Search Results (539)

Residual Kerr-induced nonlinear distortion after linear digital signal processing remains a major performance limitation in long-haul dual-polarization 16-QAM (DP-16QAM) coherent optical transmission. To address this issue, a perturbation-aided residual bidirectional gated recurrent unit (Bi-GRU) nonlinear equalizer is proposed. First-order perturbation theory is employed to extract impairment-related features as physical priors. Following PCA-based feature decorrelation and compact nonlinear projection, a two-layer Bi-GRU captures the bidirectional temporal dependencies associated with dispersion-coupled nonlinear inter-symbol interference, while a residual prediction head estimates the remaining nonlinear I/Q distortion. Numerical simulations of a 1200 km, 100 Gb/s DP-16QAM transmission link demonstrate that the proposed equalizer achieves a lower BER than linear DSP, digital backpropagation, pure Bi-GRU, a parameter-matched perturbation-aided CNN, and a perturbation-aided unidirectional GRU around the optimum launch power. Specifically, the proposed Bi-GRU achieves a mean BER of $8.785\times {10}^{-5}$, compared with $1.079\times {10}^{-4}$ for the parameter-matched CNN. Its BER is also comparable to the $8.976\times {10}^{-5}$ obtained by the perturbation-aided Bi-LSTM, while requiring 19.7% fewer trainable parameters. These results indicate that the physical perturbation prior and bidirectional temporal modeling improve nonlinear impairment compensation without introducing a substantial computational burden, enabling the proposed equalizer to achieve a favorable performance–complexity trade-off. Full article

Camera-reconstructed 3D scenes often require offline visual cleanup before inspection, presentation, or reuse as renderable virtual-scene assets. Representative applications include removing temporary furniture, parked vehicles, equipment, signage, and other distracting or obsolete objects from reconstructed indoor and outdoor environments. Such editing requires not only accurate target localization across viewpoints but also plausible recovery of the previously occluded background. Existing methods often depend on manually specified masks or category-restricted detectors, while projection-based pipelines independently inpaint multiple views and subsequently refine the 3D representation, potentially introducing cross-view appearance and geometry inconsistencies. We present OVR-GS (Open-Vocabulary Removal in Gaussian Splatting), an instruction-driven object-removal framework for pre-trained 3D Gaussian Splatting (3DGS) scenes. Given a free-form instruction, a language parser generates target-oriented queries and a textual background-completion condition. Grounding DINO and the Segment Anything Model (SAM) produce multi-view candidate masks, which are filtered using Contrastive Language–Image Pre-training (CLIP). The proposed Semantic-Aware Gaussian Selector (SAGS) aggregates rendering-contribution-weighted mask evidence, groups spatially coherent candidates, and identifies the target Gaussian subset through rendered-cluster semantic verification. After removal, new Gaussians are initialized from boundary-adjacent primitives and interior samples and optimized locally using Score Distillation Sampling (SDS), while the original background remains fixed. On IMFine, SPIn-NeRF, and Inpaint360GS, OVR-GS achieves peak signal-to-noise ratio (PSNR) values of 19.78, 17.82, and 24.62 dB and Fréchet inception distance (FID) values of 142.30, 148.60, and 34.80, respectively. The results demonstrate the effectiveness of localized Gaussian optimization for instruction-driven cleanup of reconstructed environments before visual inspection, presentation, or reuse as renderable virtual-scene assets. Full article

This paper proposes a process-mapped quality control framework for structural steel fabrication as a proof of concept for integrating normative requirements into a unified operational workflow. The study addresses the fragmentation of quality requirements in steel fabrication, where individual standards are often applied through separate inspections rather than as part of a coherent process model. The framework links material control, production preparation, assembly, welding, and anticorrosion protection through clearly defined quality gates. The framework is constructed through a qualitative design-oriented approach using SIPOC analysis and a detailed flowchart. It translates normative requirements into a coherent workflow that provides a structured basis for traceability, preventive quality assurance, and future integration with Construction 4.0. The paper suggests that process mapping can serve as a useful method for organizing quality control in steel construction projects and for transforming standards into an operational framework. Full article

