Appl. Syst. Innov., Volume 8, Issue 6 (December 2025) – 33 articles

The automation of wind turbine maintenance processes is aimed at improving the operational efficiency of wind farms through timely diagnosis of technical condition, predictive identification of potential failures, and optimization of the distribution of repair and restoration procedures. In this context, the main objective of the study is to improve the reliability and efficiency of wind energy infrastructure by developing an intelligent decision support system for wind turbine maintenance. The proposed architecture includes a module for optimizing the routes of robotic agents, which implements a hybrid method based on a combination of the A* algorithm and a modified ant algorithm with dynamic pheromone updating and B-spline trajectory smoothing, as well as a module for detecting based on a modified YOLOv3 model with integrated adaptive feature fusion and bio-inspired anchor frame optimization. The choice of the YOLOv3 architecture is due to the optimal balance between accuracy and inference speed on embedded platforms of robotic autonomous agents, which ensures the functioning of the detection module in real time with limited computing resources. The results of the computational experiment confirmed a 15–20% reduction in route length and energy consumption, as well as a 41% increase in the $F1$ detection metric relative to the baseline implementation of YOLOv3 while maintaining a performance of 42 frames per second. The set of results obtained confirms the practical feasibility and integration potential of the developed architecture into the predictive maintenance and life cycle management of wind energy infrastructure. Full article

This research proposes the development of an Entity-Relationship Diagram—PRO (ERD-PRO) to assist students in understanding the concept of developing Entity-Relationship Diagrams in designing a database. ERD-PRO is an Intelligent Tutoring System (ITS) that is built using a mixed-initiative approach to address the learning challenges by adopting Explainable Artificial Intelligence (XAI) concept to provide individualized and on-demand feedback and guidance. The effectiveness of ERD-PRO is tested on 25 participants from different educational institutions. Pre- development surveys are conducted to determine learning needs and post-development surveys are performed to measure the success. The results show that the design of ERD-PRO, guided by survey findings, successfully addresses key challenges in database design education. 65% of students agreed that the system’s explanation facilities effectively clarified difficult topics, and 90% expressed high satisfaction with the tool. The integration of XAI features within ERD-PRO has enhanced its ability to provide meaningful, scenario-based explanations, demonstrating its potential as an effective intelligent tutoring system. These findings validate the effectiveness of ERD-PRO in meeting its objectives and highlight its value in providing tailored explanations for database design instruction. Full article

Estimating the absolute scale of reconstructed camera trajectories in monocular odometry is a challenging task due to the inherent scale ambiguity in any monocular vision system. One promising solution is to fuse data from different sensors, which can improve the accuracy and precision of scale estimation. However, this approach often requires additional effort in sensor design and data processing. In this paper, we propose a novel method for fusing single-camera data with wheel odometer readings using a fuzzy system. The architecture of the fuzzy system has as inputs the wheel odometer value and the translation and rotation obtained from ORB-SLAM2. It was trained with the ANFIS tool in MATLAB 2014b. Our approach yields significantly better results compared to state-of-the-art pure monocular systems. In our experiments, the average error relative to GPS measurements was only four percent. A key advantage of this method is the elimination of the sensor calibration step, allowing for straightforward data fusion without a substantial increase in data processing demands. Full article

This study explores how research on carbon capture technologies (CCTs) has developed over time and shows how semantic text mining can improve the analysis of technology trajectories. Although CCTs are widely viewed as essential for net-zero transitions, the literature is still scattered across many subthemes, and links between engineering advances, infrastructure deployment, and policy design are often weak. Methods that rely mainly on citations or keyword frequencies tend to overlook contextual meaning and the subtle diffusion of ideas across these strands, making it difficult to reconstruct clear developmental pathways. To address this problem, we ask the following: How do CCT topics change over time? What evolutionary mechanisms drive these transitions? And which themes act as bridges between technical lineages? We first build a curated corpus using a PRISMA-based screening process. We then apply BERTopic, integrating Sentence-BERT embeddings with UMAP, HDBSCAN, and class-based TF-IDF, to identify and label coherent semantic topics. Topic evolution is modeled through a PCC-weighted, top-K filtered network, where cross-year connections are categorized as inheritance, convergence, differentiation, or extinction. These patterns are further interpreted with a Fish-Scale Multiscience mapping to clarify underlying theoretical and disciplinary lineages. Our results point to a two-stage trajectory: an early formation phase followed by a period of rapid expansion. Long-standing research lines persist in amine absorption, membrane separation, and metal–organic frameworks (MOFs), while direct air capture emerges later and becomes increasingly stable. Across the full period, five evolutionary mechanisms operate in parallel. We also find that techno-economic assessment, life-cycle and carbon accounting, and regulation–infrastructure coordination serve as key “weak-tie” bridges that connect otherwise separated subfields. Overall, the study reconstructs the core–periphery structure and maturity of CCT research and demonstrates that combining semantic topic modeling with theory-aware mapping complements strong-tie bibliometric approaches and offers a clearer, more transferable framework for understanding technology evolution. Full article

