Search Results (26,037)

Utilising the open-source design of the Federica Prosthetic Hand, we introduce a redesigned print-in-place version that minimises assembly requirements without sacrificing functionality. The original Federica hand has a complicated, time-consuming assembly process. The print-in-place design removes the process of assembling the hand, making it easier for new users to use the design. The approach results in a 21% reduction in the bill of materials and a 21% reduction in weight. Though the 8 N force of the original Federica hand was not reached in this trial, the measured results show that under the same test conditions, the print-in-place hand can match the performance of the original. In a single-specimen analysis, the print-in-place hand produced a higher peak force in four of eight orientations. The redesigned prosthetic hand includes functional joints fully integrated into its structured design with a locking mechanism, eliminating the need for several metal bolts, and a revised dorsal finger contour, reducing the requirement for additional support structures during printing. As with the original hand, the print-in-place prosthetic is open source for anyone to access. Full article

Artificial intelligence (AI) is rapidly being integrated into university curricula, yet quantitative indicators of AI use reveal little about how learners use AI as a learning resource or what educational outcomes follow. This cross-sectional survey study of 212 university students enrolled in AI-integrated courses examined the associations of AI literacy, instructor feedback, and two single-item AI use indicators—the proportion of in-class AI use and total weekly AI use time—with self-perceived cognitive learning outcomes, measured across the six cognitive processes of the revised Bloom’s taxonomy. Confirmatory factor analyses supported multidimensional and higher-order structures, but the cognitive domains overlapped substantially (interfactor correlations up to 0.943; HTMT up to 0.946), so domain-level distinctions should be interpreted with caution. A regression model with the four predictors explained 46.3% of the variance in overall self-perceived cognitive learning outcomes (R² = 0.463, adjusted R² = 0.452). When all predictors were considered simultaneously, only AI literacy showed a significant positive association (B = 0.615, β = 0.593, 95% CI [0.475, 0.754], p < 0.001); the data did not provide evidence for independent associations of instructor feedback or the two AI use indicators, whose weaker associations may partly reflect their single-item measurement. AI literacy remained significantly associated with all six cognitive domains after Benjamini–Hochberg correction. These findings suggest—within the limits of a cross-sectional, self-report design—that the quantity of AI use and learners’ competency to understand, evaluate, and self-regulate AI use are empirically distinct indicators that universities should measure separately. Full article

In high-throughput, high-concurrency, low-latency cryptographic scenarios, the throughput of public-key cryptography is a decisive performance metric. As China’s national elliptic curve cryptography standard, the SM2 signature algorithm has been widely adopted, yet scalar multiplication—the core primitive of SM2 signature—constitutes the dominant latency bottleneck. This paper proposes a high-throughput ASIC architecture that integrates precomputation with parallel task scheduling to accelerate SM2 signature generation. First, we design a Comb-algorithm-based precomputation hardware scheme for fixed-base scalar multiplication. The 256-bit scalar is partitioned into 32 segments, and 32 dedicated lookup tables are precomputed in on-chip SRAMs, which reduces fixed-base scalar multiplication to at most 31 elliptic curve point additions. Second, a six-arithmetic-unit parallel scheduling framework is developed for variable-base scalar multiplication. Equipped with two three-stage pipelined Montgomery multipliers and four modular adders, the design overlaps point addition and point doubling across pipeline stages to boost hardware resource utilization. Moreover, we build a 16-core parallel computing platform integrated with hardware task queues and DMA automatic scheduling, achieving efficient throughput scalability with the increase in core count. The proposed design has been taped out in the TSMC 28 nm CMOS process with completed physical design, including placement, clock tree synthesis, and routing. Post-layout simulation results, with full parasitic extraction (RC) and static timing analysis (STA), demonstrate that a single core achieves 89,593 signatures per second at a post-layout maximum frequency of 600 MHz. Its normalized area efficiency, measured as signatures per kilo gate equivalent (KGE), reaches 33.31 sig/s/KGE under the TSMC 28 nm process at 600 MHz. It should be noted that this metric is significantly influenced by the advanced process node and higher operating frequency; therefore, to enable a fairer assessment of intrinsic microarchitectural efficiency independent of process scaling, the frequency-normalized metric (sig/s/MHz/KGE) is adopted as the primary cross-design benchmark. Under this metric, our design achieves 55.5 × 10⁻³ sig/s/MHz/KGE, which is comparable to the 55.0 × 10⁻³ sig/s/MHz/KGE of the most area-efficient referenced design, with a marginal improvement of approximately 0.9%. The area efficiency comparison is presented only as a supplementary reference within a limited and clearly defined scope, acknowledging that the compared designs differ in functionality, process technology, and evaluation methodology. Furthermore, our design achieves the lowest Area–Time (AT) product of 30.03 KGE·ms among the compared works, indicating that our architectural innovation achieves a favorable AT trade-off for high-throughput applications rather than a fundamental shift in circuit efficiency. The 16-core parallel computing platform reaches an overall throughput of 1.03 million signatures per second. In addition, first-order arithmetic masking and key blinding are integrated into the scalar-multiplication data path to resist first-order side-channel attacks, and simulation-based TVLA evaluation indicates its leakage suppression capability under simulated conditions. Full article