The Social License to Operate (SLO) has become a key component of mining sustainability. However, the literature continues to address its main dimensions through fragmented approaches, limiting the understanding of social legitimacy as a dynamic and territorially situated process. In response to this gap, this study develops a territorial–relational analytical framework to integrate the main dimensions involved in the construction, strengthening, and loss of the SLO. Based on qualitative research involving a literature review, document analysis, and comparative case analysis, four interrelated strategic dimensions were identified: social perception and territorial management, territorial governance and citizen participation, territorial distribution of benefits and shared value creation, and institutional trust and relational coherence. The study also proposes a five-stage territorial–relational sequence that represents the dynamic, cumulative, and potentially reversible nature of the SLO throughout the life cycle of mining projects. The interpretive application of the framework to the Dominga Project shows how the interaction among these dimensions helps explain trajectories of strengthening, deterioration, and loss of social legitimacy. Overall, the study reorganizes previously scattered evidence into an integrated conceptual structure that broadens the understanding of the SLO and provides a foundation for future research aimed at its empirical validation and application in the governance of extractive projects. Full article

Isolated mixing regions (IMRs) commonly persist during steady laminar stirring and limit the attainable mixing degree. Planar laser-induced fluorescence (PLIF) and particle image velocimetry (PIV) were combined for simultaneous measurements in this study. Experiments were conducted with pure glycerol and microbubbles as PIV tracers over $Re$ = 1.76–10.58. Relatively stable IMRs were identified by PLIF throughout the investigated Re range. As $Re$ increased, the mean specific kinetic energy in the measurement plane rose from 2.84 × 10⁻⁶ m²/s² to 1.77 × 10⁻³ m²/s², whereas the steady-state mixing degree remained near 70%. PIV results showed that the velocity components obtained from boundary and center projections agreed well in magnitude and trend. At all investigated $Re$ values, the center-radial projected velocities at the IMR boundary points remained close to zero. Increasing rotational speed primarily intensified fluid motion within each region and tangential motion near the IMR boundary, without generating persistent, coherent radial exchange. Restricted boundary-normal motion is therefore an important reason for IMR persistence and the limited improvement in steady-state mixing degree. These findings provide quantitative experimental evidence for understanding steady IMRs and enhancing laminar mixing. Full article

Background/Objectives: Feature engineering remains a major challenge in metabolomics-based prediction, particularly when rich biochemical knowledge is available but underutilized. Conventional metabolomics models rely primarily on measured variables and statistically driven feature selection, overlooking the molecular and pathway context encoded in curated metabolite knowledge bases. We propose SEFA (Semantic Embedding-based Feature Augmentation), a model-agnostic framework that integrates metabolite-level textual knowledge from the Human Metabolome Database (HMDB) into structured metabolomics modeling for lung-cancer prediction. Methods: SEFA encodes HMDB metabolite descriptions as 768-dimensional MedBERT vectors and projects measured metabolite concentrations into this semantic space via concentration-weighted aggregation, producing a 928-dimensional candidate feature matrix that concatenates 11 clinical variables, 149 metabolite concentrations, and 768 semantic projection features. Sparse L1-guided feature selection reduced this representation to 24 features (2.6% of candidates) within a leakage-free cross-validation pipeline. Six classifiers were evaluated on a lung-cancer plasma metabolomics cohort of 800 participants (586 cases, 214 controls) with a stratified 80:20 split, and a controlled ablation study compared the augmented representation with a 24-metabolite-only baseline. Pathway-enrichment analysis of the metabolite sets associated with the retained embedding dimensions was performed using MetaboAnalyst 5.0. Results: Logistic regression on the 24-feature SEFA representation achieved a test ROC-AUC of 0.969, a precision-recall AUC of 0.987, and an accuracy of 94.4%, competitive with less interpretable approaches. Under the same 24-feature budget, embedding augmentation improved test ROC-AUC by 0.008, precision-recall AUC by 0.004, and accuracy by 3.1 percentage points over the metabolite-only baseline. Five retained embedding dimensions mapped onto coherent metabolic themes—sphingolipids and acylcarnitines, carnitine and glutamine metabolism, one-carbon and nitrogen handling, purine catabolism, and oxidative stress markers—and their associated metabolite sets were enriched for arginine and proline metabolism and glycine, serine, and threonine metabolism, pathways with established roles in lung-cancer biology. Conclusions: SEFA demonstrates that semantic embeddings derived from biomedical language models can convert curated metabolite annotations into patient-level features that supply complementary predictive signal while preserving biological interpretability through pathway-level analysis. The present evidence is limited to a single region-specific cohort; external, cross-platform, and cross-disease evaluation is required before broader generalization or clinical application. Full article

