Search Results (675)

Reverse engineering (RE) has been increasingly adopted in metal manufacturing to digitize legacy parts, connect “as-is” geometry to mechanical performance, and enable agile repair and remanufacturing. This review consolidates scan-to-simulation workflows that transform 3D measurement data (optical/laser scanning and X-ray computed tomography) into simulation-ready models for structural assessment and manufacturing decisions, with an explicit focus on sustainability. Key steps are reviewed, from acquisition planning and metrological error sources to point-cloud/mesh processing, CAD/feature reconstruction, and geometry preparation for finite-element analysis (watertightness, defeaturing, meshing strategies, and boundary condition transfer). Special attention is given to uncertainty quantification and the propagation of geometric deviations into stress, stiffness, and fatigue predictions, enabling robust accept/reject and repair/replace choices. Sustainability is addressed through a lightweight reporting framework covering material losses, energy use, rework, and lead time across the scan–model–simulate–manufacture chain, clarifying when digitalization reduces scrap and over-processing. Industrial use cases are discussed for high-value metal components (e.g., molds, turbine blades, and marine/energy parts) where scan-informed simulation supports faster and more reliable decision making. Open challenges are summarized, including benchmark datasets, standardized reporting, automation of feature recognition, and integration with repair process simulation (DED/WAAM) and life-cycle metrics. A checklist is proposed to improve reproducibility and comparability across RE studies. Full article

Agentic artificial intelligence (AI) is emerging as a paradigm for next-generation smart grids, enabling autonomous decision-making, adaptive coordination, and resilient control in complex cyber–physical environments. Unlike traditional AI models, which are typically static predictors or offline optimizers, agentic AI systems perceive grid states, reason about goals, plan multi-step actions, and interact with operators in real time. This review presents the latest advances in agentic AI for power systems, including architectures, multi-agent control strategies, reinforcement learning frameworks, digital twin optimization, and physics-based control approaches. The synthesis is based on new literature sources to provide an aggregate of techniques that fill the gap between theoretical development and practical implementation. The main application areas studied were voltage and frequency control, power quality improvement, fault detection and self-healing, coordination of distributed energy resources, electric vehicle aggregation, demand response, and grid restoration. We examine the most effective agentic AI techniques in each domain for achieving operational goals and enhancing system reliability. A systematic evaluation is proposed based on criteria such as stability, safety, interpretability, certification readiness, and interoperability for grid codes, as well as being ready to deploy in the field. This framework is designed to help researchers and practitioners evaluate agentic AI solutions holistically and identify areas in which more research and development are needed. The analysis identifies important opportunities, such as hierarchical architectures of autonomous control, constraint-aware learning paradigms, and explainable supervisory agents, as well as challenges such as developing methodologies for formal verification, the availability of benchmark data, robustness to uncertainty, and building human operator trust. This study aims to provide a common point of reference for scholars and grid operators alike, giving detailed information on design patterns, system architectures, and potential research directions for pursuing the implementation of agentic AI in modern power systems. Full article

The present study explores the relationship between the systemic approach, educational innovation, and the use of digital technologies in higher education, with an emphasis on military academies. The aim of the research is to shed light on how systemic thinking can support strategic planning, the quality of education, and the effective integration of innovative practices, such as artificial intelligence, information and communication technologies, and virtual reality. The methodology was based on quantitative research using a questionnaire, which was distributed to 452 members of the Hellenic Non-Commissioned Officers Academy educational community (teaching staff, cadets, and recent graduates). Data analysis showed that the adoption of a systemic approach is positively associated with the readiness of trainers, including both instructors and future professionals (cadets), to support and implement educational innovations. Furthermore, it was found that the clarity of educational objectives and the alignment of critical elements of the educational system (resources, technology, instructors, trainees, and processes) significantly reinforce the intention to adopt innovative practices. The findings also show that educators’ positive perceptions of artificial intelligence and virtual/augmented reality are associated with a higher appreciation of learning benefits, such as improved performance, trainee satisfaction, and collaboration. In contrast, demographic and professional factors have a limited effect on attitudes toward innovation. Overall, findings indicated that innovation in military academies is not limited to the technological dimension, but requires a holistic, systemic approach that integrates organizational, pedagogical, and strategic parameters. The study contributes both theoretically and practically, providing empirical evidence for the role of systemic thinking in the design and implementation of innovative educational policies in military and broader academic education. Full article