The transition toward a knowledge-based economy has elevated the strategic importance of non-financial information disclosure (NFID). Although Saudi Arabia places knowledge, innovation, and transparency at the centre of its Vision 2030 agenda, evidence on how the pillars of the knowledge economy shape the perceived quality of disclosure in Saudi organizations remains limited. This study examines the association between four knowledge-economy dimensions—human capital, information and communication technology (ICT) infrastructure, innovation systems, and the institutional environment—and the perceived quality of NFID, operationalized through transparency, relevance, reliability, timeliness, and comparability. Primary data from 368 managers, financial managers, sustainability officers, and accountants in Saudi organizations were analysed using descriptive statistics, reliability analysis, exploratory factor analysis, Pearson correlation, and multiple regression. Each organization is represented by a single informed respondent, so the outcome variable is the perceived quality of disclosure reported by knowledgeable insiders rather than quality verified in published reports. The measurement model showed strong reliability and validity (Cronbach’s alpha 0.84–0.89; composite reliability above 0.90; AVE above 0.65). The four dimensions jointly explained 55.9% of the variance in perceived disclosure quality (R² = 0.559; F = 114.99; p < 0.001). All four hypotheses were supported, with human capital the strongest correlate (β = 0.341), followed by ICT infrastructure (β = 0.270), institutional environment (β = 0.237), and innovation systems (β = 0.194). Stronger knowledge-economy capabilities in people, digital infrastructure, supportive institutions, and innovation capacity are therefore jointly associated with higher perceived quality of non-financial reporting under Vision 2030. Because the design is cross-sectional and perception-based, the findings are interpreted as associations rather than causal effects. Full article

Single-criterion feature selection for hyperspectral crop yield estimation suffers from methodological bias and limited generalisation. This study proposes a stacking-fusion feature selection framework with a ridge-regression meta-learner (STACKING_FUSION) that transfers the stacked-generalisation concept to the feature-evaluation space, integrating the Pearson correlation coefficient (PCC), grey relational analysis (GRA), and variable importance in projection (VIP) and using the out-of-fold $R^2$ as a meta-supervision signal for adaptive weighting. In field experiments (2024–2025, Gongzhuling, Jilin Province) on rice cultivars Jijing 830 and Jijing 855, leaf hyperspectral reflectance (400–2400 nm) was acquired under controlled indoor measurement conditions at the tillering, jointing, flowering, and milking stages; the study was thus conducted at the leaf scale rather than at the canopy scale of UAV or satellite remote sensing. Second-derivative spectra outperformed original and first-derivative spectra at most stages, and STACKING_FUSION with XGBoost achieved the highest accuracy ($R^2$ = 0.948, RMSE = 0.018 kg m${}^{-2}$, ratio of performance to deviation (RPD) = 4.399). Joint interpretation using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), computed on a held-out test subset, identified flowering-stage indices as the leading predictors within the model and suggested a flowering–tillering cross-stage association. With the configuration fixed from the 2024 development dataset, the 2025 analysis showed that the framework was reusable as a locked feature-engineering and algorithmic configuration rather than as a directly portable fitted predictor: under strict zero-shot application it retained high predicted–measured correlations (Pearson r≈ 0.91–0.93) but showed a consistent negative bias together with additional scale and residual error, whereas recalibration on a target-domain calibration subset (70% of the 2025 samples) achieved RPD > 3.0 in both the cross-year and cross-cultivar evaluations. These results indicate that the reusable component is the locked feature-engineering and algorithmic setting rather than the fitted coefficients. Full article

