Search Results (322)

Truck Turnaround Time (TTT), defined as the total gate-to-gate time during a truck visit to a container terminal, is a critical performance indicator of landside operations. Although machine learning has been applied to TTT prediction, existing studies typically pool import and export movements into a single model, overlooking operational heterogeneity between flows. Moreover, little attention is paid to analyzing the importance of factors that enable TTT prediction. This study develops a flow-disaggregated predictive framework for a Chilean container terminal using 754,568 export and 1,056,351 import truck visits recorded between 2017 and 2023. Four tree-based ensembles and three neural network architectures are benchmarked under a chronological train/test split. Tree-based models tend to achieve marginally lower errors, though differences are small and not uniform across flows. A pronounced asymmetry emerges: import predictions are substantially more accurate (MAE = 6.79 min; WAPE = 34.36%) than export predictions (MAE = 32.49 min; WAPE = 52.53%), and this gap persists after normalizing for differences in mean TTT. TreeSHAP analysis identifies distinct predictive structures: export TTT is primarily associated with gate congestion and maritime service activity, while import TTT is more strongly associated with intra-terminal travel distance and crane operator experience. The higher Gini concentration and bidirectionality of dominant export predictors are consistent with unobserved drivers—such as the states of inspection queues (customs, sanitary, etc.) and the off-dock truck staging area—that limit predictive accuracy beyond process variability alone. In the integrated model, flow-identifying variables rank among the most influential features, providing empirical support for flow disaggregation. These findings indicate that flow-specific modeling improves both accuracy and interpretability in operationally heterogeneous terminal processes. Full article

Leucoptera malifoliella (Lepidoptera: Lyonetiidae) is a quarantine pest of apple and other Rosaceae fruit trees whose range is expanding into new territories. Its minute size and morphological overlap with closely related Lyonetiidae make routine identification unreliable, especially for larvae and damaged specimens. We compared COI sequences from six common small Lepidoptera species found in orchards and designed the species-specific primer pair SXW-F/SXW-R. The resulting polymerase chain reaction (PCR) assay amplifies an ~500 base pairs (bp) fragment exclusively from L. malifoliella; no product was detected in any of five non-target species. The reaction tolerates annealing temperatures of 50–58 °C and consistently detects the target across all life stages (first- to third-instar larvae, pupae, adults) and all adult tissues tested (antennae, head-thorax, abdomen, wings, legs). Detection sensitivity reaches 0.03 ng/μL—approximately one-thousandth of the DNA content of a single adult. This is the first species-specific COI (SS-COI) method reported for L. malifoliella. It furnishes a rapid, specific, and sensitive diagnostic tool for quarantine inspection, field monitoring, and integrated pest management (IPM) programs. Full article

The white peach scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Hemiptera: Diaspididae), is a highly polyphagous insect pest of global phytosanitary relevance affecting fruit trees, perennial crops, and ornamental plants in temperate and subtropical regions. Its invasive success is largely driven by high thermal tolerance, a wide host range, and strong survival capacity under postharvest handling and cold-chain storage, which greatly enhances its potential for long-distance dispersal through international trade of fresh fruit and plant material. A systematic review and meta-synthesis of 105 scientific studies published between 1958 and 2026 identified key research areas, including geographic distribution and invasion pathways, host plants and varietal susceptibility, temperature-dependent life history, agricultural impact and quarantine risk, chemical and biological control strategies, and the development of phenology-based monitoring and prediction tools using thermal models and pheromones. Although recent official reports do not confirm the presence of P. pentagona in Ecuador, climate suitability modeling and evidence of long-term survival during cold storage indicate a high risk of introduction and establishment, particularly in inter-Andean valleys. Consequently, preventive phytosanitary surveillance and integrated pest management strategies tailored to Ecuadorian Andean fruit production are proposed, emphasizing phenological monitoring, nursery and agro-urban inspections, and the conservation of natural enemies as a foundation for sustainable management of this emerging pest. Full article

