Search Results (11,543)

A new formalism is introduced that makes it possible to elucidate the physical and geometric content of quantum space–time. It is based on the Minimum Group Representation Principle (MGRP). Within this framework, new results for entanglement and geometrical/topological phases are found and implemented in cosmological and black hole space–times. Our main results here are as follows: (i) We find the Berry phases for inflation and for the cosmological perturbations and express them in terms of the observables, such as the spectral scalar and tensor indices, $n_S$ and $n_T$, and the tensor-to-scalar ratio r. The Berry phase for de Sitter inflation is imaginary with the sign describing the exponential acceleration. (ii) The pure entangled states in the minimum group (metaplectic) $Mp\left(n\right)$ representation for quantum de Sitter space–time and black holes are found. (iii) For entanglement, the relation between the Schmidt type representation and the physical states of the $Mp\left(n\right)$ group is found: This is a new non-diagonal coherent state representation complementary to the known Sudarshan diagonal one. (iv) Mean value generators of $Mp\left(2\right)$ are related to the adiabatic invariant and topological charge of the space–time, (matrix element of the transition $-\infty <t<\infty$). (v) The basic even and odd n-sectors of the Hilbert space are intrinsic to the quantum space–time and its discrete levels (in particular, continuum for $n\to \infty$), they do not require any extrinsic generation process such as the standard Schrodinger cat states, and are entangled. (vi) The gravity or cosmological on one side and another of the Planck scale are entangled. Examples: The quantum primordial trans-Planckian de Sitter vacuum and the classical late de Sitter vacuum today; the central quantum gravity region and the external classical gravity region of black holes. The classical and quantum dual gravity regions of the space–time are entangled. (vii) The general classical-quantum gravity duality is associated with the Metaplectic $Mp\left(n\right)$ group symmetry which provides the complete full covering of the phase space and of the quantum space–time mapped from it. Full article

Cellular molecules interact with one another in a structured manner, defining a regulatory network topology that describes cellular mechanisms. Genetic mutations alter these networks’ pathways, generating complex disorders such as autism spectrum disorder (ASD). Boolean models have assisted in understanding biological system dynamics since Kauffman’s 1969 discovery, and various analytical tools for regulatory networks have been developed. This study examined the protein–protein interaction network created in our previous publication of four ASD patients using the SPIDDOR R package, a Boolean model-based method. The aim is to examine how patients’ genetic variations in INTS6L, USP9X, RSK4, FGF5, FLNA, SUMF1, and IDS affect mTOR and Wnt cell signaling convergence. The Boolean network analysis revealed abnormal activation levels of essential proteins such as β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD. These proteins affect gene expression, translation, cell adhesion, shape, and migration. Patients 1 and 2 showed consistent patterns of increased β-catenin activity and decreased MTORC1, RPS6, and eIF4E activity. However, patient 2 had an independent decrease in Cadherin and SMAD activity due to the FLNA mutation. Patients 3 and 4 have an abnormal activation of the mTOR pathway, which includes the MTORC1, RPS6, and eIF4E genes. The shared mTOR pathway behavior in these patients is explained by a shared mutation in two closely related proteins (SUMF1 and IDS). Diverse activities in β-catenin, MTORC1, RPS6, eIF4E, Cadherin, and SMAD contributed to the reported phenotype in these individuals. Furthermore, it unveiled the potential therapeutic options that could be suggested to these individuals. Full article

The accurate assessment of node influence is of vital significance for enhancing system stability. Given the structural redundancy problem triggered by the network topology deviation when an empirical network is copied, as well as the dynamic characteristics of the empirical network itself, it is difficult for traditional static assessment methods to effectively capture the dynamic evolution of node influence. Therefore, we propose a heuristic-based spatiotemporal feature node influence assessment model (HEIST). First, the zero-model method is applied to optimize the network-copying process and reduce the noise interference caused by network structure redundancy. Second, the copied network is divided into subnets, and feature modeling is performed to enhance the node influence differentiation. Third, node influence is quantified based on the spatiotemporal depth-perception module, which has a built-in local and global two-layer structure. At the local level, a graph convolutional neural network (GCN) is used to improve the spatial perception of node influence; it fuses the feature changes of the nodes in the subnetwork variation, combining this method with a long- and short-term memory network (LSTM) to enhance its ability to capture the depth evolution of node influence and improve the robustness of the assessment. Finally, a heuristic assessment algorithm is used to jointly optimize the influence strength of the nodes at different stages and quantify the node influence via a nonlinear optimization function. The experiments show that the Kendall coefficients exceed 90% in multiple datasets, proving that the model has good generalization performance in empirical networks. Full article

