Search Results (379)

This paper addresses bearing-only three-dimensional target localization using three cooperative UAVs under observation inconsistency and degraded geometry. A weighted point-to-line least-squares localization model is established to fuse multiple line-of-sight (LOS) observations derived from image measurements, camera calibration, and UAV poses. To handle unreliable measurements without ground truth, a reliability assessment mechanism is developed by combining geometric stability indicators with observation consistency metrics, enabling weak geometry and abnormal observations to be identified online. Based on this assessment, an adaptive optimization framework is introduced to perform residual-driven adaptive weighting and configuration optimization, thereby suppressing unreliable LOS measurements and improving the conditioning of cooperative geometry. Simulation results under four representative scenarios show that the proposed method consistently improves localization accuracy and robustness. The mean localization error is reduced from 0.545 m to 0.260 m under abnormal observations, from 0.355 m to 0.081 m under degraded geometry, and from 0.711 m to 0.280 m when both effects occur simultaneously. Statistical evaluations including RMSE, standard deviation, maximum error, confidence intervals, and box-plot analysis further demonstrate that the proposed framework effectively reduces error dispersion and improves robustness. Full article

This study proposes a barrier-oriented application of conventional failure mode and effects analysis (FMEA) and fuzzy weighted geometric mean (FWGM)-based fuzzy-FMEA for laboratory-scale solvent-based electrospinning. The process was decomposed into 14 sequential steps, and one representative failure mode was defined for each step. Severity, occurrence, and detection were rated by a five-member expert panel, and hazard-type-specific weights were assigned to chemical-dominant, electrical-dominant, fire/static-dominant, and combined-dominant hazards. Conventional FMEA identified material review/approval, equipment setup, pre-start inspection, and response to abnormalities as the highest-risk steps (RPN = 60). FWGM-based fuzzy-FMEA re-ranked tied RPN groups and identified response to abnormalities and equipment setup as the joint highest-FRPN failure modes (FRPN = 79.35), followed by pre-start inspection (77.39) and material review/approval (75.89). Barrier-oriented interpretation revealed four dominant mechanisms: upstream information-based hazards, direct high-voltage access, pre-start combined hazards, and intervention under abnormal or residual-energy states. Scenario-based post-control analysis showed that grounded enclosures, interlocks, de-energize-discharge-verify procedures, pre-start checklists, and bonding/grounding measures reduced FRPN by 25.88–43.79% for prioritized failure modes. The proposed framework supports SOP development, equipment improvement, training prioritization, and laboratory risk-assessment documentation for solvent-based nanofiber manufacturing. Full article

The pervasive integration of artificial intelligence across e-commerce ecosystems has fundamentally transformed the competitive landscape, rendering systematic and reproducible platform evaluation frameworks an operational necessity rather than an academic exercise. Conventional multi-criteria decision analysis approaches for e-commerce evaluation remain structurally constrained by their dependency on human expert panels, which introduce recruitment costs, cognitive biases, limited reproducibility, and the practical infeasibility of assembling genuinely multidisciplinary panels spanning e-commerce strategy, machine learning engineering, and financial technology simultaneously. This study proposes a novel decision support framework that integrates Large Language Model (LLM) multi-agent simulation with the Best–Worst Method (BWM) to derive reproducible priority weights for AI-driven e-commerce platform evaluation within a rigorous business intelligence architecture. Twelve domain-differentiated LLM agents—organized into three expertise groups representing e-commerce management, AI and machine learning technology, and digital payment systems—were instantiated with structured system prompts encoding professional domain knowledge and deployed across three independent simulation rounds to perform BWM pairwise comparisons across a comprehensive six-dimensional, 30-sub-criterion evaluation hierarchy. Inter-agent consensus was synthesized through geometric mean aggregation, with consistency verification conducted via BWM’s xi* indicator and inter-round stability assessed through coefficient of variation analysis. Results reveal that Transaction Security and Trust achieves the highest dimension-level weight (w = 0.248), followed by AI Recommendation Effectiveness (w = 0.213), with Personal Data Protection (G = 0.0750), Recommendation Accuracy (G = 0.0607), and Transaction Transparency (G = 0.0549) emerging as the three highest globally ranked sub-criteria. The aggregated consistency indicator xi* = 0.062 confirms logical coherence of the multi-agent judgment consensus, and all dimension weights exhibit CV values below 2.8%, demonstrating exceptional inter-round stability. Spearman rank correlations among the three domain-expertise groups exceed 0.92, confirming strong inter-group convergence. Sensitivity analysis under perturbations of ±10% and ±20% demonstrates that the top-five priority indicators are structurally stable. This study establishes LLM multi-agent BWM simulation as a methodologically rigorous, institutionally accessible, and computationally reproducible alternative to traditional expert elicitation for complex platform evaluation tasks. Full article

