Search Results (446)

Deep coalbed methane reservoirs exhibit strong heterogeneity and complex stress environments, making accurate prediction of casing loads during multi-stage hydraulic fracturing challenging. This study developed a coupled prediction framework integrating three-dimensional geomechanical modeling, modified induced stress calculation, and numerical simulation. The geomechanical model was constructed using logging, drilling, rock mechanics, and in situ stress data and validated against fracture monitoring results. The simulated fracture half-length and stimulated area differed from the monitored values by less than 10% and 8%, respectively, with a spatial matching degree exceeding 92%. A modified analytical model was then established by introducing correction coefficients for fracture net pressure and stress propagation. Among the results obtained using five parameter inversion methods, the sparrow search algorithm achieved the highest fitting accuracy, with an (R²) of 0.9371 and an RMSE of 0.3761, yielding (A = 0.7330) and (B = 0.9238). These coefficients indicate an approximately 26.7% reduction in effective net pressure and enhanced attenuation of induced stress in heterogeneous, cleat-developed coal seams. Furthermore, a multi-parameter casing stress model was developed by coupling treatment scale, injection rate, fracture spacing, and stage number. Sensitivity analysis showed that the number of fracturing stages and injection rate were the dominant factors, followed by fracture spacing and treatment scale. The proposed framework quantitatively characterizes casing stress evolution and facilitates casing load assessment under different multi-stage fracturing conditions. Full article

High-resolution range profile (HRRP) reconstruction is essential for extracting range-direction scattering characteristics in wideband radar remote sensing, particularly in synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging. Coded interrupted sampling (CIS) can improve radar low probability of intercept (LPI) performance by controlling signal transmission with a binary sequence. However, the reduced number of valid echo samples may degrade HRRP reconstruction, especially under low-duty-ratio and low signal-to-noise ratio (SNR) conditions. Conventional orthogonal matching pursuit (OMP) processes each frame independently and ignores the inter-frame continuity of scattering-center positions, which may lead to false selections and missed detections. To address this problem, this paper proposes a candidate-interval-assisted orthogonal matching pursuit (CI-OMP) algorithm based on multi-frame sequential priors. Stable scattering-center positions are extracted from historical reconstruction results and expanded into candidate intervals to guide atom matching in the current frame. Simulation results show that CI-OMP outperforms standard OMP in terms of normalized mean squared error (NMSE), tolerant support recovery rate (Tol-SRR), and peak-to-sidelobe ratio (PSLR). At a duty ratio of 0.20, CI-OMP reduces the NMSE by 1.71 dB and improves the PSLR by 7.56 dB compared with OMP. In addition, the candidate-interval strategy reduces the atom-search range by approximately 54–75% under different duty ratios and by approximately 50–83% under different SNRs, demonstrating improved search efficiency. These results demonstrate that CI-OMP improves the accuracy, robustness, and search efficiency of HRRP reconstruction for CIS radar echoes, particularly under low-duty-ratio and low-to-medium-SNR conditions. Full article

As the scaling of silicon-based devices approaches physical limits, two-dimensional transition-metal dichalcogenide field-effect transistors (2D TMD FETs) have emerged as promising candidates for logic devices in the post-Moore era. However, their design optimization relies heavily on computationally intensive TCAD simulations, thereby limiting efficient exploration of multidimensional parameter spaces. This paper proposes an efficient automated design framework for 2D TMD FETs under small-sample conditions and validates it using a monolayer MoS₂ FET as a case study. The framework integrates device design, physics-based simulation, performance prediction, and inverse design, establishing a bidirectional mapping between device parameters and electrical performance. Target-driven closed-loop optimization is achieved through TCAD-based feedback validation. Results demonstrate that, using a dataset comprising 300 TCAD samples, the forward model achieves an average coefficient of determination (${\mathrm{R}}^2$) of 0.9503. TCAD revalidation of the inverse-designed devices yields an average mean absolute error (MAE) of 0.0464 and an average mean absolute percentage error (MAPE) of 5.46% for performance metrics. Regarding computational efficiency, while a single TCAD simulation takes approximately 25 to 50 min, the trained model performs inference in under 50 ms, achieving a speedup of at least $3\times {10}^4$ during the inference phase. Accounting for the generation of the 300 TCAD samples and the training of both forward and inverse models, the framework’s one-time computational cost ranges from 160.27 to 285.27 h. Once the cumulative number of design tasks exceeds approximately 342 to 385, the total computational cost falls below that of direct TCAD simulation, with the computational advantage becoming increasingly significant as the number of tasks grows. Consequently, this method is highly suitable for large-scale parameter sweeps, device screening, and multi-objective, high-frequency design iterations. It drastically reduces repetitive TCAD calls, offering a scalable solution for the efficient, automated design of 2D TMD FETs. Full article

