Search Results (1,198)

Aircraft fuselage corrosion inspection requires both image-domain recognition and metric physical-surface localization for maintenance execution. This study develops a camera-calibrated vision measurement framework that combines PWDE-YOLOv8n-based corrosion perception, original-image coordinate restoration, lens-distortion compensation, ray-based surface mapping, and detector-center bias correction. The perception dataset comprised 2143 images and 5941 corrosion annotations and was partitioned at the physical-specimen, acquisition-session, or source-group level into 1500 training images, 429 validation images, and 214 independent detector-test images. Detailed physical localization was evaluated on a six-image metrology cohort acquired in six sessions, containing 21 corrosion boxes and 84 axial coordinates. A six-fold leave-one-image-out procedure was adopted; in each fold, the center-shift parameters were estimated from the other five images and applied unchanged to the held-out image. The proposed method achieved a mean absolute axial error of 0.641 mm (95% image-cluster bootstrap confidence interval: 0.571–0.708 mm), an RMSE of 0.809 mm, a maximum error of 3.262 mm, and a projected physical-plane bounding-box IoU of 87.12%. The expanded uncertainty of the manually established reference coordinates was 0.374 mm at k = 2, and Monte Carlo propagation produced a mean absolute error of 0.656 mm with a 95% interval of 0.618–0.693 mm. The proposed method reduced the MAE by 97.91% relative to local pixel-to-millimeter scaling and by 70.58% relative to conventional calibrated camera mapping, while producing accuracy comparable to planar homography mapping. Within ρ ≥ 1500 mm, W ≤ 150 mm, and θ ≤ 20°, the estimated curvature-induced additional axial error did not exceed 0.683 mm. A separate ten-image deployment evaluation produced a mean axial error of 2.448 mm and an average processing time of 53.35 ms/image. Full article

Image fusion integrates complementary multi-source visual information, yet plaintext fusion poses severe privacy risks. Conventional lattice-based homomorphic encryption enables ciphertext computation but incurs substantial computational overhead and exhibits poor compatibility with image fusion tasks. This work investigates lightweight privacy-preserving image fusion built upon pixel scrambling. Any pixel-scrambling technique that only rearranges pixel coordinates without modifying pixel values inherently satisfies the homomorphic properties required for pixel-level spatial fusion. In this paper, we adopt full-size random permutation matrix scrambling as a representative pixel-disordering method for systematic theoretical and experimental verification. The scheme generates a secret key matching the resolution of test images; it merely reorders pixel positions while preserving all original intensity values, allowing direct cipher-domain fusion that yields distortion-free outputs for averaging, weighted averaging, maximum-value and minimum-value fusion rules. Free from intricate lattice calculations and ciphertext expansion, the proposed lightweight framework achieves an optimal trade-off among security, computational efficiency and fusion quality for cloud computing scenarios. Full article

Responsible sourcing certification is a sociotechnical governance system that links brands, suppliers, third-party auditors, and review mechanisms. This study examines how this system is distorted when a corrupt auditor extorts genuinely compliant suppliers. Unlike collusion, where noncompliant suppliers bribe auditors to enter the sourcing system, extortion threatens genuinely compliant suppliers with erroneous rejection unless they pay. We develop a game-theoretic model in which the brand chooses a certification standard and a purchase price, while an extorted supplier either pays or enters review. Review success depends jointly on procedural effectiveness and factual verifiability. The analysis identifies four contract regimes: partial extortion, premium payment, standard contraction, and a no-extortion benchmark. Review intensity has a nonmonotonic governance effect because it both reduces erroneous rejection after refusal and compresses the corrupt auditor’s extortable surplus. We further show that eliminating observable extortion does not remove distortion but relocates it among standard exclusion, erroneous review rejection, and monetary burden. Extensions on endogenous review governance, review cost, and collusion–extortion coexistence show that certification systems may rely on incomplete review governance rather than fully restore the no-extortion benchmark. Full article

GaP and ZnTe semiconductor crystals are numerically investigated for terahertz (THz) generation driven by ultrafast 10.6 µm CO₂ laser pumping. At this wavelength, low-order multiphoton absorption is effectively suppressed, enabling the study of intrinsic performance limits governed by nonlinear optical effects, material dispersion, and THz absorption. A one-plus-one-dimensional (1+1D) frequency domain propagation model is employed, including optical rectification, cascaded nonlinear interactions, self-phase modulation, second-harmonic generation, and pulse-front-tilt-related dispersion. The simulations reveal markedly different behavior in the two crystals. In ZnTe, the large effective nonlinear coefficient enables conversion efficiencies exceeding 1% while maintaining good THz pulse quality. In GaP, strong self-phase modulation and nonlinear pulse compression can enhance the predicted efficiency but at the cost of waveform distortion and increased damage risk. MV/cm-level THz electric fields are predicted in both materials; however, ZnTe provides a more favorable compromise between conversion efficiency and pulse quality. The results provide practical guidelines for optimizing CO₂-laser-driven semiconductor THz sources. Full article