Intrusion detection systems for 5G networks are typically reported at a single operating point, obscuring the trade-off between missed attacks and false alarms that governs real deployments. Building on a lightweight conditional counter-inspection pipeline, in which a global classifier is selectively validated by curriculum-biased experts under a unanimous dissent rule, we remove the constraint that all components share one learning algorithm, assigning decision trees, random forests, extremely randomized trees, and histogram-based gradient boosting independently to the global (G), malicious-biased (EM), and benign-biased (EB) roles. Across two datasets of contrasting difficulty, 5G-NIDD and UNSW-NB15, all 14 evaluated tree-based configurations reduce missed attacks, by 36.5–79.6% on 5G-NIDD, confirming that the recovery effect is a property of the architecture rather than of decision trees. The expert assignment also selects which error the system controls: the same pipeline can be steered toward fewer false alarms, fewer missed attacks, or higher aggregate F1 without retraining the first stage. The mechanism also rescues a weak linear filter: on 5G-NIDD it cuts false positives and false negatives by 92.8% and 95.8%, and on UNSW-NB15 it raises F1 from 0.903 to 0.934 while reducing missed attacks by 35.5%. These results reframe the pipeline as a configurable validation layer matched to a deployment’s cost structure. We further show, through direct measurement on both datasets, that the conditional routing evaluates at most four of seven models per record, keeping classifier inference below 0.1 ms per record and leaving the detection stage a small contributor to overall processing cost. Full article

Deep visual recognition has achieved remarkable success across various domains, including medical imaging, autonomous vehicles, and security systems. However, the black-box nature of deep learning models poses challenges in terms of transparency and trust, especially in critical applications where human understanding is essential. Explainable AI (XAI) seeks to address these concerns by providing human-interpretable explanations for model predictions. This review explores the key techniques for explainability in deep visual recognition, including model-agnostic methods such as LIME and SHAP, model-specific approaches like saliency maps and feature visualization, and intrinsically interpretable models like decision trees and rule-based systems. We also discuss the evaluation of explainability through metrics like fidelity, consistency, and stability, and explore the challenges of balancing model performance with interpretability. Furthermore, we examine applications of XAI in medical imaging, autonomous driving, security and surveillance, agriculture, satellite imagery and remote sensing, industrial inspection, and visual forensics, highlighting how domain-specific data and operational constraints affect the required form and validation of explanations. Finally, we address current research gaps and propose future directions for enhancing the robustness and human–AI interaction in explainable visual recognition systems. As AI continues to be integrated into safety-critical domains, the development of explainable, transparent, and trustworthy AI systems will be crucial for their widespread adoption and ethical use. Full article

Sustainable forest management requires inventory workflows that provide spatially explicit structural information while retaining field-based calibration and uncertainty control. This case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory in middle-aged and near-mature larch plantations in Chuanying District, Jilin City, China. UAV laser scanning point-clouds were integrated with six 30 m × 30 m field plots to assess three individual-tree extraction algorithms: point-cloud segmentation (PCS), marker-controlled region growing (MCRG), and region-based hierarchical cross-section analysis (RHCSA). Algorithm performance was evaluated using plot-level recall, precision, F-score, localization RMSE, tree-height and crown-width accuracy, bootstrap confidence intervals, exploratory Wilcoxon signed-rank comparisons, and leave-one-plot-out stability checks. MCRG provided the most balanced numerical performance under the tested configuration, with a mean F-score of 0.845, compared with 0.808 for PCS and 0.827 for RHCSA. However, the MCRG-RHCSA paired difference was not robust across the six plots, and the analysis should be interpreted as a dataset-specific workflow comparison rather than a universal algorithm ranking. Tree height was estimated with comparatively high accuracy, whereas crown-width estimation remained weak, indicating that vertical canopy structure was more reliable than lateral crown delineation. After calibration assessment, the workflow was applied to 157.47 ha of UAV LiDAR survey areas and generated 219,996 algorithm-based detections. These outputs are best interpreted as a spatial decision-support layer for compartment updating, density screening, and field-inspection prioritization, not as an independently verified wall-to-wall stem census. Full article

Shapelet-based classifiers offer structural interpretability: discriminative subsequences form an inspectable vibration-pattern vocabulary and a shallow decision-tree ensemble produces traceable fault-type predictions. We impose explicit interpretability constraints on the model and apply Bayesian optimisation within this bounded region; the primary experimental question is whether these constraints carry a performance penalty relative to an unconstrained baseline. Evaluation uses recording-level cross-validation on CWRU (Case Western Reserve University) and MFPT (Machinery Failure Prevention Technology) bearing datasets with Hilbert envelope demodulation. The central finding is that the constraints impose no systematic performance penalty: the shapelet classifier matches ROCKET, a non-interpretable baseline, on both datasets, with cross-validated mean F1 differences smaller than the fold-to-fold standard deviation. To further characterise the selected models under the controlled laboratory conditions studied here, we assess probability calibration and conformal prediction coverage as secondary analyses. Raw probability estimates are well-calibrated, but Platt scaling degrades under cross-severity distribution shift; split conformal prediction yields valid coverage on CWRU but fails on MFPT due to class-proportion mismatch across recording-level splits. Together, these results show that structural constraints supporting interpretability are compatible with competitive performance, and identify the conditions under which reliability tools succeed and fail in this setting. Full article