Time-fractional diffusion equations describe subdiffusive transport in heterogeneous media, but their numerical treatment is complicated by the nonlocal Caputo derivative and by the weak singularity that the solution develops at the initial time. We study a global-in-time discretization that combines spectral collocation in time—on the fractional power basis ${\left\{t^{\mathsf{\ell }\alpha }\right\}}_{\mathsf{\ell }=0}^N$, evaluated at Chebyshev–Gauss–Lobatto nodes, which reproduces the leading terms of the singular expansion of the solution—with a second-order conservative finite-difference stencil in space that uses harmonic averaging of the diffusivity at the cell faces and therefore remains accurate across discontinuous media. The resulting fully discrete problem is a large, nonsymmetric, dense-in-time linear system whose two-norm condition number grows like the inverse square of the spatial mesh size, so that Krylov subspace iteration without preconditioning stalls under refinement. Exploiting the Kronecker sum structure of the discrete operator, we build a preconditioner by fast diagonalization of the spatial factor through the discrete sine transform. For constant diffusivity the preconditioner reproduces the operator exactly and yields a direct solver; for variable diffusivity it is spectrally equivalent to the operator, and we prove that the eigenvalues of the preconditioned system cluster in a disk centered at one whose radius depends only on the coefficient contrast, and not on the mesh, the number of temporal degrees of freedom, or the fractional order. Numerical experiments in one and two space dimensions confirm second-order spatial accuracy and a preconditioned iteration count that stays flat—twelve iterations from $M=32$ up to $M=1024$ in one dimension and eleven up to $M=256$ per direction in two—while the unpreconditioned count grows by more than two orders of magnitude. In time, the accuracy is spectral until round-off in the ill-conditioned Vandermonde matrix of the power basis takes over: the barrier is reached at $N=9,10,13$ for $\alpha =0.3,0.5,0.7$, where the attainable error is about ${10}^{-6}$. A benchmark against the L1 scheme on uniform and graded meshes, the Alikhanov L2-$1_{\sigma }$ scheme and Grünwald–Letnikov convolution quadrature quantifies when the global approach pays: on forced problems and on modes with $\kappa \lambda T^{\alpha }\lesssim 2$ it reaches a prescribed accuracy one to two orders of magnitude faster and with several times less memory, while for strongly damped modes the fractional power basis converges only algebraically and graded time marching is preferable below a relative error of ${10}^{-2}$. Full article

