Search Results (1,580)

Accurate reliability assessment of wind turbine gears is important for reducing maintenance costs and improving the sustainability of wind turbine gearbox operation. This study proposes a reliability modeling framework centered on shock–degradation dependence and a time-varying degradation failure threshold. First, a mapping relationship between shock signals and stress is established. Subsequently, a state-dependent shock-induced degradation increment model is introduced to describe the bidirectional mechanism by which shocks accelerate degradation and degradation states amplify shock-induced damage. Furthermore, a time-varying degradation failure threshold model is developed to characterize the dynamic attenuation of the gear load-bearing boundary during service. On this basis, a hybrid Copula function is employed to establish the joint reliability model. A numerical case study was conducted on the sun gear of a 2 MW wind turbine gearbox. The results show that, under the specified numerical operating conditions and model parameter settings, the proposed model under the fixed-threshold condition predicts a relative increase of approximately 6.1% in failure probability compared with the model assuming independent shock and degradation processes. When the time-varying degradation failure threshold is considered, the failure probability shows a relative increase of approximately 30% compared with that under the fixed-threshold condition. The proposed model can effectively characterize the failure evolution of wind turbine gears under complex loading conditions and provide theoretical support for predictive maintenance. Full article

Most bilateral actions in daily life are asymmetric: one limb sustains a steady postural load while the other performs a continuously adjusted force task. Whether the influence of such a sustained contraction on the contralateral limb is a fixed property of bilateral coordination or a strategy that higher centres rescale with task demand is unknown. In this exploratory study, we examined motor unit recruitment and common neural drive in the biceps brachii during asymmetric bilateral versus unilateral isometric force tracking, tested whether the interlimb effect depends on force level and rate of force development. Fifteen healthy adults tracked trapezoidal force profiles with the right biceps brachii at two force levels (10% and 30% of maximal voluntary contraction) and three rates of force development (5%, 10%, and 20% per second), performed unilaterally and bilaterally while the left biceps brachii held a constant 20% contraction. High-density surface electromyography was decomposed into individual motor units. Relative to unilateral tracking, the bilateral condition tended to raise recruitment threshold, lowered steady-state discharge rate, increased common-input variability, and reduced force stability, whereas action-potential amplitude and the variance explained by the common input (41–45%) were unchanged. No force- or rate-dependent interactions emerged. Although consistent with a spinal constraint, these findings cannot exclude cortical strategies and should be interpreted cautiously given the small sample. Full article

Fixed delays and single-state thresholds are difficult to adapt to dependent event sequences, short authorization windows, and multi-parameter constraints in rocket-sled ejection tests. This study presents PMT-TC, a programmable multi-event triggering and timing coordination system. It provides multi-source state validation, mission-table-driven closed-window and Boolean-predicate evaluation, predecessor-gated one-shot output, and time-correlated event recording. Compared with fixed-delay or single-speed triggering, PMT-TC changes event variables and logic between missions while preserving order, data-validity and safety gates, and records for post-test verification. An onboard–ground cooperative architecture links pre-test configuration, autonomous onboard execution, buffered recording, and post-test reconstruction on a common mission-relative time base. Validation comprised vibration and shock tests, navigation and integration tests, development-stage strategy comparisons, and two mission-scale tests. Across the two tested configurations, all six $E_1$–$E_3$ events were issued in order. In mission B, Hall-effect and BDS integer speed fields agreed at six time-aligned samples across three 500 ms windows; $E_3$ was authorized at 6.92762240 s and 211 m/s by an AND predicate combining dual-source equality, speed, and time conditions. These results support programmable mission execution and traceable reconstruction within the tested configurations. Full article