Heat dissipation significantly limits semiconductor component performance improvement. Thermal management devices are pivotal for electronic chip heat dissipation, with the enhanced thermal conductivity of materials being crucial for their effectiveness. This study focuses on single-crystal diamond, renowned for its exceptional natural thermal conductivity, investigating diamond microchannels using finite element simulations. Initially, a validated mathematical model for microchannel flow heat transfer was established. Subsequently, the heat dissipation performance of typical microchannel materials was analyzed, highlighting the diamond’s impact. This study also explores diamond microchannel topologies under high-power conditions, revealing unmatched advantages in ultra-high heat flux density dissipation. At 800 W/cm² and inlet flow rates of 0.4–1 m/s, diamond microchannels exhibit lower maximum temperatures compared to pure copper microchannels by 7.0, 7.2, 7.4, and 7.5 °C, respectively. Rectangular cross-section microchannels demonstrate superior heat dissipation, considering diamond processing costs. The exploration of angular structures with varying parameters shows significant temperature reductions with increasing complexity, such as a 2.4 °C drop at i = 4. The analysis of shape parameter k_i indicates optimal heat dissipation performance at k_i = 1.1. This research offers crucial insights for developing and optimizing diamond microchannel devices under ultra-high-heat-flux-density conditions, guiding future advancements in thermal management technology. Full article

Buildings consume significant energy, much of which is used for heating and cooling. Insulation reduces undesired heat transfer to save on heating and cooling energy usage. Radiant barriers are a type of insulation technology that reduces radiant heat absorbed by a structure. Applying radiant barriers to buildings reduces costs and improves both energy efficiency and occupant comfort. However, homes often have favorable thermal gradients that could also be used to reduce energy usage if the insulation properties were switched dynamically. This article introduces two dynamic radiant barriers intended for residential attics, which can switch between reflecting and transmitting states as needed. These radiant barriers are manufactured as a single deformable assembly using sheet materials and are compatible with various actuation mechanisms. The efficacy of these radiant barriers is reported based on a hotbox experiment and numerical calculations. The experimental results demonstrate that both proposed dynamic radiant barrier designs increase effective thermal resistance by factors of approximately 2 when comparing insulating to conducting states, and by approximately 4 when comparing the insulating state to the case without a radiant barrier. Additionally, the dynamic radiant barriers achieve heat flux reductions up to 41.9% in the insulating state compared to tests without a dynamic radiant barrier. Full article

A label-free biosensor based on a tunable MEMS metamaterial structure is proposed in this paper. The adopted structure is a one-dimensional array of metamaterial gratings with movable and fixed fingers. The moving unit of the optical detection system is a component of the MEMS structure, driven by the surface stress effect. Thus, these suspended optical nanoribbons can be moved and change the grating pattern by the biological bonds that happened on the modified cantilever surface. Such structural variations lead to significant changes in the optical response of the metamaterial system under illuminating angled light and subsequently shift its resonance wavelength spectrum. As a result, the proposed biosensor shows appropriate analytical characteristics, including the mechanical sensitivity of S_m = 11.55 μm/Nm⁻¹, the optical sensitivity of S_o = Δλ/Δd = 0.7 translated to S_o = Δλ/Δσ = 8.08 μm/Nm⁻¹, and the quality factor of Q = 102.7. Also, considering the importance of multi-biomarker detection, a specific design of the proposed topology has been introduced as an array for identifying different biomolecules. Based on the conducted modeling and analyses, the presented device poses the capability of detecting multiple biomarkers of disease at very low concentrations with proper precision in fluidic environments, offering a suitable bio-platform for lab-on-chip structures. Full article

Federated learning (FL) is a decentralized machine learning approach whereby each device is allowed to train local models, eliminating the requirement for centralized data collecting and ensuring data privacy. Unlike typical typical centralized machine learning, collaborative model training in FL involves aggregating updates from various devices without sending raw data. This ensures data privacy and security while collecting a collective learning from distributed data sources. These devices in FL models exhibit high efficacy in terms of privacy protection, scalability, and robustness, which is contingent upon the success of communication and collaboration. This paper explore the various topologies of both decentralized or centralized in the context of FL. In this respect, we investigated and explored in detail the evaluation of four widly used end-to-end FL frameworks: FedML, Flower, Flute, and PySyft. We specifically focused on vertical and horizontal FL systems using a logistic regression model that aggregated by the FedAvg algorithm. specifically, we conducted experiments on two images datasets, MNIST and Fashion-MNIST, to evaluate their efficiency and performance. Our paper provides initial findings on how to effectively combine horizontal and vertical solutions to address common difficulties, such as managing model synchronization and communication overhead. Our research indicates the trade-offs that exist in the performance of several simulation frameworks for federated learning. Full article