Ultra-wideband (UWB) inter-node ranging provides relative-distance constraints for cooperative localization in GNSS-denied UAV swarms, but dense candidate networks can exceed the available ranging slots, communication bandwidth, computation, and energy. This paper proposes a Fisher-information-based cooperative sensor node pre-selection method under heterogeneous ranging noise. All mobile nodes remain in the localization state, while the selected nodes induce the active ranging-link set. Selected-node, induced-link, ranging-slot, and normalized general-resource budgets are represented separately. Using predicted geometry and estimated link-quality weights, a gauge-free normalized Fisher information matrix combines link geometry, link-quality-dependent weights, and topology-induced coupling. A trace-based generalized GDOP (G-GDOP) criterion is optimized by a two-stage greedy heuristic with recursive matrix updates. The experiments show that G-GDOP is a local observability and information-quality metric rather than a direct predictor of topology-level nonlinear recovery error. Within the same topology, normalized local RMSE increased from 0.698 [0.673, 0.752] in the Low G-GDOP group to 1.014 [0.999, 1.068] and 1.980 [1.806, 2.180] in the Medium and High groups. Increasing the selected-node budget from K = 6 to K = 20 reduced median RMSE from 0.550 to 0.148 m while increasing the median induced-link number from 109 to 214. Additional tests covered Gaussian and heterogeneous ranging noise, deterministic NLOS bias, online link-weight errors, and predicted-position uncertainty. Direct Inversion and Woodbury Updating were numerically equivalent within a predefined tolerance in all 24 size–regime combinations, and a Woodbury runtime advantage was supported in 20 conditions. The proposed framework therefore provides an interpretable resource-aware pre-selection module without implying an unconditional real-time guarantee. Full article

This study evaluated the distribution of lead (Pb), cadmium (Cd), chromium (Cr), and arsenic (As), collectively referred to as potentially toxic elements (PTEs), in Andean dairy production systems affected by volcanic ash deposition and examined their relevance to raw milk safety. Samples of soil, forage, supplementary feed, drinking water, and raw milk were collected from 15 georeferenced dairy farms in Bilbao Parish, Ecuador. Soil and forage were assessed as interconnected environmental matrices, whereas supplementary feed and drinking water were considered additional exposure sources. PTE concentrations and selected physicochemical properties were examined using descriptive statistics, box plots, exploratory principal component analysis, and Spearman correlation analysis. Element distribution differed among matrices and did not follow a uniform or strictly linear soil–forage–milk pathway. Pb and As concentrations were lower in milk than in the environmental and dietary matrices, indicating limited occurrence in the final product under the conditions studied. Cd remained detectable in milk and therefore warrants attention because of its potential relevance to chronic dietary exposure. Cr occurred at comparatively higher concentrations in forage and supplementary feed but was substantially lower in milk. Exploratory correlations showed that Pb, Cr, and As varied positively with corrected density, protein, lactose, and total solids, whereas Cd showed inverse associations with these variables. These relationships should not be interpreted as causal because of the limited number of milk samples. Overall, the findings support an integrated, element-specific assessment of milk safety that considers environmental matrices, supplementary dietary inputs, drinking water, and milk composition. Larger seasonal studies and elemental speciation are recommended to clarify exposure pathways and potential risks. Full article

Aggregation operators are central to multi-criteria decision-making under complex fuzzy information, where the additional phase dimension of complex fuzzy sets captures periodic or cyclical uncertainty beyond the reach of classical fuzzy sets. Existing Archimedean families used in the complex-fuzzy aggregation-operator literature, namely the algebraic, Einstein, Hamacher, and Aczél–Alsina families, are all generated from integer-order kernels; complete monotonicity of a generator’s pseudo-inverse, the condition known to be necessary and sufficient for a bivariate Archimedean construction to extend consistently to an arbitrary number of arguments, has not, to our knowledge, been established or invoked as a design criterion within that literature. To address this gap, this paper introduces a new family of Complex Fuzzy Mittag-Leffler (CFML) operators generated by a two-parameter additive generator that is built from the one-parameter Mittag-Leffler function $E_{\alpha }$ and a positive exponent $\lambda$. The completely monotone character of this generator guarantees the above consistency across dimensions for every aggregation exponent no smaller than one, and the fractional order $\alpha$ supplies a tunable additional degree of freedom that reweights how criteria are compensated during aggregation. The associated operational laws and the corresponding weighted averaging and weighted geometric operators are defined and proved to be idempotent, bounded, and monotone. Notably, the classical Aczél–Alsina and algebraic product operators emerge as exact limiting cases as the fractional order tends to one. A complete multi-criteria decision-making algorithm is proposed and illustrated, for demonstration purposes only, through a hypothetical case study evaluating generative artificial-intelligence diagnostic systems, with a sensitivity analysis showing that varying the fractional order and the aggregation exponent can alter alternative rankings relative to the classical limiting operators, illustrating the added flexibility that the fractional order provides. Full article

