The use of satellite imagery to monitor the Sustainable Development Goals is increasingly common, driven by advances in remote sensing and deep learning. Introducing remote sensing into academic graduate programs at maritime institutions not only follows the current state of the research field but also raises awareness of the opportunities this technique offers for sustainable development and ecological research. For instance, remote sensing enables monitoring of ships discharging oil. A multidisciplinary approach is necessary for the highest-level research results, so to apply scientific methods effectively, an expert in the legal aspects of artificial intelligence also participated in this research. In this paper, we introduce remote sensing into seafarers’ education at the graduate level, which is now incorporated as a new course. The results indicate a high level of student engagement and demonstrate that integrating remote sensing technologies into maritime education could enhance students’ technical competencies and environmental awareness. The findings of this study highlight the potential of satellite remote sensing as an educational tool for supporting sustainability-oriented maritime education and provide practical insights for integrating emerging technologies into higher education curricula, providing practical insights for integrating emerging technologies into higher education curricula while establishing a transferable blueprint for bridging advanced data sciences with compliance-driven vocational training.
A reproducible design evaluation system is developed in this study by integrating the grey statistical method (GSM), analytic hierarchy process (AHP), and fuzzy comprehensive evaluation (FCE). Eight first-level and 40 second-level indicators were identified, covering product positioning, appearance features, function, materials, process structure, ergonomics, interactive experience, and economic efficiency. Through GSM screening, the indicator set was refined, and AHP weighting results confirmed logical consistency. Product positioning (weight = 0.190), appearance features (0.165), and interactive experience (0.151) were the most influential first-level indicators. At the second level, user-group positioning (global weight = 0.056), pattern and finish design (0.055), market positioning (0.052), door panel material (0.051), and door style design (0.051) were found to be important indicators of innovation. Interactive experience metrics such as operational convenience and labor-saving access highlighted the increasing importance of usability. FCE modeling results provided deterministic scores, reducing subjectivity and enabling reproducible evaluation. Beyond cabinetry, the GSM–AHP–FCE model can be used to evaluate sensor-integrated smart furniture, tactile interfaces, ergonomic layouts, or feedback mechanisms through various tests.
This study investigates the realization and verification of safety-related functions of the KZ-MPC-MA national modular microprocessor-based railway interlocking system for Kazakhstan. The work focuses on the realization stage of the IEC 61508 safety lifecycle and establishes traceability between station-specific safety requirements, interlocking algorithms, software implementation, hardware–software integration, and functional-safety assessment. The implemented interlocking logic incorporates 29 traffic safety conditions defined for the considered station configuration. A fail-safe control architecture based on central and distributed controller modules was implemented using certified safety-related industrial controllers and the SILworX development environment. A laboratory prototype was developed to verify route-setting functions, switch and signal control, and fail-safe system response under specified operational and failure conditions. Quantitative functional-safety assessment was performed using the probability of failure on demand (PFDavg) as a supplementary measure and the probability of dangerous failure per hour (PFH) as the governing criterion for continuous/high-demand operation within the defined assessment boundary and assumptions. The obtained PFH demonstrates that the investigated safety-related controller subsystem satisfies the specified SIL4 quantitative criterion. The study demonstrates the feasibility of realizing safety-related interlocking functions on the selected hardware–software platform and provides a basis for further system-level verification and validation.
To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge.
Tourism plays an important role in the global economy, contributing massively to the gross domestic product (GDP) and creating numerous jobs. The introduction of emerging technologies can accelerate the sector’s growth through personalization, improved user experience and better operational efficiency. The present work investigates the impact of visible light communications (VLC) in the tourism and hospitality industry based on the analysis of the recent literature published in the last decade, with the scope of improving tourist experience, operational efficiency and sustainability. Additionally, this work aims to critically evaluate the advantages and disadvantages of implementing VLC technology in the tourism and hospitality industry. For these purposes, this study presents a narrative review of the recent academic literature. The findings indicate that VLC technology can be used in a wide range of tourism-related applications, including contactless hotel services, indoor positioning and navigation, secure communications, accessibility solutions for visually impaired individuals and energy-efficient lighting infrastructure. In addition, this review demonstrates VLC’s potential to support the development of smart and sustainable tourism destinations through the integration of user-centered communication and illumination infrastructure. Finally, this work also identifies several challenges that affect large-scale deployment, including implementation costs, line-of-sight dependency and ongoing standardization issues.