Generative Artificial Intelligence (GenAI) is rapidly transforming industries and organizations through automation and augmentation. Personalization of human–system interaction is a key area that can be significantly advanced through the effective implementation of GenAI. GenAI, positioned as an intermediary between humans and systems, can transform the human experience from the pre-defined, conventional notions of personalization into a dynamic and integrated hyperpersonalization capability. This article presents the zero-shot-to-head-shot hyperpersonalization (Z2H2) framework, which aims to achieve this through the effective adoption of GenAI techniques. It is a domain-neutral framework of three incremental stages named zero-shot, few-shot, and head-shot that gradually increase the level of hyperpersonalization of the human–system interaction. The framework is further represented in a layered system design and the Z2H2 Data Modality Matrix (ZDMM), which systematically maps data types, AI capabilities, and personalization objectives for each stage. The capabilities of the framework are demonstrated in an educational setting, followed by an empirical evaluation using the Open University Learning Analytics Dataset (OULAD). Although this dataset only contains demographic and aggregated clickstream data, which is a subset of attributes relevant to the entire framework, the gradual development of zero-shot-to-head-shot hyperpersonalization is effectively demonstrated and validated on these student interactions. Full article

An increasing number of juveniles are accessing adult-oriented venues, such as bars and nightclubs, where supervision is frequently inadequate, thereby elevating their risk of both offline harm and unmonitored exposure to harmful online content. Existing facial age estimation systems, which are primarily designed for adults, have significant limitations when it comes to protecting juveniles, hindering the efficiency of supervising them in key venues. To address these challenges, this study proposes a facial age estimation solution for juvenile protection. First, we have designed a ‘detection–cropping–classification’ framework comprising three stages. This first detects facial regions using a detection algorithm, then crops the image before inputting the results into a classification model for age estimation. Secondly, we constructed the the Juvenile Protection Surveillance and Detection (JPSD) Dataset by integrating five public datasets: UTKface, AgeDB, APPA-REAL, MegaAge and FG-NET. This dataset contains 14,260 images categorised into four age groups: 0–8 years, 8–14 years, 14–18 years and over 18 years. Thirdly, we conducted baseline model comparisons. In the object detection phase, three YOLO algorithms were selected for face recognition. In the age estimation phase, traditional convolutional neural networks (CNNs), such as ResNet50 and VGG16, were contrasted with vision transformer (ViT)-based models, such as ViT and BiFormer. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visual analysis to highlight differences in the models’ decision-making processes. Experiments revealed that YOLOv11 is the optimal detector for accurate facial localisation, and that ResNet50 is the best base classifier for enhancing age-sensitive feature extraction, outperforming BiFormer. The results show that the framework achieves Recall of 89.17% for the 0–8 age group and 95.17% for the over-18 age group. However, we have found that the current model has low Recall rates for the 8–14 and 14–18 age groups. Therefore, in the near term, we emphasise that this technology should only be used as a decision-support tool under strict human-in-the-loop supervision. This study provides an essential dataset and technical framework for juvenile facial age estimation, offering support for juvenile online protection, smart policing and venue supervision. Full article

