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The wormhole attack is one of the most treacherous attacks projected at the routing layer that can bypass cryptographic measures and derail the entire communication network. It is too difficult to prevent a priori; all the possible countermeasures are either too expensive or ineffective. Indeed, literature solutions either require expensive hardware (typically UWB or secure GPS transceivers) or pose specific constraints to the adversarial behavior (doing or not doing a suspicious action). The proposed solution belongs to the second category because the adversary is assumed to have done one or more known suspicious actions. In this solution, we adopt a heuristic approach to detect wormholes in ad hoc networks based on the detection of their illicit behaviors. Wormhole and post wormhole attacks are often confused in literature; that's why we clearly state that our methodology does not provide a defence against wormholes, but rather against the actions that an adversary does after the wormhole, such as packet dropping, tampering with TTL, replaying and looping, etc. In terms of contributions, the proposed solution addresses the knock-out capability of attackers that is less targeted by the researcher's community. In addition, it neither requires any additional hardware nor a change in it; instead, it is compatible with the existing network stack. The idea is simulated in ns2.30, and the average detection rate of the proposed solution is found to be 98-99%. The theoretical time to detect a wormhole node lies between 0.07-0.71 seconds. But, from the simulation, the average detection and isolation time is 0.67 seconds. In term of packet loss, the proposed solution has a relatively overhead of [Formula: see text] 22%. It works well in static and mobile scenarios, but the frame losses are higher in mobile scenarios as compared to static ones. The computational complexity of the solution is O(n). Simulation results advocate that the solution is effective in terms of memory, processing, bandwidth, and energy cost. The solution is validated using statistical parameters such as Accuracy, Precision, F1-Score and Matthews correlation coefficient ([Formula: see text]).

According to the ER = EPR conjecture, entangled particles are connected by quantum wormholes. Under the assumption that some of the electric field surrounding an entangled charged particle leaks into the wormhole, we show that this effect will modify the hyperfine structure of the hydrogen atom. In addition, if the quantum wormholes are nontraversable, this will also lead to a nonzero total effective charge for the hydrogen atom. These effects provide strong constraints on the amplitude of this potential ER = EPR effect, given high-precision measurements of the hydrogen atom's hyperfine structure and total charge.

Quantum models of spacetime have been conjectured to hypothetically allow for the formation of Planck scale wormholes. Building on the proposal of Morris, Thorne, and Yurtsever that such microscopic spacetime structures might be enlarged to macroscopic size, we revisit Roman's analysis of a wormhole in an inflationary de Sitter background. In this context, we introduce a refined quasi-local toy mechanism, which we call the local inflation bubble. This construction inflates a compact region of spacetime and thereby magnifies the underlying microstructure. Using the Einstein equations we determine the required stress-energy to sustain the bubble and obtain intrinsic lower bounds for the corresponding energy density, while acknowledging the continued reliance on exotic matter.

In this work, we have studied the spin-dependent quantum transport of charged fermion on ( 2 + 1 ) -dimensional spacetime, whose spatial part is described by a wormhole-type geometry in the presence of constant axial magnetic flux. Choosing the solutions of the Dirac equation associated with real energy and momentum, we explored the spin-dependent transmission probabilities and giant magnetoresistance (GMR) through a single layer of wormhole graphene with an external magnetic field, using the transition matrix (T-Matrix) approach. The spin-up and spin-down components within the A and B sublattices of graphene in the matrix of 4 × 1 wave function are coupled to each other due to the wormhole structure and the magnetic field. We have found that transport properties strongly depend on the magnetic field, incident energy, and geometric parameters of the system. We observed that the transmission probability increases as the radius of the wormhole increases, and the length of the wormhole decreases. The higher energies lead to a decrease in the transmission probabilities of particles. Furthermore, we observed that the probability of the spin-flip effect is almost larger than that of the non-spin-flip effect, illustrating that electrons lose their spins during transmission. These findings highlight the complex and interesting behavior of wormhole graphene in the presence of external magnetic fields and suggest that these nano structures can have potential applications in electronic and spintronic devices.