Flying Ad Hoc Networks (FANETs) composed of highly mobile Unmanned Aerial Vehicles (UAVs) are increasingly employed in mission-critical applications. However, the open wireless medium, high mobility, and resource constraints of UAVs expose FANETs to severe security and privacy threats, particularly at the Medium Access Control (MAC) layer. Existing authentication and key agreement schemes often suffer from high computational overhead and insufficient protection against advanced attacks. To address these challenges, this paper proposes a Lightweight Key Agreement Scheme (LWKAS) based on signcryption using Hyper Elliptic Curve Cryptography (HECC) and one-way cryptographic hash functions. The proposed scheme integrates MAC-layer security while simultaneously ensuring secure authentication, confidentiality, and location privacy through the Chinese Remainder Theorem (CRT). The security analysis demonstrates that LWKAS resists multiple attacks, including Man-In-The-Middle (MITM), replay, impersonation, Ephemeral Secret Leakage (ESL), cloning, desynchronization, and DoS attacks. The proposed scheme is implemented in NS-3 and evaluated against existing state-of-the-art methods in terms of authentication delay, communication overhead, and computational cost. The results demonstrate that LWKAS achieves significantly lower computational complexity and communication overhead while providing stronger security guarantees suitable for resource-constrained FANET environments.
Against the backdrop of increasing global uncertainty and the growing acceptance of sustainable development principles, enhancing supply chain resilience has become a core issue for enterprises in managing risks and ensuring operational security. Based on a review of the literature and theoretical analysis, this study constructs an evaluation system comprising 12 third-level indicators across three dimensions: proactive defense capability, green operational capability, and collaborative recovery capability. When determining whether there are interdependent relationships among the indicators, this study introduces an extended Bayesian fusion method based on trapezoidal fuzzy numbers to evaluate and confirm these relationships, thereby reducing biases arising from subjective judgments. By quantifying experts’ assessments of the relationship strength and confidence levels between indicators using trapezoidal fuzzy numbers, this method effectively integrates the opinions of multiple experts, reducing the randomness and subjectivity associated with individual judgments. During the ANP weight calculation stage, to overcome the ambiguity and uncertainty inherent in traditional pairwise expert comparisons, trapezoidal fuzzy numbers were similarly used to quantify the comparison results. These were then defuzzified using the mean area metric to construct a precise judgment matrix. Finally, using the publicly available annual reports and ESG disclosure data from three multinational corporations—one in the semiconductor manufacturing sector (Company T), one in industrial digital manufacturing (Company S), and one in the food and beverage industry (Company N)—as empirical samples, the cross-industry applicability and validity of the constructed evaluation system were verified. The results demonstrate that this method can systematically reflect the key factors influencing sustainable supply chain resilience and their weighting structure.
Cancer incidence exhibits substantial spatial disparities linked to environmental, behavioral, built-environment, healthcare-access, and socioeconomic conditions, yet the spatial scales at which these relationships operate remain insufficiently understood. This study develops an explainable spatial epidemiology workflow that integrates Random Forest, SHapley Additive exPlanations (SHAP), Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to examine county-level incidence for all-site, colon, breast, and skin cancers across Texas, USA. Random Forest and SHAP were used to identify outcome-specific nonlinear predictor relevance, and OLS, GWR, and MGWR were used to compare global, local, and multiscale spatial associations before and after RF-SHAP feature screening. MGWR generally achieved higher model fit than OLS and GWR. Before RF-SHAP screening, MGWR R² values were 0.701 for all-site cancer, 0.516 for colon cancer, 0.499 for breast cancer, and 0.694 for skin cancer, compared with OLS R² values of 0.343, 0.338, 0.257, and 0.368. RF-SHAP reduced predictors by about one-half and consistently improved AICc. The results show that environmental exposures, activity-related conditions, transportation access, screening, food insecurity, and chronic health indicators contribute to spatial differences in cancer incidence. The framework links nonlinear machine-learning evidence with spatially explicit interpretation for transferable epidemiological analysis.