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development. Full article

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

Economic growth and the effectiveness of official development assistance (ODA) have long been a topic of debate, with the current concern being that there are high levels of aid dependency in Sub-Saharan Africa, despite low levels of structural transformation. The study analyses the growth impacts of multidimensional ODA dependency, the moderating effect of institutional quality and the possible thresholds of aid dependency in an unbalanced panel of 48 Sub-Saharan African countries over the period 2000–2025. Two-way fixed effects, System Generalized Method of Moments, Difference Generalized Method of Moments, and Common Correlated Effects Pooled are used to analyze a principal component-based ODA Dependency Index, and least-squares threshold regression is used to investigate multiple thresholds. These results show that there is a strong negative correlation between aid dependency and economic growth, while institutional quality is consistently positive to growth, but does not significantly moderate the aid–growth relationship. There are no stable thresholds for aid dependency as a function of the specification of the index. The findings highlight the need to improve domestic resource mobilization, productive investment and institutional capacity to decrease reliance on structural aid and ensure sustainable economic growth in the long term. Full article

We set out to fix a “contamination” problem in reconstruction-based Temporal Convolutional Network VAEs (TCN-VAEs) for cross-dataset IoT flow anomaly detection: when attack flows share an encoder window with benign flows, the shared latent code is allegedly distorted, inflating benign reconstruction error and producing false positive rates (FPRs) of 22–65% despite an ROC-AUC above 0.93. Our proposed fix, TCN-Pred, excludes the target flow from the encoder and scores it by next-flow prediction error, reducing FPR to 0.65–13%. We subjected this causal explanation to a battery of controlled ablations, holding architecture, decoder, loss, and thresholding fixed while varying one factor at a time. Each one falsified the original hypothesis: target inclusion/masking changes FPR by at most 0.001; context shuffling/reversing/zeroing changes it by at most 0.003; a context-blind constant-output predictor matches TCN-Pred’s FPR and F1 to three decimal places on all three datasets. The actual cause, confirmed on the original trained models with no retraining, is a scoring-granularity mismatch: the TCN-VAE threshold is calibrated from per-window errors averaged over 20 flows but applied to per-flow errors at evaluation (standard deviation $\approx \sqrt{20}\times$ higher, measured ratio 4.46 against a predicted 4.47). Recalibrating the identical model at matching granularity drops FPR from 22.7/47.6/64.6% to 0.65/5.0/12.5% on BoT-IoT, IoT-23 and ToN-IoT, closing 89–97% of the reported FPR gap without changing a single model weight. We report this diagnostic chain, together with an attack-prevalence sensitivity analysis, sample-disjoint calibration, normality diagnostics, and label-free and redundancy-aware (mRMR) feature-selection benchmarks, as a methodology other work should apply before attributing fixed-threshold performance to architecture. The pipeline is supervised source-domain feature selection followed by benign-only detector training, not fully unsupervised, a distinction we quantify later in the paper. Investigating dataset representativeness, we found that all three provided files reduce to only ≈6000 genuinely distinct flows via an undocumented row-duplication procedure, causing 97.8% BoT-IoT train/test near-duplicate overlap; a leakage-free re-evaluation changes FPR by only 0.23 percentage points. We also found that the TLS-metadata columns are already transformed upstream of every available artefact, so the proportion of genuinely TLS-encrypted flows cannot be recovered, and we soften the paper’s encrypted-traffic framing accordingly. Full article