Fish phenotyping plays an important role in growth evaluation, selective breeding, and precision aquaculture. Conventional phenotypic measurement methods are labor-intensive, time-consuming, and susceptible to observer variability. To improve measurement efficiency and reproducibility, this study proposes an automated fish phenotyping framework based on Transformer-enhanced instance segmentation. Specifically, a Mask2Former decoder was integrated into the Mask R-CNN architecture to improve boundary delineation and segmentation quality. Based on segmentation outputs, phenotypic parameters, including body length, body height, and projected area, were automatically extracted using PCA-assisted orientation estimation and geometric measurement. In addition, a standardized anatomical landmark annotation framework consisting of 12 reference points was introduced to support reproducible phenotypic description and future extensible morphometric analysis. Body weight was further estimated using polynomial regression based on extracted morphological traits. Experiments were conducted using images from three crucian carp varieties under controlled imaging conditions. The proposed framework achieved 92.7% mAP and 89.4% Boundary IoU, improving segmentation performance over the baseline model. Automated measurement yielded average relative errors of 2.16% for body length and 3.85% for body height, while weight prediction achieved an R² of 0.9479 and a mean relative error of 7.31%. These results demonstrate that Transformer-enhanced segmentation can support accurate and efficient automated phenotyping under standardized conditions and provide a foundation for future deployment in more complex aquaculture environments. Full article

Lung cancer is the deadliest cause of cancer-related deaths worldwide, and early and accurate detection is key to improving patient outcomes. IQ-OTH/NCCD CT scan images are used in this study to present an optimized computer-aided diagnosis (CAD) framework for lung cancer detection. In order to extract deep features and improve diagnostic accuracy, a weighted geometric mean (WGM) ensemble of pretrained convolutional neural networks (CNNs) called the LCD-VRD model—comprising VGG16, ResNet50V2, and DenseNet121—provides robust feature extraction and strong generalization capabilities for accurately classifying normal, benign, and malignant (cancerous) cases. To actively mitigate data imbalance and reduce model overfitting, real-time data augmentation alongside rigorous class weighting was implemented. The results show that, with 97.27% accuracy and a 97.24% F1-score, the WGM ensemble of these models performs exceptionally well. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was investigated on CT images to provide an exploratory qualitative visualization of the image regions associated with model predictions. While the proposed framework shows promise as an effective tool for automated lung cancer diagnosis, its validation is currently limited to the IQ-OTH/NCCD dataset. External dataset evaluation will be essential to fully establish robustness and clinical applicability. Full article

Long-term intensive farming has degraded the structural stability of black soil in Northeast China. This study evaluated the effects of fermentation-derived materials and fungal-derived chitin on water-stable aggregates and microbial functional potential in this soil. Four treatments were established: sterile water control (CK), uninoculated fermentation broth substrate (W), live Trichoderma asperellum fermentation broth (P), and cell-free fermentation filtrate (F). Aggregate stability was monitored during a 60-day incubation, and metagenomic sequencing was performed on the most responsive 0.5–0.25 mm dry-sieved fraction. An exogenous chitin addition experiment was also conducted to evaluate the potential contribution of fungal cell-wall-derived chitin to aggregate stabilisation. The W, P, and F treatments increased the proportion of water-stable aggregates >0.25 mm, mean weight diameter, and geometric mean diameter, while decreasing fractal dimension. Among the treatments, the uninoculated fermentation broth substrate showed the strongest effect, particularly in the 0.5–0.25 mm dry-sieved fraction. Metagenomic analysis showed that the uninoculated fermentation broth substrate altered microbial community composition, changed the relative abundances of taxa such as Sphingomonas sediminicola, Priestia megaterium, and Trichoderma asperellum, and increased the relative abundance of carbohydrate-active enzyme-related genes, including those encoding glycosyltransferases, carbohydrate esterases, and glycoside hydrolases. Chitin addition also improved aggregate stability and altered microbial community structure. These findings suggest that the uninoculated fermentation broth substrate and fungal-derived chitin improved black soil aggregate stability, potentially through shifts in microbial community composition and carbohydrate-related functional potential. This study provides a scientific basis for using fermentation-derived materials to improve the structure of degraded black soil. Full article