This work presents a case study of a Large Language Model based system for automated classification of student survey responses. The system processes 22,286 open-text responses collected from 2062 students across 12 academic programs and 21 nationalities spanning the years 2010-2025. The system architecture has been deployed on institutional servers for security, while integrating databases, an asynchronous task queue for processing, a web-based service layer, and distributed background workers that interact with remote LLM inference services. This work provides a practical reference framework for educational institutions aiming to responsibly and effectively operationalize LLMs in real-world applications.
Ensuring the cleanliness of precision components is critical in Hard Disk Drive (HDD) manufacturing, where microscopic dust contamination on the Voice Coil Motor Assembly (VCMA) can lead to positioning errors, unstable head movement, and long-term reliability failures. However, automated inspection of such contamination remains challenging because dust particles are extremely small, visually irregular, and often appear under complex microscopic backgrounds. This study presents an explainable hybrid deep learning framework for microscopic dust inspection by integrating object detection for precise localization and image classification for defect confirmation. Three YOLO architectures, namely YOLOv5, YOLOv8, and YOLOv11, were comparatively evaluated for dust detection, while three convolutional neural network (CNN) models, ResNet50, EfficientNetB0, and MobileNetV2, were implemented using transfer learning with frozen feature extraction layers for Good (G) and Not Good (NG) image-level classification. The experimental dataset consisted of annotated microscopic VCMA images, with data augmentation applied to the training subset to mitigate limited sample size and class imbalance. Experimental results showed that YOLOv8 achieved the strongest overall aggregate detection performance, whereas YOLOv5 was selected as the preferred detector for subsequent hybrid integration because it produced fewer false positives under reflective and textured microscopic backgrounds. YOLOv11 exhibited lower detection performance in the present setting, likely due to its architectural characteristics being less suited to the limited-data and high-background-complexity conditions of this study. In the present experimental setting, YOLOv5 achieved mAP@0.5 = 0.62, precision = 0.75, and recall = 0.69. For image-level classification, EfficientNetB0 achieved the highest classification accuracy of 93.10%, with F1-score = 0.932 and AUC = 0.986. In addition, Grad-CAM visualizations demonstrated that EfficientNetB0 consistently focused on physically meaningful dust-contaminated regions, thereby enhancing the interpretability of the classification results. Overall, the proposed hybrid framework integrating YOLOv5-based localization with EfficientNetB0-based defect confirmation showed promising potential for improving inspection reliability, false-alarm control, and explainability in automated VCMA quality inspection. These findings support the feasibility of explainable deep learning for microscopic defect inspection in HDD manufacturing and suggest its potential applicability to other precision manufacturing environments.
Public participation is a central component of democratic decision-making processes, particularly in planning and approval procedures. However, increasing data complexity and the growing number of submitted objections significantly raise the effort required for their review and processing. Against this background, this study developed an AI-supported objection management system that uses a large language model (LLM) to automatically pre-sort objections by topic and generate response suggestions based on historical objection texts from previous infrastructure projects. The aim is to increase efficiency in the processing workflow while maintaining consistent response quality without replacing human decision-making. The prototype development is preceded by a literature review to identify key user requirements and derive relevant use cases. Subsequently, four expert workshops with representatives from German road and rail infrastructure administrations at the state and federal level were conducted to evaluate the prototype. The results indicate significant efficiency potential, particularly through automated thematic pre-sorting of objections. However, topic structures must be adapted to the specific procedure. AI currently mainly serves as supportive pre-processing and requires human review (“human-in-the-loop”). Transparent labeling of AI use is also necessary to ensure traceability and acceptance. The findings will be incorporated into the ongoing development of the prototype within the BIM4People research project funded by the German Federal Ministry of Transport (BMV), with the aim of further improving the system’s functionality and exploring additional applications.
