Purpose: This systematic literature review (SLR) characterizes the current state of the art on digital twinning (DT) technology in tourism-related applications. We aim to evaluate the types of DTs described in the literature, identifying their purposes, the areas of tourism where they have been proposed, their main components, and possible future directions based on current work. Design/methodology/approach: We conducted this SLR with bibliometric analysis based on an existing, validated methodology. Thirty-four peer-reviewed studies from three major scientific databases were selected for review. They were categorized using a taxonomy that included tourism type, purpose, spatial scale, data sources, data linkage, visualization, and application. Findings: The topic is at an early, evolving stage, as the oldest study found dates back to 2021. Most reviewed studies deal with cultural tourism, focusing on digitising cultural heritage. Destination management is the primary purpose of these DTs, with mainly site-level spatial scales. In many studies, the physical-digital data linkage is unilateral, lacking twin synchronization. In most DTs considered bilateral, the linkage is indirect. There are more applied than theoretical studies, suggesting progress in applying DTs in the field. Finally, there is an extensive research gap regarding DT technology in tourism, which is worth filling. Originality/Value: This paper presents a novel SLR with a bibliometric analysis of DTs' applied and theoretical application in tourism. Each reviewed publication is assessed and characterized, identifying the current state of the topic, possible research gaps, and future directions.

Achieving resilience remains a significant challenge for Unmanned Aerial Vehicle (UAV) communications in 5G and 6G networks. Although UAVs benefit from superior positioning capabilities, rate optimization techniques, and extensive line-of-sight (LoS) range, these advantages alone cannot guarantee high reliability across diverse UAV use cases. This limitation becomes particularly evident in urban environments, where UAVs face vulnerability to jamming attacks and where LoS connectivity is frequently compromised by buildings and other physical obstructions. This paper introduces DET-FAIR- WINGS ( Detection-Enhanced Transformer Framework for AI-Resilient Wireless Networks in Ground UAV Systems), a novel solution designed to enhance reliability in UAV communications under attacks. Our system leverages multi-agent reinforcement learning (MARL) and transformer-based detection algorithms to identify attack patterns within the network and subsequently select the most appropriate mechanisms to strengthen reliability in authenticated UAV-Base Station links. The DET-FAIR-WINGS approach integrates both discrete and continuous parameters. Discrete parameters include retransmission attempts, bandwidth partitioning, and notching mechanisms, while continuous parameters encompass beam angles and elevations from both the Base Station (BS) and user devices. The detection part integrates a transformer in the agents to speed up training. Our findings demonstrate that replacing fixed retransmission counts with AI-integrated flexible approaches in 5G networks significantly reduces latency by optimizing decision-making processes within 5G layers.

We study a unitary matrix model of the Gross-Witten-Wadia type, extended with the addition of characteristic polynomial insertions. The model interpolates between solvable unitary matrix models and is the unitary counterpart of a deformed Cauchy ensemble. Exact formulas for the partition function and Wilson loops are given in terms of Toeplitz determinants and minors and large $N$ results are obtained by using Szegö theorem with a Fisher-Hartwig singularity. In the large $N$ (planar) limit with two scaled couplings, the theory exhibits a surprisingly intricate phase structure in the two-dimensional parameter space.

This article provides a self-contained comprehensive review of the phenomenon of synchronization in dynamical systems, with a particular focus on chaotic systems in both continuous-time and discrete-time contexts. Synchronization, initially observed by Christiaan Huygens in 1665, has evolved from the study of periodic signals to encompass chaotic systems, where the sensitive dependence on initial conditions poses unique challenges. This review pointed to both theoretical foundations and contributions (concepts and methods) and practical insights, reinforcing the relevance of chaos synchronization in physics, biology, engineering, social sciences, economics and communication systems. The study investigates various coupling schemes, such as unidirectional and bidirectional coupling, and presents stability criteria under different configurations. In a very concise way, some ongoing research carried out by the authors is also indicated, using Lorenz, Rossler and hyperchaotic Rossler systems, and quadratic maps as case studies with parameter values leading to chaotic behavior. Special attention is given to the stability of synchronized states and the role of multi-stability and bifurcations, and its implications to loss of synchronization. We highlight the role of Lyapunov exponents, eigenvalues, and Lyapunov functions in guaranteeing local and global stability of the synchronized state. We aim to contribute to a broader understanding of chaos synchronization and its practical applications in diverse fields of knowledge. This text shed light on the control and stability of coupled chaotic systems, offering new perspectives on the synchronization of non-identical systems and the emergence of complex synchronization dynamics.