Laser directed energy deposition (LDED) can fabricate Ti-6Al-4V thin-walled structures efficiently, but the deposited sidewalls usually contain adhered particles, layer steps, and waviness that limit surface quality. This study combined square-spot laser surface reshaping with machine learning-assisted parameter design to control sidewall roughness. Sixteen single-factor experiments were first conducted to clarify the effects of laser power, scanning speed, spot overlap ratio, and scan number. An 80-sample dataset was then established to train and compare random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGBoost) models, and SHapley Additive exPlanations (SHAP) were used to interpret feature contributions. RF showed the best predictive performance, with R² = 0.940 and RMSE = 1.760 μm, and was coupled with Bayesian optimization (BO) for inverse parameter design. For a target arithmetic mean roughness (Ra) of 5 μm, the optimized condition was 500 W, 2.57 mm/s, 48.09% overlap, and five scans. The predicted Ra was 5.02 μm, while the validation experiment yielded 5.76 μm, reducing the initial roughness from 28.98 μm by 80.1%. These results demonstrate that interpretable machine learning can support target-driven square-spot laser reshaping for LDED Ti-6Al-4V thin-walled structures. Full article

Radar-based Quantitative Precipitation Estimation (QPE) in semi-arid Mediterranean climates is critically challenged by systematic underestimation of shallow precipitation, yet gauge–radar merging frameworks tailored to such environments remain poorly evaluated. This study develops and assesses a merging pipeline for Cyprus, combining X-band polarimetric observations from the Paphos and Larnaca operational radar network with accumulations from a 50-station rain gauge network across 11 rainfall events spanning the 2024 wet season (January and November–December 2024). Four approaches were evaluated: raw radar mosaic, global mean field bias (MFB) correction, spatially varying local inverse distance weighting (IDW) bias correction assessed through leave-one-out cross-validation (LOOCV), and a Random Forest (RF) machine-learning retrieval trained on polarimetric, geometric, and orographic predictors and evaluated through leave-one-event-out cross-validation (LOEO-CV). Raw radar exhibited severe and highly variable underestimation, with station-level bias factors ranging from 1.4 to 200×. Global MFB correction removed systematic offset but, as a single spatially uniform scalar, could not improve spatial correspondence; it was beneficial only where the bias field was spatially coherent. Local IDW correction provided cross-validated reduction in RMSE for most events (commonly 40–53%), but this improvement reflected removal of mean bias rather than recovery of spatial pattern: only 17 January 2024 combined RMSE reduction (24.07 mm to 11.31 mm) with genuine spatial skill (leave-one-out r = 0.850, bias-field coherence r = 0.742), while several events improved in RMSE yet retained near-zero spatial correlation, and 30 and 31 January degraded outright. These results characterise the limits of distance-weighted (IDW) interpolation specifically; whether geostatistical estimators incorporating topographic external drift can restore spatial skill where the present gauge network constrains the bias field remains to be tested. When re-evaluated on the same rainy matched-pair set (N = 2378), the Random Forest reduced 10 min RMSE by only 2.6% relative to the best classical Z-R estimator (from 10.38 mm to 10.10 mm) and reduced the systematic bias from −5.06 mm to −4.25 mm, but did not improve point-to-point spatial correspondence (r ≈ 0), indicating that this mean-regression Random Forest provides effective bias-correction skill without spatial-correspondence skill, leaving the fundamental representativeness gap between CAPPI sampling and gauge point measurements unresolved. Three pre-conditions for local bias correction skill are identified as empirical diagnostics under the sample conditions of this study: a minimum of approximately 40 contributing gauges, a spatially coherent bias field, and a moderate bias range. A formal bootstrap or resampling-based uncertainty estimate for these indicators was not attempted, because eleven events constitute too small a sample for stable resampling statistics; the per-event relationships between the number of contributing gauges, the bias-factor range, the bias-field spatial autocorrelation, and the LOOCV error are therefore presented as the empirical basis for these diagnostic indicators, which should be refined and tested for statistical robustness as longer event records become available. These findings demonstrate that the suitability of spatial merging can be diagnosed from network and bias field properties prior to correction, and that machine-learning retrieval offers complementary value through systematic bias removal where spatial interpolation fails. Probabilistic merging frameworks, denser gauge networks, and ML approaches that explicitly target spatial correspondence are identified as priority developments for eastern Mediterranean QPE. Full article