In recent years, short video platforms have rapidly developed into important media for cultural dissemination. The interactions of netizens in short video comment sections not only reflect their focus on cultural content but also contain rich emotional attitudes. However, given the vast and fragmented nature of comment data, accurately extracting keywords, identifying cultural themes, and analyzing sentiment tendencies pose significant challenges in understanding netizens' cultural perceptions. To address these challenges, this study proposes a text analysis framework that integrates keyword extraction, clustering analysis, and sentiment analysis to explore the core topics and emotional characteristics of cultural dissemination in short video comment sections. Firstly, to address the challenge of balancing statistical information and semantic understanding in short-text keyword extraction, this paper proposes the TF-IDF-KeyBERT Integrated Algorithm (TKIA) keyword extraction algorithm, which integrates Term Frequency-Inverse Document Frequency (TF-IDF) and Key Bidirectional Encoder Representations from Transformers (BERT). Experiments on the CSL dataset demonstrate improvement in the F1@5 metric, showing its potential to enhance keyword extraction performance for short texts. Secondly, to address the difficulty of simultaneously considering semantic representation capability and clustering flexibility in short-text clustering analysis, this paper designs the Self-Supervised Contrastive Enhanced Clustering (SCEC) algorithm by integrating self-supervised contrastive learning with a soft clustering strategy. Compared to baseline methods, SCEC improves clustering accuracy (ACC) by 17.5% on AGNews and 6.8% on THUCNews, suggesting a more effective way to reveal the underlying structure of cultural topics. Finally, to address the challenge of effectively leveraging both text structural information and global semantic features in short-text sentiment analysis, this paper develops the BERT-GCN Cross-Attention (BGC) Model, integrating BERT embeddings and Graph Convolutional Network (GCN)-based structural features via a Cross-Attention mechanism. On the My_weibo_senti_100k dataset, the BGC model achieves a 2.45% increase in Macro-F1 and a 2.41% improvement in accuracy over strong baselines, offering its ability for high-precision modeling of user sentiment. This study offers effective data support and technical pathways for applications such as cultural content understanding, personalized recommendation, and user emotion guidance.
The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics.
Public procurement systems are prone to risks such as collusion, contractual concentration, and irregular subcontracting, which undermine transparency and accountability. Traditional fiscal oversight approaches remain largely retrospective, limiting their ability to anticipate irregularities and prevent potential losses. Addressing the gap between theoretical machine learning models and real-world institutional deployment, this study introduces an applied system innovation that integrates two complementary approaches at a national scale: a Contractual Network Model (Mallas Contractuales) and a Predictive Risk Model for Contractors. The first component uses graph-based analytics, employing an Entity-Link-Property schema to represent relationships among entities, contractors, and contracts, thereby enabling the detection of structural patterns associated with collusive or anomalous behavior. The second component implements supervised machine learning models, trained on more than 16 million contracts and 2.6 million contractors from sources such as SECOP, RUES, DIAN, and national sanction registries. Models, including Random Forests and Gradient Boosted Trees, were optimized via cross-validated hyperparameter search and evaluated on a separate hold-out set using ROC AUC and Gini metrics, achieving strong discriminatory performance under the available retrospective validation setting while maintaining operational interpretability. Both approaches were deployed in a modular architecture that integrated Databricks, i2 Analyst's Notebook, and Power BI dashboards, providing interactive visualizations and risk scores at multiple levels. Together, these systems demonstrate how the convergence of graph analytics and predictive modeling enables proactive fiscal auditing, strengthens institutional capacity, and offers a replicable framework for public sector accountability.
The growing environmental crisis, particularly water pollution from detergents, necessitates a shift from reactive compliance to proactive eco-innovation, as current methods often fail to systematically resolve trade-offs between performance, safety, and ecology. This study develops and illustrates the application of the Evolutionary-Driven Design Framework (EDDF), an integrated methodology that combines PESTEL analysis, historical evolutionary pattern analysis, Quality Function Deployment (QFD) with a novel contradiction index, Theory of Inventive Problem Solving (TRIZ), and environmental assessment. The framework was applied to redesign a conventional laundry detergent with the objectives of zero phosphates, superior biodegradability (>85%), maintained efficacy, and controlled cost. The quantitative contradiction index matrix prioritized critical unsustainable parameters (e.g., EDTA, Cocamide DEA) for substitution over mere optimization. Through an iterative feedback loop, the process evolved from a biobased concentrate to an “enzymatic power tablet” (Concept B). This waterless, solid formulation uses sodium citrate as a biodegradable builder and an encapsulated multi-enzyme system, achieving an estimated >90% biodegradability and zero phosphates while meeting technical and economic targets. The EDDF provides a structured, anticipatory roadmap that transforms regulatory and market pressures into drivers of innovation, offering companies a promising method for designing sustainable products by proactively resolving contradictions and avoiding historical mistakes.