Globally, grasslands, savannas, and wetlands are degrading rapidly and increasingly being replaced by woody vegetation. Woody Plant Encroachment (WPE) disrupts natural landscapes and has significant consequences for biodiversity, ecosystem functioning, and key ecosystem services. This review synthesizes findings from 159 peer-reviewed studies identified through a PRISMA-guided systematic literature review to evaluate the drivers of WPE, its ecological impacts, and the remote sensing (RS) approaches used to monitor it. The drivers of WPE are multifaceted, involving interactions among climate variability, topographic and edaphic conditions, hydrological change, land use transitions, and altered fire and grazing regimes, while its impacts are similarly diverse, influencing land cover structure, water and nutrient cycles, carbon and nitrogen dynamics, and broader implications for ecosystem resilience. Over the past two decades, RS has become central to WPE monitoring, with studies employing classification techniques, spectral mixture analysis, object-based image analysis, change detection, thresholding, landscape pattern and fragmentation metrics, and increasingly, machine learning and deep learning methods. Looking forward, emerging advances such as multi-sensor fusion (optical– synthetic aperture radar (SAR), Light Detection and Ranging (LiDAR)–hyperspectral), cloud-based platforms including Google Earth Engine, Microsoft Planetary Computer, and Digital Earth, and geospatial foundation models offer new opportunities for scalable, automated, and long-term monitoring. Despite these innovations, challenges remain in detecting early-stage encroachment, subcanopy woody growth, and species-specific patterns across heterogeneous landscapes. Key knowledge gaps highlighted in this review include the need for long-term monitoring frameworks, improved socio-ecological integration, species- and ecosystem-specific RS approaches, better utilization of SAR, and broader adoption of analysis-ready data and open-source platforms. Addressing these gaps will enable more effective, context-specific strategies to monitor, manage, and mitigate WPE in rapidly changing environments. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

This study investigates the incorporation of a standardised flexibility protocol within a physics-based models to enable controllable demand-side flexibility in residential energy systems. A heating subsystem is developed using MATLAB/Simulink and Simscape, serving as a testbed for protocol-driven control within a Multi-Energy System (MES). A conventional thermostat controller is first established, followed by the implementation of an OpenADR event engine in Stateflow. Simulations conducted under consistent boundary conditions reveal that protocol-enabled control enhances system performance in several respects. It maintains a more stable and pronounced indoor–outdoor temperature differential, thereby improving thermal comfort. It also reduces fuel consumption by curtailing or shifting heat output during demand-response events, while remaining within acceptable comfort limits. Additionally, it improves operational stability by dampening high-frequency fluctuations in mdot_fuel. The resulting co-simulation pipeline offers a modular and reproducible framework for analysing the propagation of grid-level signals to device-level actions. The research contributes a simulation-ready architecture that couples standardised demand-response signalling with a physics-based MES model, alongside quantitative evidence that protocol-compliant actuation can deliver comfort-preserving flexibility in residential heating. The framework is readily extensible to other energy assets, such as cooling systems, electric vehicle charging, and combined heat and power (CHP), and is adaptable to additional protocols, thereby supporting future cross-vector investigations into digitally enabled energy flexibility. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

The increasing role of automation systems in energy-efficient buildings creates a need for simulation approaches that support standardized assessment already at the design stage. This paper presents a digital twin-based simulation framework that integrates building information modeling (BIM)-derived building data with MATLAB/Simulink models to enable regulation-oriented evaluation of building automation and control strategies. The proposed approach targets scenario-based analysis of automation maturity levels, covering conventional, advanced, and predictive configurations aligned with EN ISO 52120 and the Smart Readiness Indicator (SRI). A representative academic building model is used to demonstrate how the framework supports reproducible modeling of heating, ventilation, and air conditioning (HVAC), lighting, and shading control functions and enables consistent comparison of their energy-related behavior under unified boundary conditions. The results show that the framework effectively captures performance trends associated with increasing automation sophistication and reveals interaction effects between control subsystems that are not accessible in conventional energy simulation tools. The proposed methodology provides a practical and extensible foundation for early-stage, regulation-aligned evaluation of smart building solutions and for the further development of predictive and artificial intelligence (AI)-assisted control concepts. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs. Full article

This study provides new evidence on how production digitalization investment affects firm financial performance across diverse European regions. A panel of 14,935 firm-year observations from 30 countries (2012–2022), including a focused Baltic subsample, is used alongside a refined digital capital intensity metric based on depreciated plant and machinery value. The results indicate a positive association between digital investment and operating revenue across Europe, with significantly stronger effects observed in the Baltic region. Interaction models reveal higher marginal returns for Baltic firms, suggesting that digital capital delivers amplified value in economies with lower digital saturation but greater absorptive urgency. Employee-related costs consistently predict revenue outcomes, underscoring their role in translating digital assets into performance. Intangible fixed assets exhibit a positive impact in Baltic labor-scale models but weaker effects elsewhere, indicating that institutional maturity mediates knowledge capital productivity. Implications: (1) digital investment yields asymmetric returns; (2) workforce investment enhances digital ROI; and (3) policy should prioritize organizational readiness alongside infrastructure. This study contributes by introducing a replicable proxy for production-level digitalization and by providing rare comparative evidence on digital returns in transitional versus mature European economies. Full article