In impact testing, evaluating multiple-impact signals is critical for verifying whether a test setup can reproduce penetration response signals and ensure reliable results. To overcome the limitations of traditional methods, including incomplete indicator coverage, subjective weighting, and poor consistency, this study proposes a fuzzy comprehensive evaluation (FCE) framework based on time–frequency features and combined weighting. Using multi-layer penetration response signals as the matching target, a multidimensional indicator system covering time-domain features, frequency-domain features, and signal quality and stability is established. A combined weighting method integrating AHP, EWM, and CRITIC is then developed, and subjective and objective weights are fused using the geometric mean method. A fuzzy comprehensive evaluation model is used to quantify the matching degrees of multiple sets of multiple-impact signals, and robustness is verified through weight consistency tests and sensitivity analysis. The results show that the evaluated signal sets are rated “Excellent”. Under reasonable weight combinations, the probability of obtaining an “Excellent” result reaches 99.94%, and the maximum variation caused by a ±10% perturbation in a single indicator weight is only 0.0087. The proposed framework provides a practical tool for evaluating multi-layer penetration response simulations and can be extended to other complex dynamic signal-matching problems. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Global food security and climate mitigation goals are placing unprecedented demands on agricultural systems to simultaneously improve soil productivity and reduce carbon emissions. Spent mushroom substrate (SMS), the mushroom industry’s principal waste stream, offers considerable recycling potential, yet its influence on dissolved organic carbon (DOC) chemistry and soil aggregate stability remains unclear. We tested four SMS return regimes on a medium-textured fluvo-aquic soil: CK, 0 t·ha⁻¹; ORS, 22.5 t/ha; ERS, 22.5 t/ha; and SRS, 45 t/ha in total, with 22.5 t/ha applied per SMS return event. It was found that SMS improved soil structural stability across all regimes, with SRS delivering the strongest effects. Compared with CK, SRS raised the proportions of >2 mm and 0.25–2 mm aggregates by 31.62% and 33.42%, while the mean weight diameter (MWD) and geometric mean diameter (GMD) increased by 23.25% and 22.68%. SMS also elevated aromatic carbon abundance, DOC concentration, UV254, and SUVA254. Fluorescence EEM-PARAFAC resolved DOC into three component: namely, two humic-like and one protein-like, and SMS expanded the relative contribution of the humic-like C1 fraction. Overall, under the tested fluvo-aquic soil and wheat–maize rotation conditions, SMS return was associated with changes in DOC composition, higher aggregate stability, and greater aggregate-associated carbon accumulation. These findings suggest that SMS return may be a promising strategy for improving soil structure and recycling agricultural waste under similar field conditions, but its broader applicability requires further validation. Full article

Point cloud classification and segmentation are key technologies for 3D perception and scene understanding, whose accuracy and efficiency directly affect the performance of high-level applications such as 3D modeling, object recognition, and intelligent interaction. Existing methods still exhibit obvious deficiencies in local feature representation, computational efficiency, and scene applicability. To address these issues, this paper proposes a lightweight point cloud classification and segmentation network based on adaptive feature extraction, referred to as AFE-PointNet. Firstly, an element-wise weighting set abstraction module based on the Hadamard product is designed. It leverages geometric topology learning to achieve adaptive feature enhancement, effectively improving the representation capability of local geometric structures. Meanwhile, a cascaded structure of feature aggregation and an inverted residual multi-layer perceptron (InvResMLP) is adopted for deep feature mining to achieve high-accuracy and high-efficiency point cloud classification and segmentation. Experimental results show that AFE-PointNet achieves an overall accuracy (OA) of 93.6% on the ModelNet40 dataset and 84.5% on the ScanObjectNN dataset, and attains a class mean intersection over union (Cls.mIoU) of 83.6% on the ShapeNetPart part segmentation dataset, yielding significant performance improvements over the PointNet++ model. The proposed adaptive feature enhancement and lightweight deep mining strategies effectively improve point cloud representation capability, providing a high-precision and efficient solution for 3D vision tasks. Full article