The Vietnamese textile and garment industry faces a critical sustainability paradox under the impending Carbon Border Adjustment Mechanism (CBAM). Firms are pressured to maintain high-volume export growth while aggressively minimizing carbon and resource intensity. To empirically resolve this tension, this study develops a novel three-phase benchmarking framework that integrates the Best–Worst Method (BWM), VIKOR, and Super-Efficiency Slacks-Based Measure (Super-SBM) under Variable Returns to Scale. Applied to 12 listed enterprises using 2024 fiscal data, the methodology first identifies Energy Intensity as the paramount strategic priority via the BWM. Next, VIKOR mathematically compresses six heterogeneous environmental, social, and governance (ESG) criteria into a single composite index, thereby eliminating standard Data Envelopment Analysis dimensionality constraints. The Phase 3 Super-SBM results reveal profound sector heterogeneity. The macro-scale giant VGT defines the absolute efficiency frontier with an unprecedented score of 18.6729 and zero operational slack. However, the λ reference matrix identifies mid-cap operators such as Tien Son Thanh Hoa and Binh Duong Garment as highly replicable, agile benchmarks for the broader industry. Crucially, the non-radial projection analysis uncovers hidden structural vulnerabilities. The data show that while certain firms possess massive operational buffers, others operate on the absolute edge of the efficiency frontier, leaving them dangerously exposed to impending carbon-taxation shocks. Furthermore, the model identifies critical instances of ESG decoupling in which green investments fail to yield proportional increases in export revenues. These findings suggest that addressing the sustainability paradox requires targeted structural interventions informed by diagnostic benchmarking. Policymakers and corporate executives must integrate open innovation frameworks and systematic problem-solving methodologies to structurally decouple economic output from fossil-fuel energy dependence and outdated labor-arbitrage models. Full article

Security operations can detect that an intrusion is under way, yet they cannot say how long an ongoing attack still needs to reach a critical objective such as data exfiltration. Prior work on multi-stage attacks identifies the active stage or predicts the next step, but does not estimate the residual time to compromise from real traffic using survival models. This paper addresses that gap through a temporal framework built on empirically measured stage durations, with three contributions. First, the MITRE ATT&CK taxonomy is given a temporal layer, in which each stage carries a duration distribution estimated empirically from the observed episodes of that stage. Second, a probability-weighted multi-path formulation combines these durations with stage-transition probabilities to estimate the time remaining before the objective. Third, the framework is validated on a real multi-stage campaign rather than on synthetic traffic, and three survival models are compared under a matched protocol as a benchmark of how learnable the durations are. Random Survival Forest, DeepSurv, and DeepHit are compared on DAPT 2020, a public advanced-persistent-threat dataset of 82,577 real network flows collected across five days. Random Survival Forest reaches a stable concordance index of 0.92, with a standard deviation of 0.006 across twenty repeated stratified splits on leakage-free features, and it retains a concordance of 0.79 when benign traffic is excluded entirely. When the three models are placed on a single concordance scale and trained on an identical subsample of 40,000 flows, DeepSurv reaches 0.955 and DeepHit 0.879, so the neural models are competitive at that scale. DeepSurv nevertheless fails to converge on the full flow set, returning no survival estimates in any of five seeds, whereas the forest fits successfully at every training size examined. A stage-transition graph recovered from the data, built from 25 observed transitions across ten multi-stage sessions, shows branching progression, and the residual time, reported at the entry to each stage, falls along the campaign, from about 139 min at reconnaissance to about 31 min at lateral movement, conditional on reaching the objective. All stage-level estimates rest on 74 episodes from a single campaign, of which 43 carry a positive duration, so cross-environment generalisation remains to be confirmed. The framework gives a security operations centre a data-driven estimate of the active attack effort that remains before compromise, supporting informed containment decisions. Full article

