Search Results (426)

Rotating machinery are essential elements in industrial systems and strongly present aboard vessels and maritime platforms, whose unexpected failure can lead to significant economic and operational losses, both for the maritime industry and for industry in general. Condition Monitoring (CM), through the analysis of specific parameters, aims to assess equipment health and enable the early detection of deviations from normal operating conditions. Among existing techniques, vibration analysis stands out for its effectiveness. However, when applied to naval environments, it requires human resources and equipment that are not always prepared or available. Aligned with the principles of Industry 4.0, maintenance has been integrating technologies that enhance data collection and analysis, becoming more autonomous and intelligent. The integration of Machine Learning (ML) into CM offers an alternative to conventional approaches, enabling systems to learn real operating behavior and recognize fault patterns with high accuracy and reduced human intervention. Addressing a real industrial challenge, this paper proposes an automatic framework for fault detection and diagnosis using ML models. As a case study, vibration data from rotating machinery were analyzed, encompassing common faults such as unbalance, misalignment, and the combination of both. The obtained results highlight the potential of the proposed framework for CM in maritime environments, modernizing it with new trends and making it more autonomous, efficient, and less dependent on specialized knowledge. Full article

The exponential growth of data generated by modern digital applications, including systems inspired by Industrial Internet of Things (IIoT) requirements, has accelerated the adoption of NoSQL databases due to their scalability, flexibility, and performance advantages over traditional relational systems. Among document-oriented solutions, MongoDB and RavenDB stand out due to their architectural features and their ability to manage dynamic, large-scale datasets. This paper presents a comparative analysis of MongoDB and RavenDB, focusing on the performance of fundamental CRUD (Create, Read, Update, Delete) operations. To ensure a controlled performance evaluation, a mobile and web application for managing product orders was implemented as a case study inspired by IIoT data characteristics, such as high data volume and frequent transactional operations, with experiments conducted on datasets ranging from 1000 to 1,000,000 records. Beyond the core CRUD evaluation, the study also investigates advanced operational scenarios, including joint processing strategies (lookup versus document inclusion), bulk data ingestion techniques, aggregation performance, and full-text search capabilities. These complementary tests provide deeper insight into the systems’ architectural strengths and their behavior under more complex and data-intensive workloads. The experimental results highlight MongoDB’s consistent performance advantage in terms of response time, particularly with large data volumes, while RavenDB demonstrates competitive behavior and offers additional benefits such as built-in ACID compliance, automatic indexing, and optimized mechanisms for relational retrieval and bulk ingestion. The analysis does not propose a new benchmarking methodology but provides practical insights for selecting an appropriate document-oriented database for data intensive mobile and web application contexts, including IIoT-inspired data characteristics, based on a controlled single-node experimental setting, while acknowledging the limitations of a single-host experimental environment. Full article

The maritime sector is under growing pressure to decarbonize, driving the adoption of alternative fuels such as methane, methanol, ammonia, and hydrogen. This study evaluates their thermal behavior and associated risks using Engineering Equation Solve software for heat transfer modeling and Areal Locations of Hazardous Atmospheres software for dispersion and explosion analysis in pipelines and storage scenarios. Results indicate that methane presents moderate and predictable risks, mainly from thermal effects in fires or Boiling Liquid Expanding Vapor Explosion events, with low toxicity. Methanol offers the safest operational profile, stable at ambient temperature and easily manageable, though it remains slightly flammable even when diluted. Ammonia shows the greatest toxic hazard, with impact distances reaching several kilometers even when emergency shutoff systems are active. Hydrogen, meanwhile, poses the most severe flammability and explosion risks, capable of autoignition and generating destructive overpressures. Thermal analysis highlights that cryogenic fuels require complex insulation systems, increasing storage costs, while methanol and gaseous hydrogen remain thermally stable but have lower energy density. The study concludes that methanol is the most practical transition fuel, when comparing thermal behavior and associated risks, while hydrogen and ammonia demand further technological and regulatory development. Proper insulation, ventilation, and automatic shutoff systems are essential to ensure safe decarbonization in maritime transport. Full article

