Applied System Innovation

This paper presents a systematic methodology for identifying and integrating safety and security requirements in autonomous driving systems, demonstrated through the case of an autonomous intersection. The study focuses on modeling the intelligent intersection using the MBSE Grid Framework, the SysML modeling language, and the Cameo Systems Modeler tool. Two specific use cases are modeled to illustrate the system’s functionality. A multidisciplinary approach is developed to incorporate safety and security requirements into the system model, combining theoretical foundations with practical implementation techniques. The methodology includes both a generalizable framework and domain-specific strategies tailored to autonomous driving. The proposed approach is applied and critically evaluated using the intelligent intersection as a case study. By extending SysML to systematically address safety and security concerns, the work contributes to the development of safer and more efficient autonomous transportation systems. The results provide a foundation for future research and practical applications in the field of intelligent mobility and cyber–physical systems. Full article

Mobile emergency power supply vehicles (MEPSVs), powered by diesel engines or lithium-ion batteries (LIBs), have become a viable tool for emergency power supply. However, diesel-powered MEPSVs generate noise and environmental pollution, while LIB-powered vehicles suffer from limited power supply duration. To overcome these limitations, a hydrogen-powered MEPSV incorporating a soft open point (SOP) was developed in this study. We analyzed widely used operating scenarios for the SOP-equipped MEPSV and determined important parameters, including vehicle body structure, load capacity, driving speed, and power generation capability for the driving motor, hydrogen fuel cell (FC) module, auxiliary LIB module, and SOP equipment. Subsequently, we constructed an energy management strategy for the model for MEPSV, which uses multiple energy sources of hydrogen fuel cells and lithium-ion batteries. Through simulations, an optimal hydrogen consumption rate in various control strategies was validated using a predefined load curve to optimize the energy consumption minimization strategy and achieve the highest efficiency. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Modern ports are pivotal to global trade, facing increasing pressures from operational demands, resource optimization complexities, and urgent decarbonization needs. This study highlights the critical importance of digital model adoption within the maritime industry, particularly in the port sector, while integrating sustainability principles. Despite a growing body of research on digital models, industrial simulation, and green transition, a specific gap persists regarding the intersection of port management, hydrogen energy integration, and Digital Twin (DT) applications. Specifically, a bibliometric analysis provides an overview of the current research landscape through a study of the most used keywords, while the document analysis highlights three primary areas of advancement: optimization of hydrogen storage and integrated energy systems, hydrogen use in propulsion and auxiliary engines, and DT for management and validation in maritime operations. The main outcome of this research work is that while significant individual advancements have been made across critical domains such as optimizing hydrogen systems, enhancing engine performance, and developing robust DT applications for smart ports, a major challenge persists due to the limited simultaneous and integrated exploration of them. This gap notably limits the realization of their full combined benefits for green ports. By mapping current research and proposing interdisciplinary directions, this work contributes to the scientific debate on future port development, underscoring the need for integrated approaches that simultaneously address technological, environmental, and operational dimensions. Full article

Predicting phenotypes from genomic data can significantly advance agriculture. Genomic selection, which uses genome-wide DNA markers to identify individuals with high genetic value, enhances the accuracy of breeding programs. While linear models are routinely used for genomic selection (GS), machine learning (ML) models offer complementary potential. In this study, robust ML-based models were developed to predict five phenotypic traits—three related to flowering time and two to leaf number—in Arabidopsis thaliana, a model plant with a fully sequenced genome. Using explainable artificial intelligence (XAI), specifically SHapley Additive exPlanations (SHAP) values, we identified SNPs that contributed most to trait prediction. Many of these SNPs were located in or near genes known to regulate flowering and stem elongation, such as DOG1 and VIN3, supporting the biological plausibility of the model. SHAP also enabled local interpretability at the single-plant level, revealing the genotypic basis of individual predictions. Our results indicate that integrating ML with XAI improves model interpretability and provides predictive performance comparable to traditional methods. This approach confirms known genotype–phenotype relationships and highlights new candidate loci, paving the way for functional validation. The proposed methodology offers promising applications in precision breeding and translation of insights from Arabidopsis to crop species. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