We revisit the interaction between a point charge and an inhomogeneous magnetic field that yields the magnetic quantum dot system. This magnetic field is defined by filling the whole space, except for a region of radius $r_0$. Then, we assume that there is an impenetrable potential wall located at $r_0$ and discuss the quantum effects of screw dislocation topology and the missing magnetic flux. We first show that Landau levels can be achieved even though there is the presence of an impenetrable potential wall. We go further by discussing the confinement of a point charge to a cylindrical wire. In both cases, we show Aharonov–Bohm-type effects for bound states can be obtained from the influence of the screw dislocation topology and the missing magnetic flux. Later, we discuss the influence of the screw dislocation topology and the missing magnetic flux on the magnetization and the persistent currents. Full article

Gymnosperms originated in the Middle Devonian and have experienced a long evolutionary history with pulses of speciation and extinction, which resulted in the four morphologically distinct extant groups, i.e., cycads, Ginkgo, conifers and gnetophytes. For over a century, the systematic relationships within the extant gymnosperms have been debated because different authors emphasized different characters. Recent phylogenomic studies of gymnosperms have given a consistent topology, which aligns well with extant gymnosperms classified into three classes, five subclasses, eight orders, and 13 families. Here, we review the historical opinions of systematics of gymnosperms with special reference to several problematic taxa and reconsider the evolution of some key morphological characters previously emphasized by taxonomists within a phylogenomic context. We conclude that (1) cycads contain two families, i.e., the Cycadaceae and the Zamiaceae; (2) Ginkgo is sister to cycads but not to conifers, with the similarities between Ginkgo and conifers being the result of parallel evolution including a monopodial growth pattern, pycnoxylic wood in long shoots, and the compound female cones, and the reproductive similarities between Ginkgo and cycads are either synapomorphic or plesiomorphic, e.g., the boat-shaped pollen, the branched pollen tube, and the flagellate sperms; (3) conifers are paraphyletic with gnetophytes nested within them, thus gnetophytes are derived conifers, and our newly delimited coniferophytes are equivalent to the Pinopsida and include three subclasses, i.e., Pinidae, Gnetidae, and Cupressidae; (4) fleshy cones of conifers originated multiple times, the Podocarpaceae are sister to the Araucariaceae, the Cephalotaxaceae and the Taxaceae comprise a small clade, which is sister to the Cupressaceae; (5) the Cephalotaxaceae are distinct from the Taxaceae, because the former family possesses typical female cones and the fleshy part of the seed is derived from the fleshiness of integument, while the latter family has reduced female cones and preserves no traces of the seed scale complexes. Full article

Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables efficient resource utilization but also introduces critical security vulnerabilities. An adversary could exploit this access to insert a dormant hardware trojan (HT) into the configuration bitstream, bypassing conventional security and verification measures. To address this security threat, we propose a supervised learning approach using deep recurrent neural networks (RNNs) for HT detection within FPGA configuration bitstreams. We explore two RNN architectures: basic RNN and long short-term memory (LSTM) networks. Our proposed method analyzes bitstream patterns, to identify anomalies indicative of malicious modifications. We evaluated the effectiveness on ISCAS 85 benchmark circuits of varying sizes and topologies, implemented on a Xilinx Artix-7 FPGA. The experimental results revealed that the basic RNN model showed lower accuracy in identifying HT-compromised bitstreams for most circuits. In contrast, the LSTM model achieved a significantly higher average accuracy of 93.5%. These results demonstrate that the LSTM model is more successful for HT detection in FPGA bitstreams. This research paves the way for using RNN architectures for HT detection in FPGAs, eliminating the need for time-consuming and resource-intensive reverse engineering or performance-degrading bitstream conversions. Full article

Zeolite-based catalysts efficiently catalyze the selective catalytic reduction of NO_x with methane (CH₄-SCR) for the environmentally friendly removal of nitrogen oxides, but suffer severe deactivation in high-temperature SO₂- and H₂O-containing flue gas. In this work, SSZ-39 zeolite (AEI topology) with high hydrothermal stability is reported for preparing CH₄-SCR catalysts. Mild acid leaching with oxalic acid (OA) not only modulates the Si/Al ratio of commercial SSZ-39 to a suitable value, but also removes some extra-framework Al atoms, introducing a small number of mesopores into the zeolite that alleviate diffusion limitation. Additional Co₃O₄ modification during indium exchange further enhances the catalytic activity of the resulting In-Co₃O₄/H-SSZ-39(OA). The optimized sample exhibits remarkable performance in CH₄-SCR under a gas hourly space velocity (GHSV) of 24,000 h⁻¹ and in the presence of 5 vol% H₂O. Even under harsh SO₂- and H₂O-containing high-temperature conditions, it shows satisfactory stability. Catalysts containing Co₃O₄ components demonstrate much higher CH₄ conversion. The strong mutual interaction between Co₃O₄ and Brønsted acid sites, confirmed by the temperature-programmed desorption of NO (NO-TPD), enables more stable N_xO_y species to be retained in In-Co₃O₄/H-SSZ-39(OA) to supply further reactions at high temperatures. Full article

