Lorentz invariance violation (LIV) can have multiple consequences on very-high energy gamma rays' emission, propagation, and detection, such as energy-dependent photon group velocity, photon instability, vacuum birefringence, and modified electromagnetic interaction. Depending on the underlying theoretical model, several of these effects can coexist. Nevertheless, in experimental tests of LIV, each effect is tested separately and independently. Here, we are performing a search for traces of several coexisting effects in a single analysis. We present our analysis method based on artificial neural networks and put our very first results in the context of experimental searches for LIV.

We present a search for solar axions produced through the axion-electron coupling $(g_{ae})$ using data from a novel 7-GridPix detector installed at the CERN Axion Solar Telescope (CAST). The detector, featuring ultra-thin silicon nitride windows and multiple veto systems, collected approximately 160 hours of solar tracking data between 2017-2018. Using machine learning techniques and the veto systems, we achieved a background rate of $1.06\times 10^{-5}\,\text{keV}^{-1}\text{cm}^{-2}\text{s}^{-1}$ at a signal efficiency of about $80\,\%$ in the $0.2$-$8\,\text{keV}$ range. Analysis of the data yielded no significant excess above background, allowing us to set a new upper limit on the product of the axion-electron and axion-photon couplings of g_{ae}\cdot g_{a\gamma} < 7.35\times 10^{-23}\,\text{GeV}^{-1} at $95\,\%$ confidence level. This result improves upon the previous best helioscope limit and demonstrates the potential of GridPix technology for rare event searches. Additionally, we derived a limit on the axion-photon coupling of g_{a\gamma} < 9.0\times 10^{-11}\,\text{GeV}^{-1} at $95\,\%$ CL, which, while not surpassing CAST's best limit, provides complementary constraints on axion models.

We provide a review of gauge field theories with higher spin, focusing on the classical theory of massless bosons in flat Minkowski spacetime. A brief introduction to the concept of spin is provided along with a historical review of some of the most important problems and accomplishments in higher spin theory, followed by a review of familiar lower spin theories. Using a particularly elegant formalism, we examine the free higher spin theory and the theory coupled to a generic external current. For both of these theories, we review their constrained and the unconstrained formulation, focusing our attention on the latter. We also review the existing literature, confirming the results for the non-local unconstrained formulation and correcting some calculational errors. We briefly discuss the geometrical motivation behind the construction of the basic building blocks of the theory and we entertain the possibility of there being a single equation of motion for all bosonic higher spin fields. Some explicit calculations obtained using computer-assisted methods are provided in the appendix, along with the relevant computer code.

PKS 2155-304 is a well-known high-frequency peaked BL Lac (HBL) at redshift z=0.116, which has been extensively studied across the electromagnetic spectrum due to its rapid and large-amplitude variability. Several violent outbursts in X-rays and $\gamma$-rays have been observed in the past, with intra-night variability in very-high-energy $\gamma$-rays (VHE; E > 100 GeV) detected down to the minute timescale. The alternation of quiescent and enhanced states, observed with a tentative quasi-periodicity of 1.74 $\pm$ 0.13 years in high-energy (HE; 100 MeV < E < 100 GeV) $\gamma$-rays, makes this source a key target also for ground-based $\gamma$-ray instruments and in particular for the Imaging Atmospheric Cherenkov Telescopes. Its brightness, proximity, and well-determined redshift make this $\gamma$-ray source a prime target for fundamental physics studies, including tests of Lorentz Invariance Violation (LIV), searches for axion-like particles (ALPs), and constraints on the distribution of the extragalactic background light (EBL). In the last two years, PKS 2155-304 has been independently monitored by the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes and the first Large-Sized Telescope (LST-1) of the Cherenkov Telescope Array Observatory (CTAO) located at the Roque de Los Muchachos Observatory (La Palma, Spain). The observations were carried out at large zenith angles (LZA; ZA > 55°), and the VHE data have been complemented with simultaneous observations in HE $\gamma$-rays (Fermi-LAT), X-rays (Swift-XRT) and optical wavelengths (ASAS-SN).

