Search Results (705)

Computer numerical control (CNC) machining process simulation is increasingly central to intelligent manufacturing, yet its deployment in brownfield environments remains constrained by legacy controllers, heterogeneous data semantics, limited computational resources, and rising cybersecurity requirements. While digital twins (DTs), artificial intelligence (AI), and multi-physics simulation have matured conceptually, practical adoption, particularly among small and medium-sized enterprises (SMEs), continues to lag behind theoretical capability. This paper presents a PRISMA-guided systematic review of peer-reviewed literature, standards, and industrial reports published between 2019 and 2025, focusing on CNC machining simulation, digital twin architectures, interoperability standards, and intelligent optimisation under brownfield constraints. Rather than proposing new simulation algorithms, the review synthesises fragmented evidence into a deployable, standards-aligned integration perspective. The review consolidates prior work into a seven-layer architecture grounded in ISO 23247, explicitly separating sensing, communication, digital twin entities, analytics, and human–machine interaction. It derives practical decision rules for middleware selection, edge-cloud compute placement under latency constraints, and modelling strategy selection, ranging from mechanistic and finite-element methods to hybrid reduced-order and machine-learning surrogates. An SME-oriented implementation and validation roadmap links staged retrofitting to measurable operational indicators, including overall equipment effectiveness, first-pass yield, downtime, cycle time, and energy intensity. Full article

Voltage stability assessment of a transmission interface is carried out by continuation power flow (CPF) using a fixed post-contingency operating condition. However, if legacy renewable energy sources (RESs) in the sink area are tripped during or following a fault, the actual post-fault operating point can differ from that assumed in the CPF study. This paper examines the effect of sink-area RES tripping on transmission interface voltage stability. The shift in the post-fault operating point caused by the loss of sink-area active power injection is explained using a two-bus equivalent, and the effect of reactive power support from connected RES on the transfer limit is also discussed. The proposed analysis is verified using a modified SAVNW test system in PSS/E. Two contingency scenarios were studied by applying a three-phase fault at the receiving-end bus and tripping one transmission interface line at fault clearing. The results show that sink-area RES tripping moves the post-fault operating point toward the nose point and reduces the voltage stability margin. The results also show that reactive power support from connected RES increases the transfer limit and leads to a larger margin. These effects should be considered in voltage stability assessment of transmission interfaces with legacy RES. Full article

This paper presents a low-cost retrofitting pipeline for a legacy industrial robot that uses a single RGB webcam and monocular 2D keypoint tracking to estimate human-arm posture angles ${\mathit{\theta }}^{\left(\mathrm{h}\right)}$ and map them to robot-axis joint targets ${\mathbf{q}}_{\mathrm{cmd}}^{\left(\mathrm{r}\right)}$ for A1–A3 on a KUKA KR5-2 ARC HW, while keeping the wrist orientation (A4–A6) fixed. Rather than targeting full six-DoF manipulation, the main contribution is an experimental characterization of how far monocular 2D posture-to-axis mapping can be used reliably for coarse placement and safeguarded low-speed demonstrations on a legacy robot platform. Vision-side accuracy was evaluated per axis against goniometer-based reference angles ${\mathit{\theta }}_{\mathrm{ref}}^{\left(\mathrm{h}\right)}$, showing low errors for A2–A3 within the tested range and larger errors for A1 due to monocular yaw/depth ambiguity and occlusions. The study also analyzes failure modes during simultaneous multi-joint motion, where performance degrades notably, especially for A2 and A3, and reports practical mitigation directions such as improved viewpoints, multi-view/depth sensing, and stricter dropout handling. Runtime behavior is additionally characterized through a loop timing budget, with an end-to-end latency of 185.44 ms and an effective loop frequency of 5.39 Hz, which is consistent with low-speed online operation within the demonstrated scope. The system was implemented in a fenced industrial cell with restricted access and emergency stop; no collaborative operation is claimed. Full article

Seaweeds are photosynthetic organisms with ecological, social, and economic significance, and they serve as effective bioindicators in marine ecosystems. This study assessed trace element concentrations in beach-cast seaweeds collected from four beaches along the Espírito Santo coast in southeastern Brazil—an area impacted by mining-related contamination. Samples of Zonaria tournefortii (J.V. Lamouroux) Montagne and Sargassum natans (Linnaeus) Gaillon, gathered during low tide (July–August 2022), were analyzed for 15 elements. Statistical analysis using the Kruskal–Wallis test revealed significant interspecific differences in the accumulation of several metals. Aluminum (Al), iron (Fe), and magnesium (Mg) were the most abundant (>100 mg/kg), while minor elements (<100 mg/kg) included barium (Ba), arsenic (As), zinc (Zn), vanadium (V), nickel (Ni), chromium (Cr), copper (Cu), lead (Pb), cobalt (Co), cadmium (Cd), silver (Ag), and mercury (Hg). Elemental profiles exceeded those reported in other global regions and closely resembled iron ore tailings. Most elements had relatively higher concentrations on the beaches of Imigrantes, in the north of the state. These findings are the first for beach-cast seaweeds in this region, suggesting that this contamination indicates the legacy of the mining industry from southeastern Brazil. Full article

