Open Access

The study of agricultural water usage in Almansouriya District reveals significant challenges related to water scarcity, high salinity in groundwater, and considerable evaporation rates. The study emphasizes the importance of implementing multiple strategies to achieve sustainable water use and enhance agricultural resilience in the face of climatic and environmental challenges.

This paper presents a comparative study of binarization techniques for automated defect detection in dye penetrant testing (DPT) images. We evaluate established methods, including global, adaptive, and histogram-based thresholding, against three novel machine learning-assisted approaches, Soft Binarization (SoBin), Delta Binarization (DeBin), and Convolutional Autoencoder Binarization (AutoBin), using a real-world dataset from an automated DPT system inspecting stainless steel pipes. Performance is assessed with both pixel-level and region-level metrics, with particular emphasis on the influence of defect saturation. Defect saturation is quantified as the mean saturation value of all pixels belonging to a given defect, and defects are grouped into ten categories spanning from low (60–68) to high (132–140) mean saturation. Our results demonstrate that for lower mean defect saturation values, methods such as AutoBin_Triangle , HSV_global_70 , and SoBin achieve superior Intersection over Union (IoU) and high true positive rates. In contrast, methods based primarily on global thresholding of the saturation channel tend to perform competitively on images with higher defect saturation levels, reflecting their sensitivity to stronger color signals. Moreover, depending on the method, nearly perfect region-level true positive rates ( TPRregion) or minimal false positive rates ( FPRregion) can be attained, emphasizing the trade-off that different models offer distinct strengths and weaknesses, which necessitates selecting the optimal method based on the specific quality control requirements and risk tolerances of the industrial process. These findings underscore the critical importance of defect saturation as a cue for both human and computer vision systems and provide valuable insights for developing robust automated quality control and predictive quality algorithms.

Commercial strategy games are a medium for addressing and disseminating socially controversial issues and influencing social discourse. In order to assess their didactic usefulness and suitability as educational media, this article examines the extent to which they integrate multiperspectivity into their game design. In principle, the ability to adopt perspectives is essential for understanding other human beings, their views, motives, and interests, and is therefore also an important educational objective at school. If strategy games integrate multiperspectivity, they could eventually also be used as a didactic tool to develop perspective-taking skills and to deal with controversial social issues, such as climate change or urban planning, in the classroom. In order to address this research gap and assess the potential of digital strategy games for teaching multiple perspectives in the classroom, it is first necessary to examine the extent to which and the forms in which strategy games integrate multiple perspectives. For this purpose, seventeen successful titles are examined through an empirical game analysis. It is shown that in all of them, different stakeholders and their perspectives are integrated into the games. However, the number of perspectives, the scope of the perspectives presented, and their controversial nature, as well as the language design vary considerably.

In a supersonic flow, disturbances of different parameters arise. These perturbations can have a significant impact on the interaction of the flow with the surface. When gas flow passes through a shock wave, perturbations are transformed depending on the initial parameters of the flow. Therefore, it is important to be able to correctly assess the intensity of these transformations. In this work, for the first time, a method has been proposed that allows us to estimate the dynamics of variation of disturbances of flow parameters when passing through an oblique shock wave. The influence of the shock wave inclination angle β, Mach number, intensity of disturbances of velocity, density, temperature, and pressure in front of the shock wave on perturbations of the flow parameters behind the shock wave was investigated. The Mach numbers ranged from 1.2 to 10 and the shock wave inclination angle varied from 15° to 90°. It was shown that the interaction of a supersonic gas flow with an oblique shock wave has a significant effect on the transformation of the perturbations of the flow parameters. The perturbations of temperature and pressure behind the shock wave increase significantly with the increasing angle β and Mach number in front of the shock wave. With the increasing Mach number, the velocity perturbations behind the shock wave first increase, then decrease, passing through a maximum, and afterwards the flow becomes more stable.

As global reliance on sustainable energy solutions intensifies, there is a growing need to optimise and accurately predict renewable energy outputs. Bifacial photovoltaic systems, which are capable of capturing irradiance on both their front and rear sides, represent a significant advancement over traditional monofacial systems, yielding higher energy per area. The accuracy of simulation models for these systems has a direct impact on their financial viability, necessitating the use of comprehensive and reliable simulation frameworks. This research validates BifacialSimu, an open-source simulation tool designed to enhance the prediction of bifacial PV system energy outputs by incorporating multiple simulation models. The practical validation of BifacialSimu is based on empirical data from three diverse geographic locations. The locations of Golden, United States; Heggelbach, Germany; and Florianópolis, Brazil, provide insights into the performance of bifacial PV systems across a range of environmental conditions and installation configurations. These findings underscore the practical applicability of BifacialSimu, with recommendations for simulation model selection and methodological advancements, paving the way for more precise and efficient bifacial PV system simulations across diverse scenarios. This study employs a number of validation metrics, including relative error, coefficient of determination and Normalized Root Mean Square Error, to assess the accuracy of the simulations. The findings indicate that the Ray tracing method is the most accurate of the irradiance simulation modes for most scenarios. The validation results highlight that the Ray Tracing method achieves superior accuracy in irradiance simulations, particularly under varied environmental conditions, while Variable Albedo models further enhance predictive precision by accounting for dynamic factors such as snow cover.