Sustainable Engineering and Innovation

Interactive installations and innovative design solutions using artificial intelligence

2025-07-31T20:47:44+02:00

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.

Risks of cyberattacks on accounting: Analysis of modern threats and preventive measures

2025-07-24T11:23:30+02:00

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.

Wi-Fi Direct in Android: Creating seamless device-to-device communication

2025-07-08T21:31:34+02:00

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.

Using artificial intelligence in software development processes: achievements and challenges

2025-06-30T18:27:32+02:00

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.

Comprehensive characterization of switching and conduction losses in high-ratio step-down converters for next-generation electric vehicles

2025-09-18T11:34:54+02:00

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.

Detecting gradual trends: Integrating EWMA control charts with artificial intelligence algorithms (LSTM)

2025-09-07T10:47:44+02:00

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.

Development of a novel hybrid model (PDES–ANFIS) for time series applications

2025-09-11T20:21:21+02:00

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).

Sustainable composite materials: Applications and future

2025-07-02T14:52:57+02:00

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.

Modeling and simulation of electromagnetic interference from 2G–6G mobile phones on implanted cardiac pacemakers

2025-08-16T14:52:59+02:00

Using cell phones in proximity to implanted sensitive medical electronic devices has raised concerns about the potential negative impacts of electromagnetic interference (EMI) on pacemaker functioning. This study presents a comprehensive simulation-based analysis of induced fields and voltages in pacemaker leads resulting from electromagnetic emissions of 2G through 6G mobile phones. The EMI model incorporates parameters including distance, orientation, frequency, antenna gain, and burst factor. Simulation results show that 2G and 3G phones, particularly within 10-15 cm at 0° alignment, can induce electric fields exceeding the pacemaker immunity threshold of 3 V/m. Conversely, 5G and 6G technologies, due to higher frequencies and directional emissions, exhibit minimal EMI risks. These findings support updated safety guidelines and call for revised EMI testing protocols considering emerging wireless standards.

Using fruit fly and dragonfly optimization algorithms to estimate the Fama-MacBeth model

2025-09-04T18:16:49+02:00

This research proposes the application of the dragonfly and fruit fly algorithms to enhance estimates generated by the Fama-MacBeth model and compares their performance in this context for the first time. To specifically improve the dragonfly algorithm's effectiveness, three parameter tuning approaches are investigated: manual parameter tuning (MPT), adaptive tuning by methodology (ATY), and a novel technique called adaptive tuning by performance (APT). Additionally, the study evaluates the estimation performance using kernel weighted regression (KWR) and explores how the dragonfly and fruit fly algorithms can be employed to enhance KWR. All methods are tested using data from the Iraq Stock Exchange, based on the Fama-French three-factor model. The results show that the dragonfly algorithm, particularly when using MPT and APT, demonstrates superior performance in improving the accuracy of Fama-MacBeth estimates and enhancing the effectiveness of the KWR approach.

A semi-systematic review and bibliometric analysis of life cycle assessment in solar desalination technologies (2004–2024)

2025-05-27T13:05:54+02:00

Water harvesting for human consumption faces growing challenges due to extreme climatic events, leading to the exploration of alternative sources such as groundwater and seawater. Desalination has become a viable solution despite technical and environmental limitations. Life Cycle Analysis (LCA) is widely used to assess the environmental impacts of desalination technologies, positioning solar desalination as a promising option for coastal areas. However, differences in LCA methodologies limit the identification of consistent trends. This study presents a bibliometric analysis using VOSviewer and the SCOPUS database to update the state of the art in LCA applications for desalination systems, with emphasis on solar desalination. A total of 165 documents published between 2004 and 2024 were analyzed in two periods. A significant increase in publications was observed from 2015, particularly in Asia and the Arabian Peninsula, aligning with high solar potential and financial capability. From the 29 selected papers, 12 were directly related to LCA methodologies, covering scope, typologies, impact categories, and tools. Although no single method dominates, ReCiPe has gained attention, while IMPACT 2002+ and IPCC-2013 remain in use. Commonly assessed impact categories include Global Warming Potential (GWP100a), Acidification Potential (AP), and Eutrophication Potential (EP).