The solid backfilling support robot is key equipment for intelligent backfill mining, but its dual-top-beam structure, multiple closed-loop linkages and hydraulically actuated compaction mechanism make posture representation, inverse actuator mapping and control execution strongly coupled. This study develops a unified kinematic modelling and mechanical–hydraulic-control co-simulation workflow for a ZC5160/30/50 solid backfilling hydraulic support. Closed-loop vector equations are derived for the main support mechanism, rear top-beam mechanism and compaction mechanism, and the mappings among actuator strokes, posture angles and key node positions are established. An engineering posture-index system is constructed for roof contact, support adjustment and backfilling–compaction operation. Forward and inverse kinematic modules are implemented in MATLAB/Simulink and checked using an ADAMS virtual prototype. Representative workspace sampling further shows that all 15 inverse–forward verification cases converge and satisfy actuator stroke constraints, while the residual Jacobians remain full rank with maximum condition numbers of 9.135–9.950. An ADAMS-AMESim-MATLAB/Simulink co-simulation platform is then used to evaluate actuator tracking and PID-based posture-control feasibility. The numerical comparison shows good consistency for the main rigid-body posture indices, whereas conveyor-related relative-position indices show larger deviations because the suspended conveyor motion is affected by gravity in the virtual prototype. Under two target posture cases, the top-beam and compaction-mechanism angle deviations remain within ±0.3°, and the height deviation is below 5 mm. The proposed workflow provides a simulation basis for posture perception, actuator planning and control-system design of solid backfilling support robots; the reported results should be interpreted as model-level numerical consistency and co-simulation feasibility rather than physical prototype accuracy. Full article

As large language models process ever longer contexts, the positional encoding—most commonly rotary position embeddings (RoPEs)—must remain numerically trustworthy. We give an information-geometric analysis of why the attention matrix becomes ill-conditioned as the sequence length L grows. We first show that the tempting frequency-aliasing explanation fails, because RoPE’s multi-frequency code keeps positions well separated. The mechanism is statistical: at fixed softmax temperature the attention entropy grows like $logL$, and the rows spread their mass over a collision support (inverse participation ratio) that grows polynomially as $L^a$ ($a\approx 0.9$ measured), confining the matrix’s trailing singular values and producing a numerical-rank collapse. We prove, and confirm in 50-digit arithmetic, that the spectral condition number is intrinsically ill-posed here, so the numerical rank is the correct diagnostic. From the exact entropy–temperature identity $\mathrm{d}H/\mathrm{d}\beta =-\beta {\mathrm{Var}}_p\left(e\right)$ we derive the entropy-invariant schedule ${\beta }_{★}\left(L\right)={(L/L_{\mathrm{ref}})}^{c_0}$, Curvature-Corrected RoPE (CC-RoPE), and prove that in the diffuse near-circulant regime it eliminates the collapse at a characterised conditioning–mixing cost, whereas no relative-position-preserving warp can. The two signatures are further confirmed in a trained RoPE model (Pythia-160M); the downstream perplexity effect is framed as a falsifiable prediction. Full article

