This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things (G‐IoT) is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G‐IoT. The gathered information will facilitate forecasting, decision‐making, and other activities related to smart urban services and then contribute to the incessant development of G‐IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G‐IoT approach and the knowledge gained through big data (BD) analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power‐Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data.

Objective: This study aimed to determine the prevalence of knee osteoarthritis (OA) in Saudi Arabia and the association between knee OA and modifiable and non-modifiable risk factors. Methods: A self-reported, population-based, cross-sectional survey between January 2021 and October 2021 was conducted. A large, population-representative sample (n = 2254) of adult subjects aged 18 years and over from all regions of Saudi Arabia was collected electronically using convenience sampling. The American College of Rheumatology (ACR) clinical criteria were used to diagnose OA of the knee. The knee injury and osteoarthritis outcome score (KOOS) was used to investigate the severity of knee OA. This study focused on modifiable risk factors (body mass index, education, employment status, marital status, smoking status, type of work, previous history of knee injury, and physical activity level) and non-modifiable risk factors (age, gender, family history of OA, and presence of flatfoot). Results: The overall prevalence of knee OA was 18.9% (n = 425), and women suffered more compared to their male counterparts (20.3% vs. 13.1%, p = 0.001). The logistic regression analysis model showed age (OR: 1.06 [95% CI: 1.05–1.07]; p < 0.01), sex (OR: 2.14 [95% CI: 1.48–3.11]; p < 0.01), previous injury (OR: 3.95 [95% CI: 2.81–5.56]; p < 0.01), and obesity (OR: 1.07 [95% CI: 1.04–1.09]; p < 0.01) to be associated with knee OA. Conclusions: A high prevalence of knee OA underlines the need for health promotion and prevention programmes that focus on modifiable risk factors to decrease the burden of the problem and the cost of treatment in Saudi Arabia.

The power XLindley (PXL) distribution is introduced in this study. It is a two-parameter distribution that extends the XLindley distribution established in this paper. Numerous statistical characteristics of the suggested model were determined analytically. The proposed model’s fuzzy dependability was statistically assessed. Numerous estimation techniques have been devised for the purpose of estimating the proposed model parameters. The behaviour of these factors was examined using randomly generated data and developed estimation approaches. The suggested model seems to be superior to its base model and other well-known and related models when applied to the COVID-19 data set.

The core objective of this work is to predict stock market indices’ using autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and their combination in the form of ARIMA-ANN. Financial data are, in fact, trendy, noisy and highly volatile. To tackle their chaotic nature and forecast the three considered stock markets, namely Nasdaq stock exchange, United States, Nikkei stock exchange, Japan, and France stock exchange data (CAC 40 index), we use novel approaches. The data are taken from the Yahoo Finance website for the period from 4 January 2010 to 20 August 2021. To assess the relative predictive effectiveness of the selected tools, the dataset was divided into two distinct subsets: 75% of the data was allocated for training purposes, while the remaining 25% was reserved for testing. The empirical results suggest that ARIMA-ANN produces more accurate forecasts than the separate components of all stock markets. In light of this, it may be inferred that the combining tool is more effective in analyzing financial data and provides a more accurate comparative prediction.

This work explores Routh–Hurwitz stability and complex dynamics in models for awareness programs to mitigate the spread of epidemics. Here, the investigated models are the integer‐order model for awareness programs and their corresponding fractional form. A non‐negative solution is shown to exist inside the globally attracting set (GAS) of the fractional model. It is also shown that the diseasefree steady state is locally asymptotically stable (LAS) given that R₀ is less than one, where R₀ is the basic reproduction number. However, as R₀ > 1, an endemic steady state is created whose stability analysis is studied according to the extended fractional Routh–Hurwitz scheme, as the order lies in the interval (0,2]. Furthermore, the proposed awareness program models are numerically simulated based on the predictor‐corrector algorithm and some clinical data of the COVID‐19 pandemic in KSA. Besides, the model’s basic reproduction number in KSA is calculated using the selected data (R₀ = 1.977828168)_. In conclusion, the findings indicate the effectiveness of fractional‐order calculus to simulate, predict, and control the spread of epidemiological diseases.

Background. Increasing divorce rates is a major problem in Saudi society. Divorce has become the primary solution for many couples who are experiencing problems in their relationships, and the language of divorce has become prevalent in both the daily register of Saudi courts on a daily basis and in fictional works. This trend has become a threat to marital life and is particularly damaging for children and young people. There has been previous research on the increase in divorce rates; however, no one addresses this important issue statistically. The specific research question is what are the actual reasons for the increase in divorce rates mentioned in previous studies? Objective. This study contains a statistical analysis of the underlying reasons for rising divorce rates in Saudi society. Methods. An online survey was conducted that gathered quantitative data from 800 participants. The responses of factual relevance were examined via edge plan analysis, regression analysis, and analyses with SPSS software to determine the causes for the rise in divorces in Saudi society. A predesign of edge consisting of six factors and twelve trials is selected. The design is examined in all the data for each row of it and according to the factor or reason chosen, provided that all design items are fulfilled; after that, it is analyzed in two ways. Results. Some examples are presented, so the findings indicated that differences on the couples characters and the length of time the wife spent doing housework and amount of attention to the husband are factors that contributed to divorce. Conclusion. The Saudi Arabian government needs to develop an operational plan to study these causes of divorce and take action. Future studies could analyze the data using supersaturated prototypes, where a large number of variables are studied in just a few simulation trials.