(1) Background: Climate change is reducing the climatic windows in which outdoor sport and physical activity can be safely practised. Yet, it remains unclear whether the resulting population exposure is distributed evenly across countries or is concentrated in countries with higher socio-economic vulnerability. Here, we assess the country-level distribution of population exposure to sport-relevant heat along a projected socio-economic vulnerability gradient. (2) Methods: We assess a screening-level, population-based proxy of sport-relevant heat exposure, defined by residents living in grid cells experiencing specified afternoon WBGT exceedance frequencies. We first quantify a population exposure burden by combining reconstructed afternoon wet-bulb globe temperature (WBGT) exceedance frequencies from bias-corrected CMIP6 projections (NEX-GDDP, five-model ensemble) with SSP-consistent gridded population. The resulting grid-cell exposure burden, expressed in person-days yr⁻¹, is aggregated to the country level. Countries are then ranked using a published national socio-economic vulnerability index (GVI), and the distribution of the exposure burden along this vulnerability gradient is quantified using a population-weighted concentration index (CI). The GVI is used as the ranking variable and is not incorporated into a multiplicative vulnerability-weighted risk metric. Two internally coherent futures (SSP1-2.6 and SSP2-4.5, each paired with its corresponding socio-economic pathway) are evaluated at mid-century (2055) and late century (2085). (3) Results: The population exposure burden is concentrated in more vulnerable countries in all scenario–horizon–threshold combinations (CI > 0; 0.09–0.25). Under SSP1-2.6 at mid-century, the five-model mean CI showed a descriptive increase from 0.17 for WBGT ≥ 28 °C to 0.25 for WBGT ≥ 32 °C, although the 95% paired hierarchical-bootstrap interval for the 32-minus-28 °C contrast included zero. By 2085, about 4.11 billion people live in cells experiencing at least 30 days yr⁻¹ with WBGT ≥ 32 °C under SSP2-4.5, compared with 2.14 billion under SSP1-2.6. (4) Conclusions: The results identify projected between-country inequalities in resident population exposure to sport-relevant heat conditions; they do not quantify actual sport participation, athlete exposure or health outcomes. The comparison between SSP1-2.6 and SSP2-4.5 further shows substantial differences in both climatic hazard and population exposure between these two integrated climate–demographic–socio-economic futures. Full article

Risk registers remain static governance artefacts, manually maintained and weakly coupled to operational evidence. While organisations generate continuous security telemetry from vulnerability scanners, incident reports, and audit findings, this evidence is rarely synthesised into coherent, evolving risk structures. Existing approaches address fragments of the problem: SIEM systems correlate events but do not construct risk registers; GRC platforms manage risk documentation but depend on manual entry; and LLM applications assist with summarisation but introduce non-determinism incompatible with governance requirements. This paper presents a hybrid architecture that separates stochastic LLM-based extraction from deterministic risk correlation and aggregation. The system ingests heterogeneous evidence, extracts structured claims via schema-bounded LLM processing, and correlates events into stable risk trees using anchor-based tiered matching. All correlation and projection operations are deterministic and replayable. The contribution is an architectural design pattern for integrating probabilistic inference into governance systems without compromising auditability. The walkthroughs run on a reference prototype. Replaying the stored evidence three times rebuilt the same register state, and admission scores matched the values the rules predict. An injected malformed extraction was quarantined; the register did not change. Full article