Using big data for risk analysis of construction projects is a largely unexplored area. In this traditional industry, risk identification is often based either on so-called domain expert knowledge, in other words on experience, or on different statistical and quantitative analysis of individual past projects. The motivation of this research is based on the implemented and evaluated data-driven and AI-based DARIA approach to identify financial risks in the execution phase of transportation infrastructure construction projects that shows exceptional results at an early stage of the project execution phase and has already been deployed into enterprise-wide production within the STRABAG group. Due to DARIA’s productive use, concern and doubts about the trustworthiness of its ML algorithm are certainly possible, especially when DARIA identifies risky projects while all conventional metrics within the STRABAG controlling system do not identify any problems. “If AI systems do not prove to be worthy of trust, their widespread acceptance and adoption will be hindered, and the potentially vast societal and economic benefits will not be fully realized”. Thus, and based on the results of a user study during DARIA’s successful deployment into enterprise-wide production, this paper focuses on the identification of suitable indicators to measure the trustworthiness of the DARIA ML algorithm in the interaction between individuals and systems as well as on the modeling of the reproducibility of the internal state of DARIA’s ML model. Full article

Global warming is a critical issue today, largely due to the widespread use of fossil fuels in everyday life. One promising solution to reduce reliance on conventional energy sources is to promote the use of renewable power. In particular, to encourage the use of renewable energy in industrial sectors which involve development and manufacture of the industrial artifacts, there is continuous demand for tracing energy sources within the production processes. However, given a sophisticated industrial product that involves diverse and extensive components and their suppliers, the traceability analysis across its production is a critical challenge for ensuring the full utilization of renewable energy. To alleviate this issue, this paper presents a functional framework to support tracing the usage of renewable energy by integrating the Large Language Models (LLMs) and logic programming across supply chain networks. Specifically, the proposed framework contains the following components: (1) adopting graph-based models to process and manage the extensive information within supply chain networks; (2) using the Retrieval-Augmented Generation (RAG) techniques to support the LLM for processing the information related to supply chain networks and generating relevant responses with structured representations; and (3) presenting a logic programming-based solution to support the traceability analysis of renewable energy regarding the responses from the LLM. As a case study, we use a public dataset to evaluate the proposed framework by comparing it to the RAG-based LLM and its variant. Compared to baseline methods solely relying on LLMs, the experiments show that the proposed framework achieves significant improvement. Full article

This paper investigates the reliability of a dual-axis solar tracking system using Failure Mode and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and Reliability Block Diagrams (RBD). The system’s control and data transfer subsystems are evaluated under indoor and outdoor conditions using failure rate data. Key vulnerabilities—particularly sensor degradation—are modeled through probabilistic analysis. Results show a significant drop in reliability (to 15.02%) in harsh environments, primarily due to light sensor failures. However, mitigation strategies such as Built-In Self-Test (BIST) architectures improve test coverage, thereby increasing the chance of fault detection. The findings highlight the need for reliability-focused design in solar trackers to ensure long-term energy efficiency and fault resilience. Full article

An integrated QR-based identification and multi-participant quiz system is developed for classroom and competition scenarios. It reduces the check-in latency, removes fixed buzz-in timing, and lifts hardware-imposed limits on the participant count. On the software side, a MATLAB-R2022b-based module integrates the generation and recognition of linear barcodes and QR Codes, enabling fast, accurate acquisition of contestant information while reducing the latency and error risk of manual entry. On the hardware side, control circuits for compulsory and buzz-in modules are designed and simulated in Multisim-14.3. To accommodate diverse scenarios, the team-versus-team buzz-in mode is extended to support two- or three-member teams. Functional tests demonstrate the stable display of key states—including contestant identity, buzz-in priority group ID, and response duration. Compared with typical MCU-channel-based designs, the proposed system relaxes hardware-channel constraints, decoupling the participant count from fixed input channels. It also overcomes fixed-timing limitations by supporting scenario-dependent configuration. The Participant Information Registration subsystem achieved a mean accuracy of 86.7% and a mean per-sample computation time of 14 ms. The 0–99 s configurable timing aligns with question difficulty and instructional procedures. It enhances fairness, adaptability, and usability in formative assessments and competition-based learning. Full article

