Abstract Thermal runaway tests of nominally similar lithium-ion cells can yield substantially different consequences. We examine severity variability using 365 tests from an open battery failure databank and an empirical Thermal Runaway Severity Uncertainty Amplification (TR-SUA) descriptor, defined as the ratio of post-failure to recorded pre-test coefficients of variation (CVs), not as a measured physical gain. Forty-six near-replicate groups (349 tests) were defined by recorded cell description, format, and trigger ( n ≥ 3). The median available three-descriptor input CV was 0.104%, versus output CVs of 7.577% for corrected total energy, 14.730% for ejecta energy, 21.943% for unrecovered mass, and 21.903% for unrecovered-mass fraction. With a 1%-point input-CV floor, median ratios ranged from 7.577 to 21.943 and remained sensitive to alternative input definitions and floors. Exhaustive within-group log decomposition identified 1,564 valid test pairs; among 1,094 pairs with at least 10% ejecta-energy contrast, the absolute EPR term exceeded the total-energy term in 58.0%, which describes an algebraic partition, not mechanistic causality. Cluster-bootstrap P95 intervals were appreciably broad, and model-group/format holdouts revealed poor predictive transfer despite high random-fold classification AUC. The results support reporting severity dispersion and uncertainty-qualified upper-tail outcomes, while unobserved cell/protocol conditions prevent a causal physical-amplification claim.
This study presents a compact machine-learning model for same-day biogas nowcasting in anaerobic digestion on edge-computing hardware. Implemented with Tiny Machine Learning (TinyML) and executed on an ESP32 low-cost microcontroller, the model was trained on a public dataset of 287 daily observations from a mesophilic reactor at 37 °C, using five biochemical variables, their one-day lags, and previous-day biogas production. To avoid optimistic bias, models were evaluated retrospectively with rolling-origin validation over five temporal windows, and the embedded demonstration architecture was fixed a priori rather than selected on the test data. Additional compact architectures showed discordant internal-validation and future-test rankings. A previous-day persistence baseline achieved a mean R² of 0.572 and linear autoregression 0.573, whereas the predefined 16–8 network achieved a mean R² of 0.343, showing that biogas dynamics in this stable reactor were dominated by process inertia and that biochemical variables did not improve prediction beyond the simple temporal baselines. On-device inference verification reproduced the Python reference across all 58 test vectors to within 1.67 × 10⁻⁵ NL/d at a mean latency of 48.899 µs at 240 MHz; persistence and autoregression ran in under 0.2 µs. A reduced-feature farm-scale evaluation with supervised recalibration, after meter-consistency quality control, showed a weak positive correlation but no positive skill on the later test segment (r = + 0.365, R² = −0.237, MAE = 0.30 L/d). These findings support a reproducible embedded pipeline and a rigorous benchmark, rather than claims of superior accuracy or field-system validation.
Abstract The current study focuses on optimizing AC TIG welding parameters to achieve higher hardness and better control of the heat-affected zone (HAZ) using a Taguchi L16 orthogonal array on 10 mm-thick AA5083 alloy. The study establishes a heat-input-oriented optimization framework for AC TIG welding of thick AA5083 aluminum alloy by integrating statistical optimization with metallurgical characterization. The control parameters, including AC pulse modulation frequency, peak current, and welding speed, were selected to achieve improved hardness and a smaller heat-affected zone (HAZ). Qualitative microstructural characterization using optical microscopy, SEM, and XRD indicated comparatively finer weld morphology and fewer visible defects under lower heat-input conditions, while XRD confirmed the presence of the α-Al matrix together with Al₁₃Fe₄, Mg₂Si, and minor Si phases. The results indicate that lower heat input contributes to improved hardness retention and reduced HAZ width within the investigated parameter range, providing practical guidance for AC TIG welding of thick AA5083 aluminum alloy.
Abstract This study numerically investigates and compares the glass fiber–reinforced polymer (GFRP) reinforced concrete beams flexural behaviour with four concrete types: geopolymer concrete (GPC), ordinary Portland cement concrete (OPC), and fiber-reinforced geopolymer concrete (FRGC), fiber-reinforced concrete (FRC). Despite increasing interest in sustainable and advanced material systems, particularly those integrating polypropylene fibers with FRP reinforcement, comprehensive design provisions remain limited, and GFRP-reinforced fibrous geopolymer concrete has been insufficiently explored. No new experimental testing is conducted. Instead, the study develops high-fidelity finite element models calibrated and compared with previously published experimental data by the authors. The primary contribution centers on numerical modeling and interpretation. The validated models accurately capture load-deflection response, strain distribution, cracking, stiffness loss, deformability, and failure modes for all material systems. The applicability of ACI 440.11-22 provisions is also assessed. While the code yields conservative predictions of flexural capacity, the finite element analysis (FEA) results indicate strong agreement with experimental outcomes. Including fibers increases flexural capacity by up to 20% and deformability by 24–28% versus non-fibrous versions. The study demonstrates that validated numerical modeling can reliably assess emerging FRP-concrete systems and support future code development for sustainable structures.
Abstract The Central Area of Makkah faces severe seasonal environmental pressures from heavy vehicular traffic, particularly during Hajj. Using the DPSIR framework, this study conducts a spatial and environmental diagnosis of this high-density urban core. To assess the environmental conditions, spatial sampling and field measurements were conducted at 32 strategic locations, using a calibrated Extech VPC300 Video Particle Counter for PM2.5 and PM10, along with portable multi-parameter detectors for CO and CO2. Field measurements captured two seasonal operational extremes: Phase 1 (baseline peak congestion, 20 Dhu Al-Qi’dah 1446AH) and Phase 2 (strict traffic cordon restrictions, serving as a real-world LEZ proxy, 2 Dhu al-Hijjah 1446AH). Paired-samples t-test validation indicates that during peak congestion, localised CO2 reached 1,750 ppm and noise peaked at 95 dB, while localised PM2.5 and PM10 particle counts demonstrated elevated concentrations. Methodological baseline calculations reveal that the localised prayer shuttle fleet generates a carbon footprint four times larger (57,888 tons of CO2) than the inbound regional fleet, condition compounded by the dense ‘street canyon’ morphology. Conversely, spatial analysis identifies 2.5 million m2 of vacant land. As a targeted ‘Response’, this paper introduces the Makkah Low Emission Zone (M-LEZ) framework. Distinct from conventional urban LEZs, the M-LEZ introduces a dynamically phased seasonal framework that utilises underused peripheral land for green infrastructure and transit hubs. It aims to balance mass transit logistics with microclimate sustainability and promotes public health for pedestrians.