Estimating different velocities in wrist movements from information contained in surface electromyography: Application of a machine learning technique

2025-05-27T13:03:48+02:00

The study of surface electromyography (sEMG) has several approaches. It is used to classify upper and lower extremity movements by identifying the muscle groups that have been excited to generate movements. In general, movements have certain properties related to the type of movement, the force, and the speed at which they are performed. The hypothesis of this study is that information about different speeds is contained in sEMG signals. Participants performed wrist movements at different speeds, following verbal instructions to alternate between fast and slow movements. Our objective was to estimate whether there is information in the sEMG signal that can be associated with the different speed conditions; therefore, binary differencing (two classes) was chosen to test this. These two conditions (fast and slow) were used as classes for analysis and classification based on surface electromyography signals. The moving window method was used to extract sEMG envelopes at two different speeds performed by the test subjects. A linear discriminant analysis model was created to estimate the velocities with the resulting model. Finally, cross-validation was performed to estimate sensitivity (76.67%), specificity (91.2%), and accuracy (approximately 87%).

Wavelet decomposition and statistical characterization for unbalance detection in rotating systems

2025-05-27T12:59:15+02:00

Vibration analysis is a crucial tool for the early detection of faults in rotating machines, as it allows for the prevention of major damage and avoids significant costs associated with these faults. This study examines the phenomenon of imbalance in rotating machines, using signals generated on a test bench at the Santander Technological Units, where specific fault conditions were replicated. The signals obtained were analyzed using wavelet decomposition, from which key characteristics were extracted, such as root mean square (RMS), peak value, kurtosis, and mean absolute value (MAV). These characteristics were then compared using box plots to evaluate the separation between signals from unbalanced machines and those in a fault-free state. This analysis allowed us to identify significant differences between the two conditions, demonstrating the effectiveness of the approach in detecting faults due to imbalance.

Driven gamification by AI in a time series healthcare case study: Statistical intervention analysis

2025-07-18T10:38:48+02:00

With artificial intelligence (AI), gamification has emerged as a promising strategy for improving patient engagement and rehabilitation outcomes. This study investigates the impact of AI models. (GRU, TCN, and ARIMA models ) Driven gamification on stroke rehabilitation by analyzing engagement metrics, functional independence improvement, and motivation scores. A simulated 180-day recovery dataset. SHAP analysis and performance comparisons provide insights into model interpretability and the influence of interventions performed using R. Results indicate that AI-driven gamification significantly enhances patient engagement, improving rehabilitation outcomes. The study provides a data-driven foundation for integrating AI-driven gamification in healthcare interventions. Also, this research simulates the application of a game-based cognitive therapy on day 91 and studies its effects using AI models for intervention analysis on simulated stroke recovery data with time series modeling as a forecasting model.

Modeling the interaction of virtual agents in distributed artificial intelligence systems

2025-06-14T17:46:46+02:00

Modern distributed artificial intelligence (AI) systems utilize a significant number of virtual agents that must work collaboratively to solve complex tasks. However, existing technologies for organizing their interaction are characterized by certain shortcomings: high computational complexity, simplified operating conditions, poor adaptability to changes, and significant problems in accounting for the diversity of virtual agents and their emotional reactions during decision-making. The purpose of the study is to develop a new approach for organizing virtual agent operations in distributed AI systems that aims to improve their cooperation, coordination efficiency, and adaptability. The methodological foundation of the study was an innovative approach that combined a specialized emotion model containing 100 virtual agents in a two-dimensional space with a complex network of connections between them, with machine learning methods to enhance virtual agent coordination. Computer modeling methods were applied using experiments in the Python programming environment. The research results demonstrate that effective communication methods between virtual agents significantly improve their coordination, and conflicts during task execution are substantially reduced through adaptive mechanisms. The innovative emotion model can achieve high accuracy levels and contribute to the formation of new system behavior that includes sharp changes in collective decision-making processes. It also identifies essential parameters of virtual agent cooperation to ensure stable system operation. The comprehensive approach based on combining rule-based logic with machine learning can effectively improve virtual agent coordination, especially under conditions of their diversity. The AI system demonstrates real capacity for large-scale changes, but is imperfect in reflecting negative emotional states. Such AI system research results are essential for developing autonomous systems, intelligent networks, and collaboration platforms for virtual agents.