High volume pencil manufacturing often generates substantial material waste due to a small proportion of products having missing or recessed graphite leads. Standard vision-based quality control processes discard entire wooden slats that carry any faulty pencils, causing excessive waste of usable wood and graphite resources. This study describes the design and implementation of a laser triangulation-based inspection system for lead defect detection after individual pencils are cut from the slat. The system combines a two-dimensional laser profile scanner with synchronized triggering sensors and a programmable logic controller (PLC)-controlled pneumatic rejection unit. Using the systematic design methodology for VDI 2221, a functional prototype was developed, which was then tested in a simulated production system with a throughput of up to 200 pencils per minute. The proposed system was able to detect missing and recessed leads highly accurately and correctly classified 98–100% of pencils without false rejections of acceptable products. The most common type of defect was missing or deeply recessed lead with an accuracy of 98.5%, and the less common partial-lead fractures had a lower percentage of detection of nearly 92% due to geometric sensitivity. The developed inline inspection system was successful in identifying and rejecting defective pencils without the waste of materials and provided a viable alternative of economical implementation with less than a one-year payback period. Through its increased resource efficiency and decreased raw material waste, the proposed system contributes to the United Nations Sustainable Development Goals, namely SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production). Full article

Semiconductor wafer fabrication is one of the most complex and demanding processes in industry. The process involves numerous sequential steps, including photolithography, deposition, etching, and chemical–mechanical polishing (CMP). At advanced process nodes below 5 nanometers, even angstrom-level deviations in parameters such as oxide thickness or critical dimension (CD) can lead to yield degradation or device failure. Traditional single-factor experimental methods are insufficient to capture the inherent multivariate interactions within plasma, thermal, and chemical processes. This review introduces the application of Design of Experiments (DOE) in wafer fabrication and demonstrates that it provides a statistically rigorous framework for addressing these challenges. It enables the simultaneous analysis of multiple variables, quantifying main effects and interactions, and developing predictive models with fewer runs. DOE can accelerate process development, reduce wafer consumption, enhance process robustness, and support applications in processes such as photolithography, CMP, and deposition. Beyond process optimization, DOE, combined with virtual metrology, machine learning, and digital twin technologies, provides a balanced dataset for predictive analytics and real-time control. Its functions encompass proactive monitoring, adaptive formulation optimization, and eco-efficient manufacturing aligned with sustainability goals. As wafer fabs adopt AI-assisted, simulation-driven environments, experimental design remains the foundation for knowledge-intensive, data-driven decision-making. This ensures continuous improvement in yield, manufacturability, and competitiveness in future semiconductor miniaturization processes. Full article

The planning of unbalanced three-phase distribution networks increasingly requires the coordinated integration of distributed energy resources, such as photovoltaic (PV) generators and static compensators (D-STATCOMs), to enhance system performance and reduce costs. This planning task is inherently challenging, as it leads to a mixed-integer nonlinear optimization problem driven by nonconvex voltage–current–power relationships, phase unbalances, and the temporal variability of demand and solar irradiance. This work proposes an exact Mixed-Integer Nonlinear Programming (MINLP) framework for the joint siting and sizing of PV units and D-STATCOM devices, with an objective function based on the minimization of the equivalent annual cost of energy purchases and investments. The methodology is applied to 25- and 37-bus unbalanced test systems and benchmarked against four state-of-the-art metaheuristic algorithms. The results show that the exact MINLP consistently attains the global optimum, yielding reductions in equivalent annual cost of USD 392,855 (14.36%) and USD 436,361 (14.90%) for the respective test systems, whereas the metaheuristics provide near-optimal but slightly dispersed solutions. These findings highlight the potential of exact optimization as a robust and economically sound tool for long-term distribution network planning, combining technical reliability with guaranteed global optimality. Full article

This study proposes an automated, visual–geometric fusion method for measuring pupillary height (PH) and interpupillary distance (PD), aiming to replace manual measurements while balancing accuracy, efficiency, and cost accessibility. To this end, a two-layer Ensemble of Regression Tree (ERT) is used to coarsely localize facial landmarks and the pupil center, which is then refined via direction-aware ray casting and edge-side-stratified RANSAC followed by least-squares fitting; in parallel, an RC-BlendMask instance-segmentation module extracts the lowest rim point of the spectacle lens. Head pose and lens-plane depth are estimated with the Perspective-n-Point (PnP) algorithm to enable pixel-to-millimeter calibration and pose gating, thereby achieving 3D quantification of PH/PD under a single-camera setup. In a comparative study with 30 participants against the Zeiss i.Terminal2, the proposed method achieved mean absolute errors of 1.13 mm (PD), 0.73 mm (PH-L), and 0.89 mm (PH-R); Pearson correlation coefficients were r = 0.944 (PD), 0.964 (PH-L), and 0.916 (PH-R), and Bland–Altman 95% limits of agreement were −2.00 to 2.70 mm (PD), −0.84 to 1.76 mm (PH-L), and −1.85 to 1.79 mm (PH-R). Lens segmentation performance reached a Precision of 97.5% and a Recall of 93.8%, supporting robust PH extraction. Overall, the proposed approach delivers measurement agreement comparable to high-end commercial devices on low-cost hardware, satisfies ANSI Z80.1/ISO 21987 clinical tolerances for decentration and prism error, and is suitable for both in-store dispensing and tele-dispensing scenarios. Full article