Optimal transport (OT) and the related Wasserstein metric ( W ) are powerful and ubiquitous tools for comparing distributions. However, computing pairwise Wasserstein distances rapidly becomes intractable as cohort size grows. An attractive alternative would be to find an embedding space in which pairwise Euclidean distances map to OT distances, akin to standard multidimensional scaling (MDS). We present Wasserstein Wormhole, a transformer-based autoencoder that embeds empirical distributions into a latent space wherein Euclidean distances approximate OT distances. Extending MDS theory, we show that our objective function implies a bound on the error incurred when embedding non-Euclidean distances. Empirically, distances between Wormhole embeddings closely match Wasserstein distances, enabling linear time computation of OT distances. Along with an encoder that maps distributions to embeddings, Wasserstein Wormhole includes a decoder that maps embeddings back to distributions, allowing for operations in the embedding space to generalize to OT spaces, such as Wasserstein barycenter estimation and OT interpolation. By lending scalability and interpretability to OT approaches, Wasserstein Wormhole unlocks new avenues for data analysis in the fields of computational geometry and single-cell biology. Software is available at http://wassersteinwormhole.readthedocs.io/en/latest/.

What do the typical entangled states of two black holes look like? Do they contain semiclassical interiors? We approach these questions constructively, providing ensembles of states that densely explore the black hole Hilbert space. The states contain very long Einstein-Rosen caterpillars: semiclassical wormholes with large numbers of matter inhomogeneities. Distinguishing these ensembles from the typical entangled states of the black holes is hard. We quantify this by deriving the correspondence between a microscopic notion of quantum randomness and the geometric length of the wormhole. This formalizes a "complexity = geometry" relation.

Wireless ad-hoc networks operate independently of existing infrastructure, using devices like access points to connect end-user computing devices. Current methods face issues such as low detection accuracy, structural deviation, and extended processing times. This paper proposes a cross-layer approach that leverages knowledge from the physical and Medium Access Control (MAC) layers, which is then shared with higher layers to effectively mitigate wormhole and blackhole attacks. A wormhole attack disrupts communication through tunneling, while a blackhole attack manipulates network traffic by impersonating the source. The proposed cross-layer framework integrates the network, MAC, and physical layers, and is independent of specific network protocols. The physical layer handles channel interference, the network layer manages process handling, and the MAC layer oversees bandwidth information and tracks failed transmissions. Performance metrics are measured in seconds. The Enhanced Support Vector Machine (E-SVM) algorithm, implemented using NS3 software, demonstrates superior performance compared to traditional SVM techniques across multiple metrics, including average energy consumption, average remaining energy, packets received, packet delivery ratio, delay, jitter, throughput, normalized overhead, dropping ratio, and goodput. Simulation results show that E-SVM achieves a 12.5% dropping ratio, 98.459% energy consumption, and an 89.2879% packet delivery ratio, outperforming existing SVM techniques across various network sizes.

The motivation for constructing a thin-shell wormhole from a (2+1)-dimensional rotating black hole arises from the desire to study the effects of a nonminimally coupled scalar field in this particular spacetime. By investigating the behavior of such a field in the presence of rotation, we can gain insights into the interplay between gravity and scalar fields in lower-dimensional systems. Additionally, this construction allows us to explore potential connections between black hole physics and exotic phenomena like traversable wormholes. The radial perturbation around the equilibrium throat radius is considered to explore the stable configuration for specific values of physical parameters. Then, the equations of state, specifically the phantom-like and generalized Chaplygin gas model for exotic matter is used to conduct an extensive investigation into the stability of the counter-rotating thin-shell wormholes. Our results show that the presence of a scalar field enhances the stability of the counter-rotating thin-shell wormholes.