The growing global environmental crisis calls for fundamental transformations in production and consumption systems, but the understanding of how circular economy strategies translate into quantifiable environmental benefits remains fragmented across sectors and geographies. The objective of this study is to synthesize current scientific knowledge on the circular economy as an environmental mitigation strategy, identifying conceptual convergences, methodological patterns, geographic distributions, and critical knowledge gaps. A systematic review combined with a bibliometric analysis of 62 peer-reviewed articles published between 2018 and 2024, retrieved from Scopus, Web of Science, ScienceDirect, Springer Link and Wiley Online Library, was conducted following the PRISMA 2020 guidelines. The results reveal a marked methodological convergence around life cycle assessment, with Europe dominating the scientific output (58% of the corpus). Four complementary conceptual frameworks emerged, emphasizing closed-loop material flows, environmental performance, integration of economic sustainability and business model innovation. The thematic analysis identified bioenergy and waste valorization as the most mature implementation pathways, constituting 23% of the research emphasis. However, critical gaps remain: geographic concentration limits the transferability of knowledge to diverse socioeconomic contexts; social, cultural and behavioral dimensions remain underexplored (12% of publications); and environmental justice considerations receive negligible attention. Crucially, the evidence reveals nonlinear relationships between circularity metrics and environmental outcomes, calling into question automatic benefits assumptions. This review contributes to an integrative synthesis that advances theoretical understanding of circularity-environment relationships while providing evidence-based guidance for researchers, practitioners, and policy makers involved in transitions to the circular economy. Full article

Industrial wastewater treatment processes often exhibit highly nonlinear, dynamic behavior, making accurate prediction and control difficult when using conventional modeling approaches. This study presents an enhanced Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for modeling the ion-exchange purification process based on 200 experimentally collected data samples obtained from a laboratory-scale treatment system. The initial ANFIS structure was generated using subtractive clustering to automatically derive the rule base, while hybrid learning combining backpropagation and least-squares estimation was applied to train the model. The training results demonstrated stable convergence across 100, 200, and 300 epochs, with progressive improvements in model accuracy. To further enhance performance, several meta-heuristic optimization methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Adam optimizer, were integrated within a Python 3.13-based environment to refine model parameters. Ensemble learning and an extended Boosting++ strategy was subsequently employed to reduce variance, correct residual errors, and strengthen generalization capability. The optimized ANFIS model achieved strong predictive accuracy across both training and unseen test datasets. The performance metrics for the full dataset yielded RMSE (Root Mean Square Error) = 1.3369, MAE (Mean Absolute Error) = 0.9989, and R² = 0.9313, while correlation analysis showed consistently high R-values for training (0.96745), validation (0.95206), test (0.95754), and overall data (0.96507). The results demonstrate that the combination of subtractive clustering, hybrid learning, meta-heuristic optimization, and ensemble boosting produces a highly reliable soft-computing model capable of effectively capturing the nonlinear dynamics of ion-exchange wastewater treatment. The proposed approach provides a robust foundation for intelligent monitoring and control strategies in industrial purification systems. Full article

Quick Quantitative (QQ) immunoassays have been increasingly applied for the measurement of enrofloxacin (ENR) and ciprofloxacin (CIP) residues in aquaculture due to their speed and convenience. However, their quantitative reliability remains limited because measurement uncertainty (MU) is rarely considered during field testing. To enhance the metrological reliability of QQ-based residue analysis, we developed AquaUncertainty Pal, a mobile application that embeds real-time MU computation into the QQ workflow. The software automatically evaluates uncertainty sources during sampling and pipetting, visualizes the uncertainty budget, and guides users through optimized operations. The framework was validated against ISO/IEC 17025–accredited LC–MS/MS and assessed through a user study involving 20 frontline technicians. With the integrated software, pipetting precision (RSD) at 100 μL improved from 4.1% to 1.79%, the inter-operator variability (CV) decreased by 52%, and conformity assessment accuracy for samples near the maximum residue limit (MRL) increased from 25% to 70%. This suggests that real-time MU visualization effectively guided technicians toward consistent pipetting and interpretation behavior. These results demonstrate that integrating MU into the QQ workflow is both feasible and effective, substantially improving reliability and providing a replicable digital framework for uncertainty-informed residue monitoring in aquaculture. Full article

This paper presents an integrated approach to the structural decomposition of concurrent control systems using exact transversal hypergraphs (XT-hypergraphs). The proposed method combines formal properties of XT-hypergraphs with invariant-based Petri net analysis to enable automatic partitioning of complex, concurrent specifications into deterministic and independent components. The approach focuses on preserving behavioral correctness while minimizing inter-component dependencies and computational complexity. By exploiting the uniqueness of minimal transversal covers, reducibility, and structural stability of XT-hypergraphs, the method achieves a deterministic decomposition process with polynomial-delay generation of exact transversals. The research provides practical insights into the construction, reduction, and classification of XT structures, together with quality metrics evaluating decomposition efficiency and structural compactness. The developed methodology is validated on representative real-world control and embedded systems, showing its applicability in deterministic modeling, analysis, and implementation of concurrent architectures. Future work includes the integration of XT-hypergraph algorithms with adaptive decomposition and verification frameworks to enhance scalability and automation in modern system design and integration with currently popular AI and machine learning methods. Full article