Henan Province, a major coal producing region in China, faces severe surface subsidence induced by extensive underground mining, which compromises regional ecological security and infrastructure stability. In this study, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) was applied to Sentinel-1A imagery acquired from March 2017 to February 2025 to characterize surface deformation in concentrated coal mining areas. A local validation was conducted within a representative mining area in Study Area 3 using measurements from 14 leveling benchmarks acquired between 5 May and 20 July 2023. The comparison yielded an $R^2$ of 0.816 and an RMSE of 9.22 mm, indicating good agreement between the SBAS-InSAR and leveling measurements during the validation interval. The subsidence in the study area exhibits significant spatial heterogeneity and continuous accumulation characteristics. The most negative approximate vertically projected deformation rate reached −371 mm/yr, and the maximum cumulative displacement reached −2101 mm. Scenario-based sensitivity analysis indicated potential projection errors of 6.76–8.34% for a horizontal-to-vertical displacement ratio of 0.10 and 20.28–25.01% for a ratio of 0.30, with larger uncertainty expected near subsidence trough margins. Given the difficulty of quantifying large-scale underground mining parameters, this study employs multisource environmental and topographic variables as auxiliary indicators and develops an XGBoost-SHAP model to evaluate their relative explanatory contributions to the spatial heterogeneity of mining-induced subsidence. Among the selected measurable environmental and topographic variables, groundwater table depth represents the most important measurable explanatory factor for the spatial heterogeneity of subsidence, with distinct response patterns between plain areas with thick unconsolidated layers and piedmont bedrock regions. Furthermore, wavelet coherence analysis identifies scale-dependent spatial associations between topography and subsidence. At the regional scale, elevation exhibits spatial correspondence with the geomorphological framework of contiguous subsidence basins. At the local scale, slope and aspect show localized associations with differential deformation gradients near the margins of subsidence troughs. Full article

Extreme wind speed events pose a significant threat to structural safety and are an important consideration in the design of the growing renewable energy sectors. This study models extreme wind speeds in Namibia by applying Extreme Value Theory (EVT) to quality-controlled daily maximum wind speed records from six meteorological stations, spanning 13–21 years per station and covering the 2003–2024 period. A two-rule quality-control procedure, combining a regional plausibility limit with an isolated-spike test supported by cross-station coherence checks, identified and removed 70 spurious automatic weather station records (0.24% of observations) prior to analysis. The Generalized Pareto Distribution (GDP) was fitted to declustered threshold exceedances using the Peaks-Over-Threshold method, with 95% confidence intervals for return levels obtained by profile likelihood. Model comparison based on AIC, BIC, and the negative log-likelihood indicated that the Generalized Pareto Distribution (GPD) provided a better description of the extreme wind speed tails. The results reveal a clear coastal-inland contrast; the coastal station Lüderitz experiences the strongest and most frequent extreme wind events, with a 100-year return level of 49.4 m/s (GPD; 95% profile-likelihood interval 44.9–70.3 m/s) and evidence of a bounded upper tail, while inland stations exhibit more moderate extremes with 100-year return levels 36–45 m/s. A seasonal analysis reveals stronger winter extremes at coastal Walvis Bay, while inland stations experience summer convective peaks. The study provides the first systematic station-level EVT analysis of observed wind extremes for Namibia, offering essential quantitative input for wind-sensitive infrastructure design, renewable energy project siting, and national climate adaptation planning. Full article