This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures. Full article

Routine inspections of transmission lines are essential for maintaining the reliability of the power grid. Airborne LiDAR technology provides detailed 3D corridor data for automated hazard detection, such as vegetation encroachment and structural anomalies. However, manually analyzing large point clouds is inefficient, and current segmentation methods struggle with scene complexity, scale variation, and the high cost of annotation. In this study, we present a label-efficient segmentation method built on MinkUNet, a sparse voxel convolutional network enhanced with self-attention modules in its encoder–decoder for better spatial reasoning over corridor objects (e.g., trees, buildings, towers). To further handle structural diversity and class imbalance, we adopt task-specific data augmentations and focal loss. A multi-stage pseudo-labeling strategy is then employed to enable effective cross-scene generalization with minimal labeled data. We validate our method on three real-world transmission line datasets. On the Foshan dataset, it achieves a mean Intersection over Union (mIoU) of 0.740 with an inference time of 1.31 s. Cross-scene tests at two other locations, Shumuyuan and Langwang Village, yield mIoUs of 0.762 and 0.757, respectively. These results confirm robust performance even with limited annotations. Overall, our findings demonstrate the practicality of our approach for routine power line inspections, enabling reliable hazard detection with minimal annotation effort. Full article

U.S. renewable energy consumption has expanded substantially over the past five decades, but this transition cannot be adequately characterized by aggregate growth alone. This study developed an integrated empirical, forecasting, uncertainty, reconciliation, scenario, and planning framework for U.S. renewable energy consumption using a complete monthly multisector panel from January 1973 through December 2025. The analytic dataset contained 3180 sector–month observations across 636 monthly periods and five reporting sectors: Commercial, Electric Power, Industrial, Residential, and Transportation. The framework combined data harmonization, mutually exclusive source-family construction, long-run trend analysis, source-mix diversification metrics, structural-regime diagnostics, sector–source panel analysis, rolling-origin forecast benchmarking, probabilistic interval assessment, hierarchical reconciliation, future scenario analysis, and decision-focused planning evaluation. Annual reported total renewable energy consumption increased from 2475.547 trillion Btu in 1973 to 7050.214 trillion Btu in 2025, equivalent to approximately 2.476 quadrillion Btu and 7.050 quadrillion Btu, respectively. The results show that U.S. renewable energy growth was also a source-mix transformation: the portfolio became less concentrated as wind, solar, transportation biofuels, renewable diesel, waste, and other emerging sources gained importance alongside legacy wood and hydroelectric power. Sector–source heterogeneity was substantial, with Electric Power, Industrial, and Transportation showing distinct renewable-source profiles. Forecasting performance depended strongly on model family, horizon, validation window, target group, and evaluation lens. Strong statistical baselines and feature-based tree models remained competitive or superior to several deep learning architectures, while time-series foundation models provided useful modern comparators but required calibration and horizon-specific interpretation. All five selected foundation model comparators completed successfully. ChronosBolt was the fastest and strongest completed foundation model comparator, followed in runtime by TimesFM, Moirai/Uni2TS, TimeGPT, and LagLlama; however, foundation model forecasts remained too smooth for peak-sensitive planning and did not displace the strongest feature-based tree models in point-forecast benchmarking. Probabilistic diagnostics showed that nominal coverage alone was insufficient because interval width, Winkler score, CRPS, and visual inspection revealed target-specific miscalibration, underforecast bias, and weak peak coverage. Hierarchical and decision-focused evaluation changed the model-selection narrative: bottom-up and reconciled hierarchical forecasts produced stronger planning-loss and planning-value profiles than many nominally advanced alternatives, while selected tree-based models were particularly useful for preserving source-share allocation. Scenario analysis showed that solar acceleration increased projected totals but also increased concentration and coherence divergence, whereas diversification reduced concentration but required wider uncertainty buffers. Overall, U.S. renewable energy consumption should be analyzed as a dynamic, diversified, hierarchical, and planning-sensitive system. The proposed framework provides a reproducible basis for evaluating renewable energy growth, source-mix evolution, forecast reliability, uncertainty, source allocation, scenario trade-offs, and planning value beyond single-model forecasting claims. Full article