Emerging industrial agglomeration drives green transformation through knowledge spillovers and economies of scale. However, its effects exhibit pronounced nonlinearity and heterogeneity, shaped by spatial externalities and development stages. This paper investigates 19 Chinese urban agglomerations over the period 2014 to 2023. Kernel density estimation based on enterprise-level Point-of-Interest (POI) data is used to characterize spatial agglomeration patterns across eight emerging sectors. A two-stage dynamic network super-efficiency SBM model decomposes Green Transition Efficiency (GTE) into resource utilization and pollution control sub-stages. An XGBoost-SHAP-GEO analytical framework, combined with partial dependence analysis, then identifies nonlinear driving mechanisms. The main findings are as follows: First, emerging industrial agglomeration intensifies and polarizes toward the eastern coast, whereas GTE displays a “high-west, low-east” pattern. This produces a significant spatial mismatch, rooted in the near-saturation of environmental carrying capacity in eastern regions, where congestion effects exceed knowledge spillover dividends. Second, geographic characteristics constitute the primary factor shaping GTE and operate through nonlinear interactions with industrial agglomeration and R&D investment. Notably, their moderation direction is reversible, suggesting that geographic endowments should be understood as “conditional assets” rather than fixed advantages. Third, nonlinear patterns across sectors are highly heterogeneous. The bio-industry is the only sector to achieve a J-shaped positive breakthrough. Information technology and new materials exhibit persistent inhibition, while related services display an extremely narrow threshold window with the deepest negative reversal. Thus, “moderate agglomeration” is a multidimensional concept that shifts dynamically with industry type and regional endowment. Fourth, driving mechanisms display stage-dependent evolution. The incubation stage relies on natural endowments and basic industrial pull, with the green bottleneck residing in resource utilization efficiency. The growth stage faces multiple tensions from coexisting positive and negative effects. The optimization stage shifts toward R&D innovation and industrial greening, marking a qualitative transformation from MAR externalities to Jacobs externalities. In addition, the non-significant linear coefficient in the 2SLS instrumental variable test is consistent with the inverted U-shaped nonlinear finding, further validating the necessity of a nonlinear analytical framework. These findings provide differentiated governance evidence for balancing industrial agglomeration with green sustainable development. Full article

Akerlof’s classical theory of the “Market for Lemons,” which conceptualizes adverse selection driven by information asymmetry, established the foundation of information economics. While the traditional model assumes static equilibria among a limited number of agents, analyzing its behavioral dynamics within large-scale, complex network environments remains a highly relevant task in computational social science. This study extends the classical lemon market model into a nonlinear dynamical system on adaptive networks. We mathematically elucidate macro-level social phase transitions—specifically structural exclusion such as scapegoating and collective ostracism—induced by computational cognitive limits, and evaluate optimal intervention strategies to mitigate these systemic failures. Multi-agent simulations utilizing large-scale tensor operations demonstrate that autonomous edge rewiring under incomplete information does not merely result in the uniform displacement of high-quality goods as predicted by static theory. Instead, the network self-organizes into an irreversible structural division: a core group of influential agents monopolizes high-quality information, while marginalized agents are isolated into a peripheral “lemon echo chamber” where only low-quality information circulates. To address this structural pathology under a resource constraint limiting intervention to 10% of the total agents, we evaluated two distinct approaches. The results indicate that providing informational support to influential hubs functions as a trap that exacerbates systemic inequality, superficially elevating the overall market evaluation but permanently fixing the exclusion gap. Conversely, the forced maintenance and protection of “weak ties” bridging disconnected clusters constitutes the mathematically optimal solution to dissolve fragmentation, effectively eliminating the price gap and facilitating social inclusion. Furthermore, this study demonstrates that the mechanism of social exclusion exhibits strong hysteresis effects. A distinct tipping point governs the progression toward a fragmented lemon echo chamber. Interventions implemented after crossing this critical threshold fail to restore the system to its baseline state despite identical resource expenditure, confirming the presence of an irreversible phase transition. These findings establish that the collapse dynamics outlined in the classical lemon market serve as a generalized model for explaining contemporary collective ostracism driven by information cascades. Consequently, the analysis highlights the necessity of early intervention prior to critical thresholds and the systemic preservation of structural bypasses rather than post-hoc remediation. Full article

