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First is to develop the time truncated median control chart for the Rayleigh distribution (RD) and generalized RD (GRD), respectively. Second is to evaluate the performance of the proposed attribute control chart which depends on the average run length (ARL) and third is to include real life examples for application purpose of the proposed attribute control chart.

(1) Select a random sample of size n from each subgroup from the production process and put them on a test for specified time t, where t = ? × µe. Then, count the numbers of failed items in each subgroup up to time t. (2) Step 2: Using np chart, define D = np, the number of failures, which also a random variable follows the Binomial distribution. It is better to use D = np chart rather than p chart because the authors are using number of failure rather than proportion of failure p. When the process is in control, then the parameters of the binomial distribution are n and p0, respectively. (3) Step 3: The process is said to be in control if LCL = D = UCL; otherwise, the process is said to be out of control. Hence, LCL and UCL for the proposed control chart.

From the findings, it is concluded that the GRD has smaller ARL values than the RD for specified values of parameters, which indicate that GRD performing well for out of control signal as compared to the RD.

This developed control chart is applicable when real life situation coincide with RD and GRD.

Researcher can directly use presented study and save consumers from accepting bad lot and also encourage producers to make good quality products so that society can take benefit from their products.

This article dealt with time truncated attribute median control chart for non-normal distributions, namely, the RD and GRD, respectively. The structure of the proposed control chart is developed based on median lifetime of the RD and GRD, respectively.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

Automatically extracting stance information from natural language texts is a significant research problem with various applications, particularly after the recent explosion of data on the internet via platforms like social media sites. Stance detection system helps determine whether the author agree, against or has a neutral opinion with the given target. Most of the research in stance detection focuses on the English language, while few research was conducted on the Arabic language.

This paper aimed to address stance detection on Arabic tweets by building and comparing different stance detection models using four transformers, namely: Araelectra, MARBERT, AraBERT and Qarib. Using different weights for these transformers, the authors performed extensive experiments fine-tuning the task of stance detection Arabic tweets with the four different transformers.

The results showed that the AraBERT model learned better than the other three models with a 70% F1 score followed by the Qarib model with a 68% F1 score.

A limitation of this study is the imbalanced dataset and the limited availability of annotated datasets of SD in Arabic.

Provide comprehensive overview of the current resources for stance detection in the literature, including datasets and machine learning methods used. Therefore, the authors examined the models to analyze and comprehend the obtained findings in order to make recommendations for the best performance models for the stance detection task.

The study aims to explore the enablers and barriers to the adoption of circular economy (CE) practices in micro, small and medium enterprises (MSMEs) and examine how these factors differ between developed and developing countries.

The research uses a systematic literature review (SLR) methodology to identify key enablers and barriers to CE adoption in MSMEs. The SLR process involved a detailed search and analysis of relevant academic articles from the Scopus and Web of Science databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency.

The study identifies 19 enablers and 16 barriers to CE adoption in MSMEs. Technological upgrades are the key factor helping MSMEs successfully implement CE practices, while financial constraints are the main challenge they face, according to studies from both developed and developing countries.

This research contributes to the existing body of literature by not only identifying the primary factors that either support or impede the implementation of CE by MSMEs but also by classifying them according to developed and developing countries to provide policymakers and MSME stakeholders with valuable insights on enhancing the implementation of CE in both countries by taking into account the particular barriers and enablers faced by each group individually.

The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.

This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.

The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.

The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.

This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.

Hexavalent chromium has been a benchmark corrosion inhibitor before it was phased out because of its carcinogenic properties. However, because it was phased out, many alternative corrosion inhibitors have been introduced but failed to meet the performance of this benchmark inhibitor. Consequently, benzotriazole (BTA) was reported to exhibit chromate-like inhibition performance. Subsequently, Intelli-ion was reported by researchers to exhibit chromate-like performance also with claims of being a unique alternative. This paper aims to review the inhibition performance of these two alternatives. Above all, promotes the unique inhibition performance of Intelli-ion that makes it suitable for application in many sectors.

In this paper, the corrosion inhibition performances of BTA and Intelli-ion were compared systematically by reviewing some related literatures based on the opinion of the authors.

Different methodologies for measuring the inhibition performance of BTA showed that it’s an inhibitor of choice. However, the cut edge corrosion performance of Intelli-ion and BTA corrosion inhibitors on galvanized steel of 55% Wt.% Al, 44% Wt.% Zn and 1% Wt.% Si in 5 Wt.% NaCl solution was compared when subjected to scanning vibrating electrode technique (SVET) for 24 h. The results showed faint blue-colored region depicting negative cathodic current density for the Intelli-ion while there was a high-intensity of red-colored region depicting a positive anodic current density for BTA. In other words, the Intelli-ion inhibitor had a better overall cut-edge corrosion inhibition performance than the BTA inhibitor.

This paper compares and further, summarizes the corrosion inhibition performance of Intelli-ion and BTA by evaluating SVET results from the literature. In addition, it serves as an overview and reference for the unique inhibition performance of Intelli-ion when applied in field applications.