This paper proposes a human-centric digital twin (DT) framework balancing energy efficiency with occupant well-being in existing buildings, addressing the lack of actionable insights in data-driven facility management and comfort issues common in fully automated systems. A “Human-in-the-loop” approach using dual-KPIs integrates real-time IoT data and visualization to evaluate sustainable energy use via Indoor Environmental Quality (IEQ). A novel occupancy-inference method tracks efficiency in legacy buildings without granular metering, implemented through a case study of 26 office rooms. Results indicate that the framework successfully identifies significant energy wastage and comfort anomalies without compromising well-being. Integrating real-time analytics with human oversight enables more resilient management than fully automated alternatives, particularly for detecting non-operational heating waste. The occupancy inference method was validated against ground truth, achieving 81% accuracy, with limitations regarding decay lag discussed. This research offers a cost-effective diagnostic tool for legacy buildings lacking sub-metering, lowering DT adoption barriers, and shifting maintenance from reactive to data-driven strategies. The framework leverages human expertise and infers occupancy-normalized energy metrics from standard IEQ sensors, proposing a human-centric DT framework to bridge the gap between raw sensor data and actionable facility management insights. Full article

Globally, 5.94 million accessions are conserved across 867 genebanks, of which 41.5% (2.47 million) are cereal crop accessions. Only a small portion of global germplasm diversity has been marker-genotyped or genome-sequenced. Accurate identification of genebank accessions is essential to improve the efficiency and effectiveness of global genebanking. It is crucial for preserving the legacy knowledge associated with the germplasm and for maintaining its value to current plant science and breeding efforts. Existing practices generally fall into two categories: either expensive and complex, or inefficient, labour-intensive, and inaccurate. The first relies on high-resolution genomic sequences or saturated markers, while the second relies on morphological comparisons of regenerated plants with historical records. We propose a genotyping method based on a minimal set of Single Nucleotide Polymorphism (SNP) markers and exemplify its use on a genebank scale. We identified a small, effective set of SNPs that can differentiate between the global diversity of genebank accessions of barley (Hordeum vulgare and Hordeum spontaneum) and tetraploid wheat collections (Triticum turgidum) maintained at the Germplasm Resources National Capability at the John Innes Centre, UK. This approach offers a straightforward, automatable, and inexpensive alternative to traditional genebank crop descriptors used during seed regeneration and distribution. By establishing the minimal genomic resolution needed to distinguish genetically distinct accessions, we show that as few as 24 and 25 carefully chosen SNP markers for barley and durum wheat, respectively, can effectively differentiate individual accessions. Unlike morphology-based identification, which can detect mislabelling or contamination but often cannot prevent or correct such errors, our SNP-based molecular labelling enables error correction and the retrieval of lost germplasm identity. This study highlights how accuracy and reliability in germplasm management can be improved without costly whole-genome sequencing or resource-intensive analysis. We discuss the impact of this method on enhancing quality assurance in genebanks and its broader usefulness for the user community. Full article

Extreme drought events are increasingly destabilizing European lowland oak forests, yet within-stand variation in drought legacy effects remains poorly characterized. This study integrates UAV-LiDAR canopy structural analysis with a 68-year dendrochronological record (1952–2019) to examine divergent radial growth responses to the 2012 extreme drought in Turkey oak (Quercus cerris L.) forests of Vojvodina, northern Serbia. LiDAR scanning (Wingtra Gen II, 90 m altitude, spring 2024) enabled objective classification of 180 increment cores from 90 trees across four 5–7 ha experimental plots into two structural zones: a preserved-structure zone (PS; gap fraction ≤ 10%) and a disturbed-structure zone (DS; gap fraction > 10%). Ring width index (RWI) chronologies were developed using the modified negative exponential function and analyzed with linear mixed-effects models (LMMs) incorporating AR(1) temporal autocorrelation. Lloret resilience indices (a reference window of seven years) were computed per individual tree and compared between zones using Mann–Whitney U tests with Bonferroni correction. The key finding is a statistically significant zone × period interaction in all four plots (p = 0.0009–0.033): DS zone trees exhibited a marked post-drought RWI increase (mean +0.22–0.36 units; t-test p < 0.0001 in all plots), while PS zone trees showed no significant post-drought change (p = 0.147–0.258). Pooled Lloret analysis revealed significantly higher recovery (Rt: DS median = 1.693 vs. PS = 1.237; U = 1633, p < 0.0001, r = 0.532) and resilience (Rs: DS = 1.232 vs. PS = 0.932; U = 1574, p < 0.0001, r = 0.482), while resistance (Rc) did not differ between zones (p = 0.569), indicating that DS zone trees were equally susceptible to the drought but recovered far more strongly. The equivalence of Rc between zones critically implies that divergent post-drought trajectories cannot be attributed to differential drought tolerance but instead reflect a structural mechanism operating exclusively in the post-drought period. These results are consistent with a competition release mechanism: drought-induced canopy gap formation in DS zones reduced inter-tree competition for surviving trees, enabling accelerated radial growth recovery. Full article

