Search Results (1,162)

Atmospheric correction of high-spatial-resolution (HSR) optical satellite imagery is strongly affected by the adjacency effect (AE). Conventional Atmospheric Point Spread Function (APSF)-based AE correction methods are often based on simple local averaging or distance-weighted background approximations. These are often insufficient for highly heterogeneous HSR scenes and become computationally expensive when the AE’s influence range spans far more pixels. To address these issues, this study proposes a fast AE correction algorithm with adaptive local surface constraints. The method first introduces a surface-atmosphere coupling correction based on effective reflectance. It then constructs downward- and upward-weighting kernels and incorporates local reflectance constraints into the estimation of environmental reflectance to better characterize AE intensity in HSR scenes. Since environmental reflectance estimation is retained in a kernel-weighted form, the infinite-domain integration is reformulated as a finite-window computation with truncation compensation and accelerated via fast Fourier transform (FFT) convolution, followed by a few iterations for reflectance retrieval. Validation with GaoFen-2 (GF-2) panchromatic imagery shows that, at an aerosol optical depth of about 0.4, the proposed method achieves the best performance among the compared methods, with a mean absolute error (MAE) below 0.006 relative to in situ measurements, sharpness and contrast increases of approximately $99.0\%$ and $97.9\%$, respectively, and a National Imagery Interpretability Rating Scale (NIIRS) increase of more than 0.3. For a $1024\times 1024$ image with a 501-pixel AE window diameter, the running time is below 4 s, substantially lower than that of previous APSF-based AE correction methods. The FFT implementation also avoids the quadratic dependence on window size in direct spatial convolution. Additional experiments on multiple GF-2 and Gao Fen Duo Mo scenes show that the proposed method provides stable AE correction and achieves higher image quality and visual interpretability than the compared methods in HSR imagery. Full article

Cloud and quality screening removes approximately 65% of daily TROPOMI tropospheric NO₂ pixels, creating structured data gaps that coincide with meteorological conditions driving pollution extremes. Standard gap-filling methods—kriging, Random Forests and other machine learning methods—act as statistical smoothers that systematically suppress extreme concentrations and ignore the Missing Not At Random (MNAR) character of cloud-induced missingness. Here we present a physically informed framework that treats urban NO₂ as a forced nonlinear dynamical system and reconstructs missing satellite observations through geometric navigation on a shadow manifold rather than statistical interpolation. The framework integrates five components: (i) Multivariate State-Space Reconstruction (MSSR) using multiview embeddings of continuous ground-based NO₂, O₃, and ERA5 meteorology, grounded in Stark’s forced-system embedding theorem; (ii) Short-Time Regime-Conditioned Convergent Cross Mapping (ST-RC-CCM) with a spatial-mismatch negative control for falsifiable causal validation; (iii) Inverse Probability Weighting (IPW) to correct the clear-sky sampling bias; (iv) trajectory-matrix denoising via Singular Spectrum Analysis (SSA) and Robust PCA; (v) topology-inspired fidelity metrics—Manifold Overlap Ratio (MOR) and Dynamic Trend Capture (DTC)—that penalize smoothing artefacts. The physical basis for this coupling is the shared dynamical history of surface and column NO₂: tropospheric NO₂ has a photochemical lifetime of 1–4 h near urban emission sources, comparable to the boundary layer mixing timescale, ensuring that surface and column concentrations are jointly governed by the same emission–photolysis–transport attractor. The planetary boundary layer height (PBLH), solar zenith angle (SZA), and surface O₃—all included as MSSR coordinates—are the dominant physical drivers of the instantaneous surface-to-column scaling, and their joint trajectory in state space constitutes the physically grounded basis for analogue selection. The framework is validated on a synthetic forced Lorenz-96 system, then applied to five European primary cities spanning contrasting regimes (Sofia, Milano, Stuttgart, Kraków, Hamburg) plus five N1 spatial-mismatch control stations (Plovdiv, Genova, Frankfurt, Warszawa, Berlin)—ten urban-background stations across four countries—with structured ablations (A0-A4V-A4K). Across >3600 evaluations, MOR_ext distributions for EDM and non-EDM methods are non-overlapping by a factor exceeding 5× (EDM minimum 0.59 vs. non-EDM maximum 0.10; median non-EDM MOR_ext ≤ 0.05 at every city × mask combination), while EDM achieves MOR_ext up to 0.915 (Milano Po Valley). Under a fair-comparison benchmark that withholds ground-level NO₂ from Random Forest, EDM’s RMSE advantage remains robust at a median of 3.9× (RF_FULL) and increases to 4.2× (RF_METEO), confirming that the performance gap is physical rather than an information artefact. A three-level temporal validation—within-window pseudo-cloud masking, cross-year transfer (full 2022 holdout and DJF 2023/24), and a COVID-19 out-of-distribution test—demonstrates robustness beyond standard train/test splits, with CCM library-length convergence confirmed for 60/60 ablations (p < 0.001) across all ten stations. Spatial-mismatch tests confirm local dynamical specificity at all five primary–control pairs (Δρ = 0.090–0.210), with seasonal modulation driven by orographic and synoptic mechanisms. These results establish manifold-based gap filling as a dynamically informative complement to statistical approaches, particularly in topographically confined, stagnation-prone basins where preserving extreme-event geometry is essential for exposure assessment. Full article

