Search Results (6,485)

To achieve rapid and nondestructive evaluation of subgrade compaction quality, this study proposes a subgrade compaction quality evaluation method based on the dynamic modulus obtained from a portable falling weight deflectometer (PFWD), and field experiments were conducted relying on the Gaoyi North Connection Line Project of the Hengxi Expressway in Hebei Province. PFWD tests were carried out on the roadbed under different compaction passes, combined with compaction degree tests using the cutting ring method (CRM) and falling weight deflectometer (FWD) tests, to systematically analyze the evolution law and spatial uniformity of dynamic modulus during the subgrade compaction process. The results indicate that the PFWD dynamic modulus (E_PFWD) exhibits a staged variation characteristic of “rapid increase—slow increase—tending to stability” with increasing compaction passes, and becomes basically stable after the eighth compaction pass. The overall longitudinal compaction quality of the subgrade is relatively uniform, while certain discreteness still exists among different lanes and local areas. The E_PFWD shows good linear correlations with both compaction degree and the equivalent Benkelman Beam deflection values derived from FWD measurements, among which the correlation with FWD results is stronger. The study demonstrates that PFWD can effectively characterize the overall structural stiffness and compaction uniformity of subgrade, providing a reliable basis for rapid detection and uniformity evaluation of subgrade construction quality. Full article

Spindle-current-based force estimation provides a nonintrusive alternative to dynamometer-based milling-force measurement, but its accuracy is limited by the nonlinear and time-dependent relationship between spindle current and cutting force. This study proposes a CNN–ResNet–RF model for instantaneous milling-force prediction using only spindle current signals as input. In the proposed architecture, CNN layers extract local temporal features from windowed current sequences, residual blocks refine multiscale force-related representations, and a random-forest regressor performs nonlinear force regression. Milling experiments were conducted on 7075 aluminum alloy and steel 45 using a five-axis machining center. To prevent temporal data leakage, the synchronized and preprocessed current–force data were divided at the continuous cutting trial level into training, validation, and independent test subsets. On the independent test subset, the proposed model achieved an R² value of 0.952, an MAE of 2.793 N, and an RMSE of 4.301 N, outperforming the CNN, CNN–ResNet, and RF baseline models in terms of prediction accuracy and error reduction. These results demonstrate that the CNN–ResNet–RF framework improves test-set milling-force prediction within the tested machining range. Full article

The evaluation of stents in coronary computed tomography angiography (CCTA) is still a major topic in cardiovascular imaging. Using Photon-Counting Detector CT (PCCT) may improve the assessment of coronary stents and make on-site CT-FFR analysis feasible for ruling out in-stent restenosis (ISR). In this study, patients with previous coronary stent implantation who underwent CCTA using PCCT and subsequent invasive catheter angiography (ICA) were included. Stent characteristics such as location and length were reported. CT-FFR measurements were taken 1.8 cm before and after the stent, with a value of ≤0.80 defined as hemodynamically significant under respecting the diagnostic accuracy drop in the gray zone between 0.76 and 0.80. Delta CT-FFR with a cut-off value of ≥0.06, indicating hemodynamic significance, was determined. Any ISR and interventional treatment during the following ICA was recorded. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated for post-stent CT-FFR and Delta CT-FFR in detecting ISR. Patients were followed up to evaluate the rate of major adverse cardiovascular events (MACE) 6 months after CCTA. A total of 19 patients (5 female, 14 male, median age 69 years) were enrolled in this study. In most cases, coronary stents were located in the proximal LAD with a median stent length of 70.2 mm. Pathological CT-FFR < 0.76 distal to the stent was detected in 6 cases (31.6%), while pathological Delta CT-FFR ≥ 0.06 occurred in 14 cases (73.7%). ICA was performed in three of these patients, with ISR confirmed in two cases. These findings yield sensitivity and NPV of 100% for both post-stent CT-FFR and Delta CT-FFR for excluding ISR with a superior specificity (76.5% vs. 29.4%) and overall diagnostic accuracy (78.9% vs. 36.8%) for post-stent CT-FFR. Two patients reported a myocardial infarction in follow-up; however, neither of them was located in the territory of the stented coronary artery. This study outlines the feasibility of on-site CT-FFR analysis using PCCT in excluding ISR in coronary stents with a high diagnostic accuracy. These findings highlight the need to extend the benefits of CT-FFR analysis for non-invasive assessment of possible ISR regarding personalized risk stratification and therapy planning. Full article

