matter (DM) including a Dirac fermion and a vector dark matter (VDM) under a new U(1) gauge group in the hidden sector. The model is classically scale-invariant and the electroweak symmetry breaks because of loop effects. We investigate the parameter space allowed by current experimental constraints and phenomenological bounds. We probe the parameter space of the model in the mass range 1<MV<5000\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1< M_V<5000$$\\end{document} GeV and 1<Mψ<5000\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1<M_{\\psi }<5000$$\\end{document} GeV. It has been shown that there are many points in this mass range that are in agreement with all phenomenological constraints. The electroweak phase transition has been discussed and it has been shown that there is region in the parameter space of the model consistent with DM relic density and direct detection constraints that, at the same time, can lead to first-order electroweak phase transition. The gravitational waves produced during the phase transition could be probed by future space-based interferometers such as LISA and BBO.

Generative Adversarial Network and Mutual-Point Learning Algorithm for Few-Shot Open-Set Classification of Hyperspectral Images

Existing approaches addressing the few-shot open-set recognition (FSOSR) challenge in hyperspectral images (HSIs) often encounter limitations stemming from sparse labels, restricted category numbers, and low openness. These limitations compromise stability and adaptability. In response, an open-set HSI classification algorithm based on data wandering (DW) is introduced in this research. Firstly, a K-class classifier suitable for a closed set is trained, and its internal encoder is leveraged to extract features and estimate the distribution of known categories. Subsequently, the classifier is fine-tuned based on feature distribution. To address the scarcity of samples, a sample density constraint based on the generative adversarial network (GAN) is employed to generate synthetic samples near the decision boundary. Simultaneously, a mutual-point learning method is incorporated to widen the class distance between known and unknown categories. In addition, a dynamic threshold method based on DW is devised to enhance the open-set performance. By categorizing drifting synthetic samples into known and unknown classes and retraining them together with the known samples, the closed-set classifier is optimized, and a (K + 1)-class open-set classifier is trained. The experimental results in this research demonstrate the superior FSOSR performance of the proposed method across three benchmark HSI datasets.

Quasi-solid electrolytes based on ZIF-8 to improve the electrochemical properties of lithium-ion batteries across a wide temperature range

Poor safety of liquid electrolytes and low ionic conductivity of solid electrolytes under room temperature are two major constraints for high energy density, long-term stability and safety of lithium-ion batteries (LIBs). In order to remedy the deficiencies, a quasi-solid electrolyte (QSE) is developed by immobilizing liquid electrolyte into the host of porous 2-Methylimidazole zinc salt (ZIF-8), and then mixing them with solid polymer electrolyte solvent of polyoxyethylene (PEO). The QSE has an ionic conductivity of 0.44 mS cm−1 at 20 °C and 2.27 mS cm−1 at 50 °C. The assembled LiFePO4/QSE/Li batteries exhibit excellent electrochemical performance in a wide temperature range. A reversible capacity of 129.7 mAh g−1 at 0.5 C and 20 °C and high capacity retentation of 94.7% at 0.5 C and 50 °C are achieved after 100 cycles. Especially, the LiFePO4/QSE/graphite pouch full cell exhibits a capacity retention of 97.7% at 0.5 C and 25 °C. These results indicate that QSE is a suitable candidate for enhancing the electrochemical performance of LIBs.

Phase transitions and gravitational waves in a model of ℤ3 scalar dark matter

Theories with more than one scalar field often exhibit phase transitions producing potentially detectable gravitational wave (GW) signal. In this work we study the semi-annihilating ℤ3 dark matter model, whose dark sector comprises an inert doublet and a complex singlet, and assess its prospects in future GW detectors. Without imposing limits from requirement of providing a viable dark matter candidate, i.e. taking into account only other experimental and theoretical constraints, we find that the first order phase transition in this model can be strong enough to lead to a detectable signal. However, direct detection and the dark matter thermal relic density constraint calculated with the state-of-the-art method including the impact of early kinetic decoupling, very strongly limit the parameter space of the model explaining all of dark matter and providing observable GW peak amplitude. Extending the analysis to underabundant dark matter thus reveals region with detectable GWs from a single-step or multi-step phase transition.