Predictive maintenance (PdM) is central to Industry 5.0 strategies for reducing unplanned downtime in rotating machinery. This work proposes and evaluates, as a proof of concept on a controlled single-machine testbed, a multimodal TinyML edge architecture for PdM designed to remain compatible across the application plane’s evolution toward sixth-generation (6G) networks. Three complementary modalities run local inference on commercial off-the-shelf smart sensor nodes—vibration, acoustic, and thermography—with an embedded gateway bridging per-modality decisions to a serverless cloud back-end. Using real vibration data from a controlled static-unbalance protocol, five anomaly-detection model variants, operating on ten frequency-independent time-domain features extracted from 6 s windows, are benchmarked on the actual Cortex-M4F target; the INT8-quantized fully connected autoencoder, scored by per-window reconstruction error, reaches F1 = 0.9807 with 254 µs inference latency and a 6056 B Flash footprint, well within the microcontroller budget. In a second acquisition session with the remounted sensor, the frozen model retains perfect fault recall, and a short per-installation healthy-baseline recalibration restores F1 = 0.975 without any weight retraining. The acoustic modality is classified in-sensor on log-Mel filterbank energies by the Syntiant NDP120 neural coprocessor, and the thermographic modality by a lightweight binary CNN on 96 × 96 px frames. A preliminary intra-session late-fusion analysis suggests that a logistic-regression meta-learner over the three modality confidence scores can improve on single-modality baselines when no single modality already saturates, motivating multimodal sensing primarily for robustness and redundancy. An end-to-end latency experiment shows that the cloud-uplink leg dominates the budget (79–88%), establishing edge-first inference as a necessary condition for 6G URLLC gains to be observable at the application level. All experiments are conducted over Wi-Fi and MQTT with no 5G or 6G radio, so 6G compatibility is presented as a forward-looking roadmap rather than a tested capability. Full article

Cloud central processing unit (CPU) utilization forecasting is fundamental to capacity planning, overload warning, elastic scaling, and resource provisioning in cloud computing systems. Conventional forecasting models usually optimize average point-error accuracy, whereas provisioning decisions are often more sensitive to high-load underestimation and upper-bound failures that indicate potential under-provisioning risk. This paper proposes Risk-Aware TimeMixer (RA-TimeMixer), a provisioning-oriented adaptation of Original TimeMixer for machine-level multi-step CPU utilization forecasting. RA-TimeMixer preserves the multiscale forecasting backbone and introduces two targeted risk-oriented components: batch-wise high-load weighted training and residual-based asymmetric upper-bound calibration. Experiments are conducted on a preprocessing-audited 50-machine subset of Alibaba Cluster Trace 2018 with 1 min sampling, input length 96, and prediction lengths 6, 12, and 24. At prediction length 12, RA-TimeMixer reduces High-load MAE, Under-rate high, Under-magnitude high, and Under-MAE when under by 2.72%, 1.89%, 4.06%, and 1.98%, respectively, compared with Original TimeMixer. Machine-level paired analyses, horizon and threshold studies, three-seed stability, persistence-baseline diagnostics, and fully observed-window retraining support the robustness of the observed accuracy–risk trade-off. The results indicate that RA-TimeMixer offers a transparent, risk-sensitive extension of TimeMixer for provisioning-oriented cloud CPU forecasting, while asymmetric calibration reduces empirical upper-bound violations at the cost of wider intervals and margins. Full article