Background/Objectives: Colonoscopy withdrawal time is an established quality indicator, but ongoing debate exists regarding whether its relationship with adenoma detection follows a fixed threshold or a continuous dose–response pattern. Methods: We retrospectively analyzed 31,780 colonoscopies from a multicenter endoscopy database to evaluate the relationship between withdrawal duration and lesion detection. Withdrawal time was assessed as both a continuous and categorical variable, and adenoma detection rate (ADR) and polyp detection rate (PDR) were examined across quartiles and clinically relevant intervals. Results: ADR increased progressively from 7.72% in the shortest withdrawal-time quartile to 36.47% in the longest quartile, while PDR increased from 21.22% to 67.86% (both p for trend <0.001). In the multivariable analysis adjusted for age, sex, and bowel-preparation quality, each additional minute of withdrawal time was independently associated with higher odds of adenoma detection (adjusted OR 1.14, 95% CI 1.13–1.15; p < 0.001). Although the confidence interval was narrow, reflecting the large sample size, the effect was clinically appreciable: model-predicted adenoma detection increased from 13.6% at six minutes to 21.0% at eight minutes and 28.2% at ten minutes, corresponding to approximately seven and fifteen additional adenoma-positive examinations per hundred procedures, respectively. The association remained consistent across age and sex strata. Receiver operating characteristic analysis showed moderate discrimination (area under the curve 0.701), with a Youden-optimal region of approximately 7 to 8 min. Because recorded withdrawal time incorporates interventional time, the principal analysis of inspection effort was conducted at the level of the endoscopist, using withdrawal time measured exclusively in the 14,611 examinations in which no polyp was detected and no tissue was sampled. Among 107 endoscopists contributing 26,793 procedures, inspection time was associated with adenoma detection (Spearman ρ = 0.46; p < 0.001), corresponding to an absolute increase of approximately 1.5 percentage points per additional minute, with attenuation of the gradient beyond approximately seven minutes. Conclusions: The procedure-level findings above describe the association with recorded withdrawal duration as captured in routine practice and are reported as supportive rather than as estimates of inspection effort. Together these analyses indicate that inspection time is associated with adenoma detection in a graded manner extending beyond the historical 6 min benchmark. Full article

Background: Robotic liver resections (RLRs) are rapidly expanding, yet the association between the learning curve, procedural complexity, and outcomes at intermediate-volume centers remains poorly defined. We evaluated the learning-curve trajectory and perioperative outcomes of a single surgeon’s initial RLR experience according to procedural complexity (IWATE difficulty score). Methods: We retrospectively analyzed 58 consecutive RLRs at an intermediate-volume center. We stratified outcomes by IWATE difficulty category and chronological tertile (early/middle/late). Textbook outcomes (TOs) and a composite failure endpoint (conversion, major complications [Clavien–Dindo ≥ IIIa] and 90-day mortality) were also assessed. Learning-curve behavior was examined with CUSUM and risk-adjusted CUSUM (RA-CUSUM) analyses. Results: Fifty-four procedures (93.1%) were minor resections and four were major hepatectomies; two were classified as IWATE Expert difficulty. Median estimated blood loss was 100 mL; conversion occurred in 10.3%, overall morbidity in 10.3% and severe complications (Clavien–Dindo ≥ IIIa) in 3.4%, with no mortalities within 90 days. TOs were achieved in 69.0% using the Delphi (TOLS) definition and in 46.6% using a length-of-stay-extended definition. Operative time and length of stay increased significantly with IWATE difficulty (p < 0.001 and p = 0.030), as did the composite failure endpoint (p = 0.039), whereas blood loss and complications did not. TOs decreased with difficulty under the extended definition (p = 0.016). No outcome except estimated blood loss differed across chronological tertiles (p = 0.032). Operative time was associated with the IWATE score (26.3 min per point; R² = 0.32) but not with case order (p = 0.93). CUSUM and RA-CUSUM curves showed a non-linear, multiphase pattern without an identifiable inflection point, with extremes attributable to individual high-complexity procedures. Conclusions: In this exploratory single-surgeon series, consisting predominantly of minor resections, no case-number threshold could be identified, and perioperative outcomes were more closely associated with procedural complexity than with chronological experience, supporting a complexity-adjusted interpretation of RLR outcomes rather than a fixed case-number learning threshold. Full article

This paper investigates distributed optimization-assisted output consensus for nonlinear multi-agent systems with mutually incommensurate Caputo orders, unavailable velocity-like states, bounded disturbances, measurement noise, and actuator saturation. A mixed-power exact penalty flow generates practical optimal references from local costs and intermittent neighbor broadcasts. The penalty gain and a smoothing bias bound are determined from a public interval, topology information, and certified local gradient data without prior knowledge of the aggregate optimizer. An autonomous decaying threshold provides event-triggered communication, an initial condition-independent fixed-time practical certificate for the integer-order optimizer, and exclusion of finite-time event accumulation. The physical layer is analyzed with established Caputo quadratic inequalities and agentwise Mittag–Leffler comparison. Fractional reference and command filters, a composite observer, and two-gain anti-saturation compensation form the output feedback controller, while the physical result is formulated as a finite-horizon regional verification certificate. Numerical studies include same-model and communication budget comparisons, a recent method-inspired optimizer benchmark, certificate tightening, and robustness tests for initialization, the fractional order, measurement noise, and the integration step size. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