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

Lethal foodborne illness outbreaks represent a critical intersection of public health surveillance, environmental health, and forensic toxicology. When acute gastrointestinal syndromes lead to sudden death, traditional postmortem investigation techniques are often hindered by tissue autolysis and the overgrowth of putrefactive microflora, which complicate conventional microbiological assays. This review establishes a comprehensive framework for chemical forensics by evaluating the utility of stable isotope analysis (SIA) as a supportive, probabilistic chemical proxy to complement traditional epidemiological investigations of postmortem food contamination sources. Following JBI scoping review guidelines and the PRISMA-ScR reporting framework, data from 42 peer-reviewed articles (2000–2026) were charted and synthesized to map natural isotopic variations (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H and δ³⁴S) across both forensic decedents and environmental reservoirs. The findings outline a structured, multi-tissue diagnostic cascade governed by biological metabolic turnover rates: unabsorbed gastric chyme provides a direct chemical match to contaminated source food items within a hyper-acute 0–6 h window; high-turnover visceral matrices (liver, blood plasma) shift to reflect acute exposure profiles within 1–7 days; and continuously fixed keratinized matrices (hair, nails) archive multi-month dietary and transcontinental transit histories. Furthermore, compound-specific isotope analysis (CSIA) of individual amino acids offers unprecedented structural resolution, utilizing the carbon discrimination metric (Δ¹³C_glu-phe) to differentiate pristine agricultural signatures from endogenous metabolic distortions while biochemically verifying pre-mortem physiological stress and hyper-catabolic muscle wasting. Taphonomic thresholds were explicitly defined, establishing that bulk visceral soft tissues remain isotopically stable (±0.3‰) for up to 48 h at room temperature (~21 °C) before microbially induced nitrogen enrichment (δ¹⁵N > +2.8‰) alters native profiles, whereas hair and nail keratin maintain absolute isotopic stability for over 180 days postmortem. When pristine multi-isotope signatures are coupled with mandatory chloroform–methanol lipid extraction and processed through spatial Bayesian assignment models, geographic provenance tracking via environmental isoscapes achieves a predictive accuracy of 97%. This review introduces a standardized environmental health protocol designed to harmonize field environmental sampling with medical autopsies. This protocol provides a legally robust strategy for investigating unresolved lethal foodborne illness case-outbreaks, particularly those involving pediatric mortalities linked to the consumption of counterfeit or fraudulent food products in low- and middle-income countries. Furthermore, it aims to strengthen national and municipal legal frameworks and international biosecurity enforcement. Full article

Refractory high-entropy and compositionally complex alloys are routinely compared by nominal composition and as-processed phase, yet processing changes the material that is actually tested. This is a critical, mechanism-led narrative review rather than a systematic review; the databases, complete search strings, screening sequence, inclusion and exclusion criteria, and evidence-grading rubric are reported so that coverage and selection bias can be assessed independently. This review synthesizes 186 publications around the NbTaV compositional core and compares alloys through directly measurable features of the processed state: interstitial content, local chemical order, grain-boundary chemistry, defect and grain architecture, phase morphology, compositional gradients and surfaces. Every source is assigned to a compositional tier and graded along four evidence axes: 96 of the 186 sources report Nb–Ta–V-containing states (Tier I), 58 are body-centered cubic refractory comparators (Tier II) and 32 are transferred-mechanism analogues (Tier III), and only 14 Tier I sources supply direct tensile, fracture or tensile-creep measurements. This asymmetry, rather than any disagreement between compositions, is the field’s binding evidence constraint. Direct tensile, fracture, and creep measurements are kept separate from compression, hardness, calculation, and screening evidence. This separation reconciles observations that otherwise appear to conflict: oxygen can strengthen or embrittle; lattice distortion can raise strength while lowering dislocation mobility; local order can harden the alloy, redirect defects or precede decomposition; and second phases help only within morphology- and service-specific compatibility windows. The strongest tensile behavior is obtained when mobile plasticity carriers are preserved, and interstitials, interfaces and phase continuity are simultaneously controlled. High-temperature, environmental, and irradiation performance depend additionally on the transition from the as-manufactured condition to the state that evolves during service. Quantitative matching tolerances for the convergent-state falsification test, service-condition-specific validation hierarchies, ordinal scoring rules for the phase-compatibility map, and a source-level audit of every quantitatively compared value are provided so that the framework can be tested and the synthesis independently checked. Full article