This paper presents the Adaptive Confidence-Gated Ensemble (ACGE) framework for Network Intrusion Detection Systems (NIDSs) in resource-heterogeneous fog-IoT deployments. No single algorithmic paradigm simultaneously achieves high minority-class detection accuracy, sub-millisecond inference latency, and generalization across heterogeneous IoT traffic at resource-constrained fog-IoT nodes. To address this challenge, ACGE integrates a Sequence Transformer Encoder with LightGBM via a learned and sample-adaptive gating network trained on inter-model error complementarity, formally specified within a three-mode tiered deployment architecture. Both models execute in parallel for every sample; the gating network assigns transformer-dominant weight (α ≈ 1) to 18% of samples where attention-based processing yields the greatest architectural advantage. Under true parallel inference, ACGE latency is 1.45 ms (bounded by the transformer); the measured wall-clock latency under CPU-GPU pipeline parallelism is 1.52 ms. The framework is evaluated under 5-fold stratified cross-validation with McNemar’s significance testing on IoTID20 (primary IoT benchmark) and NSL-KDD (reproducibility anchor) and validated through zero-shot transfer on CICIoT2023 and Edge-IIoTset. Notably, the Sequence Transformer Encoder and ACGE process L = 5 consecutive flow records per decision, providing five times the information available to single-flow baseline models; approximately 57% of the transformer’s performance advantage is attributable to architectural design rather than extended input access. On IoTID20 multiclass classification, ACGE is the only evaluated configuration yielding a negative accuracy-to-macro-F1 gap (−0.51 pp), reaching 99.12 ± 0.03% accuracy and 98.67 ± 0.08% balanced accuracy, demonstrating genuine minority-class improvement. LightGBM achieves the optimal fog-tier profile (0.97 ms latency, concept drift sensitivity of −0.53 pp over three months). On Edge-IIoTset zero-shot transfer, ACGE achieves 93.89% binary accuracy and 85.78% multiclass macro-F1. Ransomware detection (12.34–66.34% F1 across all models) is identified as the primary capability gap; targeted data collection, few-shot adaptation, and federated learning are recommended as the most critical future directions. All inter-paradigm performance differences are reported as the mean ± SD and verified by McNemar’s test with Bonferroni correction. Full article

This study establishes a field-measurement-based wideband modeling and station-level simulation framework for conducted high-frequency (HF) disturbance studies in modular multilevel converter-based high-voltage direct-current (MMC-HVDC) stations. Full-scale engineering-site frequency-response measurements are used to identify kHz-to-MHz terminal models of the arm reactor and the valve-side winding of the converter transformer. The arm reactor is fitted in the admittance domain by vector fitting and synthesized as a passive parallel network containing the main inductive path and multiple damped resistor–inductor–capacitor (RLC) branches. The transformer valve-side winding is represented by a Foster I/II hybrid $\pi$-type terminal network reconstructed from two single-phase port-impedance measurements. The validated equipment models are integrated into a representative Power Systems Computer-Aided Design (PSCAD) station model. A 2 ms valve-side source sequence, constructed from nearest-level-control switching instants and a parameterized switching-transient template, is applied in paired injection and zero-injection simulations. For the representative event, the source peak is 605.6 V. Over the first 4 $\mathsf{\mu }$s, the arm-reactor terminal reaches 972.6 V, while the direct-current (DC)-side, valve-side alternating-current (AC), and point-of-common-coupling (PCC) responses reach 534.2, 438.4, and 151.9 V, respectively. The corresponding peak changes relative to the source are $+4.11$, $-1.09$, $-2.81$, and $-12.01$ dB. The DC-side response contains a dominant damped oscillation near 0.61 MHz, and the AC/PCC transfer varies markedly across 0.2–2.0 MHz. In a separate control-identical comparison over the first 2.5 $\mathsf{\mu }$s, the field-identified and lumped models give DC-side peaks of 171.9 and 1.23 V and PCC peaks of 121.4 and 3.93 V under the same excitation. The framework connects field-identified equipment terminal behavior with station-level time-domain propagation analysis and provides a modeling basis for broadband resonance screening and conducted electromagnetic-interference (EMI) assessment. Full article

Low organic carbon utilization efficiency is a core bottleneck restricting the application of biofloc technology (BFT) in intensive aquaculture, accompanied by limited total nitrogen removal, excessive biofloc accumulation, and underutilized chemical energy in organic wastes. To address this issue, this study develops a novel biofloc technology–microbial fuel cell (BFT-MFC) coupled system that exploits surplus carbon sources in BFT as electron donors for bioelectricity generation while synergistically enhancing water purification performance. Compared with a conventional standalone BFT system, the coupled system was systematically evaluated in terms of water quality regulation, biofloc control and electricity generation performance. Results showed that the BFT-MFC system maintained relatively stable dissolved oxygen, pH, and temperature throughout the operation period. The ammonia nitrogen concentration remained relatively low in the BFT-MFC system, although a transient increase to approximately 0.35 mg/L occurred around day 20 before subsequently declining, and total nitrogen fluctuated within 4.29–12.87 mg/L, with substantially less accumulation than that observed in the BFT system, in contrast to the control group, where TN continuously rose to a peak of 23.55 mg/L. Total organic carbon was stabilized within a narrower range of 150–245 mg/L, compared with the wide fluctuation of 129.6–360 mg/L in the single BFT system. Additionally, the coupled system exhibited lower net biofloc accumulation based on floc-volume measurements, while maintaining effective water-quality regulation, and the integrated MFC delivered a maximum output voltage of 295.9 mV and a peak power density of 1716.8 mW m⁻². Overall, the BFT-MFC coupled system integrates wastewater purification, biofloc regulation and energy recovery into a single unit, offering a promising sustainable strategy for industrial recirculating aquaculture. Full article