To address the multi-objective conflicts among cost, time, and carbon emissions in Guangxi cross-border container multimodal transport—under the influence of factors such as tariffs, fluctuating customs clearance efficiency, and carbon emission policies—this paper develops a multi-objective optimization model that integrates mixed time windows and scenario analysis. The model incorporates multiple elements, including transportation cost, transshipment cost, tariff cost, time value of cargo, carbon tax cost, in-transit and customs clearance time, and transshipment-related carbon emissions, making it more aligned with real-world cross-border operational scenarios. To effectively solve this complex model, an improved NSGA-II algorithm (I-NSGA2) is designed, which introduces an adaptive crossover and mutation operator along with an elite retention strategy to enhance convergence speed and solution diversity, while embedding a scenario parameter response mechanism to accommodate dynamic fluctuations in key parameters. Subsequently, an evaluation framework is constructed using the entropy weight–TOPSIS method to select recommended routes with favorable cost–time–carbon trade-offs from the Pareto frontier. A case study based on the Nanning–Kuala Lumpur route is conducted for validation. Experimental results demonstrate that the I-NSGA2 algorithm significantly outperforms MOPSO, MOEAD, and the standard NSGA-II in terms of IGD and HV metrics; time-sensitive cargo tends to favor rail-dominated routes, while low-cost cargo prefers combined road–water–rail routes. This study effectively addresses the route selection problem for Guangxi cross-border container multimodal transport under varying key parameters, and also provides a research foundation for optimizing cross-border multimodal transport route selection in other regions. Full article

Large-scale field video archiving under real-world operational conditions presents four compounding challenges: heterogeneous edge devices with inconsistent hardware capabilities, low-light and high-variance illumination environments, intermittent satellite and mobile network connectivity, and arbitrary camera mounting orientations. Existing QR-based video ingestion pipelines fail to address these challenges jointly, resulting in high decode failure rates and unreliable cloud archiving in distributed field deployments. This paper presents a framework that directly addresses all four challenges through a unified edge-to-cloud pipeline. Rather than introducing new computer-vision primitives, the contribution lies in the robust system architecture that integrates and orchestrates established techniques, and in its engineering validation at scale. The pipeline combines four engineered components: (i) a visibility-aware adaptive transcoding strategy; (ii) a priority-weighted non-uniform temporal sampling scheme; (iii) a 23-angle rotation ensemble decoder; and (iv) a checkpoint-resumable block-staged cloud synchronization mechanism with deferred reclassification. The framework is validated on 132 independent edge deployments across 849 operational regions, processing 87,873 production videos totaling 1.05 TB over a 10-day observation window (27 May–5 June 2026). The evaluation demonstrates 92.1% QR recall at 26.8% of full-scan CPU cost, 98.0% upload reliability, and 33.5% deferred recovery of initially unresolvable codes (p < 0.001, Cohen’s d > 0.97). To the best of our knowledge, this constitutes the largest reported real-world evaluation of an edge QR-based video ingestion framework and the first to characterize recall variance across more than 100 independent field deployments. Full article

Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under uncertainty. Open forecasting benchmarks operators rely on report-only point predictions. The dispatch models that turn forecasts into firm commitments assume a constant round-trip efficiency, so the committed flexibility is systematically over-scheduled. This study contributes two complementary modules, validated separately on public data. The first is a calibrated probabilistic charging forecaster that provides, to our knowledge, the first prediction intervals with reported empirical coverage on the UrbanEV benchmark. It is a gradient-boosted quantile-regression model that combines each zone’s own-history lags with adjacency-weighted neighbor-mean features and exogenous price and calendar inputs. It is calibrated by conformalized quantile regression and scored over thirty zones across a 120-day hourly window. The second is a deliverable-flexibility envelope whose returnable-energy bounds are set by measured, state-of-charge- and rate-dependent vehicle-to-grid (V2G) discharge efficiency rather than a constant round-trip number. These bounds are fit to the measured discharge traces of three V2G-capable vehicles in the Esser bidirectional-charging dataset. Chosen as a lightweight, reproducible baseline, the forecaster keeps its prediction intervals within a five-percentage-point coverage tolerance at both the 80% and 90% nominal levels. Measured coverage is 0.823 and 0.911. It also improves on the continuous ranked probability score of its conformalized-point counterpart at matched point accuracy. This calibration holds across the hyperparameter neighborhood and under data deficiency. On the delivery side, a leave-one-vehicle oracle shows the efficiency-aware envelope short-delivers less than the constant-average-efficiency aggregator on held-out vehicles. Its residual shortfall is 1.21% against the aggregator’s 2.03% at the conservative operating point. The margin widens as commitments grow more aggressive and discharges reach the lowest states of charge. Each of these two measured properties, calibrated demand-side uncertainty and state-dependent discharge physics, imposes a material, separately validated constraint on how much contracted EV flexibility can be delivered, a constraint the point-forecasting frontier leaves unaddressed. Full article