Machine learning (ML) is increasingly applied to geochemical baseline estimation and anomaly detection in soils and sediments, yet the methodological conditions under which machine learning outperforms traditional approaches—and which preprocessing and validation decisions most consequentially determine that advantage—remain incompletely characterized across environmental and mineral exploration domains. A structured systematic scoping review of 146 records from the Web of Science Core Collection applied sequential filtering to yield 78 thematically eligible studies, from which 20 were prioritized through a composite index integrating age-adjusted citation impact, platform usage, and semantic relevance. Four cross-cutting findings emerge. First, performance gains in environmental applications were driven primarily by spatial model structure rather than algorithm selection: incorporating a spatial covariate derived from geographically weighted regression raised test-set explained variance from $R^2=0.80$ to $R^2=0.96$ for cadmium mobility prediction in a geochemically heterogeneous karst setting, a gain the source study supported with a held-out test set and a Monte Carlo analysis of sensitivity to data size. Second, isometric or centered log-ratio preprocessing was applied in the majority of mineral exploration studies (three of five classical and hybrid studies and four of five deep-learning studies) but in none of the seven environmental studies, representing a systematic methodological gap with direct consequences for covariate importance estimates under compositional closure. Third, Shapley additive explanations and accumulated local effects functioned as instruments of operational value, enabling element-specific anomaly threshold derivation, training sample diagnosis, and grid-cell anomaly type classification; this evidence demonstrates that the accuracy–interpretability trade-off commonly assumed in the machine learning literature is not fundamental in geochemical applications but contingent on algorithm selection. Fourth, 90% of the 20 synthesized studies (18 of 20 by study-area location—13 in China and five in Iran) were evaluated under within-domain validation designs, and the consistently high performance metrics reported should be interpreted as interpolation estimates rather than evidence of transferable predictive capability. Geographic diversification of training datasets and spatially explicit cross-regional validation are identified as structural prerequisites for regulatory-grade applicability. Full article

Background/Objectives: Contemporary patient-specific subperiosteal implants have re-emerged as graftless solutions for oral and maxillofacial rehabilitation, driven by advances in digital planning, CAD/CAM workflows, additive manufacturing, and biomaterial engineering. Their indications have progressively expanded from severely atrophic edentulous jaws to segmental defects, single-tooth replacement, congenital craniofacial anomalies, salvage situations, and oncologic reconstruction. This scoping review aimed to map the current evidence on modern patient-specific subperiosteal implants, focusing on indications, workflow, design principles, materials, outcomes, complications, and maintenance. Methods: A scoping review was conducted according to PRISMA-ScR principles to identify clinical studies, case series, case reports, systematic and scoping reviews, technical notes, finite element analyses, in vitro studies, and relevant translational investigations dealing with contemporary custom-made or CAD/CAM subperiosteal implants. The evidence was narratively synthesized according to clinical indication and thematic domains, including full-arch rehabilitation, sectional and single-tooth applications, congenital and post-oncologic defects, rescue indications, biomechanics, material selection, surface response, prosthetic protocols, and complication management. No quantitative meta-analysis was performed because of the scoping design and the substantial heterogeneity of study types, indications, implant systems, outcome definitions, and follow-up durations. Results: The final evidence map included 116 records, of which 56 were unique human clinical records with extractable denominators and 60 were biomechanical, in vitro, surface-biology, review, consensus, historical, or conceptual records. Of the 56 unique clinical records, 49 were mapped within the six indication-level clinical sections, while seven were retained as cross-cutting clinical evidence addressing patient-reported outcomes, design-related complications, bone apposition, anchorage strategy, comparative graftless rehabilitation, or reconstructive/prosthetic principles. The six indication-level sections included 52 clinical-record assignments: 15 for full-arch rehabilitation, 13 for segmental or sectional rehabilitation, one for single-tooth rehabilitation, four for congenital or craniofacial indications, 13 for post-oncologic or post-ablative reconstruction, and six for rescue or salvage indications. Because three records addressed more than one indication, these counts represent indication-level assignments rather than mutually exclusive clinical records. Reported survival in most short- to mid-term clinical series was generally high, commonly ranging from 90% to 100%, although lower values of 70–80% were reported in selected longer-term cohorts and survival clearly overestimated clinical success in some studies. Expanding applications include posterior mandibular and maxillary defects, lateral incisor agenesis, cleft-related or syndromic deformities, maxillectomy reconstruction, obturator support, and hybrid rehabilitation with endosseous implants; however, evidence for the indications at the extremes of this spectrum—single-tooth replacement and primary oncologic reconstruction—remains limited to small, largely single-group case series and reports. Soft-tissue events, including dehiscence, mucositis, recession, and framework exposure, were the dominant complications and showed wide variability, with reported recession/exposure rates ranging from approximately 10% in some sectional and full-arch series to as high as 65% in bilateral maxillary cohorts; their clinical significance varied from asymptomatic stable findings to progressive inflammatory complications requiring revision. Conclusions: Patient-specific subperiosteal implants represent a promising and increasingly versatile reconstructive option; however, the present findings should be interpreted as evidence mapping rather than as definitive comparative evidence. Their clinical use should remain highly selective, prosthetically driven, and supported by meticulous planning, rigid fixation, soft-tissue management, and structured maintenance. Standardized success criteria, longer follow-up, and comparative prospective studies are required. Full article