The present study investigates the dynamics of nonlocal beams by establishing a consistent stress-driven integral elastic using the Physics-Informed Neural Network (PINN) approach. Specifically, a PINN is developed to compute the first eigenfunction and eigenvalue arising from the underlying sixth-order ordinary differential equation. The PINN is based on a feedforward neural network, with a loss function composed of terms from the differential equation, the normalization condition, and both boundary and constitutive boundary conditions. Relevant eigenvalues are treated as separate trainable variables. The results demonstrate that the proposed method is a powerful and robust tool for addressing the complexity of the problem. Once trained, the neural network is less computationally intensive than analytical methods. The obtained results are compared with benchmark analytical solutions and show strong agreement.

Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory operating in the energy range from 20 GeV up to 300 TeV, with two sites in La Palma (Spain) and Paranal (Chile). It will consist of telescopes of three sizes, covering different parts of the large energy range. We report on the performance of Large-Sized Telescope prototype (LST-1) in the detection and characterization of extragalactic gamma-ray sources, with a focus on the reconstructed gamma-ray spectra and variability of classical bright BL Lacertae objects, which were observed during the early commissioning phase of the instrument. LST-1 data from known bright gamma-ray blazars - Markarian 421, Markarian 501, 1ES 1959+650, 1ES 0647+250, and PG 1553+113 - were collected between July 10, 2020, and May 23, 2022, covering a zenith angle range of 4 deg to 57 deg. The reconstructed light curves were analyzed using a Bayesian block algorithm to distinguish the different activity phases of each blazar. Simultaneous Fermi-LAT data were utilized to reconstruct the broadband $\gamma$-ray spectra for the sources during each activity phase. High-level reconstructed data in a format compatible with gammapy are provided together with measured light curves and spectral energy distributions (SEDs) for several bright blazars and an interpretation of the observed variability in long and short timescales. Simulations of historical flares are generated to evaluate the sensitivity of LST-1. This work represents the first milestone in monitoring bright BL Lacertae objects with a CTAO telescope.

Recent advances in robotics have paved the way for robots to replace humans in perilous situations, such as searching for victims in burning buildings, in earthquake-damaged structures, in uncharted caves, traversing minefields or patrolling crime-ridden streets. These challenges can be generalized as problems where agents have to explore unknown mazes. We propose a cooperative multi-agent system of automated mobile agents for exploring unknown mazes and localizing stationary targets. The Heat Equation-Driven Area Coverage (HEDAC) algorithm for maze exploration employs a potential field to guide the exploration of the maze and integrates cooperative behaviors of the agents such as collision avoidance, coverage coordination, and path planning. In contrast to previous applications for continuous static domains, we adapt the HEDAC method for mazes on expanding rectilinear grids. The proposed algorithm guarantees the exploration of the entire maze and can ensure the avoidance of collisions and deadlocks. Moreover, this is the first application of the HEDAC algorithm to domains that expand over time. To cope with the dynamically changing domain, succesive over-relaxation (SOR) iterative linear solver has been adapted and implemented, which significantly reduced the computational complexity of the presented algorithm when compared to standard direct and iterative linear solvers. The results highlight significant improvements and show the applicability of the algorithm in different mazes. They confirm its robustness, adaptability, scalability and simplicity, which enables centralized parallel computation to control multiple agents/robots in the maze.

We show that for general spherically symmetric configurations, contributions of general gravitational and mixed gauge-gravitational Chern-Simons terms to the equations of motion vanish identically in $D>3$ dimensions. This implies that such terms in the action do not affect Birkhoff's theorem or any previously known spherically symmetric solutions. Furthermore, we investigate the thermodynamical properties using the procedure described in an accompanying paper. We find that in $D>3$ static spherically symmetric case Chern-Simons terms do not contribute to the entropy either. Moreover, if one requires only for the metric tensor to be spherically symmetric, letting other fields unrestricted, the results extend almost completely, with only one possible exception --- Chern-Simons Lagrangian terms in which the gravitational part is just the $n=2$ irreducible gravitational Chern-Simons term.

In this paper we consider the Koopman operator associated with the discrete and the continuous time random dynamical system (RDS). We provide results that characterize the spectrum and the eigenfunctions of the stochastic Koopman operator associated with different types of linear RDS. Then we consider the RDS for which the associated Koopman operator family is a semigroup, especially those for which the generator can be determined. We define a stochastic Hankel DMD (sHankel-DMD) algorithm for numerical approximations of the spectral objects (eigenvalues, eigenfunctions) of the stochastic Koopman operator and prove its convergence. We apply the methodology to a variety of examples, revealing objects in spectral expansions of the stochastic Koopman operator and enabling model reduction.