Existing Cyber Resilience Assessment Guidelines, including those of the Bank of Korea (BoK), focus on governance-oriented compliance and lack quantitative criteria for measuring the operational effectiveness of security technologies—a Policy–Technology Gap also common in general enterprise settings. To address this gap, this study proposes D3-CREF, a technology-centric cyber resilience evaluation framework that maps the MITRE D3FEND taxonomy to financial security domains and introduces a Normalized Resilience Index (NRI) aggregating four dimensions—Coverage, Maturity, Automation, and Timeliness—via a closed-form weighted geometric mean with AHP-elicited weights (consistency ratio CR = 0.04). All NRI indicators are anchored to MITRE ATT&CK techniques and exemplar CVE entries, enabling threat-informed measurement. The framework was validated through a three-round Delphi study with 50 experts (Kendall’s W = 0.78, p < 0.001; Cronbach’s α = 0.89; CVR 0.68–0.92) and a Cyber Range-based simulation. For three institutions with identical BoK scores (92/100), NRI yielded discriminative values of 0.83, 0.44, and 0.09 (CV = 0.68 vs. 0.00 for the baseline), confirming a shift from compliance-based to performance-driven assessment. Full article

Cascade hydropower alters river hydrological regimes and threatens aquatic ecosystems, calling for robust ecological risk assessment (ERA). Conventional assessments often rigidly apply the full five-layer Driving Force–Pressure–State–Impact–Response framework, leading to indicator redundancy and unbalanced weighting. Single weighting methods also fail to reconcile expert judgment with data variability. To address these issues, we developed a three-layer (target–element–indicator) evaluation system embedding DPSIR logic without its full structure, focusing on hydrological regime, water environmental quality, and aquatic ecology with ten indicators. We used an improved group AHP-CRITIC coupling method for weighting: AHP aggregates expert judgments via geometric mean, and CRITIC integrates data variability and inter-indicator conflict. Multi-attribute utility theory normalized indicators into a unified security index, applied to four cascade stations in the middle Jinsha River using 66-year (1953–2018) hydrological and seven-year (2013–2019) in situ monitoring data. The evaluation obtained a comprehensive index of 0.71 to 0.74, which is generally safe. River connectivity loss was the primary limiting factor. Hydrological alteration was mild overall with a value of 0.139, while extreme flow decline rate variation reached a high level of 0.83. Weekly regulated stations achieved over 97% ecological flow guarantee, which is much higher than daily regulated stations. This streamlined framework improves interpretability for cascade basins and supports sustainable watershed management. Full article

Accurate body-weight measurement is important for precision pig farming, but conventional weighing methods are labor-intensive and may disturb normal animal activity. Although three-dimensional sensing systems can provide reliable geometric information, their deployment cost limits large-scale application in commercial farms. This study proposes a non-contact pig weight estimation framework based on standard RGB images. The framework combines SAM 2 foreground extraction with a transformer-based dorsal segmentation network to obtain stable body contours under complex farm conditions. Cross-covariance attention and local patch interaction modules are introduced to preserve both global body structure and local boundary details during segmentation. A hybrid loss function combining focal loss and label-distribution-aware margin loss is further adopted to address foreground-background imbalance. After segmentation, 17 morphometric features are extracted from the dorsal region and used for weight prediction with XGBoost regression. Experiments were conducted on the public PIGRGB-Weight dataset containing 12,476 RGB images from 124 pigs. The proposed method achieved a mean absolute error of 2.983 kg and an $R^2$ value of 0.9891. Compared with a DeepLabV3+-based baseline under the same regression protocol, the proposed framework reduced the prediction error by 24.1%. The results indicate that improving dorsal segmentation quality can substantially enhance the stability of morphometric feature extraction from low-cost RGB images. Full article