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) is characterized by high recurrence rates despite appropriate treatment and surveillance. Identifying inexpensive and readily available biomarkers capable of improving risk stratification remains an important clinical challenge. The platelet-to-lymphocyte ratio (PLR), a marker of systemic inflammation, has emerged as a potential prognostic indicator in several malignancies. This study aimed to evaluate the association between preoperative PLR, tumor recurrence, and recurrence-free survival (RFS) in NMIBC patients treated with intravesical Bacillus Calmette–Guérin (BCG) or thermochemotherapy. Methods: This retrospective study included 153 patients diagnosed with NMIBC between January 2020 and January 2024. All patients underwent transurethral resection of bladder tumor (TURBT) followed by intravesical BCG (n = 123) or thermochemotherapy (n = 30). Preoperative PLR was calculated from complete blood counts obtained before surgery. Receiver operating characteristic (ROC) analysis was used to determine the optimal PLR cut-off value. Recurrence-free survival was evaluated using Kaplan–Meier survival analysis and Cox proportional hazards regression models. Results: During a mean follow-up period of approximately 19 months, recurrence was observed in 35.8% of patients treated with BCG and 30% of those treated with thermochemotherapy. ROC analysis demonstrated good discriminatory ability for recurrence prediction (AUC = 0.831, 95% CI: 0.761–0.901, p < 0.001) and identified an optimal PLR threshold of 120. Patients with elevated PLR values demonstrated higher recurrence rates and shorter recurrence-free survival. Kaplan–Meier analysis revealed a clear separation of survival curves according to PLR status. In multivariable Cox regression analysis, PLR > 120 remained independently associated with recurrence-free survival in the BCG group (HR = 2.703, 95% CI: 1.118–6.534, p = 0.027), whereas only a borderline association was observed in the thermochemotherapy group (HR = 23.265, 95% CI: 0.952–568.336, p = 0.054). Conclusions: Elevated preoperative PLR was associated with recurrence and recurrence-free survival in patients with NMIBC. The prognostic value of PLR appeared to be more pronounced in patients receiving intravesical BCG therapy. Given its low cost, accessibility, and ease of calculation, PLR may serve as a useful adjunctive biomarker for clinical risk stratification when used alongside established clinicopathological prognostic factors. Further prospective multicenter studies are required to validate these findings. Full article

As a critical insulation component in cable systems, the cable terminal is susceptible to defects caused by human and environmental factors during manufacturing, installation, and service. Such defects may lead to local electric-field distortion and insulation weaknesses at the cable terminal, posing a severe threat to the safe operation of the cable system. In this study, an electric-field simulation model of a 10 kV cable terminal was implemented to investigate the effects of various defects, such as insufficient stress-cone overlap, axial scratch, ring-cut defect, and moisture ingress on the cable terminal. The results show that insufficient stress-cone overlap produces a severe field distortion, and the distortion level is strongly correlated with the misalignment distance. For mechanical damage defects, axial scratches and ring-cut defects mainly distort the electric field inside the air gap, and defect position induces a stronger distortion level than that of defect depth. With increasing ring-cut depth, the maximum value of distorted electric field first decreases and then rises slightly. For moisture defects, the distorted field primarily occurs at the angle between the water-film tip and the stress cone, where the maximum value appears near the XLPE/SIR interface. These results provide a theoretical basis for defect diagnosis, structural optimization, and assembly process control of cable terminals. Full article