This study analyzes an 11-year cosmic ray dataset of positrons and antiprotons for planetary dependencies related to gravitationally focused dark matter streams. By projecting event timestamps onto planetary heliocentric longitudes, structured flux modulations are found. The results resemble previously reported planetary relationships in solar and terrestrial observables. This supports the possibility that streaming dark matter particles are focused toward Earth by solar system bodies, including the Moon. The findings suggest further searches using other cosmic ray channels, including existing very high-energy gamma-ray data.

Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we dub this field 'social physics' and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migrations, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines.

We study solar differential rotation (DR) for solar cycle No. 19 (1954-1964) by tracing sunspot groups on the sunspot drawings of Kanzelhoehe Observatory for Solar and Environmental Research (KSO). Our aim is to extend previous DR analysis from the KSO data (1964-2016) to the years prior to 1964 to create a catalog of sunspot group positions and DR parameters from KSO sunspot drawings and white light images. Synodic angular rotation velocities were first determined using the daily shift (DS) and robust linear least-squares fit (rLSQ) methods, then converted to sidereal velocities, and used to derive solar DR parameters. We compare the DR parameters obtained from different sources and analyse the north-south asymmetry of rotation for solar cycle No. 19. It has been shown that our results for the equatorial rotation velocity (parameter A) and the gradient of DR (parameter B) coincide with earlier results from the KSO data (performed with a different method), as well as with results from the Kodaikanal Solar Observatory (KoSO) and the Yunnan Observatories (YNAO). In contrast, the values of parameter A from three different earlier studies based on the Greenwich Photoheliographic Results (GPR) exhibit statistically significant differences when compared to the values of parameter A derived from KSO, KoSO and YNAO. These findings suggest that the GPR data have the largest inconsistency compared to the other three data sources, highlighting the need for further analysis to identify the causes of these discrepancies. The analysis of the north-south asymmetry in the solar rotation profile using two different methods shows that the DR parameters of the hemispheres coincide, indicating a rotational symmetry around the equator. This is consistent with previous results from KSO and YNAO data. However, all sources indicate slightly higher equatorial rotation velocities in the southern hemisphere.

Coastal water quality management is a public health concern, as poor coastal water quality can harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of $Escherichia\ Coli$ and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their relationships with environmental stressors. Gradient Boosting (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with measurements from all sampling sites and used to predict $E.\ Coli$ and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R$^2$ values of 0.71 and 0.68 for predicting $E.\ Coli$ and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both $E.\ Coli$ and enterococci levels. Finally, the spatial and temporal accuracy of both ML models were examined at sites with the lowest coastal water quality. The spatial $E. Coli$ and enterococci models achieved strong R$^2$ values of 0.85 and 0.83, while the temporal models achieved R$^2$ values of 0.74 and 0.67. The temporal model also achieved moderate R$^2$ values of 0.44 and 0.46 at a site with high coastal water quality.

PG 1553+113 is one of the few blazars with a convincing quasi-periodic emission in the gamma-ray band. The source is also a very high-energy (VHE; >100 GeV) gamma-ray emitter. To better understand its properties and identify the underlying physical processes driving its variability, the MAGIC Collaboration initiated a multiyear, multiwavelength monitoring campaign in 2015 involving the OVRO 40-m and Medicina radio telescopes, REM, KVA, and the MAGIC telescopes, Swift and Fermi satellites, and the WEBT network. The analysis presented in this paper uses data until 2017 and focuses on the characterization of the variability. The gamma-ray data show a (hint of a) periodic signal compatible with literature, but the X-ray and VHE gamma-ray data do not show statistical evidence for a periodic signal. In other bands, the data are compatible with the gamma-ray period, but with a relatively high p-value. The complex connection between the low and high-energy emission and the non-monochromatic modulation and changes in flux suggests that a simple one-zone model is unable to explain all the variability. Instead, a model including a periodic component along with multiple emission zones is required.