Internal phosphorus loading is a key process sustaining eutrophication in stratified Baltic Sea coastal systems, yet its long-term dynamics in the Archipelago Sea remain poorly quantified due to limited deep-water monitoring and the absence of sediment time series. This study provides a multidecadal assessment of internal loading from the early 1980s to 2025 using two complementary indicators: (i) seasonal accumulation of total phosphorus in the surface layer (ΔTP) and (ii) the covariation between near-bottom oxygen depletion and dissolved inorganic phosphorus (DIP) release. Temporal associations with external phosphorus inputs from marine fish farming—highly variable during the study period—were analyzed to evaluate whether cumulative loading trajectories coincided with phases of intensified ΔTP. New measurements of drifting filamentous macroalgae from 2025 were additionally used to assess their seasonal contribution to the internal phosphorus pool and their relevance for mitigation. Results show a pronounced multidecadal strengthening of internal loading signals in the mid and inner Archipelago Sea. At the Seili station, ΔTP increased by approximately 6.8 µg L⁻¹ (≈3.4-fold) since the early 1980s. This trend coincided with long-term deterioration of near-bottom oxygen conditions and increasing DIP concentrations, consistent with enhanced sediment phosphorus release. Although cumulative aquaculture loading exhibited simple correlations with ΔTP, detrended analyses indicate that these relationships largely reflect shared long-term trends rather than direct causal linkages. Drifting filamentous macroalgae formed a substantial seasonal phosphorus reservoir (≈146 t P). Overall, internal phosphorus input to the Archipelago Sea has intensified markedly—by an estimated ~70% since the 1980s—highlighting the growing importance of sediment–water feedbacks and legacy phosphorus. Effective mitigation therefore requires strategies that address both internal recycling processes and external nutrient inputs. Targeted removal of drifting filamentous macroalgae may provide a complementary nutrient-export pathway in coastal management. Full article

The current literature on electrical system maintenance highlights three technology domains—Building Information Modeling (BIM), Digital Twin (DT), and extended reality (XR)—that have independently demonstrated strong potential for improving lifecycle information management, predictive analytics, and operational support. However, their convergence remains largely underexplored, particularly in electrical system maintenance. This paper provides a structured review of BIM–DT–XR convergence in electrical system lifecycle management, examining their roles across lifecycle phases and their integration through literature synthesis and cross-domain implementation evidence. BIM is analyzed as a basis for modeling and integrating facility management with electrical asset lifecycles; DT as a framework for dynamic system representation and applications in electrical and power systems; and XR as a means of visualizing and interacting with BIM-DT environments. Cross-domain implementation evidence from an industrial electrical facility and a tertiary smart-building pilot shows that BIM–DT–XR integration is technically feasible at pilot scale. However, the analysis identifies five structural integration gaps: semantic misalignment between building-oriented IFC and grid-oriented CIM ontologies; fragmented standard adoption; inconsistent data governance and naming practices; validation approaches focused on syntactic rather than dynamic model fidelity; and the separation of XR visualization from predictive DT capabilities. The implementation evidence further indicates that real-world deployment remains constrained by data quality limitations, integration complexity, cost factors, and interoperability with legacy systems. The review concludes that, despite the maturity of individual technologies, their effective application depends on advances in semantic alignment, lifecycle data governance, validation of dynamic models, and scalable integration frameworks, enabling the transition toward integrated, interoperable, and lifecycle-aware infrastructures for electrical system maintenance. Full article

