Amr Elbrashy

This study introduces an integrated, non-destructive method for real-time soil moisture measurement using a 2.4 GHz microwave/RF sensing system combined with machine learning. It overcomes the limitations of traditional gravimetric techniques, which are destructive, energy-intensive, and have a 24-hour delay, by providing quick, on-site estimates of volumetric water content (VWC) for urgent geotechnical decisions. The system pairs a 2.4 GHz radar platform with edge computing via a Raspberry Pi 5 to gather and analyze reflected signal power, which correlates strongly with soil dielectric properties. A convolutional neural network (CNN) classifies soil texture (sand, clay, or mixed), allowing for the selection of optimal polynomial regression models for precise prediction. Validation with target water contents of 0.05, 0.15, 0.25, 0.35 and 0.45 L showed high agreement between measured and predicted values, with average accuracies around 99.8%, 99.7%, and 99.4% for sand, clay, and mixed soil, respectively. The system dramatically reduces measurement time to real-time, maintaining reliability compared to the standard gravimetric method, which usually takes 24 h. This approach offers a cost-effective, scalable solution for continuous soil monitoring in civil engineering and agriculture, improving the assessment of foundational stability and enabling proactive risk management.

AIArtificial intelligenceANFISAdaptive neural fuzzy inference systemANNArtificial neural networksASHPAir source heat pumpsCSICurrent-source inverterDASDistributed acoustic sensingDASTDual-axis sola...

The worldwide need for effective thermal management solutions keeps increasing, driven by uses ranging from cooling electronic parts to regulating temperatures in homes and vehicles [1]. In this co...

Optimizing solar still design and developing cost-effective and evaporating bio-materials are critical to improving freshwater production. This study experimentally investigates the performance enhancement of a tubular solar still (TSS) through three innovative scenarios incorporating biomaterials. The first scenario involves coating the absorber plate with carbon quantum dots (CQDs) and graphite nanoparticles (GNPs). The second scenario incorporates natural porous materials: sponge and loofah on the absorber plate. The third scenario combines CQDs and sponges and compares them with conventional black paint. Performance evaluations were conducted based on hourly and cumulative water productivity and thermal efficiency. The experimental results reveal that the CQDs and sponge combination achieved the highest cumulative water productivity of 3.66 L/m ² and the highest thermal efficiency of 57.28%, outperforming black paint by 28.8% and conventional sponges by 3.7%. This improvement is attributed to the synergistic effect of the high heat absorption of CQDs and the porous sponge structure, which improves energy distribution and water evaporation. These results provide valuable insights for developing solar-powered desalination technologies to sustainably and efficiently produce freshwater.

Energy, food, and water are the most essential demands for the human community. In this study, a novel hybrid solar photovoltaic/thermal (PV/T) solar dryer integrated with a water recovery unit is designed, developed, and experimentally evaluated for drying agricultural products. The system combines a PV/T air collector that simultaneously generates electricity and hot air with a thermoelectric cooling (TEC) unit that condenses water vapor from the drying exhaust. This theoretical research aims to develop a new concept for drying systems based on thermoelectric coolers, using PV/T as a solar collector and an electric supplier. The scientific innovation lies in utilizing the waste heat from the hot side of the TEC units and introducing it into the drying chamber to enhance the evaporation rate. Meanwhile, the water vapor expelled through the cold side of the units is condensed, transforming the drying-to-drinking (D2D) process without energy or mass loss. Furthermore, drying 1 kg of tomato leads to two sources of condensed water (tomato moisture + atmospheric water). While an airflow rate of 0.05 kg/s corresponds to an inlet of atmospheric air at 1440 kg during 8 h, the water recovery unit produced nearly 3.9 L of water during an 8-hour drying cycle. Thus, addressing the dual challenge of food preservation and water scarcity.