Conventional industrial manipulators are often costly and come with steep learning curves, which limits their scalability in hands-on robotics education. This paper presents a compact and modular vision-guided sorting platform based on a 4-DOF SCARA robot, designed for rapid assembly, reconfiguration, and beginner-friendly deployment in laboratory courses. A collaborative visual perception strategy is proposed, which introduces a lightweight YOLOv8 algorithm for robust material category recognition, while HSV-based color segmentation and Hough circle localization are utilized to extract sub-pixel centroid features. The pixel measurements are mapped to the robot base frame through an integrated nine-point hand-eye calibration model, and joint commands are generated via a joint-space quintic polynomial interpolation algorithm to ensure continuity and avoid kinematic singularities. The overall system adopts a hierarchical architecture in which the vision host communicates target commands to a motion controller via TCP/IP, while joint actuators are driven through a CAN bus. Feasibility is first verified in a Webots digital prototype with synchronized conveyor and manipulator control, and is then validated on a physical platform equipped with a compliant TPU-based soft gripper to improve grasp tolerance under localization noise. Experiments demonstrate that the system achieves an average recognition accuracy of 98.1% and a mean positioning error of 0.189 mm. The proposed platform provides an extensible testbed for teaching kinematics, perception-to-control integration, and modular robotic system development.
The rapid growth of Distributed Energy Resources (DERs) has intensified operational challenges in modern distribution networks, especially with respect to observability, bidirectional power flow, feeder model accuracy, and fast event detection. This review critically examines the role of Distribution Phasor Measurement Units (D-PMUs) in this transition. Rather than only listing reported applications, the paper evaluates the technical and practical conditions under which D-PMUs provide meaningful value beyond conventional monitoring technologies. Particular attention is given to state estimation, event detection, ancillary operation, communication latency, synchronization vulnerability, economic viability, and the limited evidence from field deployment. The review shows that D-PMUs are especially attractive at feeder heads, DER interconnection points, switching locations, and microgrid boundaries, where synchronized phase-angle measurements improve visibility of dynamic and unbalanced phenomena. However, widespread deployment is still constrained by cost, communication infrastructure, interoperability, timing security, and the scarcity of publicly documented utility-scale results. The paper concludes by identifying the most promising research directions, including physics-aware learning, graph-based analytics, edge processing, and application-driven placement strategies for DER-rich distribution systems.
The semiconductor industry has long relied on Statistical Process Control (SPC) for yield and reliability management. In early technology nodes, classic univariate tools such as Shewhart charts, cumulative sums (CUSUM), exponentially weighted moving averages (EWMA), and the Cp/Cpk exponent could effectively monitor a finite set of key variables. However, sub-5nm and emerging 3 nm technologies have fundamentally changed the statistical environment. Advanced patterning, high-aspect-ratio etching, atomic layer deposition (ALD), chemical-mechanical polishing (CMP), and novel materials have drastically narrowed the process window. At these scales, nanometer-level deviations in critical dimensions (CD), overlay, or surface roughness can significantly impact yield. Simultaneously, modern wafer fabs generate massive amounts of high-frequency sensor data and high-dimensional metrology data. Traditional SPC assumptions—such as independence, normality, low dimensionality, and stationarity—often do not hold. Semiconductor data exhibits: (i) extremely high-dimensionality and strong intervariate correlations; (ii) a hierarchical structure encompassing fab → tooling → chamber → recipe → batch → wafer → field; and (iii) metrological delays and sampling limitations leading to incomplete and asynchronous observations. To address these challenges, this paper reviews advanced statistical methods applicable to wafer fabrication. These methods include multivariate statistical process control (MSPC) approaches such as Hotelling T2 statistics, PCA/PLS combining T2 and Q statistics, contribution diagnostics, time-series drift and change point detection, and Bayesian hierarchical modeling for uncertainty-aware monitoring in data-limited scenarios. Furthermore, we discuss how to integrate these methods with fault detection and classification (FDC), line-to-line monitoring (R2R), advanced process control (APC), and manufacturing execution systems (MES). This paper focuses on scalable, interpretable, and maintainable implementations that transform statistical analysis from a passive monitoring tool into an active component of data-driven fab control.