This work proposes an intelligent strategy for the coordinated management of active and reactive power in Battery Energy Storage Systems (BESSs) within AC microgrids operating under both grid-connected (GCM) and islanded (IM) modes to minimize daily operational costs. The problem is formulated as a mixed-variable optimization model that explicitly leverages the control capabilities of BESS power converters. To solve it, a Parallel Particle Swarm Optimization (PPSO) algorithm is employed, coupled with a Successive Approximation (SA) power flow solver. The proposed approach was benchmarked against parallel implementations of the Crow Search Algorithm (PCSA) and the JAYA algorithm (PJAYA), both in parallel, using a realistic 33-node AC microgrid test system based on real demand and photovoltaic generation profiles from Medellín, Colombia. The strategy was evaluated under both deterministic conditions (average daily profiles) and stochastic scenarios (100 daily profiles with uncertainty). The proposed framework is evaluated on a 33-bus AC microgrid that operates in both grid-connected and islanded modes, with a battery energy storage system dispatched at both active and reactive power levels subject to network, state-of-charge, and power-rating constraints. Three population-based optimization algorithms are used to coordinate BESS schedules, and their performance is compared based on daily operating cost, BESS cycling, and voltage profile quality. Quantitatively, the PPSO strategy achieved cost reductions of 2.39% in GCM and 1.62% in IM under deterministic conditions, with a standard deviation of only 0.0200% in GCM and 0.2962% in IM. In stochastic scenarios with 100 uncertainty profiles, PPSO maintained its robustness, reaching average reductions of 2.77% in GCM and 1.53% in IM. PPSO exhibited consistent robustness and efficient performance, reaching the highest average cost reductions with low variability and short execution times in both operating modes. These findings indicate that the method is well-suited for real-time implementation and contributes to improving economic outcomes and operational reliability in grid-connected and islanded microgrid configurations. The case study results show that the different strategies yield distinct trade-offs between economic performance and computational effort, while all solutions satisfy the technical limits of the microgrid. Full article

Detecting small-scale objects remains a critical challenge with limited pixel information, complex backgrounds, and varying imaging conditions. To tackle these challenges, we propose an innovative high-precision detection framework (DRF-YOLO) that integrates a dilated-wise residual (DWR) module and an asymptotic feature pyramid network (AFPN). The DWR module enhances contextual representation and enriches spatial detail, while AFPN optimizes multi-scale feature fusion and semantic alignment. Extensive evaluations were carried out on the DUT-Anti-UAV and Det-Fly datasets, which contain images taken in complex aerial environments. The DRF-YOLO model achieved an mAP@50 of 86.9 and 91.1% on the two respective datasets, showing performance gains of 1.5% and 3.3% compared to the YOLOv8 reference model, and yielded mAP@50:95 gains of 1.1 and 2.3%, respectively. The synergistic effect of the DWR module and AFPN architecture enables significant enhancement in mAP@50, mAP@50:95, precision, and recall, demonstrating an optimal balance between accuracy and object coverage. The model also demonstrates improved robustness under complex backgrounds and occlusion, underscoring its potential for accurate UAV detection. Full article

In response to the demands for high reliability, excellent dynamic response, and high-precision control of advanced aircraft actuation systems, this study focuses on the control technology for the master-master operating mode of dual-redundancy electro-hydrostatic actuation (EHA) systems. A multi-domain coupling model integrating motor magnetic circuit saturation, hydraulic viscosity-temperature characteristics, and mechanical clearances was established, based on which a current-loop decoupling technique using vector control was developed. Furthermore, the study combined adaptive sliding mode control (ASMC) and an improved active disturbance rejection control (ADRC) to enhance the robustness of the speed loop and the disturbance rejection capability of the position loop, respectively. To address the key challenges of synchronous error accumulation and uneven load distribution in the master-master mode, a dual-redundancy dynamic model accounting for hydraulic coupling effects was developed, and a two-level cooperative control strategy of "position synchronization-dynamic load balancing" was proposed based on the cross-coupling control (CCC) framework. Experimental results demonstrate that the position loop control error is less than ±0.02 mm, and the load distribution accuracy is improved to over 97%, fully meeting the design requirements of advanced aircraft. These findings provide key technical support for the engineering application of power-by-wire flight control systems in advanced aircraft. Full article