This study investigates the effectiveness of a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) model for predicting the temperature of switchgear within electrical power systems. Given the critical importance of temperature monitoring for operational safety and stability, this research integrates CNNs and LSTMs to leverage their respective strengths in spatial feature extraction and temporal data processing. Utilizing a dataset from 2020 comprising hourly data points along with comprehensive environmental and operational variables, the model aims to deliver precise temperature predictions. Initial results indicate a high level of accuracy, with the CNN-LSTM model achieving an $R^2$ score of 0.95 and a mean absolute error of 0.12 °C, highlighting its significant potential to enhance the monitoring and management of safety in power systems. Full article

This article develops a method for detecting malware based on the multi-scale recurrent architecture (time-aware multi-scale LSTM) with salience gating, multi-headed attention, and a sequential statistical change detector (CUSUM) integration. The research aim is to create an algorithm capable of effectively detecting malicious activities in behavioural data streams of executable files with minimal delay and ensuring interpretability of the results for subsequent use in forensic audit and cyber defence systems. To implement the task, deep learning methods (training LSTM models with dynamic consideration of time intervals and adaptive attention mechanisms) and sequence statistical analysis (CUSUM, Kulback–Leibler divergence, and Wasserstein distances), as well as regularisation approaches to improve the model stability and explainability, were used. Experimental evaluation demonstrates the proposed approaches’ high efficiency, with the neural network model achieving competitive indicators of accuracy, recall, and classification balance with a low level of false positives and an acceptable detection delay. Attention and salience profile analysis confirmed the possibility of interpreting signals and early detection of abnormal events, which reduces the experts’ workload and reduces the number of false positives. This study introduces the new hybrid architecture development that combines the advantages of recurrent and statistical methods, the theoretical properties formalisation of gated cells for long-term memory, and the proposal of a practical approach to the model solutions’ explainability. The developed method implementation, implemented in the specialised software product form, is shown in a forensic audit. Full article

A significant number of digital marketers use unethical marketing methods that violate Search Engine Optimization guidelines, with the objective of deceiving engines into displaying a specific website as the top result. The practice of fake comments constitutes a violation of Search Engine Optimization policies and is directly impeding market transparency. In addition, the absence of established standards between search engines, evaluation platforms and other trusted agencies makes exploitation easy. Therefore, in order to ensure fair competition among digital businesses, we propose a decentralized system for detecting fake comments, leveraging Blockchain technology for verification. The implementation of smart contracts as self-executing agreements will be achieved by utilizing the Ethereum network and the Truffle Suite. The Ethereum smart contracts will immutably record every comment as a transaction, eliminating any central authority. When a comment is flagged as suspicious, a digital business can trigger a verification request. Stakeholders or reviewers then vote on authenticity. Smart contracts collect these votes and issue a definitive verdict on whether the comment is fake. Full article

Permanent magnet synchronous motors are the dominant technology in industrial applications such as elevator systems. Their unique advantages over induction motors give them higher energy efficiency and significant reduction in energy consumption. Accordingly, the elevator is one of the basic means of comfortable and safe transportation. More generally, in elevator systems, electric motors are characterized by continuous use, increasing the risk of possible failure that may affect the operation of the system and the safety of passengers. The application of appropriate monitoring and artificial intelligence techniques contributes to the predictive maintenance of the motor and drive system. The main objective of this paper is a literature review on the application of modern monitoring methodologies using smart sensors and machine learning algorithms for early fault diagnosis and predictive maintenance generally. Thus, by exploiting the advantages and disadvantages of each method, a technique based on a multi-fault set is developed that can be integrated into an elevator control system offering desired results of immediate predictive maintenance. Full article