Against China’s dual carbon peaking and carbon neutrality strategic goals, nationwide digital transformation brings both carbon abatement dividends and potential energy rebound risks, and its full low-carbon potential is constrained by local industrial foundations and regional economic development stages. Most existing studies merely treat industrial upgrading as an intermediate transmission channel, with little discussion of its moderating influence. Moreover, few threshold analyses take the comprehensive level of regional economic development as the core threshold variable to capture the boundary conditions of digital decarbonization effects. Based on balanced panel data covering 30 provincial-level regions of China from 2011 to 2023, this paper constructs a multi-dimensional digital economy index via the entropy weight method. Prior to formal regression, we conduct Pearson correlation analysis and mean-centered VIF multicollinearity diagnostics to avoid biased estimation. Two-way fixed-effects regression, moderation tests, Bootstrap-based regional heterogeneity comparison and Hansen’s single threshold model are adopted for empirical analysis. The results show that digital economy development significantly curbs carbon emission intensity; a one-standard-deviation increase in the digital economy composite index is associated with an approximately 9.7% decline in carbon emission intensity. The mean-centered interaction term DIG × UIS is significantly negative at the 1% level, proving that service-oriented industrial upgrading strengthens the carbon reduction effect of digitalization. The mitigation effect displays distinct spatial divergence: the estimated coefficient equals −2.638 for eastern provinces, −3.585 for central regions and −1.700 for western areas. Bootstrap inter-group coefficient tests confirm statistically significant gaps between east–west and central–western subgroups. Threshold regression identifies a single threshold of logarithmic per capita GDP at 11.94. After crossing this economic development threshold, the inhibitory coefficient of the digital economy rises markedly from −0.844 to −1.473. This study enriches the theoretical system of digital low-carbon transition by jointly uncovering the moderating role of industrial upgrading and the stage threshold constraint of economic development and offers differentiated digital low-carbon policy guidance for provincial governments. Full article

Geochemical gold datasets from shear zone-hosted systems frequently contain extreme outliers that distort statistical structure, shift population boundaries, and undermine the reliability of concentration–area (C–A) fractal modeling. Conventional treatments such as discarding anomalous samples or applying fixed Winsorization thresholds often fail to preserve the multivariate relationships that control geochemical dispersion. In this research, an intelligent random forest (RF)-based model was developed to reconstruct an extreme Au outlier in stream sediment samples from the Saqqez shear zone belt by leveraging available multielement geochemical information. This study introduces a hybrid correction framework based on a machine learning algorithm and targeted Winsorization (MLA–TW) that integrates TW with RF regression to reconstruct a realistic and geochemically plausible value for a highly influential Au outlier. Three scenarios were examined: (1) modeling with the original dataset containing a 739 ppb outlier, (2) modeling after removing the outlier, and (3) modeling with a reconstructed value obtained from the MLA–TW approach. The RF model showed reliable predictive capacity (R² = 0.85), and the reconstructed value preserved both geological plausibility and nonlinear multivariate structure. Application of the C–A fractal model demonstrated that the MLA–TW scenario yielded the most stable population breaks, the most robust anomaly thresholds, and the highest spatial fidelity, successfully identifying verified gold prospects and deposits in the region. Overall, the MLA–TW framework stabilizes the C–A model and improves its robustness by reducing the statistical leverage of extreme values while preserving the nonlinear geochemical patterns essential for anomaly detection. The results confirm that this intelligent hybrid approach provides an objective and geologically meaningful methodology for refining Au threshold determination and delineating shear zone-related gold targets with improved accuracy. Full article