The integration of artificial intelligence (AI) into environmental monitoring, climate action, and sustainability has become increasingly important, particularly in tackling the global challenges associated with climate change. In this article, we critically assess the role of AI within the frameworks of the United Nations’ Sustainable Development Goals (SDGs) 11 (Sustainable Cities and Communities) and 13 (Climate Action). This study shows how AI applications can significantly enhance climate resilience and promote sustainable urban development. AI technologies enable extensive data collection and analysis through various methods, including remote sensing and sensor networks, which improve assessments of air and water quality, land use changes, and biodiversity monitoring. Another significant advantage of AI is its ability to optimize energy consumption across various sectors, leading to substantial reductions in greenhouse gas emissions. Additionally, we demonstrate that AI enhances waste management strategies by identifying recycling opportunities and streamlining logistics, ultimately contributing to waste reduction and fostering a circular economy. In the fields of agriculture and forestry, AI systems have proven effective in improving crop management and pest control practices, mitigating environmental impacts while increasing food productivity. Furthermore, AI plays a vital role in wildlife conservation efforts by assisting in tracking endangered species and analyzing satellite imagery to combat illegal activities. We also illustrate how AI supports the development of predictive climate models, enabling policymakers to create science-based strategies for effectively navigating extreme weather and climate risks, thereby fostering resilient infrastructure decisions. While the transformative potential of AI in climate initiatives is clear, we address several limitations and ethical challenges associated with its application, including concerns about data quality and accessibility, biases in AI algorithms, and the need for transparency in AI-driven decision-making. These challenges emphasize the importance of interdisciplinary collaboration and robust engagement strategies to ensure that AI applications promote equitable access to solutions for marginalized populations affected by climate change. Moreover, we examine how the COVID-19 pandemic has reshaped the landscape of AI for sustainable development, revealing both challenges and opportunities in implementing AI technologies. The pandemic has led to increased investment in remote monitoring technologies while highlighting the need for collaborative approaches to address the complex interdependencies between public health and climate resilience. Through a synthesis of relevant literature and case studies, we advocate for a holistic and collaborative approach to utilizing AI technologies in environmental management to achieve the SDGs successfully. By fostering innovation and inclusivity in decision-making processes, society can leverage AI's potential to create a sustainable and resilient future, thus advancing comprehensive climate action in alignment with the United Nations’ mission.

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

The basic reproduction number values give an initial prediction of the disease because the values predict of end of the disease if the values are less than one or the disease converts to epidemic if the values are more than one. We apply the SIRD epidemiology model for estimating the basic reproduction number of the new coronavirus disease for multiple different countries. For estimating of the basic reproduction number values, we fit the SIRD model using the Runge-Kutta simulation method in addition to the analytical solution of parts of the model. We use the collected data of the new coronavirus pandemic reported up to date July 30, 2020 in India, the Syrian Arab Republic, the United States, France, Nigeria, Yemen, China and Russia. We find that the basic reproduction numbers of the new coronavirus disease are located in the range [1.0011-2.7936] for the different location countries and the values of the ratio between the rate of recovery and the rate of mortality are between 1.5905 for Yemen and 44.0805 for Russia. Also, we find the dates of the actual decreasing of Covid-19 cases in five countries. We find that the basic reproductive number is between 1.0011 for the smallest value and 2.7936 for the greatest value. The most important thing is that the values of the basic reproduction number of the new coronavirus disease in all considered countries are more than one which means that the new coronavirus disease is epidemic in all of considered countries.

In this paper, we apply an algorithm for simulating the susceptible-infected-recovered-dead pandemic model for demonstrating the forecasting and the spreading of the new coronavirus disease. For this purpose, we apply an implicit analytical solution for parts of the model, in addition to applying the finite difference methods for other parts of the model. Based on the collected data of the number of infected cases, recovered cases, and deceased cases of the new coronavirus pandemic as of 30 May 2020, we find the values of the coefficient of infection, the coefficient of recovery, and the coefficient of mortality of the new coronavirus pandemic for four different countries, namely, China, the United States, Russia, and the Syrian Arab Republic. Besides, we find the ratio of the average rate of recovery to the average rate of death of the new coronavirus pandemic for the same four countries. For the following months, we predict the number of the infected cases, the recovered cases, and the deceased cases of the pandemic for the United States, Russia, and the Syrian Arab Republic using the methods normally used for the epidemic model. We find that the number of infected cases of the new coronavirus disease may increase to about two million cases in the United States, about eight hundred thousand cases in Russia, and about three hundred cases in the Syrian Arab Republic. We believe that the algorithm we use for simulating the spreading and the forecasting of the new coronavirus disease can be applied for finding other related coefficients of the pandemic such as the basic reproduction numbers with a certain population.

Morse potential interaction is an important type of the vibrational potentials, especially, in the quantum mechanics which is used for the describing of general vibrational cases rather than the harmonic one. Morse potential has three fitting parameters, the depth of the Morse interaction, the distance of equilibrium bond and the range parameter which determines the range of the well. The Morse interaction specific bond volume is a three dimensional image of the bond length in its molar case, and this specific volume is the generalisation in three dimensions. In this study, the integral equation theory of the simple fluids has been applied for deriving a novel formula of the specific bond volume for Morse potential based on one of the approaches in the theory and based on the boundary conditions. We find that the specific bond volume of Morse potential depends on the absolute temperature via logarithmic function and square root function, besides, the specific bond volume of Morse potential decreases when the temperature decreases for different values of the molar volume and for different values of the depth of Morse well. In addition to that, the specific bond volume of Morse potential increases when the depth of the well decreases for different temperature values. Also, it is found from the formula which we derive that the specific bond volume of Morse potential increases via linear function with the molar volume of the system for different values of temperatures. We apply the formula of the specific bond volume of Morse potential for finding this specific volume for two molecules of the hydrogen halogens, which are the hydrogen chloride, and hydrogen fluoride. We find that the specific bond volume of the hydrogen chloride is greater than the one of the hydrogen fluoride. Also, we apply the formula for the two simple molecules gases which are the hydrogen molecules, and the nitrogen molecules. Besides, we apply the formula for the slab–slider system in two cases: hard and soft materials, and we concluded that the changes of the specific bond volume of the soft materials is faster than the hard materials. We believe that the formula which is found of the specific bond volume of Morse potential is general and can be applied for multiple materials.