Financial institutions face data quality (DQ) challenges in regulatory reporting due to complex architectures where data flows through multiple systems. Data consumers struggle to assess quality because traditional DQ tools operate on data snapshots without capturing temporal event sequences and business contexts that determine whether anomalies represent genuine issues or valid behavior. Existing approaches address either semantic representation (ontologies for static knowledge) or temporal pattern detection (event processing without semantics), but not their integration. This paper presents CEDAR (Contextual Events and Domain-driven Associative Representation), integrating financial ontologies with event-driven processing for context-aware DQ assessment. Novel contributions include (1) ontology-driven rule derivation that automatically translates OWL business constraints into executable detection logic; (2) temporal ontological reasoning extending static quality assessment with event stream processing; (3) explainable assessment tracing anomalies through causal chains to violated constraints; and (4) standards-based design using W3C technologies with FIBO extensions. Following the Design Science Research Methodology, we document the first, early-stage iteration focused on design novelty and technical feasibility. We present conceptual models, a working prototype, controlled validation with synthetic equity derivative data, and comparative analysis against existing approaches. The prototype successfully detects context-dependent quality issues and enables ontological root cause exploration. Contributions: A novel integration of ontologies and event processing for financial DQ management with validated technical feasibility, demonstrating how semantic web technologies address operational challenges in event-driven architectures. Full article

As a critical parameter for describing oil–water two-phase flow behavior, relative permeability curves are widely applied in field development, dynamic forecasting, and reservoir numerical simulation. This study addresses the issue of relative permeability anisotropy, focusing on the seepage characteristics of two typical bedding structures in sandstone reservoirs—tabular cross-bedding and parallel bedding—through multi-directional displacement experiments. A novel anisotropic relative permeability testing apparatus was employed to conduct displacement experiments on cubic core samples, comparing the performance of the explicit Johnson–Bossler–Naumann (JBN) method, based on Buckley–Leverett theory, with the implicit Automatic History Matching (AHM) method, which demonstrated superior accuracy. The results indicate that displacement direction significantly influences seepage efficiency. For cross-bedded cores, displacement perpendicular to bedding (Z-direction) achieved the highest displacement efficiency (75.09%) and the lowest residual oil saturation (22%), primarily due to uniform fluid distribution and efficient pore utilization. In contrast, horizontal displacement exhibited lower efficiency and higher residual oil saturation due to preferential flow path effects. In parallel-bedded cores, vertical displacement improved efficiency by 18.06%, approaching ideal piston-like displacement. Microscale analysis using Nuclear Magnetic Resonance (NMR) and Computed Tomography (CT) scanning further revealed that vertical displacement effectively reduces capillary resistance and promotes uniform fluid distribution, thereby minimizing residual oil formation. This study underscores the strong interplay between displacement direction and bedding structure, validating AHM’s advantages in characterizing anisotropic reservoirs. By integrating experimental innovation with advanced computational techniques, this work provides critical theoretical insights and practical guidance for optimizing reservoir development strategies and enhancing the accuracy of numerical simulations in complex sandstone reservoirs. Full article

Advanced Persistent Threats (APTs) exhibit covert behaviors, long attack cycles, and fragmented intelligence, creating challenges for correlation analysis and attribution. This work proposes a unified knowledge-graph-based framework for multi-level APT correlation. We construct an APT ontology and automatically extract entities and relations from threat reports using NER and relation extraction models. The resulting multi-source intelligence is normalized and integrated into a Neo4j knowledge graph containing 15,682 entities and 42,713 relations. Multi-level correlation analysis is then performed through explicit structural reasoning, semantic embedding models such as TransE and RotatE, and a temporal evolution module based on T-GCN to capture dynamic attack-path patterns. Experiments demonstrate that the proposed framework achieves an F1-score of 0.91 for relation extraction and improves APT correlation prediction accuracy by 17.3% over rule-based baselines. The system supports large-scale attack-chain reasoning and sector-oriented threat analysis, providing enhanced attribution and decision support for cybersecurity defense. Full article

Cyber bots have become prevalent across the Internet ecosystem, making behavioral understanding essential for threat intelligence. Since bot behaviors are encoded in traffic payloads that blend with normal traffic, honeypot sensors are widely adopted for capture and analysis. However, previous works face adaptation challenges when analyzing large-scale, diverse payloads from evolving bot techniques. In this paper, we conduct an 11-month measurement study to map cyber bot behaviors through payload pattern analysis in honeypot traffic. We propose TrafficPrint, a pattern extraction framework to enable adaptable analysis of diverse honeypot payloads. TrafficPrint combines representation learning with clustering to automatically extract human-understandable payload patterns without requiring protocol-specific expertise. Our globally distributed honeypot sensors collected 21.5 M application-layer payloads. Starting from only 168 K labeled payloads (0.8% of data), TrafficPrint extracted 296 patterns that automatically labeled 83.57% of previously unknown payloads. Our pattern-based analysis reveals actionable threat intelligence: 82% of patterns employ semi-customized structures balancing automation with targeted modifications; 13% contain distinctive identity markers enabling threat actor attribution, including CENSYS’s unique signature; and bots exploit techniques like masquerading as crawlers, embedding commands in brute-force attacks, and using base64 encoding for detection evasion. Full article