Bismuth layered structure ferroelectrics (BLSFs), also known as Aurivillius phase materials, are ideal for energy-efficient applications, particularly for solar cells. This work reports the first comprehensive detailed theoretical study on the fascinating structural, electronic, and optical properties of XNb₂Bi₂O₉ (X = Ca, Ba, Be, Mg, Sr). The Perdew–Burke–Ernzerhof approach and generalized gradient approximation (GGA) are implemented to thoroughly investigate the structural, bandgap, optical, and electronic properties of the compounds. The optical conductivity, band topologies, dielectric function, bandgap values, absorption coefficient, reflectivity, extinction coefficient, refractive index, and partial and total densities of states are thoroughly investigated from a photovoltaics standpoint. The material exhibits distinct behaviors, including significant optical anisotropy and an elevated absorption coefficient > 10⁵ cm⁻¹ in the region of visible; ultraviolet energy is observed, the elevated transparency lies in the visible and infrared regions for the imaginary portion of the dielectric function, and peaks in the optical spectra caused by inter-band transitions are detected. According to the data reported, it becomes obvious that XNb₂Bi₂O₉ (X = Ca, Ba, Be, Mg, and Sr) is a suitable candidate for implementation in solar energy conversion applications. Full article

Wireless Local-Area Networks (WLANs), as a popular internet access solution, are widely used in numerous places, including enterprises, campuses, and public venues. As the number of devices increases, large-scale deployments will cause the problem of dense wireless networks, including a lot of energy consumption. Thus, the optimization of energy-efficient wireless AP devices has become a focal point of attention. To reduce energy consumption, we have proposed the active access-point (AP) configuration algorithm for WLANs using APs with a dual interface. This uses the greedy algorithm combined with the local search optimization method to find the minimum number of activated APs while satisfying the minimum throughput constraint. However, the previous algorithm basically satisfies only the average throughput among the multiple hosts associated with one AP, wherein some hosts may not reach the required one. In this paper, to overcome this limitation, we propose an enhanced active AP configuration algorithm by incorporating the throughput request satisfaction method that controls the actual throughput at the target value (target throughput) for every host by applying traffic shaping. The target throughput is calculated from the single and concurrent communicating throughput of each host based on channel occupancy time. The minimum throughput constraint will be iteratively adjusted to obtain the required target throughput and achieve the fair throughput allocation. For evaluations, we conducted simulations using the WIMNET simulator and experiments using the testbed system with a Raspberry Pi 4B for APs in four topology cases with five APs and ten hosts. The results show that the proposed method always achieved the required minimum throughput in simulations as well as in experiments, while minimizing the number of active APs. Thus, the validity and effectiveness of our proposal were confirmed. Full article

We formulate a data-independent latent space regularization constraint for general unsupervised autoencoders. The regularization relies on sampling the autoencoder Jacobian at Legendre nodes, which are the centers of the Gauss–Legendre quadrature. Revisiting this classic allows us to prove that regularized autoencoders ensure a one-to-one re-embedding of the initial data manifold into its latent representation. Demonstrations show that previously proposed regularization strategies, such as contractive autoencoding, cause topological defects even in simple examples, as do convolutional-based (variational) autoencoders. In contrast, topological preservation is ensured by standard multilayer perceptron neural networks when regularized using our approach. This observation extends from the classic FashionMNIST dataset to (low-resolution) MRI brain scans, suggesting that reliable low-dimensional representations of complex high-dimensional datasets can be achieved using this regularization technique. Full article

Exploring species diversity along elevational gradients is important for understanding the underlying mechanisms. Our study focused on analyzing the species diversity of fungal communities and their subcommunities at different trophic and taxonomic levels across three high mountains of the Korean Peninsula, each situated in a different climatic zone. Using high-throughput sequencing, we aimed to assess fungal diversity patterns and investigate the primary environmental factors influencing fungal diversity. Our results indicate that soil fungal diversity exhibits different elevational distribution patterns on different mountains, highlighting the combined effects of climate, soil properties, and geographic topology. Notably, the total and available phosphorus contents in the soil emerged as key determinants in explaining the differences in diversity attributed to soil properties. Despite the varied responses of fungal diversity to elevational gradients among different trophic guilds and taxonomic levels, their primary environmental determinants remained remarkably consistent. In particular, total and available phosphorus contents showed significant correlations with the diversity of the majority of the trophic guilds and taxonomic levels. Our study reveals the absence of a uniform diversity pattern along elevational gradients, underscoring the general sensitivity of fungi to soil conditions. By enriching our understanding of fungal diversity dynamics, this research enhances our comprehension of the formation and maintenance of elevational fungal diversity and the response of microbial communities in mountain ecosystems to climate change. This study provides valuable insights for future ecological studies of similar biotic communities. Full article