Tafila Technical University

Errors in the representation of the drop size distribution are a major source of uncertainty in rainfall estimation, since both radar reflectivity and microwave attenuation depend nonlinearly on precipitation microphysics. These uncertainties propagate directly into the specific attenuation–rain rate (k–R) relationship through the interaction between electromagnetic waves and hydrometeors, leading to systematic biases when globally fixed coefficients are used. In standard practice, the k–R relationship is expressed as a power law of the form k=aRb, where the coefficients a and b are typically taken from the International Telecommunication Union (ITU) recommendations and assumed to be globally applicable. The use of the ITU coefficients implicitly assumes stationary rainfall microphysics, which is physically inconsistent under varying cloud and rain regimes. This highlights the need for stratified parameterizations in which the coefficients are optimized for different microphysical conditions. In this context, cloud phase information from geostationary satellites provides a physically meaningful basis for clustering the k–R relationship, as different cloud phases are associated with distinct precipitation formation processes and drop size distributions.The objective of this study is to derive cloud phase dependent k–R parameterizations and to assess their performance across a large disdrometer network. A global disdrometer dataset (Ghiggi et al., 2021, DISDRODB) covering multiple climatic regions is used to simulate k–R relationships across a wide frequency range from 5 to 100 GHz using the T-matrix scattering method. SEVIRI MSG observations are used as input to the Cloud Physical Properties (CPP) product provided by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), from which cloud phase is classified into water, supercooled water, mixed phase, deep convective, cirrus, and opaque ice categories. Frequency dependent k–R coefficients are derived separately for each cloud type. The framework is evaluated across more than 100 independent disdrometer sites, primarily concentrated in Europe.Relative to the ITU recommended model (ITU-R P.838-3), the cloud phase adaptive parameterization substantially reduces root mean square error (RMSE), with the strongest improvements observed at 5 to 8 GHz. At these frequencies, more than 90 percent of sites show lower RMSE, with average reductions reaching up to 1.5 mm.h-1. More moderate improvements are found at higher frequencies from 60 to 100 GHz, where around 60 percent of sites show RMSE reductions, with average improvements below 0.5 mm.h-1.These results show that cloud phase informed k–R parameterizations can significantly improve rainfall estimation from commercial microwave links and indicate potential applicability to radar systems.Reference:Ghiggi, G., Billault-Roux, A. C., Candolfi, K., Pillac-Mage, L., Unal, C., Schleiss, M., Uijlenhoet, R., Raupach, T., and Berne, A.: DISDRODB – A global disdrometer archive of raindrop size distribution observations, PrePEP 2025, Karlsruhe, Germany, 10–12 March 2025, https://indico.kit.edu/event/4015/contributions/18545/, 2025. This work was supported by the Czech Science Foundation (GACR), Czech Republic, under Grant No. 24-13677L (MERGOSAT).

Dynamic Message Signs (DMSs) play a critical role in conveying real-time traffic information to drivers; however, their effectiveness heavily relies on how messages are structured and displayed, particularly regarding phasing duration and content length. This study examines the influence of these two factors on driver readability, comprehension, and gaze behavior using an advanced virtual reality (VR) driving simulator. Controlled experiments simulated four DMS scenarios, combining two phasing intervals (2.5 and 4 s) with short and long message formats, adhering to Michigan Department of Transportation (MDOT) guidelines. The experiment integrated eye-tracking technology to measure fixation duration and frequency, while statistical methods, including survival analysis and LASSO regression, were employed to identify significant predictors of message readability. Results revealed that shorter messages with shorter phasing intervals led to the highest comprehension rates and reduced cognitive strain. Furthermore, individual characteristics such as gender, driving speed, and highway driving experience significantly affected how drivers engaged with DMS messages. These findings contribute to the development of more effective DMS deployment strategies and provide practical design recommendations to enhance traffic safety and information delivery on high-speed roadways.

PurposeThis study aims to compare the dosimetric characteristics of 3D-printed bolus materials acrylonitrile butadiene styrene (ABS), polylactic acid (PLA) with recently introduced natural rubber (NR) bolus.Materials and MethodsWe employed Monte Carlo simulation to evaluate ABS, PLA, and NR boluses of thicknesses 0.5, 1.0, and 1.5 cm under 6, 9, and 16 MeV electron beam irradiation. Percentage depth dose (PDD) data was analyzed to evaluate dosimetry parameters. Dosimetric stability under varying air-gap conditions was assessed by analyzing PDD curves under air-gap sizes of (0, 1, 3, 5 mm).ResultsNR and ABS showed similar dosimetric profiles, whereas PLA showed enhanced deeper-tissue protection and provided 0.7–10.2% higher surface dose (SD). PLA also exhibited the highest stability, with SD and R90 deviations limited to 1% and 1.2%, respectively, under varying air-gap sizes. At 6 and 9 MeV, a 0.5 cm bolus failed to provide a single dose of 90% of maximum dose for nearly all tested materials.ConclusionNR demonstrated comparable dosimetric performance to ABS, serving as a viable substitute. PLA was optimal for maximizing SD and distal tissue sparing while exhibiting the lowest air-gap sensitivity. For tumors located several centimeters deep from the surface, 16 MeV electron beams were suitable. Furthermore, Dual hotspots were identified a substantial risk of localized normal tissue toxicity.

This paper attempts to retrofit the diesel generating systems - used for electrification of the Middle East and North Africa (MENA) off-grid remote areas - with hybrid renewable energy (RE) systems, in which biomass is integrated with solar. For this purpose, five alternative RE systems comprised of different configurations of the following components: photovoltaic (PV) array, biogas fueled generator (BGG) and batteries (BATT) are investigated to identify the most appropriate configuration to be used for rural electrification of the aforesaid areas. These five systems are simulated, optimized and analyzed based on their techno-economic performance with the condition of fully meeting the load demand. The results unveil that – among the optimal configurations – of the investigated systems, the BGG-PV-BATT hybrid RE system is the most appropriate one for off-grid rural electrification. This system is able to entirely meet the load demand with the least cost of electricity (COE), and it has the lowest net present cost (NPC). Results of investigating the possibility of extending the electricity grid to the site disclose that - based on the site’s distance from the grid - the grid extension option is more expensive than the off-grid BGG-PV-BATT hybrid system one. Based on this fact and considering all of the obtained results, the BGG-PV-BATT proved to be the most appropriate solution - from environmental and techno-economic points of view - for off-grid rural electrification in the remote off-grid MENA sites and in other sites in the globe having the same conditions.

The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.