This study was conducted to evaluate the efficacy of Saccharomyces cerevesiae as a growth promoting agent in tomato. Soaking the seeds in yeast suspension at 5 g/L for 12h increased germination percentage, root length, root fresh and dry weight, plant height, foliage fresh and dry weight, attained 88.5% ; 8.1 cm ; 84.3 mg ; 7.03 mg ; 10.75 cm ; 839 mg and 37.75 mg compared with 80% ; 5.33 cm ; 39 mg ; 4.8 mg ; 7.35 cm ; 608 mg and 25.5 mg in seedlings grown from non treated seeds respectively. Similar results were obtained with seedling from seeds soaked in S. cerevesiae filtrate for 12 hrs. with values of 77.5% ; 6.875 cm ; 91.5 mg ; 7.5 mg ; 9.5 cm ; 777 mg and 40.35 mg compared to 66% ; 5.8 cm ; 57.7 mg ; 5.03 mg ; 5.9 cm ; 493 mg and 27.28 mg in control (non treated seeds) for the same above criteria respectively. Watering the soil together with spraying the foliar parts with S. cerevesiae suspension at 5 and 8 g/L were found to be more effective than watering and spraying the plants separately in plant growth stimulation under plastic house conditions. The leaf contents of chlorophyll attained to 60.4 and 61.17 SPAD unit compared with 50.37 SPAD units in control respectively and leaf area reached to 3124 and 3119 cm2 / plant compared with 1904 cm2 / plant in control for the two concentrations respectively. The treatment induced also an increasing in plant high ; fresh and dry weights which attained 222 cm ; 223.3 cm ; 1485.7 g ; 1489 g ; 340.7 g ; 341.7 compared to 186 cm ; 1169.3 g ; 286 g in control for the two concentrations respectively. Similar increasing in root length , root fresh and dry weight and yields which attained 30.33 cm ; 30.7 cm ; 61 g ; 61.33 g ; 14.33 g ; 14.33 g ; 6.9 kg / plant and 6.95 kg / plant compared to 24.13 cm ; 46 g ; 10 g and 4.22 kg / plant in control , were found. The stimulations of plant growth criteria was found in concomitance with increase of N ; P and K in treated plant leaves which reached 2.293 ; 2.3 ; 0.4007 ; 0.402 ; 0.5506 and 0.5723% compared to 1.458 ; 0.2283 and 0.1226% in control for the two concentrations respectively . In addition increasing in total solid soluble material (TSS), 5.2 and 5.2023% compared to 3.867% in control treatment were observed.
Transportability refers to the ease with which people, goods, or services may be transferred. When transportability is high, distance becomes less of a limitation for activities. Transportation networks are frequently represented by a set of locations and a set of links that indicate the connections between those places which is usually called network topology. Hence, each transmission network has a unique topology that distinguishes its structure. The most essential components of such a framework are the network architecture and the connection level. This research aims to demonstrate the efficiency of the road network in the Al-Karrada area which is located in the Baghdad city. The analysis based on a quantitative evaluation using graph th
... Show MoreThe current research aims to prepare a proposed Programmebased sensory integration theory for remediating some developmental learning disabilities among children, researchers prepared a Programme based on sensory integration through reviewing studies related to the research topic that can be practicedby some active teaching strategies (cooperative learning, peer learning, Role-playing, and educational stories). The Finalformat consists of(39) training sessions.
Cover crops (CC) improve soil quality, including soil microbial enzymatic activities and soil chemical parameters. Scientific studies conducted in research centers have shown positive effects of CC on soil enzymatic activities; however, studies conducted in farmer fields are lacking in the literature. The objective of this study was to quantify CC effects on soil microbial enzymatic activities (β-glucosidase, β-glucosaminidase, fluorescein diacetate hydrolase, and dehydrogenase) under a corn (Zea mays L.)–soybean (Glycine max (L.) Merr.) rotation. The study was conducted in 2016 and 2018 in Chariton County, Missouri, where CC were first established in 2012. All tested soil enzyme levels were significantly different between 2016 and 2018
... Show MoreThe purpose of this paper is to introduce and study the concepts of fuzzy generalized open sets, fuzzy generalized closed sets, generalized continuous fuzzy proper functions and prove results about these concepts.
Objective: The goal of this research is to load Doxorubicin (DOX) on silver nanoparticles coupled with folic acid and test their anticancer properties against breast cancer. Methods: Chitosan-Capped silver nanoparticles (CS-AgNPs) were manufactured and loaded with folic acid as well as an anticancer drug, Doxorubicin, to form CS-AgNPs-DOX-FA conjugate. AFM, FTIR, and SEM techniques were used to characterize the samples. The produced multifunctional nano-formulation served as an intrinsic drug delivery system, allowing for effective loading and targeting of chemotherapeutics on the Breast cancer (AMJ 13) cell line. Flowcytometry was used to assess therapy efficacy by measuring apoptotic induction. Results: DOX and CS-Ag
... Show MoreVehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for