The fourth industrial revolution has been discussed generously in literature, as it centers around offering high value and customized products or services to the consumer by harnessing the potential of cutting-edge technologies. It comes as no surprise that it has brought about a paradigm shift in the manufacturing and services sector; however, it is imperative to analyze the variables which influence its adoption. Although there has been an increasing number of studies helping us to understand the adoption of Industry 4.0, there is no structured and process-oriented implementation roadmap that brings together contextual factors for the adoption, nor a step-by-step methodology regarding improvements. Therefore, the authors have conducted a review in which the barriers to Industry 4.0 adoption have been analyzed in a manufacturing context and their corresponding drivers have been discussed. The study reveals that top management commitment, clear strategy, and a skilled workforce play a significant role in the adoption of Industry 4.0 technologies. Afterwards, the authors have developed a conceptual framework for Industry 4.0 adoption by combining DMAIC with a Technology–Organization–Environment (TOE) framework. The recommended framework is designed to facilitate sustainable digital transformation, helping organizations navigate through a structured ability-building process, upskill their workforce, and embrace technologies that align with sustainability objectives. From an academic perspective, the research makes key contributions to technology management literature by utilizing the TOE approach in a proper manner through DMAIC principles. For practitioners, the research work provides an easy four-step process that can assist them in adopting Industry 4.0 technologies in a proper manner. Full article

Under 10 min sparse Automatic Identification System (AIS) sampling, the reliability of point-wise motion statistics degrades substantially, and conventional classification methods rely on trajectory interpolation, which may introduce spurious motion patterns. This study proposes a feature-driven framework for fishing vessel type identification that eliminates the need for interpolation preprocessing. A 39-dimensional feature set is constructed using robust statistics, including the median and interquartile range, to characterize trajectory-level behavioral patterns. Adaptive speed interval thresholds are derived through a data-driven approach grounded in Bayesian decision boundaries, thereby removing the dependence on manually defined cut-off values. A backward ablation procedure guided by feature importance ranking identifies a lightweight 12-dimensional feature subset that retains 98.7% of the classification accuracy at a compression rate of 69%. Evaluated on 18,320 fishing vessel trajectories in the East China Sea, the full 39-dimensional feature set achieves a 5-fold cross-validation accuracy of 91.92% (Macro-F1 = 0.919, Kappa = 0.879), with inter-fold standard deviations ranging from 0.002 to 0.004. Comparative experiments demonstrate that three tree-based classifiers all exceed 90% accuracy on the same feature set, confirming that feature robustness, rather than model selection, constitutes the dominant performance factor. LightGBM achieves the optimal trade-off between accuracy and training efficiency, whereas the cross-validation standard deviation of LSTM is approximately 7.5 times greater, indicating that hand-crafted robust features provide superior stability under sparse sampling conditions. The proposed framework requires no fishery-specific prior knowledge and offers a transferable paradigm for sparse AIS trajectory analysis. Full article

Forecasting multi-asset volatility spillovers and turning the forecasts into risk-aware portfolios requires methods that uncover directional information flow between assets, compress the state into a minimal sufficient representation, deliver calibrated uncertainty, and respect explicit tail-risk limits. We propose TDV (Transfer-entropy, Dynamic-graph-attention, Variational-information-bottleneck), an information-theoretic artificial intelligence framework that couples a time-varying transfer entropy network with a graph attention encoder regularised by a variational information bottleneck, and demonstrates the practical value of the calibrated predictive distribution through a downstream entropy-regulated, CVaR-constrained portfolio application. We establish three theoretical results: $L^2$ consistency of the k-nearest-neighbour transfer entropy estimator on $\alpha$-mixing returns with rate $O_P\left(n^{-2/(2+d)}\right)$, a PAC–Bayes generalisation bound of order $O\left(\sqrt{\left(I\right(X;Z)+log(1/\delta \left)\right)/n}\right)$ for the bottleneck-encoded forecaster, and asymptotic CVaR feasibility of the plug-in allocation. In simulations across sparse Granger networks, contagion DCC–GARCH ensembles, and regime-switching factor models, the framework cuts spillover forecasting errors by 24 to 42 percent against LSTM, vanilla GAT, and Transformer baselines, and it recovers 1.6 additional nats of mutual information with the realised connectedness matrix. On a 32-asset global panel covering 2014 to 2025, the model delivers an out-of-sample $R^2$ of 0.331, an annualised Sharpe ratio of 1.46 against 0.83 for an equally weighted benchmark, a maximum drawdown of 7.8 percent, and 95 percent CVaR reductions of 28 to 36 percent across sub-periods relative to a shrinkage minimum-variance baseline. Full article