We study a unitary matrix model of the Gross-Witten-Wadia type, extended with the addition of characteristic polynomial insertions. The model interpolates between solvable unitary matrix models and is the unitary counterpart of a deformed Cauchy ensemble. Exact formulas for the partition function and Wilson loops are given in terms of Toeplitz determinants and minors and large $N$ results are obtained by using Szegö theorem with a Fisher-Hartwig singularity. In the large $N$ (planar) limit with two scaled couplings, the theory exhibits a surprisingly intricate phase structure in the two-dimensional parameter space.

In this paper, we propose a multi-user downlink system for two users based on the orthogonal time frequency space (OTFS) modulation scheme. The design leverages the generalized singular value decomposition (GSVD) of the channels between the base station and the two users, applying precoding and detection matrices based on the right and left singular vectors, respectively. We derive the analytical expressions for three scenarios and present the corresponding simulation results. These results demonstrate that, in terms of bit error rate (BER), the proposed system outperforms the conventional multi-user OTFS system in two scenarios when using minimum mean square error (MMSE) equalizers or precoder, both for perfect channel state information and for a scenario with channel estimation errors. In the third scenario, the design is equivalent to zero-forcing (ZF) precoding at the transmitter.

Synthetic datasets are beneficial for machine learning researchers due to the possibility of experimenting with new strategies and algorithms in the training and testing phases. These datasets can easily include more scenarios that might be costly to research with real data or can complement and, in some cases, replace real data measurements, depending on the quality of the synthetic data. They can also solve the unbalanced data problem, avoid overfitting, and can be used in training while testing can be done with real data. In this paper, we present, to the best of our knowledge, the first synthetic dataset for Unmanned Aerial Vehicle (UAV) attacks in 5G and beyond networks based on the following key observable network parameters that indicate power levels: the Received Signal Strength Indicator (RSSI) and the Signal to Interference-plus-Noise Ratio (SINR). The main objective of this data is to enable deep network development for UAV communication security. Especially, for algorithm development or the analysis of time-series data applied to UAV attack recognition. Our proposed dataset provides insights into network functionality when static or moving UAV attackers target authenticated UAVs in an urban environment. The dataset also considers the presence and absence of authenticated terrestrial users in the network, which may decrease the deep networks ability to identify attacks. Furthermore, the data provides deeper comprehension of the metrics available in the 5G physical and MAC layers for machine learning and statistics research. The dataset will available at link archive-beta.ics.uci.edu

In-band full-duplex transmission allows a relay station to theoretically double its spectral efficiency by simultaneously receiving and transmitting in the same frequency band, when compared to the traditional half-duplex or out-of-band full-duplex counterpart. Consequently, the induced self-interference suffered by the relay may reach considerable power levels, which decreases the signal-to-interference-plus-noise ratio (SINR) in a decode-and-forward (DF) relay, leading to a degradation of the relay performance. This paper presents a technique to cope with the problem of self-interference in broadband multiple-input multiple-output (MIMO) relays. The proposed method uses a time-domain cancellation in a DF relay, where a replica of the interfering signal is created with the help of a recursive least squares (RLS) algorithm that estimates the interference frequency-selective channel. Its convergence mean time is shown to be negligible by simulation results, when compared to the length of a typical orthogonal-frequency division multiplexing (OFDM) sequences. Moreover, the bit-error-rate (BER) and the SINR in a OFDM transmission are evaluated, confirming that the proposed method extends significantly the range of self-interference power to which the relay is resilient to, when compared with other mitigation schemes.

We consider smooth solutions of the wave equation, on a fixed black hole region of a subextremal Reissner-Nordstr\"om (asymptotically flat, de Sitter or anti-de Sitter) spacetime, whose restrictions to the event horizon have compact support. We provide criteria, in terms of surface gravities, for the waves to remain in $C^l$, $l\geq 1$, up to and including the Cauchy horizon. We also provide sufficient conditions for the blow up of solutions in $C^1$ and $H^1$.

A customized finite-difference field solver for the particle-in-cell (PIC) algorithm that provides higher fidelity for wave-particle interactions in intense electromagnetic waves is presented. In many problems of interest, particles with relativistic energies interact with intense electromagnetic fields that have phase velocities near the speed of light. Numerical errors can arise due to (1) dispersion errors in the phase velocity of the wave, (2) the staggering in time between the electric and magnetic fields and between particle velocity and position and (3) errors in the time derivative in the momentum advance. Errors of the first two kinds are analyzed in detail. It is shown that by using field solvers with different $\mathbf{k}$-space operators in Faraday's and Ampere's law, the dispersion errors and magnetic field time-staggering errors in the particle pusher can be simultaneously removed for electromagnetic waves moving primarily in a specific direction. The new algorithm was implemented into OSIRIS by using customized higher-order finite-difference operators. Schemes using the proposed solver in combination with different particle pushers are compared through PIC simulation. It is shown that the use of the new algorithm, together with an analytic particle pusher (assuming constant fields over a time step), can lead to accurate modeling of the motion of a single electron in an intense laser field with normalized vector potentials, $eA/mc^2$, exceeding $10^4$ for typical cell sizes and time steps.