Real-time vessel tracking and environmental assessment in developing regions face significant challenges due to the high cost and proprietary constraints of commercial Automatic Identification System (AIS) services. We introduce MAGI, an open-source, low-cost, IoT-distributed architecture that integrates Orange Pi 5 edge nodes with software-defined radio (SDR) AIS receivers and containerized microservices to capture, preprocess, and stream AIS messages. During a ten-day field campaign in Panama, our decentralized deployment processed over 500,000 AIS transmissions, achieving 99% uptime and delivering vessel position and speed updates with sub-second latency. Based on the collected data, we also evaluated system scalability, energy consumption, and per node cost, demonstrating that a complete coastal network can be deployed for under USD 1200 per site. These results confirm that MAGI is a scalable, secure, and affordable IoT solution for AIS-based vessel tracking and environmental monitoring in resource-constrained settings. Full article

The issue of food waste is more relevant than ever, both for emerging and developed economies. Information technologies have the potential to contribute to reducing this problem, and our research aims to present a viable prototype that uses on-cloud image classification and specific OCR techniques. The result of our study is a low-cost, high-performance mobile application prototype that paves the way for further research. We used advanced application integration concepts, including mobile architectures, Firebase machine learning components, and OCR techniques to highlight how close food products are to their expiration date. In contexts with no printed date, the system computes an indicative shelf-life estimate from conservative category priors. These estimates are not safety judgments and do not replace manufacturer date labels or national food-safety guidance. These results give our article clear elements of authenticity and contribution to the field of knowledge, improving the economic efficiency of warehouses and food stores. The implications of our study are technical, economic, and social. Full article

In the evolving landscape of digital education, there is an increasing need to enhance traditional Learning Management Systems (LMSs) by integrating innovative pedagogical practices that promote active participation and learner autonomy. This study presents the transformation of a Greek LMS platform into an open learning ecosystem, incorporating three key educational innovations: collaborative hackathons, microcredentials, and blended learning support. The primary goal was to modernize the LMS in a way that encourages deeper engagement, social learning, collaboration, and mixed learning. To accomplish this objective, the system integrated advanced innovative tools designed to facilitate structured collaborative processes including hackathons, microcredentials aligned with specific learning objectives, and blended learning through flexible content delivery and student learning tracking tools. The use of these tools in the educational process contributes to the creation of a more dynamic and participatory learning environment, where knowledge is co-shaped and learning acquires a social character. In addition, the tools promote differentiated learning, allowing students to engage at their own pace and in their own way. Full article

The paper presents methods of applying AI to generate mathematical and programming exercises for the purpose of creating courses on an educational platform. Various challenges and advantages are highlighted and discussed in the context of a new interactive platform—Compass. The proposed learning methods based on user–platform interaction are described, along with the results of evaluations conducted among university students who learned with Compass. Full article

The acquisition of images of road surfaces not only establishes a theoretical foundation for road maintenance by relevant departments but also is instrumental in ensuring the safe operation of highway transportation systems. To address the limitations of traditional road surface image acquisition systems, such as low collection speed, poor image clarity, insufficient information richness, and prohibitive costs, this study has developed a high-speed binocular-vision-based system. Through theoretical analysis, we developed a complete system that integrates hybrid anti-shake technology. Specifically, a hardware device was designed for stable installation at the rear of high-speed vehicles, and a software algorithm was implemented to develop an electronic anti-shake module that compensates for horizontal, vertical, and rotational motion vectors with sub-pixel-level accuracy. Furthermore, a road surface image fusion algorithm that combines the stationary wavelet transform (SWT) and nonsubsampled contourlet transform (NSCT) was proposed to preserve multi-scale edge and textural details by leveraging their complementary multidirectional characteristics. Experimental results demonstrate that the fusion algorithm based on SWT and NSCT outperforms those using either SWT or NSCT alone across quality evaluation metrics such as QAB/F, SF, MI, and RMSE: at 80 km/h, the SF value reaches 4.5, representing an improvement of 0.088 over the SWT algorithm and 4.412 over the NSCT algorithm, indicating that the fused images are clearer. The increases in QAB/F and MI values confirm that the fused road surface images retain rich edge and detailed information, achieving excellent fusion results. Consequently, the system can economically and efficiently capture stable, clear, and information-rich road surface images in real-time under high-speed conditions with low energy consumption and outstanding fidelity. Full article