Direction-of-arrival (DOA) estimation with coprime arrays can synthesize an enlarged virtual aperture from a limited number of physical sensors. However, coherent incident sources lead to rank deficiency of the source covariance matrix, while unknown nonuniform sensor noise mainly contaminates the zero-lag component of the difference-coarray covariance. These two effects jointly degrade conventional Coarray Root-MUSIC, Coarray ESPRIT, and interpolation-based virtual-array methods. To address this problem, this paper proposes a Toeplitz–Hankel structured covariance reconstruction method for coherent-source DOA estimation with coprime arrays under unknown nonuniform noise. The method first performs redundancy-aware difference-coarray lag averaging. The zero-lag component is then suppressed during missing-lag interpolation to reduce the bias caused by sensor-dependent noise powers. A Toeplitz positive semidefinite projection is used to enforce covariance validity, and a relaxed Hankel truncated-singular-value-decomposition refinement is introduced to enhance the low-rank spectral structure of the reconstructed virtual covariance sequence. Finally, multi-scale forward–backward spatial smoothing MUSIC is applied for coherent-source DOA estimation. Simulation results with a coprime array of $M=4$ and $N=5$ show that the proposed method provides more accurate and stable DOA estimates than Coarray Root-MUSIC, Coarray ESPRIT, and RV-TSI. Compared with CVX-based THSCR, the proposed method avoids semidefinite programming and nuclear-norm optimization and reduces the average runtime from approximately $11.2$ s per trial to approximately $0.11$ s per trial under the tested setting. Full article

Assessing long-term hydroclimatic variability in central Mexico is essential to understand regional water availability and groundwater recharge for urban centers such as Querétaro. This study developed a multi-century winter–spring precipitation reconstruction for the Sierra Gorda Biosphere Reserve (SGBR) using ring width chronologies of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco. Standard dendrochronological techniques were applied to develop a 284-year master chronology (1731–2015). Following the accepted Subsample Signal Strength criterion (SSS ≥ 0.85) for chronology reliability, the reconstruction was restricted to the 1744–2015 period, yielding a statistically robust 271-year December–April precipitation record. A bootstrapped ordinary least-squares regression model relating tree-ring indices to instrumental December–April precipitation was calibrated and validated using split-sample cross-validation, explaining 46% of the instrumental precipitation variance (R² = 0.46) and yielding positive verification statistics (RE = 0.38–0.58; CE = 0.37–0.57). Spatial field correlations against gridded climate data (CRU TS4.08) confirmed a broad regional hydroclimatic signal centered over the Sierra Madre Oriental. Continuous wavelet transform (CWT), spectral analysis, superposed epoch analysis (SEA), and wavelet coherence (WTC) revealed significant interannual (2–8 years) and decadal (10–20 years) variability associated with large-scale ocean–atmosphere climate modes, including the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Tropical North Atlantic (TNA) index. The pronounced sensitivity of these conifer forests to pre-monsoonal moisture deficits highlights their vulnerability to projected warming and increasing spring evapotranspiration stress. Although the reconstruction is limited to pre-monsoonal (December–April) precipitation, it provides a robust centuries-long baseline for contextualizing regional hydroclimatic variability and supports water-resource management, groundwater conservation, and climate-adaptation strategies in central Mexico. Full article

Greening urban spaces is one of the fundamental elements in strengthening urban resilience to climate change and introducing sustainable urban lifestyles. The subject of this research is innovative urban solutions, situated in the context of cities’ aspirations to attain the title of being the greenest and most sustainable city in Europe. The conducted research is based on a comparative analysis of greening initiatives in 41 cities in 13 European countries, united in the international project Green Cities Europe, carried out from 2020 to 2023. The aim of the research is to evaluate urban solutions implemented in the context of social, economic, environmental, and spatial values, as well as the use of blue–green infrastructure, renewable energy sources, and new technologies. The study employed a mixed-methods approach combining qualitative and quantitative analyses. The research included a review of planning and strategic documents, a comparative analysis of 131 urban greening projects, and an evaluation of 45 projects nominated for the Green Cities Europe Award (GCEA). The findings were synthesised using comparative statistical analyses and descriptive evaluation. Based on the conducted analysis and assessment, we demonstrate that the studied cities, which are pursuing development and climate policies, implement urban greening actions with varying, often limited, degrees of innovation. We show that the implemented solutions place significant emphasis on environmental and social values, but only minimally incorporate elements of renewable energy sources and new technologies. This study indicates the need for cities to pursue a coherent development policy that fosters the planning and implementation of innovative urban solutions. Full article

