Abstract
Car rental and car sharing are two established mobility concepts which traditionally have been offered by specialized providers. Presumably to increase utilization and profitability, most recently, car rental providers began to offer car sharing in addition, and vice versa. To assess and quantify benefits and drawbacks of combining both into a single mobility concept with one common fleet, we consider such combined systems on an aggregate level, replicating demand patterns and rentals throughout a typical week. Our systematic approach reflects that, depending on a provider's status quo, different business practices exist, for example with regard to the applied revenue management approaches. Methodologically, our analyses base on mathematical optimization. We propose several models that consider the different business practices and degrees to which the respective new mobility concept is offered. To support mobility providers in their strategic decision-making, we derive managerial insights based on numerical studies that use real-life data.

Abstract
Accessibility in digital labor is a research line that has been trending over the last few years. The usage of crowdsourcing, especially in the form of microtasks, can become an inclusive solution to support accessible digital work. Integrating cognitive abilities tests and task fingerprinting has proven to be effective mechanisms for microtask personalization when considering neurotypical people. In this article, we report the elaboration of usability tests on microtask personalization with neurodivergent people. The preliminary study recruited six participants with autism, attention deficit hyperactivity disorder, and dyslexia. The results obtained indicate that this solution can be inclusive and increase the accessibility of crowdsourcing tasks and platforms. One limitation of this study is that it is essential to evaluate this solution on a large scale to ensure the identification of errors and/or features of cognitive personalization in microtask crowdsourcing.

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.