Accurate and real-time localization for unmanned vehicles in complex motion environments is challenged by asynchronous multi-sensor measurements, time-varying measurement quality, and the growing computational burden of long-term factor graph optimization. To address these problems, this study proposes AVFGO, a sliding-window incremental factor graph optimization framework for motion-state-aware adaptive multi-sensor fusion localization. IMU pre-integration is used as the primary state-propagation backbone, while asynchronous LiDAR odometry, AHRS, wheel odometry, and barometer measurements are uniformly represented as factor-graph constraints. A sliding-window marginalization mechanism bounds the optimization scale by retaining historical information as prior factors. A motion-state-aware fusion-rate strategy controls the insertion density of external factors, and a residual-driven vector-wise adaptive weighting model adjusts the covariance of heterogeneous sensors in different measurement dimensions. Field experiments on a GNSS-denied wheeled unmanned vehicle dataset show that AVFGO achieves a 3D position RMSE of 0.380 m, reducing the error by 65.36% relative to IFGO and 52.71% relative to SWIFGO. The mean single-step optimization time is 0.0389 s, corresponding to an 11.9× speedup over IFGO and a 75.81% reduction relative to SWIFGO. These results indicate that the proposed framework improves accuracy and real-time performance while remaining limited by the single-platform field-test scope, which is explicitly discussed as a direction for future validation. Full article

Smart grids with high renewable penetration are increasingly evaluated through long-horizon production simulation, but conventional decomposed simulation mainly reports energy balance and unit feasibility, while power-quality stress remains weakly quantified in the storage correction layer. This paper presents a power-quality-proxy-guided state-replay framework for renewable-rich smart grids. Instead of claiming feeder-level electromagnetic simulation, the method defines planning-level proxy indicators that can be exported by production-simulation software: a voltage-deviation proxy obtained from net-power sensitivity, a net-load ramp proxy, an inverter/charger harmonic-risk proxy, and a renewable-curtailment exposure proxy. These normalized indicators are combined into a composite score $S_{PQ}$, which is then used to distinguish two storage values: charge retention during renewable-surplus voltage-rise intervals and discharge support during voltage-dip, ramp-stress, or inverter-stress intervals. A base decomposed production-simulation schedule is first obtained. The proposed layer then constructs storage accounting cycles independent of monthly and rolling-window boundaries, attaches the proxy ledger to each interval, backtracks terminal residual storage energy to low-value charging actions, and reallocates physically feasible discharge to high-$S_{PQ}$ intervals. The corrected storage path is projected onto power and energy limits and replayed before storage and conventional-unit states are inherited by the next monthly solve; cycles outside the replay validity envelope are escalated to full redispatch rather than counted as successful corrections. An eight-interval case reports explicit $S_{PQ}$ values and shows that a trajectory ending 45 MWh above the 30 MWh reference can be corrected by trimming 35 MWh of low-proxy-value charging and adding 10 MWh of discharge in two high-score intervals. A 96-interval experiment further shows that the full method reduces explicitly discarded residual energy from 214 MWh to 31 MWh, provides 128 MWh of proxy-guided support, and lowers weighted PQ-proxy exposure by 46.3%. The framework links smart-grid data analysis, renewable integration, and power-quality improvement within a traceable production-simulation workflow. Full article