Enhancing the optimization of resource distribution for eMMB and URLLC services within 5G wireless network architectures

2025-07-08T08:49:42+02:00

The complex dilemma of resource allocation and management in the 5G network priority system, particularly for eMBB and URLLC services, is a pressing and critical issue that necessitates comprehensive research and strategic actions to enhance the performance and user experience of modern digital communications. This situation urgently requires the development of innovative spectrum sharing strategies, prioritization methods, and adaptive algorithms to cope with real-time fluctuations in network conditions. The fusion of machine learning and artificial intelligence can significantly enhance these methods by predicting traffic trends and proactively adjusting resources, ensuring that both eMBB and URLLC services meet their respective quality of service standards. This paper introduces a Q-learning-based particle swarm optimization algorithm for efficient resource allocation techniques. The implementation of edge computing can further alleviate some of these challenges by performing data processing close to the user, thereby reducing latency and improving the response time of URLLC applications while meeting the high throughput requirements of eMBB.

The effect of climate on water resources in Iraq using AI

2025-07-01T23:57:13+02:00

Climate change is increasingly affecting global water resources, considering their availability, quality, and distribution. The temperature rise, altered rainfall pattern, and extreme weather incidents further water challenges brought gains in vulnerable but dry areas. In this regard, the study adopted the utilization of AI, in particular, machine learning approaches for climate adaptation sciences concerning water resources. The models of decision tree, Naive Bayes, and linear regression evaluate relationships between temperature, humidity, wind speed, evaporation, and subsequent water balance using climatic data from 1991 to 2021 for three Iraqi governorates: Diwaniya, Najaf, and Karbala. The discovered trend indicates that rising temperature causes an increase in evapotranspiration brought about by water deficiency that persists. The application of AI in the research reflected that while the models can capture long-term phenomena at a gross scale, they are limited in making precise predictions, thereby making it imperative to develop solutions with ensemble learning and deep neural networks. Another thing gleaned from the study is the importance of AI as a complementary tool for water resource management based on data in the face of climate change. Another factor worthy of attention would be how to address the limits of the present when it comes to using data, model interpretability, and interdisciplinary integration so that we can define and implement sustainable climate adaptation options for tomorrow's water security. This study fills the gap in knowledge as it adopted a novel model using AI to predict the effect of climate change on water resources in Iraq. This study also opened a wide gate for future research in this domain.

A cross-sectional statistical survey analysis on consumer perceptions of domestic relative to foreign goods in Iraq

2025-07-01T23:27:46+02:00

The research aims to identify the most important factors affecting the acquisition of local or imported goods by designing a questionnaire form that was distributed to a sample of shoppers in Iraq. The Chi-square test was used in the statistical analysis, while the characteristics that were used in the analysis were demographic characteristics, namely sex, age, educational attainment, profession, culture, intelligence, economic status, marital status, number of children, residence, in addition to the personal characteristics that the consumer is accustomed to and other aspects that were addressed. The analysis showed a set of conclusions, in general, that the majority of shoppers prefer cheaper goods if they are of the same quality, and there is a preference for some local needs over imported ones, such as dairy, meat, sweets, and curtains, while imported goods are preferred over local ones when purchasing clothes and furniture.

