One of the goals of adding adjuvants to agricultural spray solutions is to enhance the droplet size characteristics of this spray. Droplet size, in turn, has an influence in the deposited spray quality, in addition to the drift and losses of spray to off-target places. The aim of this research was to evaluate the effect of adding adjuvants to two types of water from different sources on the droplet size characteristics. Two types of adjuvants were employed in the tests: the active substance content of the first adjuvant was a 50% aqueous solution of sodium salt of alkylbenzenesulfonic acid—10% (HY), whereas the second was from rapeseed oil (natural origin)—85% (OL). Both adjuvants were tested in two concentrations: the first was with the concentration recommended by the manufacturer (100%), whereas the second concentration was 50% of the recommended dose. Two types of water from different sources were used in the tests: the first was from a village in the eastern part of Poland (WS), whereas the second was tap water from the city of Lublin, Poland (WUP). Dv0.1, Dv0.5, or volume median diameter (VMD), Dv0.9, Sauter mean diameter (SMD), relative span (RS), and the droplet size distribution were measured and calculated as characteristics of the droplet size. Results showed that the source of water affected the influence of adding adjuvant to the spray solution. Water from the WS source with adjuvant resulted in a numerical decrease in the Dv0.5 values in the percentage of droplet size range below 150 µm, whereas water from the WUP source resulted in an increase in these values (except when adding the HY adjuvant at 50% concentration). Adjuvant concentration significantly (p < 0.05) influenced the features of Dv0.9, SMD, and RS. Adding the OL adjuvant type numerically decreased the percentage of droplet size below 150 µm, and the Dv0.5 values, but only when the WS water source was used.
Abstract :H.pylori is an important cause of gastric duodenal disease, including gastric ulcers, Mucosa-associated lymphoid tissue (MALT), and gastric carcinoma. biosensors are becoming the most extensively studied discipline because the easy, rapid, low-cost, highly sensitive, and highly selective biosensors contribute to advances in next-generation medicines such as individualized medicine and ultrasensitive point-of-care detection of markers for diseases. Five of ten patients diagnosed with H.pylori ranging in age from 15–85 participated in this research. who [gastritis, duodenitis, duodenal ulcer (DU), and peptic ulcer (PU)] Suspected H.pylori colonies w
... Show MoreEco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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