High-resolution soil depth maps are valuable for environmental modelling, yet reliable data remains scarce in the tropics. This study evaluates the feasibility of mapping depth to bedrock (DTB) in Sri Lanka using a legacy dataset (n = 88) and global environmental covariates (n = 247). A robust machine learning workflow was employed—including feature selection, hyperparameter tuning, and a stacked ensemble of four algorithms (Random Forest, XGBoost, Cubist, SVM)—to test the limits of global data for local mapping. Despite rigorous optimization, the final ensemble model achieved a performance of R² = 0.197 (RMSE = 35.4 cm) under spatial cross-validation. While still modest, this result significantly outperforms existing global products and quantifies the “prediction gap” inherent in using ~1 km resolution global covariates to model micro-scale soil variability. An initial exploration involved log-transforming the target variable; however, following rigorous testing, the untransformed depth was modelled directly to avoid bias in back-transformation. A robustness experiment was further conducted, reducing predictors from 24 to 12, which degraded performance, confirming that the model captures complex, physically meaningful climatic interactions rather than fitting noise. The study concludes that while global covariates can capture regional meso-scale trends (explaining ~20% of variance), they are insufficient for resolving local micro-relief (<50 m). The resulting map and uncertainty products provide a critical “baseline” for national planning, but effectively demonstrate that future improvements will require investment in higher-resolution local covariates (e.g., LiDAR) rather than more complex algorithms. Full article

Artificial intelligence (AI) is increasingly integrated into cybersecurity tools to improve threat detection, anomaly identification, and incident response. However, organisations, particularly small- and medium-sized enterprises (SMEs), often struggle to discover, evaluate, and effectively use AI-enabled cybersecurity solutions due to skills gaps, usability challenges, and fragmented tool ecosystems. This paper presents the advaNced cybErsecurity awaReness ecOsystem for SMEs (NERO), a human-centric cybersecurity ecosystem that combines a cybersecurity marketplace with a competency-based training and awareness platform to support the practical adoption of advanced cybersecurity technologies. The NERO Marketplace enables structured discovery, comparison, and assessment of cybersecurity tools based on usability, operational relevance, and competency alignment. Complementing this, the NERO Training Platform delivers modular, multi-modal training aligned with the European Cybersecurity Skills Framework (ECSF) to develop the human competencies required to operate advanced cybersecurity systems. This study contributes a socio-technical framework that addresses the gap between AI tool availability and organisational readiness through ECSF role-based competency mapping and iterative design-based evaluation. The platform targets technical roles like Cybersecurity Implementer to ensure training is aligned with the operational requirements of critical infrastructure protection. Results from cross-sector SME training activities show measurable improvements in cybersecurity awareness, knowledge, and user satisfaction, with knowledge gains exceeding 30% in some modules. Finally, the paper provides a structural mapping of these cross-sectoral results to the maritime defence domain, specifically addressing legacy OT systems and intermittent connectivity constraints. Full article

Patterns of resource selection are driven by the decision-making processes of animals occurring at multiple scales from where to establish a home range (i.e., second order selection) to which resource patches to use within the home range (i.e., third order selection). Elk (Cervus canadensis) were reintroduced to southwestern Virginia, USA, from 2012 to 2014 following successful translocations onto reclaimed surface coal mines in the region. We sought to understand how elk have acclimated following their translocation using location data from GPS-collared adult female elk (n = 33) collected from 2019 to 2022 along with remotely sensed terrain and land cover data. We utilized continuous-time movement models paired with generalized linear mixed-effects modeling to describe seasonal resource selection at second and third orders. At both scales of selection and throughout the year, female elk selected reclaimed surface mines, conifer forests, ridgetops, and areas with lower terrain roughness, while avoiding mixed hardwood and oak (Quercus spp.) forests. Unmined open land was only selected at the third order during periods of forage scarcity (i.e., winter) and increased metabolic requirements (i.e., late gestation). Although surface coal mining leaves legacy environmental impacts on the landscape, management of these sites provides benefits to elk and maintains open habitat that is otherwise limited. Full article

