This study evaluates the reliability and suitability of long-term water quality data collected from a sustainable aquaponics system equipped with a pH, dissolved oxygen, and temperature regulation control strategy based on a PID algorithm. Although PID control was implemented to maintain parameters within optimal biological ranges, natural fluctuations and out-of-range measurements were recorded, particularly in pH. Rather than being considered anomalies, these deviations represent realistic environmental variations that must be captured for comprehensive system analysis. A thorough data validation process was conducted, including descriptive statistics, outlier detection, correlation analysis, principal component analysis (PCA), and temporal stability evaluation. Results confirmed the absence of missing data, the presence of controlled variability in dissolved oxygen and temperature, and meaningful correlations between parameters, with pH showing the highest variability. Autocorrelation and long-term trend analyses indicated stable measurement patterns that reflect real-world aquaponic dynamics. The validated dataset provides a robust foundation for future studies, particularly for the development of artificial intelligence (AI)-based predictive models aimed at early detection of fish distress or mortality.

The study examines the characteristic features, capabilities, and performance of Wi-Fi Direct as a device-to-device communication protocol in Android. During the research, the Wi-Fi Direct's effectiveness, connectivity speed and stability, energy consumption, and the possibility to transmit large-sized files, were assessed, comparing them to similar Bluetooth characteristics. The research data were taken from testing using Google Pixel 4 and Samsung Galaxy S10 smartphones with Android, and the results were compared with secondary data findings. The test presupposed transmission of three different-sized files to measure performance outcomes in terms of power consumption, data transfer quality, connection speed, and reliability. The research revealed how Wi-Fi Direct works with the data transmission speed and connection stability compared to Bluetooth in Android and identified the consequences of Wi-Fi Direct use for the energy consumption of Android devices. The study findings show that Wi-Fi Direct is associated with better outcomes in the areas of file transfer speed, especially for large data files, while Bluetooth has proven to be more energy-efficient and easier to use for smaller tasks. These results align with the secondary data findings and highlight the potential of combining both communication protocols. Finally, the study emphasizes the growing relevance of Wi-Fi Direct for high-bandwidth mobile applications, irrespective of setup complexity and higher power consumption.

The increasing digitalization of accounting systems has amplified vulnerabilities to cyberattacks, yet practical strategies to address sector-specific risks remain underexplored. This study explored prevalent cyber threats in the accounting industry, such as phishing directed at enterprise resource planning systems, unauthorized access utilizing mobile devices, and invoice fraud. The investigation was conducted in a manner that combined interviews with accounting and cybersecurity professionals and comparative case reviews of organizational practices. A statistical analysis showed that multi-factor authentication reduced cyber-attack likelihood by 58% while regular employee training reduced the risk by 37%. Organizations with combined Information Technologies (IT) and accounting teams took 40 percent less time resolving security incidents than organizations with separate departments. The results showed that mid-sized firms generally had poor preparedness, while bigger firms showed adherence to international security standards. They also highlighted the need for mandatory cybersecurity training, collaboration across departments, and strategic investments in adaptive security frameworks. Regulatory incentives for small and medium enterprises to adopt cost-effective mitigating safeguards are also a policy implication. The results help bridge theoretical cybersecurity models and real accounting practice by providing actionable advice on making operations more sustainable while mitigating the increasing threat of cyber incidents.

Artificial intelligence (AI) technologies have increasingly penetrated the field of modern design, particularly in the creation of interactive installations. This study aimed to analyze the impact of AI-generated interactive installations on user experience and aesthetic perception. The research employed a comparative approach, content analysis, and case study methodology. A total of 50 scholarly sources were collected and analyzed using the PRISMA protocol to ensure systematic selection and review. The findings demonstrated that key AI tools used in interactive installations include generative adversarial networks (GANs), variational autoencoders (VAEs), computer vision systems, natural language processing (NLP), behavioral analytics, and adaptive machine learning algorithms. These tools, while powerful, require high levels of digital competence, precise configuration, and substantial financial investment. Case analyses of installations such as Living Light, The AI Van Gogh Museum, AI-Driven Storefront, and AI Classcape revealed the following benefits: enhanced interactivity, user personalization, innovative use of AI capabilities in aesthetic experiences, and the emergence of AI as a co-author in artistic creation. AI-driven interactive installations offer significant potential in design and digital art. However, their effectiveness is currently limited by the lack of intuitive human-like creativity, reliance on pre-programmed datasets, and the cost of implementation. The results highlight both the transformative potential and the current limitations of AI as a creative agent in modern design environments.