Closed-set heterogeneous domain adaptation (HDA) for Internet of Things (IoT) intrusion detection aims to transfer detection capabilities across environments that differ in devices, telemetry, feature schemas, attack implementations, label taxonomies, and target supervision availability. Although recent HDA methods report strong performance, their deployment meaning is often unclear because improvements over a weak source-only baseline do not show how much target supervision headroom has been recovered or whether adaptation is preferable to direct target-side labelling under the same budget. This paper presents a controlled, anchor-based benchmark for closed-set HDA in IoT intrusion detection. Edge-IIoTset is used as the main fixed target dataset, with transfer from CICIDS2017, UNSW-NB15, CICIDS2017 + UNSW-NB15, and CICIDS2017 + NSL-KDD under single-source and multi-source settings. The benchmark defines fixed resolved contexts, Intersection and Union representation contracts, a five-class closed-set label contract, leakage-safe preprocessing, and an anchor ladder consisting of source-only, correlation alignment (CORAL), matched-budget target-only, and oracle target-only references. Geometric Graph Alignment (GGA) and the Joint Semantic Transfer Network (JSTN) are evaluated as the primary selected native single-source semi-supervised HDA (SS-HDA) and multi-source semi-supervised HDA (MS-HDA) exemplars, while the Prototype-Matching Graph Network (PMGN) and Conditional Weighting Adversarial Network (CWAN) provide $1:10$ method coverage checks. Each method–context–ratio configuration is evaluated across twenty fixed seeds, and DA-versus-target-only differences are tested using paired seed-level statistical evidence. A compact second-target confirmatory experiment using ToN-IoT assesses whether the qualitative headroom recovery and same-budget deployment patterns remain visible under a different IoT/IIoT target. The results show that primary native HDA can recover substantial source-only-to-oracle headroom, but not uniformly. At the $1:10$ labelled target ratio, GGA recovers $0.633$–$0.835$ of the available headroom across C1–C4, while JSTN recovers $0.776$–$0.897$ in the contemporary-source MS-HDA family and $0.872$–$0.926$ in the mixed-vintage family. Same-budget comparisons show that DA is deployment-competitive only in some contexts; in others, direct target-side supervised learning is stronger. The benchmark therefore shows that closed-set HDA should be evaluated as target-conditioned, context-resolved evidence rather than as a pooled method leaderboard. Full article

The rapid advancement of payment technologies and potential disintermediation pressure make it important to monitor how actively commercial banks participate in payment and settlement systems. This study conceptualizes bank participation as a multidimensional construct and develops a Bank Payment Participation Index (BPPI or ANR BPPI) using publicly available Reserve Bank of India data for 2011–2012 to 2022–2023. BPPI integrates Financial Capacity (FC), Technological Readiness (TR), Payment Performance (PI), and a PPI-based Technological Advancement/Disintermediation proxy (TAD). TAD, measured as the share of PPI transactions in total payment volumes, enters the index as (1−TAD) because rising non-bank payment penetration reduces banks’ intermediation share; a higher TAD represents a structural drag on bank payment participation, and (1−TAD) converts this drag into a participation-compatible scale. The index applies min–max normalisation, equal-weighted sub-index aggregation, and geometric mean composition with lagged input dimensions. Computations show that the four-component BPPI rises from 230.794 in 2013–2014 to 797.453 in 2022–2023, indicating a strong long-run increase in banking-system participation. The BPPI remains strongly associated with GDP over the 2013–2014 to 2022–2023 sample, with R Square = 0.906 and adjusted R Square = 0.894. Diagnostic tests indicate that the validation is best interpreted as association-based evidence rather than causal proof. The BPPI is proposed as a decomposable monitoring and diagnostic framework that equips regulators and banks to track participation trends and detect structural vulnerabilities over time, subject to future refinement using fixed policy goalposts, bank-level data, and CBDC-specific transaction data. In its present form, the BPPI constitutes a model-stage prototype framework subject to future operationalisation with fixed expert-determined benchmarks and bank-level disaggregated data. Full article