A coaxial twin-rotor helicopter features a compact structure by eliminating the tail rotor. Although offering advantages in lift capability and maneuverability, the design presents challenges from its mechanical complexity and the aerodynamic interference between the counter-rotating rotors. During blade intersection, the collision risk of the blades depends not only on the blade-tip distance, but also on the intersection phase and the blade-tip position. In our study, we defined a blade collision warning parameter, d, to represent a fused safety clearance in coaxial twin-rotor helicopters, and proposed a correlated uncertainty propagation model for the measurement chain. The proposed model incorporates uncertainty sources from radar ranging, phase determination, geometric consistency, phase-synchronized triggering, sensor-point substitution, and model discrepancy through covariance terms. Experimental validation is performed on a single-rotor blade-intersection platform under controlled conditions. With the simulated blade rotated at 420 r/min, the combined standard uncertainty ranges from 0.677 to 0.996 mm over the reference warning parameter range of 99–990 mm. The event-level residual-compatibility rate is 86.8%, with localized non-compatibility observed at several reference points. Additional tests at 300 and 600 r/min demonstrated millimeter-level stability. Our uncertainty analysis identified radar ranging as the dominant contributor, followed by model discrepancy and sensor-point substitution uncertainty. Full article

In some scenarios, motorsport simulators have been used to enable the controlled acquisition of dense telemetry with high similarity to real-world data, reducing cost when assessing driving performance. However, although popular, performance analyses traditionally treat human control as deterministic and overlook the stochasticity of driving behavior. In fact, existing coaching methods which improve driving performance have to deal with two distinct outcomes: a driver who restructures his race control strategy and a driver who merely repeats it faster. This article presents a Behavior-First framework for interpretable driver behavior analysis that separates them. We characterize control signals with two information-theoretic descriptors: Jensen–Shannon divergence, which quantifies distributional distance from a proficiency-matched reference and whose square root satisfies the triangle inequality, and Permutation Entropy to measure the ordinal complexity of the input sequence. A deterministic, physics-informed heuristic layer then identifies kinematic performance gaps and emits structured tokens that a Large Language Model translates into natural-language coaching narratives. We evaluated the framework in an exploratory case study. The three beginners who received generated coaching messages and the single uncoached comparison participant exhibited different lap-time and information-theoretic trajectories. Because the groups were small and non-randomized, these observations describe within-driver evolution and do not estimate a causal coaching effect. Full article

Early-stage offshore wind development in the Gulf of Tonkin (Bac Bo Gulf) requires environmental information beyond conventional wind-resource mapping. Waves, currents, directional relationships, and extreme conditions all affect structural loading, operability, and the reliability of preliminary feasibility assessments. Wind, wave, current, and bathymetric data for 1993–2024 were analysed at three representative fixed-bottom screening sites: northern Gulf of Tonkin (NGT), central Gulf of Tonkin (CGT), and southern Gulf of Tonkin (SGT). The analysis characterises long-term conditions, seasonal variability, directional misalignment, wind-conditioned sea states, and marginal extremes with quantified uncertainty. The central site was the most energetic, with a mean wind speed of 7.97 m s⁻¹ at 150 m and a 50-year marginal wind return level of 37.67 m s⁻¹; the southern site had the strongest background current and the most persistent wind–wave misalignment. These differences do not follow a simple north–south gradient, so a single regional environmental description would misrepresent at least one of the three sites. The resulting conditional, directional, event-associated, and marginal-extreme statistics were organised into an IEC-oriented screening framework rather than treated as completed design-load-case inputs. This framework gives developers a basis for comparing site-specific environmental conditions before committing to costly site investigations. However, joint environmental modelling, water levels, tidal and storm currents, site measurements, and load-response analysis remain necessary before detailed design and certification. Full article