We show that the Riemannian Gaussian distributions on symmetric spaces, introduced in recent years, are of standard random matrix type. We exploit this to compute analytically marginals of the probability density functions. This can be done fully, using Stieltjes-Wigert orthogonal polynomials, for the case of the space of Hermitian matrices, where the distributions have already appeared in the physics literature. For the case when the symmetric space is the space of $m \times m$ symmetric positive definite matrices, we show how to efficiently compute by evaluating Pfaffians at specific values of $m$. Equivalently, we can obtain the same result by constructing specific skew orthogonal polynomials with regards to the log-normal weight function (skew Stieltjes-Wigert polynomials). Other symmetric spaces are studied and the same type of result is obtained for the quaternionic case. Moreover, we show how the probability density functions are a particular case of diffusion reproducing kernels of the Karlin-McGregor type, describing non-intersecting Brownian motions, which are also diffusion processes in the Weyl chamber of Lie groups.

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.

Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices for their benefits during UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on time series approaches for anomaly detection where the signal available in resource blocks are decomposed statistically to find trend, seasonality, and residues, while the second is based on newly designed deep networks. The joined technique is suitable for UAVs because the statistical model does not require heavy computation processing but is limited in generalizing possible attack's identification. On the other hand, the deep network can classify attacks accurately but requires more resources. The simulation considers the location and power of the jamming attacks and the UAV position related to the base station. The statistical method technique made it feasible to identify 84.38 % of attacks when the attacker was at 30 m from the UAV. Furthermore, the Deep network's accuracy was approximately 99.99 % for jamming powers greater than two and jammer distances less than 200 meters.

We compute analytically greybody factors for asymptotically flat spherically symmetric black holes with stringy higher derivative corrections in d dimensions in the high frequency limit. Our calculations include both the eikonal limit - where the real part of the frequency of the scattered wave is much larger than the imaginary part - and the highly damped case - where the imaginary part of the frequency is much larger than the real part -, addressing the emission of gravitons and test scalar fields, and yielding full transmission and reflection scattering coefficients.

We used the random walk to model the problem of reserves. The classic case of a stochastic process is the example of random walks, which are used to study a set of phenomena and, particularly, as in this article, models of reserves evolution. Random walks also allow the construction of significant complex systems and are also used as an instrument of analysis, being used in the sense of giving a theoretical characteristic to other types of systems. Our goal is primarily to study reserves to see how to ensure that pension funds are sustainable. This classic approach to the study of pension funds makes possible to draw interesting conclusions about the problem of reserves.

After the fundamental work of Livschitz in [1; 2], various research directions emerged, among which the following stand out: (i) the study of cocycles with values in groups and semigroups beyond R, as well as the investigation of corresponding regularity results; (ii) the analysis of how a certain degree of regularity of the cocycle can confer corresponding regularity to the solution of the cohomological equation; and (iii) the study of higher-dimensional cohomology naturally associated with the action of groups other than Z or R. The aim of this article is to present, as self-contained as possible, a review of the natural generalizations of the notions of cocycles and cochains, as well as their corresponding results, in the study of cohomological equations.

We show in experiments that a long, underdense, relativistic proton bunch propagating in plasma undergoes the oblique instability, that we observe as filamentation. We determine a threshold value for the ratio between the bunch transverse size and plasma skin depth for the instability to occur. At the threshold, the outcome of the experiment alternates between filamentation and self-modulation instability (evidenced by longitudinal modulation into microbunches). Time-resolved images of the bunch density distribution reveal that filamentation grows to an observable level late along the bunch, confirming the spatio-temporal nature of the instability. We calculate the amplitude of the magnetic field generated in the plasma by the instability and show that the associated magnetic energy increases with plasma density.

Given the busy period and busy cycle major importance in queuing systems, it is crucial the knowledge of the respective distribution functions that is what allows the calculation of the important probabilities. For the M|G|$\infty$ queue system, there are no round form formulae for those distribution functions. But, for the M|D|$\infty$ queue, due the fact that its busy period and busy cycle have both Laplace transform expression round forms, what does not happen for any other M|G|$\infty$ queue system, with an algorithm created by Platzman, Ammons and Bartholdi III, that allows the tail probabilities computation since the correspondent Laplace transform in round form is known, those distribution functions calculations are possible. Here, we will implement the algorithm through a FORTRAN program.