In this study, the milling performance of Inconel 718 alloys produced by forging (WP1), Inconel 718 produced by Selective Laser Melting (SLM) (WP2), and Inconel 718 (WP3) subjected to heat treatment after SLM, under different cooling/lubrication conditions, was evaluated using experimental and artificial intelligence-based approaches. Microstructural analysis showed a homogeneous fine-grained structure in WP1, while WP2 exhibited dendritic features and porosity. Heat treatment improved the microstructural homogeneity of WP3. The hardness values of WP1, WP2, and WP3 were 457 Hv, 303.33 Hv, and 391 Hv, respectively. Milling experiments yielded cutting forces of 336.5–1185.9 N, surface roughness values of 0.22–1.39 µm, and cutting temperatures of 168–658 °C. Compared with dry machining, MQL reduced average cutting force and cutting temperature by 15.5% and 18.65%, respectively, while improving tool wear and surface integrity. Machine learning models including LR, DTR, SVR, and GPR were developed to predict machining responses. GPR provided the highest prediction accuracy, achieving 98.72% for cutting force and 98.99% for cutting temperature. The results demonstrate that manufacturing route and cooling strategy significantly affect the machinability of Inconel 718 and that machine learning techniques can effectively support machining process optimization. Full article

Five-axis CNC machining involves strong coupling among machining quality, material removal efficiency, and operational safety, making it difficult to obtain adaptive and feasible process parameters using conventional scalar-objective optimization methods. To address this problem, this study proposes a physics-constrained multi-objective multi-agent deep deterministic policy gradient framework, termed MOMADDPG, for Pareto-oriented optimization of five-axis machining parameters. A data-calibrated digital twin simulation environment is constructed to model five-axis kinematics, tool-workpiece engagement, cutting force, chatter tendency, spindle power, tool wear, actuator bounds, and collision risk. The PHM Society 2010 milling dataset is used to calibrate the cutting force and tool wear sub-models, while five-axis motion, tool orientation variation, and engagement conditions are generated within the digital twin environment. In the proposed framework, three heterogeneous agents are assigned to quality preservation, efficiency improvement, and safety assurance, respectively. A hierarchical attention Actor is designed to enhance feature extraction under partially observable machining conditions, while vector-valued dual Critics preserve objective-specific value information. Physical constraints are handled using adaptive Lagrangian multipliers, and a Pareto archive-guided preference curriculum is introduced to improve the diversity of feasible non-dominated solutions. Simulation results show that MOMADDPG achieves a task success rate of 98% and a hypervolume value of 0.674 after training. Compared with representative baselines, including DQN, MADDPG, MAAC, and MAPPO, the proposed method provides better Pareto-front approximation, higher task feasibility, and stronger robustness under process perturbations in the data-calibrated five-axis simulation environment. The results demonstrate the potential of combining digital twins and multi-objective multi-agent reinforcement learning for safe and adaptive parameter optimization in five-axis machining simulations. Further validation on physical five-axis CNC systems is still required before industrial deployment. Full article