Background/Objectives: The lactate/albumin ratio (LAR) integrates tissue hypoperfusion and nutritional reserve into a single dimensionless index. As a late-course measurement (last available value before the primary outcome), it may reflect terminal metabolic deterioration rather than early prognostic signal. We examined the association of the final LAR with in-hospital mortality after adjustment for validated severity scores (APACHE II, SOFA) in critically ill patients receiving CRRT, and evaluated its incremental value beyond lactate alone. Methods: A single-centre retrospective observational study was conducted, including 110 adult patients treated with CRRT/CVVHDF at Konya City Hospital (January 2024–January 2026). Multivariable logistic regression was performed across four hierarchically adjusted models. APACHE II was designated the primary adjustment covariate. ROC analyses compared the final LAR with final lactate and albumin alone. Results: In-hospital mortality was 70.9% (78/110); complete paired final lactate and albumin values were available for 109/110 patients (78 non-survivors, 31 survivors). After APACHE II adjustment, each 0.1-unit increase in the late-course LAR remained significantly associated with in-hospital mortality (OR 1.70; 95% CI 1.16–2.49; p = 0.007), a finding replicated in the ICU-stay sensitivity cohort (n = 100, 73 events; OR 1.63; 95% CI 1.09–2.42; p = 0.016). The LAR trajectory (ΔLAR; AUC 0.725) was significantly greater in non-survivors (+0.104 vs. −0.060; p < 0.001); however, ΔLAR did not significantly outperform the lactate trajectory alone (Δlactate; AUC 0.706; p = 0.212), and lost statistical significance after severity-score adjustment. Furthermore, the association was attenuated and non-significant after SOFA adjustment (OR 1.35; p = 0.077), and the final LAR did not provide statistically significant discriminative advantage over lactate alone (AUC 0.768 vs. 0.761; LRT p = 0.435). Conclusions: The late-course LAR remained significantly associated with mortality after APACHE II adjustment. Three qualifications temper this finding: the association was attenuated after SOFA adjustment, the final LAR did not outperform lactate alone, and the measurement design is susceptible to reverse causality. The LAR trajectory (ΔLAR; AUC 0.725) showed comparable discrimination to the final LAR but did not significantly outperform the lactate trajectory alone (Δlactate; AUC 0.706; p = 0.212) and lost statistical significance after severity-score adjustment, so it should likewise be regarded as hypothesis-generating rather than confirmatory; prospective evaluation with time-stamped serial measurements and landmark analyses in multi-centre CRRT cohorts is warranted. Full article

Material extrusion (MEX) enables rapid fabrication of single-use polymer casting patterns for foundry and mechanical engineering applications, where handling strength and surface texture are critical. This study examines how infill pattern, density, and orientation affect the mechanical and surface properties of samples printed from ELEGOO PLA on a Bambu Lab A1 using Bambu Studio 2.5.0. Two infill types (triangle, grid), two densities (50%, 70%), and five orientations were evaluated via uniaxial tension, fixed-deflection three-point bending, line-profile and areal surface texture measurements, and SEM fracture analysis. Tensile strength ranged from 23.60 to 29.19 MPa. Raising infill density from 50% to 70% increased mean tensile strength from 24.62 to 27.65 MPa. The highest tensile strength, 29.19 MPa, occurred for a 70% grid infill at 75° orientation; the highest bending load at 4 mm midspan deflection, 107.97 N, occurred for a 70% grid infill at 15°. Across the descriptive surface dataset, differences among the top, side, and bottom surfaces were greater than the variations associated with infill orientation. Top, side, and bottom surfaces formed via different mechanisms; the relatively high bottom-surface roughness arose from replication of the textured PEI build plate. The selected lower-density fracture surfaces exhibited more pronounced visible gaps and inter-bead discontinuities in the SEM images. Within the tested range, the 70% grid infill produced the best overall mechanical performance. These findings are specific to the ELEGOO PLA–Bambu Lab A1–Bambu Studio 2.5.0 system and should not be interpreted as intrinsic PLA properties, used for direct cross-material benchmarking, or assumed to represent behavior under industrial foundry conditions. Instead, they provide system-specific screening data for selecting candidate infill configurations and for the subsequent evaluation of this commercial MEX printing platform for producing single-use polymer casting patterns for complex-geometry components in mechanical engineering under representative foundry conditions. Full article