Surface drip irrigation is one of the most conservative irrigation techniques that help control providing water directly on the soil through the emitters. It can supply fertilizer and providing water directly to plant roots by drippers. One of the essential needs for trickle irrigation nowadays is to obtain more knowledge about the moisture pattern under the trickling source for various types of soil with various discharge levels with trickle irrigation. Simulation numerical using HYDRUS-2D software, version 2.04 was used to estimate an equation for the wetted area from a single surface drip irrigation in unsaturated soil is taking into account water uptake by roots. In this paper, using two soil types were used, namely

This study investigated the bioethanol production from green algae Chlorella vulgaris depending on its carbohydrate-enriched biomass. Four different phosphorous concentrations were employed to stimulate bioethanol production from Chlorella vulgaris. The impact of various phosphorous values on Chlorella vulgaris growth rate as well as primary product (carbohydrate) were evaluated. High performance liquid chromatography was utilized in this work. The stationary phase was identified as day 14, 12, 10 and 6 in treatments 6, 4, 2 and g/L, respectively. The findings suggest that the treatment without phosphorous addition had the highest record of carbohydrate content (22.64% dry weight) as well as the highest bioethanol yield (20.66% dry weight).

In this work, the dyes Rhodamine B and Coumarin 102 containing titanium dioxide nanoparticles were used as scattering centers to fabricate a random gain medium. The laser dye was dissolved in hexanol and methanol solvent respectively. The titanium dioxide nanoparticles were synthesized by DC reaction magnetron spraying technique. The random-gain medium was made by adding 2.5 mg of titanium dioxide nanoparticles to Rhodamine and coumarin 102 dyes by coating the glass cell with two-sided titanium dioxide with high spectral efficiency and low production cost. A narrow line optical emission was detected at 565 nm for Rhodamine B and 534 nm for coumarin 102, where it was found that rhodamine B dye has FWHM 8 nm and coumarin dye 102 has FWHM 9 nm

The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

Carbon dioxide geo-sequestration (CGS) into sediments in the form of (gas) hydrates is one proposed method for reducing anthropogenic carbon dioxide emissions to the atmosphere and, thus reducing global warming and climate change. However, there is a serious lack of understanding of how such CO2 hydrate forms and exists in sediments. We thus imaged CO2 hydrate distribution in sandstone, and investigated the hydrate morphology and cluster characteristics via x-ray micro-computed tomography in 3D in-situ. A substantial amount of gas hydrate (∼17% saturation) was observed, and the stochastically distributed hydrate clusters followed power-law relations with respect to their size distributions and surface area-volume relationships. The layer-

Automated detection of Dubas palm infestation by image processing techniques has practical significance as it can improve agricultural efficiency, increase crop yield and quality, protect the environment, and provide data-driven insights. It also reduces the human effort required for pest control and enhances sustainability. In this study, we aimed to automate the detection of Dubas bug infestation in palm trees using deep learning with transfer learning residual neural networks. Based on four models: InceptionResNetV2, ResNet18, ResNet50, and ResNet101, the data used in this study were obtained by drone photography, many images were taken, and then the infected area was extracted. Using two types of data, 185 infected images and 185 health