For processing timeliness, standardizing formats, and reflecting the variety of massive strong motion observation data of the National Seismic Network Center, we developed a strong motion data processing system applicable to different types of strong motion observation stations, which enables rapid data collection, processing, and archiving. It provides a human–machine interaction data processing interface to preprocess the acceleration record of seismic waveforms and analyzes the acceleration event waveform data by calculating ground motion, including peak ground acceleration, peak ground velocity, peak ground displacement, instrumental intensity, duration, Fourier spectrum, response spectrum, and triple spectrum. The system exports metadata and seismic record waveforms to archive and store the data. The system enables platform unity, function integration, and data completeness, playing an effective role in data processing and management for emergency and damage assessment, and scientific research on earthquakes. Full article

The world is being orchestrated by dramatic changes caused by technological and innovative disruptions. Accordingly, Industry X.0 terminology was coined because the revolutionary numbers could not represent this industrial disruption. Coping with these technological disruptions is essential for an organization’s sustainability and resilience. Therefore, defining the technological gaps, as well as mapping the potential innovative disruptions for industrial systems, becomes compulsory. Technology Readiness Levels is a standardized method widely adopted to evaluate the maturity of a technology, using a scale from 1 (concept) to 9 (commercialized solution). This framework helps stakeholders to benchmark different industrial systems from a technology innovation perspective. However, TRL sometimes fails to capture the maturity of breakthrough innovations and lacks quantification. In this paper, a comprehensive framework for assessing technological readiness levels is proposed. The automotive industry was selected as one of the top technology-related industries to validate this framework. This framework maps the technological readiness levels of the following three main industry components: product, engineering, and operations. A tailored Data Envelopment Analysis (DEA) model has been employed as a benchmarking approach to evaluate the technological readiness gaps and map the technological footprint position of a selected automotive company across the best practices in the automotive industry. Full article

The use of simulated environments for the development and validation of Automated Guided Vehicles (AGVs) has proven to be an effective approach for reducing costs and accelerating the testing process. Simulated environments offer a safe and controlled means for performance analysis and controller parameter adjustment. However, most simulators employed for AGVs and mobile robots rely on kinematic models, which limits the fidelity of the tests. This work introduces a physics-driven Unity framework that leverages the NVIDIA PhysX engine to model AGV dynamics—including payload variation, wheel–ground interactions, and suspension effects—addressing a critical gap in surveyed studies. A factory-floor virtual environment was developed, and a holonomic AGV was implemented with RigidBody and WheelCollider components. PID controllers were tuned via Exhaustive Search and Ziegler–Nichols methods across loads from 0 kg to 100 kg. Exhaustive Search achieved a mean lateral error of just 0.0069 cm and a standard deviation of 1.33 cm at 50 kg—58% lower variability than Ziegler–Nichols. Meanwhile, controller tuning using Ziegler–Nichols required only up to 40 min per load but exhibited up to 84% inter-operator gain variability. Performance was validated on infinity-shaped track, demonstrating Unity’s utility for quantitative performance benchmarking. As contributions, this study (i) presents a novel dynamic AGV simulation framework, (ii) proposes a dual validation workflow combining on-site tuning and systematic optimization, and (iii) integrates an embedded evaluation suite for reproducible control- strategy comparisons. Full article

Architectural robotics enables physical spaces and their components to act, think, and grow with their inhabitants. However, this is still a relatively new field that requires further improvements in portability, customizability, and flexibility. This study integrates spatial embedding knowledge, small-space design principles based on human scales and behaviors, and robotic kinematics to propose a prototype robot capable of efficient batch storage, habitability, and autonomous mobility. Based on the spatial distribution of its user’s dynamic skeletal points, determined using a human–computer interaction design system, this prototype robot can automatically adjust parameters to generate a customized solution aligned with the user’s behavioral habits. This study highlights how considering the inhabitant’s personality can create new possibilities for architectural robots and offers insights for future works that expand architecture into intelligent machines. Full article

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