Agricultural subsidy policies have increasingly shifted from production-oriented support toward incentives that reward environmental stewardship and the provision of ecosystem services. Despite their rapid expansion, evidence regarding the environmental effectiveness, economic efficiency, market implications, and food-security consequences of these eco-centric agricultural subsidies remains fragmented across policy frameworks and regions. This review synthesizes current evidence on eco-centric agricultural subsidies by comparatively evaluating their environmental, economic, and policy outcomes across developed and developing economies. The review was conducted using a structured literature search following PRISMA-informed review procedures, drawing upon peer-reviewed articles, systematic reviews, policy evaluations, and international institutional reports retrieved from major scientific databases and policy sources. The evidence indicates that eco-centric subsidies generally improve biodiversity conservation, soil health, carbon sequestration, water quality, and reductions in chemical inputs when payments are appropriately targeted and supported by effective monitoring and institutional capacity. Performance-based and results-oriented payment schemes frequently demonstrate greater environmental additionality and cost-effectiveness than conventional practice-based payments; however, their broader implementation remains constrained by monitoring costs, verification requirements, administrative complexity, and regional institutional capacity. Economic outcomes are more heterogeneous, with benefits depending on program design, agroecological conditions, market structures, and farm characteristics. While these subsidies can enhance environmental returns on public investment, challenges including land-value capitalization, unequal benefit distribution, transaction costs, market distortions, and potential short-term productivity trade-offs remain important policy concerns. Evidence regarding food-security impacts is similarly context-dependent and varies across production systems and geographical regions. Overall, the review demonstrates that no single subsidy instrument is universally effective. Instead, the greatest environmental and economic benefits are achieved through integrated policy portfolios combining targeted incentives, outcome-based payments, robust monitoring systems, digital technologies, carbon-market integration, and equitable program design. The review also identifies important evidence gaps concerning developing-country experiences, long-term cost-effectiveness, and standardized evaluation frameworks, providing priorities for future research and policy development. Full article

3D geovisualisation is increasingly used to represent spatial phenomena in engaging and interactive ways. However, 3D thematic methods and their user-centred evaluation remain challenging due to issues such as view distortion, variable scale, and complex interactions. Previous research emphasises that effective 3D thematic design requires careful treatment of visual variables and interactive camera control to mitigate overlap and occlusion. Building on this foundation, we present 3D-Geo-Vis, a web-based application with open-source code. The application visualises air temperature using seven methods of 3D geovisualisation and supports real-time adjustment of method-specific visual variables through a dedicated side panel. We report a usability study with 54 participants that combines (i) eye-tracking (Tobii Pro Spark, 60 Hz), (ii) interaction logging using our MapLogger tool, and (iii) a post-test questionnaire including the User Experience Questionnaire (UEQ) and open-ended feedback. Participants completed a structured scenario comprising free exploration and targeted analytical tasks. Across all sessions, MapLogger captured 15,457 interactions. The success rate of fully completed tasks was generally high for tasks involving the search for a specific value or modification of interface parameters (Task 2: 87.0%; Task 3: 85.2%), while the voxel-based analytical task showed slightly lower completion (Task 4: 77.8%), reflecting higher cognitive and interaction demands. UEQ results indicate a slightly positive overall user experience, with speed-related items rated most negatively. Triangulating gaze behaviour, interaction logs, and subjective feedback reveals key usability issues (e.g., insufficient salience of method switching, attention concentration on the side panel, and performance limitations of voxel rendering) and yields concrete recommendations for improving onboarding, feedback, and control discoverability in interactive 3D visualisation environments. These findings and resulting recommendations informed the development of the revised 3D-Geo-Vis 2.0 application. Full article

Manufacturing tolerances and winding-layout variations can introduce phase-to-phase mismatches in stator resistance and leakage inductance. Under such unbalanced phase impedances, conventional field-oriented control (FOC), typically designed under balanced-parameter assumptions, may produce unequal phase currents, distorted airgap MMF, reduced efficiency, increased torque ripple, and undesired vibro-acoustic behavior. This paper investigates an open-end winding (OEW) induction machine (IM) drive, in which each phase is independently driven by an H-bridge inverter fed by the same DC source. To mitigate phase–current imbalance without parameter estimation, an RMS-based phase–current-balancing controller is proposed. The controller continuously calculates the RMS value of each phase current and adaptively scales the corresponding reference-phase voltage in a low-bandwidth outer loop, while preserving the classical FOC structure. The balancing law is derived directly from the phase-impedance imbalance model; convergence of the three coupled per-phase loops is proven via a Lyapunov argument, and stability of the cascaded structure is established through an analytical bandwidth-separation analysis shown to be robust to ±30% machine-parameter variation and across the 500–1500 rev/min speed range. Simulation and experimental results across multiple operating points demonstrate effective phase–current equalization. Full article