This study consolidates contemporary methodologies for applying artificial intelligence in software engineering. Using the PRISMA protocol, an analysis of 60 peer-reviewed publications was conducted. Findings indicate that the use of generative tools (such as GitHub Copilot), AI-based testing platforms (like Testim.io and Diffblue), and DevOps automation systems (e.g., Harness.io) can lead to a 20–40% reduction in development time, while also enhancing code quality and minimizing errors. A key academic contribution of the research is the introduction of a three-tier classification of integration barriers – technical, organizational, and legal – that hinder the seamless adoption of AI technologies within the Software Development Life Cycle (SDLC), as well as the lack of standardized methodologies. The recommendations provided in this work are particularly relevant to software engineers, IT project leaders, and academic researchers, as they address crucial concerns related to model interpretability, system instability, the absence of unified standards, and regulatory ambiguity. The practical relevance of the study lies in presenting actionable strategies for the responsible, scalable, and ethically grounded deployment of AI-driven tools in industrial, academic, and research settings.

Sustainable energy has become a critical focus due to the environmental and economic limitations of traditional fossil fuels. One of the most prominent applications in this field is electric vehicles (EVs), which rely on high-voltage DC battery packs (typically 400V or 800V) as their primary energy source. These batteries supply power to AC motors via inverters that convert direct current (DC) to alternating current (AC). Additionally, EVs incorporate DC-DC converter systems to step down the high-voltage DC for auxiliary systems such as infotainment units, control modules, and lighting. The step-down DC-DC converter is composed of various components, including switches (such as MOSFETs or IGBTs) and diodes. These components are subject to different types of losses—namely switching, conduction, and thermal losses—which can significantly impact system efficiency and performance. This article investigates these losses through simulation using the PLECS software across multiple operating scenarios.

Most time series with a clear overall trend in their data and graphs require a model that effectively addresses the overall trend. If the time series also includes various fluctuations and random variations, nonlinear models are the ideal approach. To improve the prediction error and make it very small, a new model was applied to the time series of annual cancer cases in Iraq for the period from 1976 to 2023. This series contains a general trend covering more than 85% of the data, in addition to various random fluctuations and variations. The proposed hybrid model consists of two parts: the first part addresses the strong overall trend in a linear manner by partitioning the series into an optimal number of parts according to the optimal division that gives the lowest value for RMSE and MAPE, and applying a double exponential smoothing method to all parts to address the upward trend. The second part detects nonlinear patterns in the residuals of the first model using an adaptive network-based fuzzy inference system (ANFIS). The proposed hybrid model, Partitioned Double Exponential Smoothing (PDES-ANFIS), has proven to be more efficient compared to the unpartitioned hybrid model and single models by using the root mean square error (RMSE).

Control charts are widely used in statistical process control (SPC) to detect small, gradual shifts in process behavior, although effective at mitigating noise, such as the exponentially weighted moving average (EWMA). Traditional EWMAs, however, face significant challenges and limited adaptability in complex and dynamic environments. In this paper, we propose an improved hybrid approach that integrates EWMAs with artificial intelligence algorithms, such as anomaly detection models, deep learning networks, and unsupervised learning, to enhance the early detection of non-random variations and subtle process trends. Simulations and real-world datasets were used to validate the effectiveness of the integrated model in identifying slow-developing faults.

Sustainable composite materials represent a critical step towards a more environmentally friendly future. They are an alternative to traditional composites by reducing waste. Sustainability of materials can be achieved through various studies. Composite materials will become more efficient and environmentally friendly with the various findings of researchers. They will play an important role in overcoming challenges such as resource scarcity. Sustainable composites significantly decrease environmental problems related to the production of biodegradable engineered materials. The production of sustainable composites results in less energy consumption and lower toxic gases. Sustainable composites are used in packaging, construction, automobiles, sports, and furniture equipment. In this study, sustainable composite materials are examined and criticized considering their benefits, innovations, applications and challenges, and future perspectives.