Complex products continue to face low iterative-design efficiency and poor cross-generation data compatibility, while existing product-gene research is still constrained by the predominance of qualitative approaches, ambiguous representations of hierarchical associations, and insufficient standardization. Based on the principles of decomposition and reconstruction and the systems thinking of genetic engineering, this study develops a generic three-level framework for product genes at the platform, assembly, and component levels. Hierarchical mapping functions and parameter-constraint equations are introduced to enable quantitative representation, and a quantitative product-gene information system is established, including a core-parameter quantification model and inter-/intra-level association-strength models. By integrating multiple international standards, the study further constructs a tripartite standardized description system covering metadata, semantics, and format, and proposes a mathematical mapping method from product information to standardized formats. A case study of Company A’s Platform B and Concept Vehicle C shows that the association-strength model achieves the required adaptation threshold, thereby validating the proposed framework. This study provides quantitative theoretical support for the platform-based and intelligent development of complex products and offers an implementable technical solution for product-gene reuse and data sharing, particularly in the new energy vehicle industry.
Background: Maritime operations increasingly rely on integrated, secure, and resilient architectures, yet the associated body of knowledge remains fragmented across conceptual, operational, logical, methodological, and governance-oriented perspectives. Objective: Our aim is to systematically review the literature on maritime integrated systems architecture in order to identify dominant themes, methodological tendencies, enabling technologies, and unresolved research gaps. Eligibility criteria: Peer-reviewed studies published in English were included when they addressed integrated systems architecture, or closely related architectural approaches, in maritime or naval contexts. Studies centred exclusively on isolated components, non-maritime settings without clear architectural transferability, or insufficient technical or methodological detail were excluded. Information sources: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and IMarEST. Searches were carried out between January and March 2025, with the final search update for all sources completed in March 2025. Methods: The review was conducted and reported in accordance with PRISMA 2020. Three reviewers independently screened titles, abstracts, and full texts. Two reviewers independently extracted data, assessed methodological limitations and risk of bias using a review-specific qualitative appraisal framework, and evaluated the risk of bias due to missing results at the synthesis level. Disagreements were resolved through discussion and consensus, with third-reviewer arbitration when necessary. The synthesis combined qualitative thematic analysis across eleven predefined analytical categories with descriptive bibliometric and thematic mapping procedures. Results: Of 300 identified records, 60 studies met the inclusion criteria. Across non-mutually exclusive analytical categories, the literature was concentrated in Integrated Systems Architecture (52 studies), Development Processes (42), and Conceptual Models (37), whereas Zachman-based Methodology (4) and Maturity Models (3) were only marginally represented. Three recurrent patterns were observed across the corpus: the central role of cybersecurity and risk governance in architectural design; the growing importance of information technology and operational technology convergence for resilient monitoring, coordination, and decision support; and the increasing use of model-based and model-driven approaches to address architectural complexity. Overall confidence in the principal synthesized findings was judged to be moderate. Limitations: The review was limited to six databases and English-language publications, and the included studies varied in reporting depth, methodological transparency, and degree of empirical validation. Conclusions: The review organizes the field into a multilevel taxonomy spanning conceptual and operational models, logical and layered views, development processes, reference architectures, enabling technologies, and maturity-related perspectives. Taken together, the findings suggest that research in this area has progressed more clearly in architectural representation and integration than in long-term evaluation, particularly with regard to the practical operationalization of Zachman-based approaches and the development of maritime-specific maturity assessment frameworks. Funding: This review received no external funding. Registration: The review was not prospectively registered, and no publicly accessible protocol was prepared.