Smart-port decarbonisation requires operations-research decision support under day-ahead uncertainty. We present a rolling-horizon Model Predictive Control Energy Management System, formulated as a Mixed-Integer Linear Program with five forecast streams, and benchmark it against a deterministic rule-based controller on an identical configuration. A full-year proof-of-concept at the Port of Ancona (8760 hourly steps over the 2024 Italian Day-Ahead Market, 6.5 MWp PV, 1.0 MWh BESS) combines realised 2024 market, photovoltaic and auxiliary-demand series with a post-AFIR projected cold-ironing demand—the dominant load—and is therefore an operational proof-of-concept rather than a fully metered baseline. The principal MPC outcome is structural: anticipatory dispatch raises the mean BESS state of charge from 13.6% to 46.0% and cuts residence at the minimum SoC from 81% to 6% of hours. The forecasting layer attains sub-7% sMAPE on cold-ironing-loaded demand and 9–18% on the remaining streams (seasonal MASE₂₄ ≤ 0.74 on demand and price streams). At the relay-constrained 0.08 C pilot, the realised savings is 0.44% (€14,463 yr⁻¹; 95% moving-block bootstrap CI [€12,842, €15,742]); benchmarked against an enhanced rule-based controller that is itself permitted price-threshold grid charging, the residual value of predictive optimisation is €5652 yr⁻¹ (0.17%), with the remainder of the gap being the value of enabling grid charging. A C-rate sweep shows the savings doubling to 0.93% at 0.5 C, and a direct 20 MWh/±10 MW simulation yields a €0.57 M yr⁻¹ gross arbitrage savings whose net value, after a realistic battery-degradation penalty, is substantially smaller. Controller-level operational CO₂ rises marginally (+6.2 t, +0.13%), an effect distinct from—and dwarfed by—the system-level cold-ironing decarbonisation. The framework is reproducible in open-source Python (PuLP/HiGHS) from the actual data and is portable to other single-node smart city energy hubs. Full article

Seawater intrusion remains a critical threat to coastal groundwater, where subsurface cut-off walls are commonly used for mitigation. This study employs 2D variable-density numerical modeling to investigate the impacts of hydraulic conductivity anisotropy (rk = 0.02–50) and seasonal inland groundwater fluctuations on residual saltwater desalination, quantified by means of RRSM and RRSL. Our results revealed that rk is inversely correlated with final desalination efficiency. Lower rk values (0.02–0.1) achieve exhaustive salt removal despite requiring longer flushing durations. Conversely, higher rk values significantly suppress efficiency and induce violent oscillations in desalination rates under seasonal forcing. A critical failure mechanism was identified: intensified vertical flow dynamics allow saltwater to “overtop” the barrier during low inland groundwater stages, triggering severe secondary intrusion. These findings underscore that conventional cut-off wall designs may be inadequate under dynamic boundaries, necessitating taller barrier configurations and precise anisotropy assessments to ensure long-term functional resilience in coastal aquifer management. Full article

Citrus peel is examined here as a naturally evolved protective surface, with the goal of developing a transferable quantitative framework for extracting engineering-relevant descriptors from biological protective surfaces and using them as design templates for biomimetic counterparts. A single-specimen-per-species design is adopted to map intra-fruit geometric variation across regions and magnifications; absolute descriptor values are therefore reported as ordinal indicators of inter-species ranking rather than as population means. Five citrus species (lemon, orange, mandarin, grapefruit, and bitter orange) were characterised by mechanical testing (cutting, puncture, and compression; five replicates per fruit), gravimetric peel density and thickness, and scanning electron microscopy (SEM) at 100×–10,000×. The 135-image SEM dataset was processed with an automatic-calibration pipeline performing per-image scale-bar detection, multilevel-Otsu segmentation of albedo air space, cell-bounded surface segment (CBSS) and oil-gland segmentation on flavedo, and grey-level co-occurrence matrix (GLCM) texture analysis with a directional anisotropy index $A_F$. Calibration was consistent across all images (FoV × magnification $=\mathrm{403,273}\pm 410$ $\mathsf{\mu }$m$·\times$, $\pm 0.10\%$). Principal component analysis separated flavedo and albedo at every magnification (PC1 + PC2 = 84–92%). Within this dataset, grapefruit showed the densest CBSS cover (1072 ${\mathrm{mm}}^{-2}$) together with the highest oil-gland density (2.77 ${\mathrm{mm}}^{-2}$); bitter orange showed the largest CBSS area (23.7 $\mathsf{\mu }{\mathrm{m}}^2$) and the thickest peel (13.1 mm); mandarin showed the most directionally oriented flavedo film ($A_F=0.0885$); and lemon showed the most open albedo (${\phi }_{2D}=36.2\%$). Oil-gland equivalent diameter was essentially invariant (∼45 $\mathsf{\mu }$m) across the five fruits, while gland density varied 4.4-fold. The structural metrics define a layered descriptor space—a dense isotropic surface relief versus a thick cellular bulk—that supplies two distinct bioinspired-design priors: dense surface films as a structural prior for selective-permeability membranes and layered cellular cores as a prior for impact-absorbing panels. A modified-atmosphere packaging (MAP)-compatible biomimetic film is identified as one downstream design hypothesis requiring direct gas-permeability verification on synthetic membranes. Full article