This article presents a comprehensive rapid review of the current disaster risk reduction (DRR) efforts in Saudi Arabia, a country exposed to a variety of hazards such as extreme heat, droughts, floods, dust, and sandstorms, along with threats from terrorism and violence. Employing a rapid review approach, our aim was to provide timely insights into DRR strategies, with an emphasis on the unique geographical and socio-political context of Saudi Arabia. This study serves as a valuable reference for similar hazard-prone regions worldwide. Our review encompasses Saudi Arabia’s progress in key areas, such as improving building codes and infrastructure, developing early warning systems, raising public awareness, and strengthening emergency response capabilities. While Saudi Arabia has made commendable strides in implementing international best practices for DRR, our review also identified specific areas where further development and enhancement are needed. These include the need for more sophisticated early warning systems, expanded public awareness campaigns, and continual enhancements in emergency response capabilities. This review offers key insights into the challenges and opportunities within Saudi Arabia’s DRR efforts, highlighting the steps that Saudi Arabia has taken towards resilience. Drawing from specific examples of past disasters, our findings shed light on practical considerations for improving disaster risk management, with the potential to inform policy, enhance public awareness, and contribute to building a safer and more resilient future in Saudi Arabia.

The evolution of aircraft power systems has been driven by increasing electrical demands and advancements in aviation technology. Background: This study provides a comprehensive review and experimental validation of on-board electrical network development, analyzing power management strategies in both conventional and modern aircraft, including the Mi-24 helicopter, F-22 multirole aircraft, and Boeing 787 passenger airplane. Methods: The research categorizes aircraft electrical systems into three historical phases: pre-1960s with 28.5 V DC networks, up to 2000 with three-phase AC networks (3 × 115 V/200 V, 400 Hz), and post-2000 with 270 V DC networks derived from AC generators via transformer–rectifier units. Beyond theoretical analysis, this work introduces experimental findings on hybrid-electric aircraft power solutions, particularly evaluating the performance of the Modular Power System for Aircraft (MPSZE). The More Electric Aircraft (MEA) concept is analyzed as a key innovation, with a focus on energy efficiency, frequency stability, and ground power applications. The study investigates the integration of alternative energy sources, including photovoltaic-assisted power supplies and fuel-cell-based auxiliary systems, assessing their feasibility for aircraft system checks, engine startups, field navigation, communications, and radar operations. Results: Experimental results demonstrate that hybrid energy storage systems, incorporating lithium-ion batteries, fuel cells, and photovoltaic modules, can enhance MEA efficiency and operational resilience under real-world conditions. Conclusions: The findings underscore the importance of MEA technology in the future of sustainable aviation power solutions, highlighting both global and Polish research contributions, particularly from the Air Force Institute of Technology (ITWL).

This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grates, potholes, and cobblestones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network’s effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.

Observations from permanent GNSS stations are actively used for the research and monitoring of geodynamic processes. Today, with the use of modern scientific programs and IGS products, it is possible to determine GNSS station coordinates and velocities at the level of a few millimeters. However, the scientific community constantly faces the question of increasing the accuracy of coordinate definitions to obtain more reliable data in the study of geodynamic phenomena. One of the main sources of errors is systematic measurement errors. To date, the procedure for their removal is still incomplete and imperfect. Also, during the processing of long-term GNSS measurements, it was found that the coordinate time series, after the removal of trend effects, are also characterized by seasonal variations, mainly of annual and semi-annual periods. We estimated the daily coordinate time series of 10 permanent GNSS stations in the central-eastern part of Europe from 2001 to 2019 and calculated the seasonal variation coefficients for these stations. The average value of the coefficients for the annual cycle for the N, E, and H components is −0.7, −0.2, and −0.7 mm, and for the semi-annual cycle the average value is 0.3, 0.4, and −0.5 mm. The obtained coefficients are less than 1 mm, which is why it can be argued that there is no seasonal component in the coordinate time series or that it is so small that it is a problematic task to calculate it. This practical absence of a seasonal component in long-term time series of GNSS coordinates, in our opinion, is partly compensated by the use of modern models of mapping functions (such as VMF3) for zenith tropospheric delays instead of the empirical GMF. To test the obtained results, we calculated the coefficients of seasonal variations for the sub-network of GNSS stations included in the category of the best EPN stations—C0 and C1. The values of the coefficients for the stations of this network are also less than 1 mm, which confirms the previous statement about the absence of a seasonal component in the long-term time series of coordinates. We also checked the presence of seasonal changes in the time series using the well-known decomposition procedure, which showed that the seasonal component is not observed because the content does not exceed 10% for additive decomposition and 20% for multiplicative decomposition.

Współcześnie, choć technologia rozwinęła się na niewyobrażalną skalę, wciąż istnieją czynniki, które mogą zakłócić bezpieczne i sprawne funkcjonowanie wielu obszarów codziennego życia. Jednym z nich są opóźnienia. Niewątpliwie są one elementem niepożądanym, a w niektórych przypadkach nawet niebezpiecznym. Szczególnym przypadkiem może być ruch lotniczy, który jest jednym z najbardziej zaawansowanych technologicznie obszarów. Jednak występujące dość często opóźnienia w ruchu lotniczym sprawiły, że pożądane stało się badanie tego obszaru w oparciu o modelowanie przepustowości lotnisk i metody uczenia maszynowego, z głównym naciskiem na algorytmy drzew decyzyjnych. W oparciu o te metody drzew decyzyjnych, wynikiem pozyskiwania i przetwarzania danych i zmiennych było stworzenie konkretnych modeli, które mogą wspierać zarządzanie ruchem lotniczym, a w konsekwencji niwelowanie powstałych opóźnień.

W pracy przedstawiono problem w zarządzaniu bezpieczeństwem ruchu na płycie lotniska. Celem artykułu było opracowanie metody minimalizującej ryzyko zdarzeń niebezpiecznych w naziemnych operacjach lotniskowych realizowanych przez statki powietrzne. Opracowanie metody wymagało zdefiniowania modelu decyzyjnego zarządzania ryzykiem wypadków w naziemnych operacjach lotniskowych. Wyznaczony w pracy model jest modelem decyzyjnym składającym się z następujących etapów, tj. opracowanie danych wejściowych, zmiennych decyzyjnych, ograniczeń oraz funkcji kryterium. Istotne znaczenie w zarządzaniu bezpieczeństwem ruchu na płycie lotniska ma bezkolizyjne sterowanie ruchem statków powietrznych. W celu wyznaczania tras jazdy statków powietrznych po płycie lotniska opracowano algorytm mrówkowy. Metodę bazująco na algorytmie mrówkowym zweryfikowano. Weryfikacja potwierdziła wysoką jej skuteczność. Weryfikacji metody dokonano na danych rzeczywistych z lotniska Chopina w Warszawie. Ruch pojazdów obsługi naziemnej został zamodelowany przy wykorzystaniu symulatora ruchu GlobSim.