Agricultural marketing increasingly integrates Agriculture 4.0 technologies—Blockchain, AI/ML, IoT, and recommendation systems—yet systematic evaluations of computational maturity and deployment readiness remain limited. This Systematic Literature Review (SLR) examined 99 peer-reviewed studies (2019–2025) from Scopus, Web of Science, and IEEE Xplore following PRISMA protocols to assess algorithmic performance, evaluation methods, and Technology Readiness Levels (TRLs) for agricultural marketing applications. Hybrid recommendation systems dominate current research (28.3%), achieving accuracies of 80–92%, while blockchain implementations (15.2%) show fast transaction times (<2 s) but limited real-world adoption. Machine learning models using Random Forest, Gradient Boosting, and CNNs reach 85–95% predictive accuracy, and IoT systems report >95% data transmission reliability. However, 77.8% of technologies remain at validation stages (TRL ≤ 5), and only 3% demonstrate operational deployment beyond one year. The findings reveal an “efficiency paradox”: strong technical performance (75–97/100) contrasts with weak economic validation (≤20% include cost–benefit analysis). Most studies overlook temporal, geographic, and economic generalization, prioritizing computational metrics over implementation viability. This review highlights the persistent validation–deployment gap in digital agriculture, urging a shift toward multi-tier evaluation frameworks that include contextual, adoption, and impact validation under real deployment conditions. Full article

As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and their correlation with perceived organizational readiness. Utilizing a quantitative survey of 53 specialized practitioners (N = 53), we assessed maturity across the six dimensions of the Bank of Thailand’s Cyber Resilience Assessment regulatory framework: Governance, Identification, Protection, Detection, Response, and Third-Party Risk Management. While descriptive statistics indicate high overall maturity ($\overline{\mathrm{x}}$ = 4.19, S.D. = 0.37), multiple regression analysis uncovers a critical “Protection Paradox”. Specifically, the “Protection” dimension exhibits a statistically significant negative impact on readiness (β = −0.432, p = 0.01), suggesting that over-engineered technical controls induce operational friction. In contrast, “Identification” emerged as the primary positive driver of readiness (β = 0.627, p < 0.01), highlighting visibility as a superior strategic lever. Furthermore, a structural disconnect was identified between strategic “Governance” and “Third-Party Risk Management” (r = 0.46), highlighting a “Silo Effect” where board-level policy fails to effectively mitigate supply chain risks. These findings suggest that financial institutions must pivot from volume-based compliance to risk-optimized integration to bridge these strategic and operational gaps. Full article

Pancreatic ductal adenocarcinoma (PDAC) is highly lethal, and many patients cannot undergo curative surgery due to frailty. Multimodal prehabilitation: combining exercise, nutrition, and psychological support improves functional readiness, but its biological impact on the PDAC tumor microenvironment (TME) is unclear. In this exploratory pilot study, we profiled resected PDAC tissues from prehabilitation-treated patients and matched controls using NanoString GeoMx Digital Spatial Profiling across immune, tumor, and stromal compartments (n = 4). Transcriptomic signatures were analyzed via differential expression, pathway enrichment, and MCP-counter deconvolution; protein-level validation used multiplex immunofluorescence (n = 8). Ligand–receptor modeling assessed cell–cell communication, and prognostic relevance was evaluated in TCGA-PDAC (n = 178). Prehabilitation was associated with increased NK-cell cytotoxicity, interferon response, and chemokine recruitment, as well as higher neutrophil signatures (p < 0.01) and reduced fibroblast signatures (p < 0.05). Tumor regions showed lower MAPK and PI3K/AKT activity, while stroma exhibited decreased TGF-β and Wnt signaling. Immunofluorescence confirmed neutrophil infiltration and reduced fibroblast density. TCGA analysis linked neutrophil-high/fibroblast-low profiles to longer survival (1044.6 vs. 458.7 days, p = 0.0052). These findings suggest prehabilitation may promote a more immune-active, less fibrotic TME in PDAC, resembling transcriptional states associated with improved survival. Prospective studies integrating biological and clinical endpoints are warranted. Full article

The increasing complexity of modern electrical substations—driven by renewable integration, advanced automation, and asset aging—necessitates a transition from reactive maintenance toward intelligent, data-driven strategies. Predictive maintenance (PdM), supported by artificial intelligence, enables early fault detection and remaining useful life (RUL) estimation, while Digital Twin (DT) technology provides synchronized cyber–physical representations for situational awareness and risk-free validation of maintenance decisions. This study proposes a five-layer DT-enabled PdM architecture integrating standards-based data acquisition, semantic interoperability (IEC 61850, CIM, and OPC UA Part 17), hybrid AI analytics, and cyber-secure decision support aligned with IEC 62443. The framework is validated using utility-grade operational data from the SS1 substation of the Badra Oil Field, comprising approximately one million multivariate time-stamped measurements and 139 confirmed fault events across transformer, feeder, and environmental monitoring systems. Fault detection is formulated as a binary classification task using event-window alignment to the 1 min SCADA timeline, preserving realistic operational class imbalance. Five supervised learning models—a Random Forest, Gradient Boosting, a Support Vector Machine, a Deep Neural Network, and a stacked ensemble—were benchmarked, with the ensemble embedded within the DT core representing the operational predictive model. Experimental results demonstrate strong performance, achieving an F1-score of 0.98 and an AUC of 0.995. The results confirm that the proposed DT–AI framework provides a scalable, interoperable, and cyber-resilient foundation for deployment-ready predictive maintenance in modern substation automation systems. Full article