The preconditioned Crank–Nicolson (pCN) Markov chain Monte Carlo method is a standard tool for high-dimensional Bayesian inverse problems because its proposals preserve the prior and its performance degrades gracefully under mesh refinement. Yet its efficiency depends heavily on the step-size parameter $\beta$, which practitioners must tune manually for each problem. To remove this burden, we propose an acceptance rate-adaptive pCN algorithm that calibrates $\beta$ automatically during a finite burn-in phase via a Robbins–Monro stochastic approximation driven by cumulative acceptance rate feedback. We also studied a dual-signal extension that supplements acceptance rate adaptation with an auxiliary effective sample size (ESS) feedback term, activated gradually through a smooth logistic switch. To contextualize the proposed methods, we conducted a systematic comparison with the adaptive Metropolis (AM) and the Metropolis-adjusted Langevin algorithm (MALA). In both adaptive pCN variants, adaptation is confined to the burn-in and the step size is then frozen, so that the post-burn-in samples are produced by a time-homogeneous Metropolis–Hastings kernel targeting the correct posterior. Numerical experiments on five linear-Gaussian benchmarks with a controlled spectral structure (dimensions: 10 to 100, condition numbers: 2 to 200, SNR from $-9$ dB to $+8$ dB) and a nonlinear PDE-constrained inverse problem governed by the cubic–quintic nonlinear Schrödinger equation showed that the acceptance rate-adaptive pCN yields consistent improvements over vanilla pCN, reaching approximately an $1.8\times$ higher ESS in ill-conditioned regimes with low inter-seed variance. The AM achieved a superior ESS on well-conditioned and low-dimensional problems, while the MALA excelled in low-SNR regimes, but degraded under sharp posteriors. A sensitivity analysis confirmed the robustness to hyperparameter choices, and multi-seed experiments validated the reproducibility of all findings. These results indicate that automatic step-size adaptation can substantially improve the practical efficiency of pCN for challenging moderate-dimensional Bayesian inverse problems without manual tuning. Full article

This work introduces a Targeted Emulsified-Solvent Injection (TESI) process for extracting bitumen from oil sands. In TESI, a solvent is emulsified near the emulsion phase inversion point (PIP), where the interfacial tension and the emulsion stability are very low. This allows the solvent to be easily emulsified and then deposited onto the bitumen-coated porous media (under lower shear conditions, where the emulsion breaks), mixing with bitumen, decreasing bitumen viscosity, and enabling mobilization and diluted bitumen recovery. The design of the surfactant-solvent formulation was guided by the Hydrophilic-Lipophilic-Difference and Net-Average-Curvature (HLD-NAC) frameworks. The HLD-NAC was used to identify a formulation with less than 1% surfactant exhibiting ultralow interfacial tension (~10⁻³ mJ/m²), at the PIP, where HLD = 0. This formulation was injected into columns packed with bitumen-coated sands at varying salinities and water-to-solvent ratios. Using optimal conditions, bitumen recoveries of up to 83% can be obtained at room temperature, without the need for steam or high-pressure injection, a condition suitable for intermediate-depth reservoirs. The effluent emulsion of diluted bitumen can be gravity-separated, allowing for the recycling of the aqueous solution containing the surfactant. The recovery curves were modeled using a continuous stirred tank reactor (CSTR) model coupled with a Capillary number model for thin viscous films that allowed the prediction of effluent diluted bitumen viscosities and an estimation of the pressure drops in the column that were consistent with experimental observations. Full article