This work investigates the generalization behavior of deep neural networks (DNNs), focusing on the phenomenon of "fooling examples," where DNNs confidently classify inputs that appear random or unstructured to humans. To explore this phenomenon, we introduce an analytical framework based on maximum likelihood estimation (MLE), without adhering to conventional numerical approaches that rely on gradient-based optimization and explicit labels. Our analysis reveals that DNNs operating in an overparameterized regime exhibit a collapse in the output feature space. While this collapse improves network generalization, adding more layers eventually leads to a state of degeneracy, where the model learns trivial solutions by mapping distinct inputs to the same output, resulting in zero loss. Further investigation demonstrates that this degeneracy can be bypassed using our newly derived "wormhole" solution. The wormhole solution, when applied to arbitrary fooling examples, reconciles meaningful labels with random ones and provides a novel perspective on shortcut learning. These findings offer deeper insights into DNN generalization and highlight directions for future research on learning dynamics in unsupervised settings to bridge the gap between theory and practice.

We develop a nonperturbative definition of RMT_{2}: a generalization of random matrix theory that is compatible with the symmetries of two-dimensional conformal field theory. Given any random matrix ensemble, its n-point spectral correlations admit a prescribed modular-invariant lift to RMT_{2}, which moreover reduce to the original random matrix correlators in a near-extremal limit. Central to the prescription is a presentation of random matrix theory in Mellin space, which lifts to two dimensions via the SL(2,Z) spectral decomposition employed in previous work. As a demonstration we perform the explicit RMT_{2} lift of two-point correlations of the GUE Airy model. We propose that in AdS_{3} pure gravity, semiclassical amplitudes for off-shell n-boundary torus wormholes with topology Σ_{0,n}×S^{1} are given by the RMT_{2} lift of JT gravity wormhole amplitudes. For the three-boundary case, we identify a gravity calculation which matches the RMT_{2} result.

Fluid-driven flow of granular material leads to complex behaviour and emergent instabilities in many natural and industrial settings. However, the effect of using fluid flow to vertically drive a dense bed of sedimenting grains is not well documented. Here we find contrasting behaviours in a submerged fluid-driven silo, including fingering patterns, porous flow, classical silo flow, and the formation of straight, semi-dilute wormhole-like channels. Once formed, these channels rapidly propagate towards the outlet and act as a bypass of the wider packing. The onset of this instability occurs when the gravity-driven grain flow at the free surface is insufficient to supply the fluid-assisted central region below the interface. Balancing empirical models of these flows predicts the height at which channels emerge as a function of grain size and flow rate. These findings provide a framework for predicting and controlling fluid-grain interactions in natural hazards, industrial processing, and geophysical flows.

The technique of matrix acidification or acid fracturing is commonly utilized to establish communication with natural fractures during reservoir reconstruction. However, this process often encounters limitations due to filtration, which restricts the expansion of the primary acid-etching fracture. To address this issue, a computational model has been developed to simulate the expansion of an acid-etching wormhole by considering various factors such as formation process, injection duration, pressure build-up, and time-varying acid percolation rate. By analyzing the pumping displacement of acid-etching wormholes, this model provides valuable insights into the time-dependent quantities of acid percolation. It has been revealed that the filtration rate of acid-etching wormholes is strongly influenced by pumping displacement, viscosity, and concentration of the acid fluid used in stimulation as well as physical properties of the reservoir itself. Notably, viscosity plays a significant role in determining the effectiveness of acid fracturing especially in low-viscosity conditions. Acid concentration within 15% to 20% exhibits maximum impact on successful acid fracturing while concentrations below 15% or above 20% show no obvious effect. Furthermore, it was found that pumping displacement has a major influence on effective fracturing. However, beyond a certain threshold (> 5.0 m3/min), increased pumping displacement leads to slower etching distance for acids used in construction purposes. The simulation also provides real-time distribution analysis for acidity levels within eroded fractures during matrix-acidification processes and quantifies extent of chemical reactions between acids and rocks within these fractures thereby facilitating optimization efforts for design parameters related to matrix-acidification.

The concepts of the multiverse and wormholes in dimensions beyond our physical space have long captivated curiosity and imagination, yet experimental demonstrations remain elusive. In this work, we employ nonlocal artificial materials to construct a photonic analogy of parallel spaces, where two distinct effective optical media coexist within a single artificial material, each accessible through different material boundaries. Enhanced by deep learning, this method further enables the analogies of two fascinating phenomena: photonic wormholes as invisible optical tunnels, and photonic multiple realities, where two different optical devices or scatterers function independently at the same location as if they exist in separate dimensions. Our findings empower optical designs to transcend the limitations of physical dimensions effectively, paving the way for an unprecedented degree of freedom in multiplexing.

Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance. This improvement is achieved by incorporating a Gaussian barebone mechanism, a wormhole strategy, and an elimination strategy into the MFO. To assess the effectiveness of BWEMFO, a series of comparison experiments is conducted, comparing it against conventional metaheuristic algorithms, advanced metaheuristic algorithms, and various MFO variants. The experimental results reveal a significant enhancement in both the convergence speed and the capability to escape local optima with the implementation of BWEMFO. The scalability of the algorithm is confirmed through benchmark functions. Employing BWEMFO, we optimize the kernel parameters of the kernel-limit learning machine, thereby crafting the BWEMFO-KELM methodology for medical diagnosis and prediction. Subsequently, BWEMFO-KELM undergoes diagnostic and predictive experimentation on three distinct medical datasets: the breast cancer dataset, colorectal cancer datasets, and mammographic dataset. Through comparative analysis against five alternative machine learning methodologies across four evaluation metrics, our experimental findings evince the superior diagnostic accuracy and reliability of the proposed BWEMFO-KELM model.

The paper examines the dynamics of asymmetric thin shell wormholes that connect two distinct spacetimes using the cut and paste technique. The focus is on analyzing the linear stability of these wormholes by considering radial perturbations and utilizing the modified generalized Chaplygin gas equation of state. The specific case of an asymmetric wormhole connecting Schwarzschild-Rindler spacetime to Schwarzschild-Rindler-de Sitter space-time is analyzed using this formalism. Our investigation uncovers the existence of both stable and unstable regions, which are contingent upon the appropriate selection of various parameters within the metric spacetime and equation of state. Additionally, we determine that stability regions exist as a consequence of the square speed of sound. By increasing the value of the cosmological constant, the stability region is expanded. Furthermore, the stability regions are augmented by the influence of Rindler parameters, while the stability regions are also affected by adjustments in the equation of state parameters, leading to their enlargement.

The Internet of Things (IoT) is revolutionizing diverse sectors like business, healthcare, and the military, but its widespread adoption has also led to significant security challenges. IoT networks, in particular, face increasing vulnerabilities due to the rapid proliferation of connected devices within smart infrastructures. Wireless sensor networks (WSNs) comprise software, gateways, and small sensors that wirelessly transmit and receive data. WSNs consist of two types of nodes: generic nodes with sensing capabilities and gateway nodes that manage data routing. These sensor nodes operate under constraints of limited battery power, storage capacity, and processing capabilities, exposing them to various threats, including wormhole attacks. This study focuses on detecting wormhole attacks by analyzing the connectivity details of network nodes. Machine learning (ML) techniques are proposed as effective solutions to address these modern challenges in wormhole attack detection within sensor networks. The base station employs two ML models, a support vector machine (SVM) and a deep neural network (DNN), to classify traffic data and identify malicious nodes in the network. The effectiveness of these algorithms is validated using traffic generated by the NS3.37 simulator and tested against real-world scenarios. Evaluation metrics such as average recall, false positive rates, latency, end-to-end delay, response time, throughput, energy consumption, and CPU utilization are used to assess the performance of the proposed models. Results indicate that the proposed model outperforms existing methods in terms of efficacy and efficiency.

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In the infrared limit, a nearly anti-de Sitter spacetime in two dimensions (AdS_{2}) perturbed by a weak double trace deformation and a two-site (q>2)-body Sachdev-Ye-Kitaev (SYK) model with N Majoranas and a weak 2r-body intersite coupling share the same near-conformal dynamics described by a traversable wormhole. We exploit this relation to propose a symmetry classification of traversable wormholes depending on N, q, and r, with q>2r, and confirm it by a level statistics analysis using exact diagonalization techniques. Intriguingly, a time-reversed state never results in a new state, so only six universality classes occur-A, AI, BDI, CI, C, and D-and different symmetry sectors of the model may belong to distinct universality classes.