Monitoring the health of bearings in industrial rotating machines is a major challenge for ensuring the reliability and continuous operation of installations. Conventional fault detection methods, based on multivariate control charts such as Hotelling’s T², multivariate exponentially weighted moving average, or multivariate cumulative sum control chart, are limited by the complex nonlinear dynamics of the system. In this article, we propose an innovative monitoring approach based on the Koopman operator, allowing the linearization of a nonlinear system in an observed space and the application of drift detection techniques via an extended T² control chart. The study is based on two experimental approaches: one using controlled simulated data to analyze the responsiveness and robustness of the model, and the other applied to real data from an industrial turbogenerator monitoring the vibrations, temperatures, and speeds of the front and rear bearings. Comparative results show that the Koopman-based T² map detects defects earlier, with better accuracy under noise and a reduced false alarm rate compared to conventional methods. The integration of wavelet preprocessing, statistical feature extraction by sliding windows, and PCA representation of the trajectories enhances the robustness and interpretability of the model. Full article

To address the limited edge-preserving capability of traditional guided filtering caused by fixing the filtering window center at the target pixel, an edge-preserving guided filtering algorithm based on edge tangent flow and side window (EWGF) is proposed. Rather than relying on local intensity statistics, the proposed method exploits local geometric structures through the integration of edge-aware weighting, tangent direction estimation, and adaptive side window filtering. Firstly, an edge-aware weighting strategy is introduced to construct a weighted gradient representation, enabling characterization of edge intensity and local structural features. Secondly, based on the weighted gradient field, a structure tensor constrained by local geometric information is established, and edge tangent flow is estimated through eigenvalue decomposition to capture local edge orientations. Furthermore, a tangent-guided one-dimensional side window guided filtering mechanism is developed, in which the filtering direction is adaptively aligned with local edge structures to suppress noise and textures while preserving edge sharpness and structural continuity. Finally, comparative experiments are conducted on the BSD500, Set5, and Set14 datasets, with PSNR and SSIM employed as evaluation metrics. Experimental results demonstrate that, compared with the traditional guided filtering algorithm, the proposed method improves PSNR by an average of 5.69 dB and SSIM by an average of 0.11, validating its performance in structure preservation and noise suppression. Full article

Secure Shell (SSH) intrusions against Linux servers remain a dominant vector of opportunistic and targeted cyber incidents, yet operational honeypots treat attacker commands either as isolated lookup keys for static templates or as input to a single uniform language model. Two research directions partially address this gap: LLM-driven response generation raises interaction realism but incurs a fidelity–latency trade-off, while semantic command analytics classifies attack stages from embeddings yet applies a single fine-tuned model uniformly across sessions. This work introduces the phase-adaptive model routing framework (PAMR), which explicitly couples both views. A lightweight online phase estimator infers the current attack stage—reconnaissance, exploitation, or persistence—from a sliding window of recent commands via compact semantic embeddings; a router then dispatches each command to one of several heterogeneous LLM backends according to estimated phase, command complexity, and a confidence-weighted latency budget. The routing decision is formulated as a constrained optimization problem and realized at runtime as an $O\left(1\right)$ lookup in a precomputed dispatch table. PAMR is evaluated on a controlled, reproducible benchmark of 412 stage-annotated command sessions that combines representative Linux command–response pairs with synthesized attacker traces; we explicitly state that this is a laboratory benchmark rather than live attacker traffic, and we scope our claims accordingly. Relative to uniform-model baselines, PAMR reduces mean response latency by approximately 38% against an API-hosted high-capacity backend and by approximately 45% against a locally hosted mid-capacity backend, while keeping token-level response-fidelity metrics close to the high-capacity baseline (cosine similarity ≈ 0.39 vs. 0.40) and maintaining an online stage-classification macro-F1 above 0.87. We further provide a first-order analytical treatment of the timing side-channel that any backend-routing architecture introduces, and we frame the contribution as a latency/token-fidelity trade-off, leaving validation of operational realism against live adversaries to future work. Full article

