The need for sustainable development, coupled with the growth in industrialization, creates a complex environment in which businesses strive to achieve and maintain a competitive advantage. Information now forms a vital part of how firms perform in today’s globalized corporate world. This paper explores the impact of information systems on sustainable organizational operations. Furthermore, it observes how IT infrastructure and information security policy (ISP) play vital roles in the changing business environment. The importance of information security culture (ISC) as a mediator in developing the association between the independent and dependent variables is also investigated. Reviewing these categories’ interactions within the context of transitional economics is the main goal. To assess and predict the impact of ISs, ISP, ITI, and ISC on sustainable organizational performance (SOP), 214 businesses took part in a structured survey. For data cleaning and reliability analysis, SPSS software was used; for mediation analysis, the Preacher and Hayes approach was applied; and, for multiple linear regression analysis, Python was applied. The study is significant for developing countries in the role of IS for the effectiveness of IT governance and strategic integration. The findings indicate that organizational performance is substantially impacted by information security policy (ISP), IT infrastructure (ITI), and information security culture (ISC).

A wide range of mathematical constructs, theories, and algorithms have been leveraged by cryptographers to secure valuable and sensitive digital images. Among these, image encryption based on scrambling algorithms has been extensively utilized. These scrambling techniques operate on 1D, 2D, and 3D constructs, relying on pixel-level swapping to enhance security. This research introduces a novel scrambling algorithm that employs a row- and column-based scrambling approach. The encryption process begins with an input image, from which a 2D scrambled image is generated. The columns of pixels from the input image are randomly placed into the columns of the scrambled image. Once all columns are inserted, the process is repeated to intensify the scrambling effect. A similar procedure is then applied to the rows of the image. To further enhance security, a diffusion process is applied using an XOR operation between the scrambled image and a random number stream. These random numbers are generated using the 3D Rössler chaotic system, where two chaotic streams contribute to the scrambling effect, and a third stream is used for diffusion. Both machine simulations and exhaustive security analyses demonstrate that the proposed image cipher is highly resistant to various cryptanalytic attacks, including brute-force attacks, differential attacks, and noise and data cropping attacks. Given these promising results, we recommend the proposed method for real-world applications to fully utilize its security potential.

Urban air pollution remains a critical challenge for public health and environmental sustainability. This study investigates the predictive capabilities of five machine learning (ML) models: Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) for forecasting the Air Quality Index (AQI) using the widely adopted Air Quality dataset from the UCI ML Repository. Although collected in 2004–2005, the dataset continues to serve as a benchmark in recent literature and provides a reproducible testbed for methodological evaluation. After structured pre-processing, feature engineering, and chronological train–validation–test splitting, models were rigorously tuned and assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2), with 95% bootstrap confidence intervals and corrected resampled t-tests confirming statistical significance. Ensemble models achieved the best performance, with Random Forest obtaining the lowest RMSE (12.48) and MAE (9.35), and XGBoost achieving the highest R2 (0.89). Feature importance analysis identified NOx, PM2.5, and CO as the most influential predictors. We incorporated Shapley Additive exPlanations (SHAP) analyses and case-level visualizations to support interpretability, providing transparent insights for practical decision-making. While the study is limited by the absence of external validation and genetic variables (e.g., APOE), it establishes a reproducible, interpretable, and computationally efficient ML framework for AQI forecasting. The findings highlight the continuing relevance of benchmark datasets for reproducible evaluation and demonstrate the potential of interpretable ML-based approaches for smart city air quality management and public health policy.

The successful execution and management of Offshore Software Maintenance Outsourcing (OSMO) can be very beneficial for OSMO vendors and the OSMO client. Although a lot of research on software outsourcing is going on, most of the existing literature on offshore outsourcing deals with the outsourcing of software development only. Several frameworks have been developed focusing on guiding software system managers concerning offshore software outsourcing. However, none of these studies delivered comprehensive guidelines for managing the whole process of OSMO. There is a considerable lack of research working on managing OSMO from a vendor’s perspective. Therefore, to find the best practices for managing an OSMO process, it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s perspective. This study validated the preliminary OSMO process model via a case study research approach. The results showed that the OSMO process model is applicable in an industrial setting with few changes. The industrial data collected during the case study enabled this paper to extend the preliminary OSMO process model. The refined version of the OSMO process model has four major phases including (i) Project Assessment, (ii) SLA (iii) Execution, and (iv) Risk.

The generation, storage, and transmission of digital images have become ubiquitous in today’s interconnected world. Ensuring the security of these images is a critical challenge that demands immediate attention. This study proposes a novel image encryption algorithm designed to address these concerns effectively. The proposed framework leverages the unique properties of three key constructs: the Wireworld cellular automaton, the 1D logistic chaotic map, and the piecewise linear chaotic map. The 1D logistic chaotic map is employed to generate random numbers, which are used to initialize the Wireworld cellular automaton. The automaton, in turn, introduces scrambling effects into the plaintext image, effectively disrupting its pixel arrangement. Additionally, the piecewise linear chaotic map is utilized to achieve diffusion effects, further enhancing the security of the encryption process. Extensive security analyses and machine experiments have yielded highly promising results. The proposed algorithm has been rigorously evaluated using a variety of validation metrics, including key space analysis, correlation coefficient, Cartesian and polar histograms, information entropy, histogram variance, and peak signal-to-noise ratio (PSNR). In particular, we got an entropy of 7.9975 and histogram variance 251.9867. These metrics collectively demonstrate the algorithm’s strong security characteristics and its resilience against potential attacks. The findings suggest that the proposed image cipher is not only highly secure but also practical for real-world applications. It holds significant potential for safeguarding digital images across diverse domains, including healthcare, military, and multimedia communication. This study underscores the viability of the proposed approach as a reliable solution for ensuring the security of images in an increasingly digital world.