Lung cancer, the leading cause of cancer-related mortality worldwide, necessitates better methods for earlier and more accurate detection. To this end, this study introduces LCxNet, a novel, custom-designed convolutional neural network (CNN) framework for computer-aided diagnosis (CAD) of lung cancer. The IQ-OTH/NCCD lung CT dataset, which includes three different classes—benign, malignant, and normal—is used to train and assess the model. The framework is implemented using five optimizers, SGD, RMSProp, Adam, AdamW, and NAdam, to compare the learning behavior and performance stability. To bridge the gap between model complexity and clinical utility, we integrated Explainable AI (XAI) methods, specifically Grad-CAM for decision visualization and t-SNE for feature space analysis. With accuracy, specificity, and AUC values of 99.39%, 99.45%, and 100%, respectively, the results demonstrate that the LCxNet model outperformed the state-of-the-art models in terms of diagnostic performance. In conclusion, this study emphasizes how crucial XAI is to creating trustworthy and efficient clinical tools for the early detection of lung cancer. Full article

This paper addresses deficiencies in existing spray carts and suspended sprayers regarding operational scenarios, spray coverage, versatility, and wall film thickness adjustment by designing a rail-mounted rotating nozzle application robot. Static analysis of the robot frame verifies compliance with strength and stiffness requirements. Motor torque calculations ensure stable and reliable nozzle rotation. Geometric modeling derives optimal link parameters for automated nozzle angle control. ANSYS Fluent simulations characterize static spray coverage, analyzing quantitative relationships between nozzle height, angle, and spray distance. SolidWorks Motion establishes a coupled model of nozzle rotation and cart translation to obtain spray trajectories under varying speeds. Coupled Fluent simulations further evaluate wall film thickness distribution patterns under dynamic spraying conditions. The findings provide a theoretical foundation and technical reference for structural optimization and precise spraying control in greenhouse spraying robot systems. Full article

At present, traditional X-ray inspection is used to inspect the welds of the bottom, barrel section and short shell parts of the launch vehicle, which has the disadvantages of low automation, complicated process and low efficiency, and cannot meet the fast-paced development needs of multiple models at present. Moreover, the degree of digitization is low, the test results are recorded in the form of negatives, data statistics, storage and access are difficult, and the circulation efficiency is low, which is not conducive to product quality control and traceability; At the same time, it cannot adapt to and meet the needs of digital and intelligent transformation and development. In this paper, a dual-robot collaborative digital radiographic inspection system for rocket tank welds is developed by combining dual-robot control technology and digital radiographic inspection technology. The system can be directly applied to digital radiographic inspection of tank bottom, barrel section and short shell welds of multiple types of launch vehicles; meanwhile, the dual-robot path planning technology based on the dual-mode is studied. Finally, the imaging software platform based on VS and Twincat3.0 VS2015 software combined with QT upper computer is designed. Experiments show that compared with the existing traditional ray detection methods, the detection efficiency of the system is improved by 5 times, the image sensitivity reaches W14, the resolution reaches D10, and the standardized signal-to-noise ratio reaches 128, which far exceeds the requirements of process technology A, and meets the current non-destructive detection work of multi-model rocket tank welds. Full article

The automotive industry is at a critical juncture, facing increasing pressure from stringent environmental regulations, resource scarcity, and global supply chain disruptions. This study aims to identify, model, and prioritize the key factors influencing the adoption of circular economy principles in the automotive sector. The Decision-Making Trial and Evaluation Laboratory method was applied to analyze the interdependencies among ten critical factors. Based on a contribution from a panel of six experts from a European automotive distributor with its main operating points in France and Romania, the study reveals a clear cause-and-effect relationship among the factors. Causal factors such as organizational culture, internal integration, regulatory policies, circular business models, and R&D capacity were identified as key drivers. Conversely, factors like top management commitment, stakeholder engagement, technological capability, sustainable materials management, and ecodesign were classified as effect factors, meaning they are influenced by other variables. The key practical contribution of this research is a strategic prioritization framework for decision-makers, offering guidance on how to effectively boost key factors, either directly or indirectly, to achieve a more efficient alignment with circular economy principles. By strategically leveraging these interdependencies, organizations can trigger a positive chain reaction, leading to improved performance in the dependent effect factors and ultimately accelerating the transition to a circular economy. Full article