Three-dimensional inversion of airborne transient electromagnetic (ATEM) data is computationally demanding because it requires repeated forward simulations and sensitivity calculations. Previous Krylov-subspace studies have substantially improved the efficiency of three-dimensional transient electromagnetic forward modeling, while a recently developed model order reduction (MOR)-based sensitivity framework has enabled efficient three-dimensional ATEM inversion for an ideal step-off excitation. However, this MOR inversion framework is derived from a source-free step-off initial-value problem and cannot readily incorporate the time-dependent source associated with a realistic transmitter waveform. Directly introducing such a source term would substantially complicate the corresponding MOR sensitivity derivation and implementation. In this study, we extend the existing MOR inversion framework to arbitrary transmitter waveforms using a convolution-based transformation. The step-off responses and sensitivities are first computed within the rational-Krylov MOR framework and are then transformed to those corresponding to the actual transmitter waveform. Gaussian quadrature and interpolation operations are assembled into a precomputed full-waveform transformation matrix. Because this matrix is independent of the subsurface conductivity model, it can be reused for all receivers, model parameters, and inversion iterations, thereby avoiding repeated interpolation and convolution operations. Forward modeling tests for trapezoidal, half-sine, triangular, and VTEM-type waveforms and show that the proposed method accurately reproduces the full-waveform responses, with off-time relative errors generally below 5%. Synthetic inversions demonstrate that the step-off approximation may cause conductivity overestimation, boundary distortion, spurious anomalies, and degraded convergence for long turn-off waveforms, whereas the waveform-corrected MOR inversion provides more stable and reliable results. A field ATEM example further provides an initial field-scale assessment of the feasibility of the method, yielding a conductivity model consistent with the known fault-controlled geological setting. The field inversion requires approximately 4.96 h for 14 iterations. Full article

Single-source remote sensing image (RSI) cannot simultaneously meet high-spatial and high-temporal resolution requirements, failing to provide decision-makers with timely and accurate monitoring data. Spatiotemporal fusion (STF) of multi-source RSIs represents an efficient and convenient means of producing land-cover observations with high-temporal and high-spatial resolutions. However, current STF approaches still suffer from severe prediction distortion under abrupt changes, long-interval temporal variations, and land-cover type transitions, as well as poor robustness against disturbances in prior data. To address these challenges, a temporal-variation-resistant bidirectional convolution-Transformer generative adversarial network (TRB-GAN) for RSI STF, which comprises a temporal-variation-resistant bidirectional convolution-Transformer generator (TRBG) and a multiresolution input convolution-Transformer discriminator (MICTD), is devised to improve the robustness in predicting time-varying information and enhance STF capability. First, the TRBG designs a temporal-variation-resistant bidirectional encoder to capture prior information and arbitrary time-varying local–global features, enhancing prediction robustness and representation capability for time-varying information. Second, the TRBG designs a dual-guided triple-attention fusion decoder (DTAFD), incorporating dual-guided cross convolution-attention fusion and decision attention fusion. DTAFD dynamically calculates correlations among spectral, spatial, and time-varying information to aggregate heterogeneous features and adaptively performs stepwise weighting and integration, effectively mitigating the adverse impacts from heterogeneous imaging mechanisms and significant resolution gaps. Finally, MICTD and deep supervision enable adversarial learning of local–global structures and spectra across resolutions, providing feedback to the TRBG for producing finer images. Ablation and comparative experiments demonstrate the TRB-GAN achieves superior STF performance and stronger robustness to time-varying disturbances for the widely used CIA and LGC datasets. Full article

