Implementation of variational iteration method for various types of linear and nonlinear partial differential equations

There are various linear and nonlinear one-dimensional partial differential equations that are the focus of this research. There are a large number of these equations that cannot be solved analytically or precisely. The evaluation of nonlinear partial differential equations, even if analytical solutions exist, may be problematic. Therefore, it may be necessary to use approximate analytical methodologies to solve these issues. As a result, a more effective and accurate approach must be investigated and analyzed. It is shown in this study that the Lagrange multiplier may be used to get an ideal value for parameters in a functional form and then used to construct an iterative series solution. Linear and nonlinear partial differential equations may both be solved using the variational iteration method (VIM) method, thanks to its high computing power and high efficiency. Decoding and analyzing possible Korteweg-De-Vries, Benjamin, and Airy equations demonstrates the method’s ability. With just a few iterations, the produced findings are very effective, precise, and convergent to the exact answer. As a result, solving nonlinear equations using VIM is regarded as a viable option

Effects of planting date and spraying with organic fertilizers on vegetative growth indices of dill plant (Anethum graveolens L.)

A factorial field experiment in a split-plot design was carried out in Kannan district, Diyala Governorate, mid-east of Iraq, during the agricultural season 2020-2021 to study the effect of planting date and spraying with organic fertilizers on growth and qualitative characteristics of dill plant (Anethum graveolens L.). The experiment included three replicates, each comprising 15 factorial treatments. Experiment factors included three planting dates (20/9, 10/10 and 1/11) and three types of organic fertilizers (humic fertilizers, seaweed extracts and amino acids). Organic fertilizers were sprayed three times during the vegetative growth stage. Results indicate that the plants growing on the first date (20/9) significantly outperformed in plant height, the number of leaves, chlorophyll content and dry matter percentage, which amounted to 42.48 cm, 5.8 leaf plant-1, 28.06 mg per 100 g fresh weight and 8.52%, respectively. The second date (10/10) were significantly superior in vegetative yield which amounted to 26.791ton ha-1. All fertilizers were significantly outperformed control treatment in plant height, the number of leaves and branches, chlorophyll percentage, vegetative yield and dry matter percentage

A systematic review of trustworthy artificial intelligence applications in natural disasters

Artificial intelligence (AI) holds significant promise for advancing natural disaster management through the use of predictive models that analyze extensive datasets, identify patterns, and forecast potential disasters. These models facilitate proactive measures such as early warning systems (EWSs), evacuation planning, and resource allocation, addressing the substantial challenges associated with natural disasters. This study offers a comprehensive exploration of trustworthy AI applications in natural disasters, encompassing disaster management, risk assessment, and disaster prediction. This research is underpinned by an extensive review of reputable sources, including Science Direct (SD), Scopus, IEEE Xplore (IEEE), and Web of Science (WoS). Three queries were formulated to retrieve 981 papers from the earliest documented scientific production until February 2024. After meticulous screening, deduplication, and application of the inclusion and exclusion criteria, 108 studies were included in the quantitative synthesis. This study provides a specific taxonomy of AI applications in natural disasters and explores the motivations, challenges, recommendations, and limitations of recent advancements. It also offers an overview of recent techniques and developments in disaster management using explainable artificial intelligence (XAI), data fusion, data mining, machine learning (ML), deep learning (DL), fuzzy logic, and multicriteria decision-making (MCDM). This systematic contribution addresses seven open issues and provides critical solutions through essential insights, laying the groundwork for various future works in trustworthiness AI-based natural disaster management. Despite the potential benefits, challenges persist in the application of AI to natural disaster management. In these contexts, this study identifies several unused and used areas in natural disaster-based AI theory, collects the disaster datasets, ML, and DL techniques, and offers a valuable XAI approach to unravel the complex relationships and dynamics involved and the utilization of data fusion techniques in decision-making processes related to natural disasters. Finally, the study extensively analyzed ethical considerations, bias, and consequences in natural disaster-based AI.</p