Abstract
Forest fires are becoming increasingly common, and they are devastating, fueled by the effects of global warming, such as a dryer climate, dryer vegetation, and higher temperatures. Vegetation management through selective removal is a preventive measure which creates discontinuities that will facilitate fire containment and reduce its intensity and rate of spread. However, such a method requires vast amounts of biomass fuels to be removed, over large areas, which can only be achieved through mechanized means, such as through using forestry mulching machines. This dangerous job is also highly dependent on skilled workers, making it an ideal case for novel autonomous robotic systems. This article presents the development of a universal perception, control, and navigation system for forestry machines. The selection of hardware (sensors and controllers) and data-integration and -navigation algorithms are central components of this integrated system development. Sensor fusion methods, operating using ROS, allow the distributed interconnection of all sensors and actuators. The results highlight the system's robustness when applied to the mulching machine, ensuring navigational and operational accuracy in forestry operations. This novel technological solution enhances the efficiency of forest maintenance while reducing the risk exposure to forestry workers.

Abstract
Following the widespread interest of both the scientific community and companies in using autonomous vehicles to perform deliveries, we propose the 'Drone-Assisted Vehicle Routing Problem with Robot Stations' (VRPD-RS), a problem that combines two concepts studied in the autonomous vehicles literature: truck-drone tandems and robot stations. We model the VRPD-RS as a mixed-integer linear program (MILP) for two different objectives, the makespan and operational costs, and analyze the impact of adding trucks, drones, and robots to the delivery fleet. Given the computational complexity of the problem, we propose a General Variable Neighborhood Search (GVNS) metaheuristic to solve more realistic instances within reasonable computational times. Results show that, for small instances of 10 customers, where the solver obtains optimal solutions for almost all cases, the GVNS presents solutions with gaps of 0.7% to the solver for the makespan objective and gaps of 0.0% for the operational costs variant. For instances of up to 50 customers, the GVNS presents improvements of 21.5% for the makespan objective and 8.0% for the operational costs variant. Furthermore, we compare the GVNS with a Simulated Annealing (SA) metaheuristic, showing that the GVNS outperforms the SA for the whole set of instances and in more efficient computational times. Accordingly, the results highlight that including an additional drone in a truck-drone tandem increases delivery speed alongside a reduction in operational costs. Moreover, robot stations proved to be a useful delivery element as they were activated in almost every studied scenario.

Abstract
The medical imaging field contends with limited data for training deep learning (DL) models. Our study evaluated traditional data augmentation (DA) and Generative Adversarial Networks (GANs) in enhancing DL models for identifying stomach precancerous lesions. Classic DA consistently outperformed GAN-based methods with ResNet50 (0.94 vs 0.93 accuracy) and ViT (0.85 vs 0.84 accuracy) models achieving higher accuracy and other performance metrics with DA compared to GANs. Despite this, GAN augmentation showed significant improvements when compared to train with the original dataset, highlighting its role in diversifying datasets and aiding generalization across different medical imaging datasets. Combining both augmentation techniques can enhance model robustness and generalisation capabilities in DL applications for medical diagnostics, leveraging DA's consistency and GANs' diversity.

Abstract
One of the key tasks in ensuring national security is the ability of the state and society to recognise and effectively assess the conditions for disasters, and to prevent them from threatening the sustainable development of the country. The Kyrgyz Republic is highly vulnerable to the influence of climate change, which in turn affects the frequency and intensity of disasters. The Kyrgyz Republic is exposed to almost all types of geological and man-made hazards, including earthquakes, landslides, debris flows, flash floods, outbursts of mountain lakes, dam failures, avalanches, droughts, extreme temperature, epidemics and releases of hazardous substances. Analysis of information on existing risks and their control systems used to reduce their negative impact makes it possible to assess the degree of probability, the expected consequences of threats, determine the degree of risk, the adaptive potential of communities and select appropriate protective measures. Therefore, this study is conducted to assess the hazard, vulnerability and exposure of Suzak district (Jalal-Abad oblast) in order to quantify the risk of the study area using multi-parameter holistic assessment with field collecting of primary data and utilizing Index-based Risk Assessment approach based on applying INFORM Risk model. Collected data was used to downscale subnational INFORM Risk model for municipal and district level using a multi-layered structure. A risk score is calculated by combining 72 indicators that measure three main dimensions: hazard & exposure, vulnerability, and lack of coping capacity. These findings provide an opportunity to develop a more effective disaster risk management at the local and national levels, by prioritizing relevant actions and investments for municipalities – districts which are demonstrated relatively highest risk scores. Also, the possibility of applying localized risk assessment procedures provides an opportunity to obtain more accurate sub-national (district/oblast based) and national levels with effective assessing dynamics of risk.

Abstract
Tight relationships exist in the local Universe between the central stellar properties of galaxies and the mass of their supermassive black hole (SMBH)1-3. These suggest that galaxies and black holes co-evolve, with the main regulation mechanism being energetic feedback from accretion onto the black hole during its quasar phase4-6. A crucial question is how the relationship between black holes and galaxies evolves with time; a key epoch to examine this relationship is at the peaks of star formation and black hole growth 8-12 billion years ago (redshifts 1-3)7. Here we report a dynamical measurement of the mass of the black hole in a luminous quasar at a redshift of 2, with a look back in time of 11 billion years, by spatially resolving the broad-line region (BLR). We detect a 40-mu as (0.31-pc) spatial offset between the red and blue photocentres of the H alpha line that traces the velocity gradient of a rotating BLR. The flux and differential phase spectra are well reproduced by a thick, moderately inclined disk of gas clouds within the sphere of influence of a central black hole with a mass of 3.2 x 108 solar masses. Molecular gas data reveal a dynamical mass for the host galaxy of 6 x 1011 solar masses, which indicates an undermassive black hole accreting at a super-Eddington rate. This suggests a host galaxy that grew faster than the SMBH, indicating a delay between galaxy and black hole formation for some systems. Using the GRAVITY+ instrument, dynamical measurement of the black hole mass in a quasar at a redshift of 2.3 (11 billion years ago) shows how the relationship between galaxies and black holes evolves with time.

Abstract
Underwater wireless communications are crucial for supporting multiple maritime activities, such as environmental monitoring and offshore wind farms. However, the challenging underwater environment continues to pose obstacles to the development of long-range, broadband underwater wireless communication systems. State of the art solutions are limited to long range, narrowband acoustics and short range, broadband radio or optical communications. This precludes real-time wireless transmission of imagery over long distances. In this paper, we propose SAGE, a semantic-oriented underwater communications approach to enable real-time wireless imagery transmission over noisy and narrowband channels. SAGE extracts semantically relevant information from images at the sender located underwater and generates a text description that is transmitted to the receiver at the surface, which in turn generates an image from the received text description. SAGE is evaluated using BLIP for image-to-text and Stable Diffusion for text-to-image, showing promising image similarity between the original and the generated images, and a significant reduction in latency up to a hundred-fold, encouraging further research in this area.