We present a spectral-action framework for connecting generalized trace dynamics (GTD) to the structural form of the low-energy action of the observed Universe. The fundamental single-STM-atom Lagrangian is decomposed exactly into a purely bosonic sector, boson–fermion cross terms, and bifermionic terms. This sectorwise decomposition furnishes a dictionary to almost-commutative spectral geometry: the bosonic sector supplies a quadratic GTD Dirac functional built from the six split-biquaternionic differential directions together with octonionic vector/gauge fluctuations; the cross-sector supplies, under an explicit localization hypothesis, a sesquilinear fermionic pairing; and the bifermionic sector supplies the scalar/internal channel that is bosonized into the Higgs bridge field. We also record the principal-symbol link between the $\mathrm{SO}(3,3)$ BF variables and the four-dimensional leafwise Dirac operator. The two four-dimensional leaves of the six-dimensional base overlap in two common directions; from the observed (gravitational) leaf, the two nonintersecting directions of the complementary leaf are internal, so the second leaf is reinterpreted as the weak-interaction sector rather than as an independent spacetime—a reinterpretation stated here as an explicit hypothesis. Under stated assumptions—spontaneous localization, Euclidean continuation, six- to four-dimensional BF reduction, and a candidate observed-leaf finite geometry compatible with the $E_6/J_3\left({\mathbb{O}}_{\mathbb{C}}\right)$ inputs—the bosonic heat-kernel expansion yields the structural low-energy classes of terms: Einstein–Hilbert gravity, Yang–Mills kinetic terms, and scalar kinetic and potential terms. In addition, we provide a candidate finite spectral triple with explicit finite trace invariants, verify that the localization map respects the one-generation lepton/quark representation split, identify visible color-singlet scalar channels with electroweak quantum numbers $(\mathbf{1},\mathbf{2},\pm 1/2)$, and exhibit a smooth regulator family with explicit cutoff moments $(f_0,f_2,f_4)$. Conversely, the assembled low-energy spectral action admits a natural inverse bilinear lift back to split bioctonionic trace dynamics. Every arrow of the construction is classified as an exact algebraic identity, an imported result, a working hypothesis, or an open problem. Under this classification, the paper offers a possible architecture for obtaining low-energy gauge–gravity physics from GTD, with conditional consistency checks and reductions; it is not a completed first-principles derivation of the Standard Model coupled to gravity. Full article

Background: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder that elevates the risk of cardiometabolic conditions, including metabolic syndrome (MetS). Aortic knob width (AKW) is a simple radiographic marker reflecting cumulative vascular remodeling and subclinical atherosclerosis. AKW has been associated with cardiometabolic disorders in various populations. However, its clinical relevance in women with PCOS remains unclear. This study aimed to evaluate AKW in women with PCOS and investigate its relationship with MetS. Methods: This retrospective case–control study comprised 200 women diagnosed with PCOS in accordance with the Rotterdam criteria, alongside 200 healthy controls matched for age and body mass index. Clinical, anthropometric, biochemical, and hormonal parameters were recorded. AKW was measured on posteroanterior chest radiographs by two independent observers. MetS was defined based on the 2009 international consensus criteria. Associations between AKW and MetS components were assessed using correlation and covariance analyses. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of AKW in detecting MetS. Results: The AKW was significantly higher in the PCOS group than in the control group (30.43 ± 5.02 mm vs. 28.47 ± 5.21 mm, p < 0.001). The prevalence of MetS was markedly higher in women with PCOS (39%) than in the control group (9.5%). AKW increased progressively with the number of MetS components, with a more pronounced trend in the PCOS group. Significant correlations were observed between AKW and all MetS components, including blood pressure, waist circumference, fasting glucose, and triglycerides (all p < 0.001), while high-density lipoprotein cholesterol showed an inverse correlation. ROC analysis demonstrated moderate-to-good discriminatory performance of AKW for identifying MetS in women with PCOS (area under the curve: 0.80; 95% confidence interval: 0.72–0.86; p < 0.001), with an optimal cutoff value of 33.05 mm (78% sensitivity and 80% specificity). Conclusions: AKW was significantly associated with the presence and burden of metabolic syndrome among women with PCOS. Although AKW demonstrated moderate-to-good discriminatory performance for identifying MetS, prospective multicenter studies with external validation are required before its potential clinical utility can be established. Full article