Iterative dynamics-based mesh discretisation for multi-scale coastal ocean modelling

Flow in coastal waters contains multi-scale flow features that are generated by flow separation, shear-layer instabilities, bottom roughness and topographic form. Depending on the target application, the mesh design used for coastal ocean modelling needs to adequately resolve flow features pertinent to the study objectives. We investigate an iterative mesh design strategy, inspired by hydrokinetic resource assessment, that uses modelled dynamics to refine the mesh across key flow features, and a target number of elements to constrain mesh density. The method is solver-agnostic. Any quantity derived from the model output can be used to set the mesh density constraint. To illustrate and assess the method, we consider the cases of steady and transient flow past the same idealised headland, providing dynamic responses that are pertinent to multi-scale ocean modelling. This study demonstrates the capability of an iterative approach to define a mesh density that concentrates mesh resolution across areas of interest dependent on model forcing, leading to improved predictive skill. Multiple design quantities can be combined to construct the mesh density, refinement can be applied to multiple regions across the model domain, and convergence can be managed through the number of degrees of freedom set by the target number of mesh elements. To apply the method optimally, an understanding of the processes being model is required when selecting and combining the design quantities. We discuss opportunities and challenges for robustly establishing model resolution in multi-scale coastal ocean models.

Effectively answering why questions on structural graph clustering

The pSCAN algorithm is widely acknowledged for its efficiency in structural graph clustering across diverse graph applications, frequently utilized to detect significant clusters within graphs. The results of pSCAN are constrained by two parameters: (1) the structural similarity constraint ϵ; (2) the density constraint μ. Nevertheless, determining appropriate values for these parameters proves challenging as users often lack the requisite professional knowledge. Consequently, users may inquire about the inclusion of unexpected vertices in specified clusters, posing why questions. In this paper, we aim to address these inquiries by investigating the challenge of answering why questions on structural graph clustering. The objective is to refine the initialized clustering parameters to exclude unexpected vertices from specified clusters. Initially, we introduce the crucial concept of label-vertices and delve into a unified explanation framework designed for addressing why questions on structural graph clustering. Subsequently, we propose two effective refining algorithms, namely FReEPS and FReMU, specifically tailored to modify the similarity constraint ϵ and the density constraint μ independently. Moreover, in our approach to enhancing efficiency, we introduce two baseline explanation algorithms, ReParFirstMUand ReParFirstEPS, simultaneously refining the parameters ϵ and μ to exclude unexpected vertices from specified clusters. Additionally, we explore two improved algorithms, FReParFirstMUand FReParFirstEPS, incorporating a process of pruning unnecessary parameter combinations and computations. This enhancement aims to improve the efficiency of simultaneously refining the two parameters for addressing these questions. Finally, we conducted comprehensive experiments on real social network datasets. The results illustrate that the explanation efficiency of FReEPS and FReMU surpasses that of the state-of-the-art explanation algorithms by 1-2 orders of magnitude. Moreover, FReMu exhibits faster performance than FReEPS. Furthermore, simultaneous refinement of the parameters ϵ and μ yields better-refined parameters. Additionally, FReParFirstMU and FReParFirstEPS outperform ReParFirstMU and ReParFirstEPS, respectively.

Optimal enzyme profiles in unbranched metabolic pathways.

How to optimize the allocation of enzymes in metabolic pathways has been a topic of study for many decades. Although the general problem is complex and nonlinear, we have previously shown that it can be solved by convex optimization. In this paper, we focus on unbranched metabolic pathways with simplified enzymatic rate laws and derive analytic solutions to the optimization problem. We revisit existing solutions based on the limit of mass-action rate laws and present new solutions for other rate laws. Furthermore, we revisit a known relationship between flux control coefficients and enzyme abundances in optimal metabolic states. We generalize this relationship to models with density constraints on enzymes and metabolites, and present a new local relationship between optimal reaction elasticities and enzyme amounts. Finally, we apply our theory to derive simple kinetics-based formulae for protein allocation during bacterial growth.

Optimization algorithm to generate a robust magnetic force of the magnetic navigation system against position error of a magnetic robot

We propose an optimization algorithm to generate a robust magnetic force against position error to achieve good controllability of the magnetic robot in the region of interest of a magnetic navigation system comprising electromagnets. We formulate an optimization problem that minimizes the norm of the spatial derivatives of the magnetic force subject to the constraints of the magnetic flux density and magnetic force. The robust magnetic force generated by the proposed algorithm was compared with conventional pseudo-inverse matrix method and verified through an experiment in which a magnetic robot was successfully steered along a bifurcated glass tube to reach a target position.