Thermomechanical coupling effects significantly influence the dynamic response of flexible multibody systems operating in thermal environments. Accurate simulation using conventional projection-based reduced-order models remains challenging due to strong nonlinearity and the time-varying nature of thermal fields. This work proposes a projection-free sparse-solving framework for thermomechanical coupling dynamics. The elastic and thermal fields are represented using sparse POD coefficients, and the governing equations are directly sampled online without Galerkin projection. The sparse coefficients are recovered via l₁ norm optimization at each time step. To ensure stable recovery under thermal-mechanical coupling, a unit-norm tight frame-based preconditioner is introduced to reduce the coherence of the underdetermined system matrix. The proposed method is validated through three numerical examples. Results show that the method maintains stable accuracy over long-time simulations, reduces computational time by over 40%, and exhibits improved robustness compared with the discrete empirical interpolation method in thermomechanical coupling problems. The adaptive basis selection capability and the necessity of unit norm tight frame preconditioning are confirmed. The method offers an efficient and reliable alternative for thermomechanical multibody dynamics simulation. Full article

Artificial Intelligence (AI) is being integrated into the architectural profession via processes such as generating images based on design parameters; providing support to help create written descriptions of designs; assisting in the visualisation of buildings before they are built; allowing for greater use of parameters in construction; providing documentation to help complete projects in record time; and assisting in making design decisions. The success of integrating AI into practice depends not only on the use of tools but also on firms’ maturity in integrating AI into their workflows, employees’ capabilities, project teams’ operational efficiency, the impact of investment decisions, and the overall governance of the profession. This research evaluates the maturity of AI integration in architectural practice in the Kurdistan Region of Iraq. To do so, it develops and operationalises the AI Integration Maturity Index (AIMI), an eight-component formative composite index scored 0–37 and organised into five maturity bands (Non-adopter, Exploratory, Occasional, Integrated, and Advanced Strategic). The index comprises adoption, usage, diversity of tools, breadth of workflows, project penetration, staff involvement, training/capacity building, and governance/strategic focus. The AIMI is treated as a literature-derived formative diagnostic tool rather than a universal weighting standard, and was developed through a structured literature synthesis, expert pilot review, and internal-structure validation. Accordingly, its component logic and internal statistics are reported as a transparency and coherence check rather than as reflective reliability claims: Cronbach’s alpha (0.922) is presented descriptively to show component co-movement given the formative specification, while inter-coder reliability (kappa = 0.96) supports the qualitative benchmark coding. The study employs a mixed-methods descriptive comparative methodology consisting of a structured survey instrument administered to 100 architectural firms operating in the local market and structured asynchronous text-based interviews with 10 international architectural firms, comparing the resulting profiles with selected, generally accepted benchmarks and with a sample of leading firms engaged in architectural practice worldwide. On average, total AIMI scores in the local sample were 18.14 out of 37, indicating that local firms are broadly adopting and using AI (82% currently use AI on a regular or occasional basis). By contrast, the international sample yielded a mean AIMI score of 28.60, indicating that local firms exhibit a significantly lower level of maturity than the international benchmark, with the largest gaps in staff involvement, project penetration, and overall engagement with AI use. The paper concludes that the primary challenge facing architectural firms in the Kurdistan Region is no longer basic awareness or technological infrastructure, but rather the transition from broad and superficial AI adoption to a systematic, structured, project-based, and well-governed integration of AI within the architectural profession. Full article