A lubricant protective film (LP) formed on automotive Zn-coated steel sheets is a functional surface layer that controls shear resistance at the die–sheet interface while also affecting the electrical contact state during resistance spot welding and the surface reactivity during paint pretreatment. In this study, the effect of LP coating weight on surface friction, interfacial electrical resistance, and degreasing–phosphate reactivity was analyzed for 340 MPa-grade galvannealed (GA) steel sheets within a unified surface-governed framework. The LP coating weight was controlled in the range of 0–1008 mg/m² on a single-sided basis. The friction coefficient, cup-drawing limit blank holding force (BHF), resistance spot welding current range, resistance–time product obtained by integrating dynamic resistance with respect to time, residual LP after degreasing, phosphate coating formation behavior, and forming simulation results using experimentally measured friction coefficients as input were comparatively evaluated. With increasing LP coating weight, the friction coefficient decreased from approximately 0.163 to 0.130 and then increased again to approximately 0.145 in the high-coating-weight regime. This surface-state change increased the limit BHF during cup drawing, whereas it narrowed the current range and increased the resistance–time product during resistance spot welding. In addition, under conditions above approximately 550 mg/m², residual LP after degreasing increased, and local no-growth regions of the phosphate coating were identified. These results show that, within the present test conditions, LP coating weight is not merely the amount of lubricant applied but a surface-state variable that concurrently influences frictional, electrical, and chemical responses. Therefore, within the scope of the present laboratory-scale framework, an LP coating weight of approximately 300–550 mg/m² should be interpreted not as a universal optimum, but as an operational surface window derived by balancing formability, the RSW process window, and phosphate reactivity under the present experimental conditions. Full article

The menopausal transition is a key period marked by changes in cardiometabolic health and adrenal function, representing an important window for targeted interventions to improve women’s health. Glucagon-like peptide-1 receptor agonists improve metabolic parameters, while physical exercise provides well-established benefits; however, their effects during the menopausal transition remain insufficiently explored. This study examined the effects of liraglutide (0.186 mg/kg; corresponding to the human equivalent dose of 1.8 mg/day used for type 2 diabetes treatment) and exercise on the adrenal gland in a rat model of menopausal transition. Three-month-old females served as young controls (CY), while 16-month-old acyclic females were assigned to control (C), liraglutide (L), exercise (E), or combined treatment (L+E). Compared with CY, the C group showed increased body mass and adrenal alterations, including reduced adrenal weight and volume, cortical atrophy, increased collagen content, decreased STAR, and increased pAMPKα optical density (p ≤ 0.05). Sf1 and Star were upregulated in L, E, and L+E compared with C, most prominently in L (p ≤ 0.05), while Cyp11b2 was increased in L and L+E (p ≤ 0.05). Hormone analysis showed reduced 17β-estradiol, corticosterone, and aldosterone in C compared with CY. Corticosterone was further reduced in L compared with C, while aldosterone increased in L and L+E compared with C (p ≤ 0.05). In conclusion, the menopausal transition induced adrenal morpho-functional remodeling. Liraglutide intervention had the greatest impact on steroidogenic output, both alone and in combination with exercise, while exercise alone showed no significant effect. Full article

Human-mediated species translocations are increasingly interacting with global climate change, yet empirical insights into how introduced populations achieve long-term demographic stability in new frontiers remain limited. This study systematically investigates the post-translocation establishment and multi-generational persistence of the endemic cyprinid Squalidus multimaculatus at its newly identified northern distribution limit in Goseong, Republic of Korea, following historical human-mediated introduction. Utilizing nationwide multi-decadal occurrence records, we mapped the species’ spatio-temporal dynamics across three temporal phases (T₀, T₁, and T₂). Long-term biogeochemical water quality indices and thermal regimes were compared between the native baseline (Yeongdeok) and Goseong. Furthermore, demographic shifts and growth trajectories were evaluated using 676 field-sampled specimens through length–weight relationships, condition factor (K_F) analysis, age structure mixture modeling, and the von Bertalanffy growth function (VBGF). Our results indicate that the Goseong habitat provides generally favorable biogeochemical conditions characterized by low nutrient loading and high dissolved oxygen stability. Crucial to this transition, a distinct winter warming shift expanded the available thermal window, reducing the frequency of extreme winter cold-stress events (≤2.0 °C) from 40.0% to 6.9%. Concurrently, the post-translocation population exhibited successful demographic stability, characterized by a self-sustaining age structure (ages 0+ to 4+), an inferred spawning window during June–July, and a growth trajectory (L_∞ = 95.69 mm) broadly comparable to native benchmarks. These findings establish a reliable empirical baseline framework linking human-mediated introductions with long-term environmental transitions. Globally, this case study suggests that post-translocation success depends on the interplay between introduction pathways and climate suitability, offering key insights into population survival and ecosystem evolution under global change. Full article