A scalable and explainable framework for detecting Ponzi schemes in Ethereum smart contracts using a stacking model

2025-06-05T15:49:23+02:00

Blockchain technology has reshaped digital finance, enabling decentralized applications (DApps) on platforms like Ethereum. However, these innovations have also facilitated fraudulent schemes such as Ponzi schemes, which deceive users with false promises of high returns. These schemes cause financial losses and weaken trust in blockchain systems. Existing detection methods face key challenges, including limited labeled data, over-reliance on transaction history, and failure to identify scams early. To address these issues, we propose a framework that combines static and dynamic features of smart contracts for early Ponzi detection. Our feature set includes opcode patterns, developer behavior, temporal trends, and metadata, crafted to work independently of transaction data. We enhance feature representation using TF-IDF, CountVectorizer, and Word2Vec for deeper semantic understanding. These features are used to train multiple machine learning and deep learning models such as Random Forest, XGBoost, CNNs, and BiGRUs. A stacking ensemble with a neural meta-learner integrates predictions for improved performance. The model achieves 99% accuracy and an AUC of 0.9522 on a curated Ethereum dataset, handling class imbalance through oversampling and synthetic data generation. We also employ SHAP for model explainability, offering insights into feature importance and promoting transparency. Our framework is scalable and supports real-time monitoring of contracts, helping prevent financial damage by detecting fraud at deployment. This solution enhances the security and reliability of decentralized finance platforms.

A new compact CPW-UWB antenna for advanced healthcare monitoring applications

2025-05-13T11:53:51+02:00

This paper presents the design and simulation for a microstrip antenna, specifically designed to operate within the ultra-wideband (UWB) frequency range of 3.4226 GHz to 13.4 GHz. The antenna employs a coplanar waveguide (CPW) feeding technique and features a slotted patch mounted on an FR4 substrate. The findings of this study indicate that the antenna effectively spans the entire UWB frequency spectrum, achieving an operational bandwidth of 9.9774 GHz while upholding an input reflection coefficient of less than -10 dB. Notably, this design exhibits bidirectional radiation patterns, distinguishing it from traditional planar or microstrip patch antennas. Its lightweight construction, advantageous emission characteristics, and broad frequency range render this compact antenna highly suitable for various medical applications, including Wireless Body Area Networks (WBANs) and healthcare stations. Accordingly, it presents a promising technological solution for enhancing wireless communication and sensing capabilities in future healthcare initiatives.

Shared manufacturing and the sharing economy ideal: Strategic limits in a fragmenting world

2025-05-19T04:06:50+02:00

This study offers a strategic critique of shared manufacturing (SharedMfg), a concept rooted in the broader sharing economy (SE) and promoted as a mechanism for optimizing industrial capacity through peer-to-peer coordination. While such frameworks emphasize digital platforms, scheduling efficiency, and resource pooling, they frequently neglect the deeper constraints that govern the real-world feasibility of manufacturing. In particular, SharedMfg models are often constructed atop idealized abstractions, treating manufacturing units as modular, cyber-physical assets within an Industry 4.0 ecosystem, while overlooking the material, energetic, and geopolitical foundations on which all manufacturing ultimately depends. Extending beyond critique, we explore the conceptual underpinnings of SharedMfg within its systemic context, a prelude to the layered pyramid model advanced in this study. This paper argues that manufacturing does not evolve autonomously, but rather reflects the socio-political order in which it is embedded. To address this oversight, we propose a layered conceptual framework – a manufacturing transformation pyramid – that begins not with coordination, but with the substrate: matter, energy, and institutional structure. We contend that genuine transformation in manufacturing systems must be grounded in these foundational realities, rather than in digital optimization alone. Absent this grounding, SharedMfg/SE risks becoming a transient theoretical exercise, bounded by the specific conditions of its historical moment and detached from the structural realities that shape industrial capacity.