Abstract Camera images and LiDAR point clouds provide complementary information for 3D object detection by supplying visual cues and geometric measurements, respectively. Existing bird’s-eye-view (BEV) fusion methods typically map modality-specific features into a shared BEV space and integrate them into a joint representation. Ideally, the fused representation should retain information from both modalities while distinguishing object regions from the surrounding background. However, in many existing BEV fusion methods, the fused representation is optimized primarily through the downstream detection objective, without direct constraints on modality information retention or foreground–background discrimination at the fusion stage. To remedy this shortcoming, we present a Camera–LiDAR 3D object detection framework with two complementary forms of auxiliary supervision for the fused BEV representation. Specifically, Dual-Modality BEV Feature Reconstruction uses two separate decoders to reconstruct the pre-fusion camera and LiDAR BEV features from the fused representation. The corresponding losses encourage modality-specific information to remain recoverable after fusion. In parallel, BEV Foreground Supervision derives class-agnostic targets from the BEV footprints of ground-truth 3D boxes and uses them to supervise a dense foreground map predicted from the fused feature, encouraging clearer foreground–background discrimination. Experiments on nuScenes show that the proposed method achieves 73.4% NDS and 70.8% mAP on the validation split, together with 74.6% NDS and 71.9% mAP on the test split. Ablation studies further confirm that each objective improves the baseline individually, while their combination delivers the best performance among the evaluated configurations.
Abstract Micro-grid planning conventionally treats the equipment model as a pre-assigned condition and optimizes only the installed capacity, thereby ignoring the differences among candidate models in their rated operating parameters (e.g. the energy-storage charging and discharging efficiency), their economic parameters (e.g. the procurement and fixed operation and maintenance costs) and their operational constraints (e.g. the maximum battery capacity), and hence the influence of the equipment model on the planning scheme and on its annual net revenue. This paper therefore proposes a joint planning model for equipment selection and capacity matching that maximizes the annual net revenue. A wind-PV-storage output model and a set of candidate equipment models are first constructed, and a mixed-integer linear programming model is then formulated with the ANR as its objective, covering the equipment-selection, capacity, operation and power and energy balance constraints, so that the equipment model and the installed capacity are decided in a single unified framework rather than sequentially. The model is solved by a commercial mixed-integer programming solver on the MATLAB platform, and the optimal equipment selection scheme and capacity configuration are obtained. In an industrial micro-grid case study the proposed model raises the ANR by up to 4.05% with respect to benchmark schemes with pre-assigned equipment models while maintaining zero load loss throughout the year, thereby removing the disconnection between equipment selection and capacity configuration and improving the economy, the capacity scheme and the equipment-type compatibility of micro-grid planning. It provides theoretical support and a methodological reference for the planning and design of micro-grid projects.
Abstract Decked-charge blasting includes axially discontinuous or decoupled charge configurations, with air-deck blasting as one subtype. Here, air-deck denotes an intentional air or gaseous void, whereas decked-charge includes air, water, expanded-polystyrene (EPS), and polyethylene (PE) decks. Published fragmentation results remain inconsistent because rock conditions, blast geometry, deck design, response definition, and reporting differ across studies. This study develops a literature-derived, source-aware evidence-synthesis and exploratory interpretable-modelling framework. A dataset of 42 observations and 114 fields from six sources was reconstructed; 30 observations from four sources were regression-eligible, comprising 20 air-deck, two water-deck, one EPS-deck, one PE-deck, and six conventional/no-deck cases. Random Forest achieved leave-one-out MAE = 82.29 mm, RMSE = 126.50 mm, and R 2 = 0.391. Bayesian optimization improved tuning-stage performance, but nested leave-one-source-out validation remained weak. Independent external validation on 31 field cases from three unseen publications yielded a best pooled result of MAE = 134.64 mm, RMSE = 158.27 mm, and R 2 = − 0.194; all evaluated models had negative external R 2 . Explainability identified response family, rock strength, burden-to-diameter ratio, hole depth, observation scale, hole diameter, specific charge, stemming, bench-height-to-burden ratio, and deck position as influential. Sensitivity analysis showed that within-dataset associations were not driven solely by response family, while cross-source transfer remained limited by source and response-definition heterogeneity. Source-level comparison found smaller fragmentation in 11 of 18 paired cases (mean − 21.91%; median − 31.48%). Air-decking should therefore be treated as a site- and configuration-dependent option requiring local verification, and the framework as an evidence-synthesis tool rather than a direct blast-design predictor.
Abstract Rapid advances in AI and robotics are extending manufacturing digital twins beyond passive monitoring to closed-loop control and physical intervention. Yet in manufacturing QA, the term “digital twin” is used for both systems that only observe and systems that act on the physical process. This distinction matters because once a digital twin acts, model validity is no longer enough; the intervention must also be authorized, bounded, traceable, and verified. Existing maturity frameworks capture connectivity and automation but give less attention to the assurance requirements that arise when digital-twin outputs are translated into physical action. We address this gap by introducing an Embodiment-Authority-Assurance (EAA) framework that distinguishes how quality information leads to physical action, who has authority over that action, and how the intervention is assured. We apply the framework to a systematic review of 235 manufacturing QA studies, selected from 868 publications identified through Scopus and Web of Science between 2017 and 2026, classifying them across five maturity levels from digital shadows to governed adaptive QA twins. We found that the frontier of manufacturing QA digital twins is shifting from monitoring and advisory support toward human-approved and bounded autonomous intervention, with higher-maturity applications becoming increasingly visible in recent years. Yet these advances remain fragmented across the literature, with no reviewed study bringing them together into a fully assured intervention chain. By revealing this shift, our study reframes manufacturing QA digital-twin maturity around a new frontier: not simply whether digital twins can act, but whether increasingly autonomous interventions can be governed and assured.
Delivering affordable, reliable, and sustainable electricity in lower-middle-income countries requires spatially explicit tools that translate remote-sensing and infrastructure data into decision support for sustainable development planning. Focusing on Egypt, we update the national wind-energy suitability map using a Geographic Information System (GIS) and a multi-criteria decision analysis framework. High-resolution spatial inputs—remote-sensing-derived land use/land cover and updated road and electrical-grid networks—are integrated and weighted across eight criteria using the Analytic Hierarchy Process (AHP). The results indicate that approximately 1.60