Global supply chains face intensifying pressures from disruption, regulatory complexity, and sustainability mandates, requiring a shift toward more resilient and adaptive coordination. While artificial intelligence (AI) and blockchain have been recognised as complementary enablers, their implementation remains largely fragmented, existing as isolated tools linked by manual data exchange rather than integrated, programmable logic. This paper addresses this orchestration gap by proposing the Dynamic Resource Orchestration Framework for AI-Blockchain Integrated Supply Chains (DROF-AIBC). Grounded in Resource Orchestration Theory (ROT) and Dynamic Capabilities Theory (DCT), the framework provides a theoretical foundation for the strategic and autonomous orchestration of digital resources. Unlike classic supply chain orchestration, which focuses on the linear coordination of physical assets and legacy systems, DROF-AIBC conceptualises an “intelligent conductor” as a coordination mechanism combining AI-driven analytics and smart contract-based execution. This mechanism supports the configuration, optimisation, and monitoring of resources in response to changing external signals, effectively closing the loop between analytical insights and verifiable execution. The paper further substantiates how this autonomous capability serves as a foundational roadmap for the Industry 5.0 paradigm, embedding human-centricity through Explainable AI (XAI) to provide a “provenance of logic”, promoting circular economy sustainability, and fostering systemic resilience in turbulent environments. The framework aims to provide both a theoretical foundation and a practical roadmap for orchestrating AI and blockchain to advance resilient, sustainable and adaptive supply chains. Full article

Mercury (Hg) bioaccumulation in freshwater fish represents a major pathway of human exposure, particularly in cascade reservoir systems where hydrological retention and legacy contamination can enhance methylmercury (MeHg) formation and trophic transfer. This study quantified total mercury (THg) concentrations in seven tissues of seven fish species from the Arda River cascade (Bulgaria). Multi-tissue measurements were integrated with morphometric predictors, multivariate statistical analyses, and combined deterministic and probabilistic human-health risk assessments. Muscle and liver contained the highest THg concentrations, whereas gills and gonads exhibited the lowest levels. Predatory species and larger individuals accumulated significantly more Hg, reflecting trophic magnification and size-dependent exposure. A longitudinal gradient across the cascade reservoirs suggests hydrological retention effects influencing mercury distribution. Species- and tissue-specific size–Hg relationships further indicate heterogeneous bioaccumulation dynamics among taxa. Risk assessment indicated acceptable exposure for adults and pregnant women at average consumption (140 g·week⁻¹), but elevated exposure for children consuming high-Hg predators. Monte Carlo simulations (N = 30,000) revealed upper-tail risks, while Safe Weekly Intake thresholds provided species-specific consumption limits. These findings highlight the value of integrating multi-tissue monitoring with probabilistic risk modelling to support evidence-based fish-consumption advisories in contaminated freshwater systems. Full article

The 2025 approval of the selective NaV1.8 blocker suzetrigine for acute pain marked a pivotal advance in analgesic drug development. Yet the subsequent failure of Vertex’s next-generation NaV1.8 inhibitor VX993 to demonstrate clinical analgesia underscores enduring challenges in translating mechanistic promise into patient benefit. This review examines why promising targets and compounds, spanning NaV and TRP channels, often falter and outlines a path toward more reliable target selection and validation. I first summarize the pain pathway, from nociceptor transduction through spinal processing to cortical perception, emphasizing how inflammation and peripheral sensitization reshape excitability. Historically serendipitous, pain drug discovery now prioritizes molecular precision. Most approved chronic pain therapies act in the CNS and are limited by modest efficacy and adverse effects. Nociceptor-enriched targets (NaV1.7/1.8/1.9; TRP channels) remain attractive, yet redundancy among NaV subtypes and the necessity of blocking targets at the correct anatomical sites complicate translation. Human genetics and multi-omics provide a powerful, unbiased engine for target discovery. Rare high-impact variants offer strong causal hypotheses, while common polygenic contributions illuminate broader susceptibility. Large biobanks increasingly reveal a mismatch between legacy pain targets and genetically supported candidates across neuronal and non-neuronal cells. Human DRG transcriptomics highlight NaV channel redundancy. Human in vitro electrophysiology and PK/PD analyses show suzetrigine achieves ~90–95% NaV1.8 engagement, yet neurons can still fire unless additional channels are blocked. Species differences and drug distribution (including BBB/PNS penetration and P-gp efflux) critically influence efficacy; centrally accessible blockade (e.g., for NaV1.7 or TRPA1) may be necessary to achieve robust analgesia, challenging peripherally restricted strategies. Osteoarthritis illustrates how obesity-driven metabolic inflammation, synovial immune activation, subchondral bone remodeling, and specific nociceptor subtypes converge to drive mechanical pain. Multi-omic integration across diseased human tissues can pinpoint causal processes and cell types, enabling more selective and safer target choices. I propose a practical framework for target validation that integrates: (i) rigorous human genetic support; (ii) cell-type and site-of-action mapping; (iii) human-relevant electrophysiology and PK/PD with verified target engagement; (iv) species-appropriate models; (v) consideration of modality (small molecule, biologic, RNA, targeted protein degradation). Advancing genetically and anatomically aligned targets, tested at the right sites and exposures, offers the best path to genuinely effective, better-tolerated pain therapeutics. Full article