The principal problem with the analysis of nonlinear dynamical systems is that it is repetitive and inefficient to simulate every initial condition and parameter configuration individually. This not only raises the cost of computation but also constrains scalability in the exploration of a large parameter space. To solve this, we restructured and extended the computational framework so that variation in the parameters and initial conditions can be automatically explored in a unified structure. This strategy is implemented in the brain-inspired nonlinear dynamical model that has three parameters and multiple coupling strengths. The framework enables detailed categorization of the system responses through statistical analysis and through eigenvalue-based assessment of the stability by considering multiple initial states of the system. These results reveal clear differences between periodic, divergent, and non-divergent behavior and show the extent to which the strength of the coupling $k_{ij}$ can drive transitions to stable periodic behavior under all conditions examined. This method makes the analysis process easier, less redundant, and provides a scalable tool to study nonlinear dynamics. In addition to its computational benefits, the framework provides a general method that can be generalized to models with more parameters or more complicated network structures. Full article

Accurate behavioral segmentation of vessel trajectories from Automatic Identification System (AIS) is essential for maritime safety and traffic management. Existing methods often rely on predefined thresholds or emphasize geometric criteria and offer limited behavioral interpretability for mobility analysis. This paper introduces an unsupervised behavioral segmentation framework that integrates clustering with matheuristic optimization. Trajectories are cleaned with a forward sliding window, and three smoothed movement features, namely speed, acceleration, and turning rate, are computed for each point. Each feature is discretized by the Jenks Natural Breaks algorithm to extract key feature points and pointwise feature labels. Segment boundaries are near-optimally chosen from these key feature points using a Matheuristic Fixed Set Search (MFSS) that minimizes a Minimum Description Length (MDL) objective. This ensures behavioral consistency within each segment and clear separation between adjacent segments. Experiments on an AIS dataset from the Qiongzhou Strait, China, demonstrate that our proposed method yields more compact, distinctly differentiated segments than baseline methods, while preserving intra-segment behavioral continuity. These segments exhibit strong semantic coherence, making them well-suited for downstream tasks such as traffic risk assessment and route planning. Full article

With the trend of internationalization, maritime traffic density has gradually increased. Since 2002, the International Maritime Organization (IMO) has required various types of vessels to be equipped with the Automatic Identification System (AIS). Through AIS static and dynamic data, more complete navigational information of vessels can be obtained. As the Port of Kaohsiung is currently transitioning into a smart port, this study focuses on inbound and outbound vessels of the Second Port of Kaohsiung. It considers both the safety monitoring of the smart port and environmental security, integrating a big data database to provide early warnings for abnormal navigation conditions. This study builds an integrated database based on vessel AIS data, conducts AIS big data analysis to extract useful information, and establishes a random forest model to predict whether a vessel’s course and speed during port navigation deviate from normal patterns, thereby achieving the goal of early warning. This study also helps reduce the risk of collisions caused by abnormal vessel operations and thus prevents marine pollution in the port area due to oil spills or hazardous substance leakage. Through real-time monitoring and early warning of navigation behavior, it not only enhances navigation safety but also serves as the first line of defense against marine pollution, contributing significantly to the protection of the port’s ecological environment and the promotion of sustainable development. Full article

This study investigates the determinants of Fraudulent Financial Reporting (FFR) in the banking sector from 2021 to 2024 by integrating the Fraud Hexagon framework within a risk and financial management perspective. Using panel data comprising 140 bank-year observations (35 banks over four years), the research applies an empirical analysis to examine six key elements—pressure, opportunity, rationalization, capability, arrogance, and collusion—that shape fraud risk behavior in financial institutions. The results indicate that leverage does not significantly influence fraud incentives, suggesting that financial pressure alone is insufficient to drive fraudulent reporting without weak governance structures. In contrast, factors related to ineffective monitoring, auditor switching, and director change show varying effects on FFR. The findings also reveal that CEO image does not reflect arrogance, which has no significant effect on FFR, and political connections of entities do not automatically reduce fraud risk unless supported by strong and independent governance mechanisms. The study underscores the crucial moderating role of the audit committee in enhancing financial reporting integrity. From a policy perspective, the research provides strategic insights for regulators and supervisory bodies such as the Financial Services Authority (OJK) to strengthen governance frameworks, enforce stricter disclosure requirements, and integrate fraud risk management practices into corporate oversight. Overall, this study contributes to the financial governance literature by demonstrating how effective risk management and governance alignment can reduce fraudulent reporting and improve the sustainability of the banking sector. Full article