Multi-source urban indicator datasets are frequently used in clustering, machine learning, and regression analyses without prior statistical auditing, potentially distorting results through non-normality, multicollinearity, and cross-source measurement disagreement. This study applies an integrated five-stage pre-modeling diagnostic protocol, comprising coverage auditing, normality assessment, outlier detection, correlation analysis, and VIF screening, to a 71-variable dataset compiled from six international sources for 106 arid and semi-arid cities across nine world regions. The protocol evaluates coverage completeness, distributional normality, outlier structure, inter-variable correlation architecture, and multicollinearity severity. Results show that 89% of continuous variables are non-normal, 17 of 23 predictors exceed the conventional Variance Inflation Factor threshold of 10, and 2 major international sources exhibit near-zero agreement for electricity access (Pearson r = −0.02). Gulf Cooperation Council cities form a structurally coherent outlier cluster requiring robust scaling rather than exclusion. The study produces an eight-action priority matrix to guide downstream analytical decisions and provides a replicable protocol for multi-source urban sustainability datasets. Full article

Multimodal online reviews, which integrate textual content with user-uploaded images, have emerged as a significant information source for consumer decision-making and enterprise service enhancement in electronic commerce. Sarcastic reviews, characterized by positive textual statements that are contradicted by negative visual evidence, systematically distort sentiment analysis and subsequent evaluations. Most existing research on multimodal sarcasm detection is limited to binary classification. In contrast, many conventional review-based decision models permit cross-criterion trade-offs, allowing favorable performance on secondary attributes to obscure deficiencies in tolerance-sensitive attributes. To bridge the gap between misleading review content and reliable decision support, this study introduces an integrated three-module framework, the RDSF-MSD-SC. The first module, the Deep Feature Fusion Sarcasm Detection Model (DFF-SDM), combines RoBERTa-based contextual text analysis with CLIP-based cross-modal alignment to detect reviews in which linguistic and visual cues diverge. The second module, the Large Language Model-Driven Structured Semantic Correction Mechanism (LLM-SSCM), reinterprets identified sarcastic reviews and translates their implicit negative sentiment into structured evaluation units (problem dimension, problem severity, and correction confidence) that can be directly utilized by decision models. The third module, Non-Compensatory Decision-Making based on Contextual Tolerance and Mismatch Rate (NCDM-CTMR), aggregates the resulting severity matrix using context-specific tolerance vectors, penalizing breaches of tolerance rather than averaging them out. An empirical analysis of 8623 multimodal reviews from eight major U.S. passenger airlines demonstrates that DFF-SDM achieves an F1-score of 0.86, LLM-SSCM attains an expert-assessed correction quality score of 0.92, and NCDM-CTMR identifies Delta as the most context-appropriate airline with zero mismatch in a time-sensitive business travel scenario. Delta maintained the top ranking in 99.82% of the 20,000 Monte Carlo simulations conducted under a simplified mismatch formulation. Ablation analysis showed that the full pipeline recovered negative evidence from approximately 87% of the 2847 expert-labeled sarcastic reviews, a rate bounded by DFF-SDM’s recall of 0.87; the no-correction and no-detection variants recovered approximately 0%. Compared to the WSM, TOPSIS, and VIKOR baselines, the proposed framework yields a more threshold-sensitive differentiation among mid-tier alternatives. Full article

Various inverter applications such as electronic vehicles, renewable energy systems, and uninterrupted power supplies have been the motive behind the increasing focus on the DC-AC inverters field. Therefore, DC-AC inverters have been evolving with the recent topology being the Z-source inverters (ZSIs). Though the ZSI overcame most of the limitations faced by the previous topologies such as the Voltage-Source Inverters (VSIs) and Current-Source Inverters (CSIs), they had shortcomings such as the increase in switching devices’ voltage stress and hence the deterioration of power quality with the increase in the boost factor. As a result, several ZSI-based topologies have been proposed in the literature to further improve the performance of a ZSI. This paper also proposes a different Z-source inverter topology called the Shunt Capacitor-Boosted Z-Source Inverter (SCB-ZSI) which seeks to improve the boost factor and lower the voltage stress. This inverter strategically adds two shunt capacitors on the impedance network of a traditional Z-source inverter, hence the Shunt Capacitor-Boosted-ZSI. The SCB-ZSI was mathematically modeled and simulated on MATLAB Simulink R2024a version. The SCB-ZSI was found to have a high boost factor compared to the ZSI for the same input DC voltage and modulation index. The SCB-ZSI was also found to incur less switching voltage stress across the switching devices compared to the ZSI for the same input DC voltage and modulation index. The simulation test results showed that the selection of shunt capacitors of a ZSI at 1% of those of the original ZSI improves the boost factor by 56% and reduces the switch voltage stress ration by 40% on an SCB-ZSI for the same set of input parameters. Though the SCB-ZSI is one of the promising ZSI-based inverter topologies, more work still has to be done before they can be industrially applied, such as developing an algorithm to design the shunt capacitors. Full article