A complex interplay between the crystal structure and electron behavior within borophene renders this material an intriguing 2D system with many of its electronic properties still undiscovered. Experimental insight into those properties is additionally hampered by the limited capabilities of the established synthesis methods, which in turn inhibits the realization of potential borophene applications. In this multi-method study, photoemission spectroscopies and scanning probe techniques complemented by theoretical calculations have been used to investigate the electronic characteristics of a high-coverage, single-layer borophene on Ir(111) substrate. Our results show that the binding of borophene to Ir(111) exhibits pronounced one-dimensional modulation and transforms borophene into a nano-grating. The scattering of photoelectrons from this structural grating gives rise to the replication of electronic bands. In addition, the binding modulation is reflected in the chemical reactivity of borophene and gives rise to its inhomogeneous aging effect. Such aging is easily reset by dissolving boron atoms in iridium at high temperature followed by their reassembly into a fresh atomically-thin borophene mesh. Besides proving electron-grating capabilities of the boron monolayer, our data provides a comprehensive insight into the electronic properties of epitaxial borophene which is vital for further examination of other boron systems of reduced dimensionality.

Microchannel heat sinks are an efficient cooling method for semiconductor packages. However, to properly cool increasingly complex and thermally dense circuits, microchannel designs should be improved and expanded on. In this paper, microchannel designs with secondary channels and with ribs are investigated using computational fluid dynamics and are coupled with a multi-objective optimization algorithm to determine and propose optimal solutions based on observed thermal resistance and pumping power. A workflow that combines Latin hypercube sampling, machine learning-based surrogate modeling and multi-objective optimization is proposed. Random forests, gradient boosting algorithms and neural networks were considered during the search for the best surrogate. We demonstrated that tuned neural networks can make accurate predictions and be used to create an acceptable surrogate model. Optimized solutions show a negligible difference in overall performance when compared to the conventional optimization approach. Additionally, solutions are calculated in one-fifth of the original time. Generated designs attain temperatures that are lower by more than 10% under the same pressure limits as a convectional microchannel design. When limited by temperature, pressure drops are reduced by more than 25%. Finally, the influence of each design variable on the thermal resistance and pumping power was investigated by employing the SHapley Additive exPlanations technique. Overall, we have demonstrated that the proposed framework has merit and can be used as a viable methodology in microchannel heat sink design optimization.

In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around the VIRGO gravitational waves observatory sited near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.

This research addresses the challenge of performing search missions in dynamic environments, particularly for drifting targets whose movement is dictated by a flow field. This is accomplished through a dynamical system that integrates two partial differential equations: one governing the dynamics and uncertainty of the probability distribution, and the other regulating the potential field for ergodic multi-agent search. The target probability field evolves in response to the target dynamics imposed by the environment and accomplished sensing efforts, while being explored by multiple robot agents guided by the potential field gradient. The proposed methodology was tested on two simulated search scenarios, one of which features a synthetically generated domain and showcases better performance when compared to the baseline method with static target probability over a range of agent to flow field velocity ratios. The second search scenario represents a realistic sea search and rescue mission where the search start is delayed, the search is performed in multiple robot flight missions, and the procedure for target drift uncertainty compensation is demonstrated. Furthermore, the proposed method provides an accurate survey completion metric, based on the known detection/sensing parameters, that correlates with the actual number of targets found independently.

Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging or inaccessible environments. This is why introducing unmanned aerial vehicles (UAVs) can be of great help to enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved in the mission. Motivated by this, we design and experiment with autonomous UAV search for humans in a Mediterranean karst environment. The UAVs are directed using Heat equation-driven area coverage (HEDAC) ergodic control method according to known probability density and detection function. The implemented sensing framework consists of a probabilistic search model, motion control system, and computer vision object detection. It enables calculation of the probability of the target being detected in the SAR mission, and this paper focuses on experimental validation of proposed probabilistic framework and UAV control. The uniform probability density to ensure the even probability of finding the targets in the desired search area is achieved by assigning suitably thought-out tasks to 78 volunteers. The detection model is based on YOLO and trained with a previously collected ortho-photo image database. The experimental search is carefully planned and conducted, while as many parameters as possible are recorded. The thorough analysis consists of the motion control system, object detection, and the search validation. The assessment of the detection and search performance provides strong indication that the designed detection model in the UAV control algorithm is aligned with real-world results.