The measurement of a probe impedance performed during eddy current inspections enables detection of flaws in electrically conductive materials. A correct interpretation of the measured impedance values constitutes a key aspect that determines the effectiveness of the inspections, and for this purpose, mathematical models are employed. Such models, which are becoming more and more frequently an integral part of eddy current measurement systems, enable carrying out the calculation of the probe impedance, through depicting the measurements being performed. What offer the shortest calculation time while maintaining high accuracy are analytical solutions. In this paper, to the best of the authors’ knowledge, this is the first time an analytical model of an eddy current probe placed over a small diameter cylinder containing a hole has been presented. The final formulas were obtained using the truncated region eigenfunction expansion (TREE) method, and then implemented in Matlab. The calculated values of the probe resistance and reactance were compared with the measurement results obtained for cylinders with a through defect. The tests were conducted on components made of several conductive materials with different geometric dimensions. The measurement error in all of the tests was small, i.e., it did not exceed 3% across the entire frequency range. The proposed solution can be used in defectoscopy for eddy current testing of tubes, pucks, washers, and any cylindrical elements. Full article

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible. Full article

Mitochondrial genomes are widely used in insect taxonomy and phylogenetics, but their signals may conflict with morphology and nuclear genomic evidence because the mitochondrial genome represents a single maternally inherited locus. Here, we assembled complete mitochondrial genomes of four pygmy grasshoppers, Zhengitettix transpicula, Formosatettix sp., Gibbotettix parvipulvillus, and Bolivaritettix sp., using PacBio HiFi reads. The four mitogenomes ranged from 15,152 to 17,976 bp and contained the typical 37 mitochondrial genes. Mitochondrial phylogenies inferred by maximum likelihood and Bayesian methods were topologically identical and recovered several well-supported tetrigid relationships, including a close relationship between Formosatettix sp. and Bolivaritettix sp. However, Z. transpicula was unexpectedly placed near Macromotettixoides rather than close to other Zhengitettix representatives. In contrast, a morphology-based tree recovered Z. transpicula with Z. triangularis, and comparison with a published nuclear single-copy ortholog tree based on 1962 loci supported a non-mitochondrial placement of Zhengitettix inconsistent with the anomalous mitochondrial position of Z. transpicula. Independent assembly from the original HiFi reads, read-depth inspection, protein-coding gene checks, and nuclear-genome screening for NUMT-like sequences supported the authenticity of the assembled Z. transpicula mitogenome. These results document mito–nuclear and cyto-morphological discordance in Tetrigidae and highlight the need for integrative interpretation of mitochondrial phylogenies in taxonomically complex insect groups. Full article

The early and accurate recognition of fruit tree pests and diseases is essential for safeguarding fruit yield, quality, and sustainable agricultural production. Conventional manual inspection methods are inadequate for meeting the demands of continuous, objective, and real-time monitoring in large-scale orchards. Following the framework of “model evolution–key challenges–edge-intelligent deployment,” this review systematically summarizes advances in deep learning-based recognition of fruit tree pests and diseases, and compares the effectiveness and limitations of representative methods from the perspectives of data complexity, model generalization and robustness, real-time inference, cross-modal fusion, and trustworthy diagnosis. Existing studies indicate that CNNs, attention mechanisms, Transformers, multimodal fusion, and lightweight networks have promoted the transition of fruit tree pest and disease recognition from image classification to object detection, lesion segmentation, and edge deployment; however, sample scarcity, class imbalance, insufficient cross-domain generalization, black-box decision-making, energy constraints, and long-term robustness remain major bottlenecks for field application. Future research should focus on open orchard environments and develop data-efficient, interpretable, low-power, and continuously updatable edge-intelligent recognition systems, thereby advancing precision agriculture and smart orchards. Full article

Autonomous inspection of discrete obstacles (e.g., tree trunks in orchards and forests) requires UAVs to visit every target with proper observation distance and heading, while simultaneously exploring the unknown environment. Existing space-guided exploration methods focus on eliminating unknown space and are inherently agnostic to the inspection targets themselves, leading to incomplete coverage and redundant traversal. We observe that the obstacles themselves encode the spatial topology of the environment and can serve as natural planning anchors. Based on this insight, we propose ORACLE, an Object-centric Autonomous Coverage Exploration framework that shifts the planning paradigm from space-guided to target-guided exploration. ORACLE integrates: (1) an online target detection and persistent identification module via occupied-voxel connected component labelling, (2) a density-aware global coverage planner that modulates ATSP costs to prioritize target-dense regions, and (3) a target-guided local planner that replaces frontier viewpoints with direct obstacle observation points in a Sequential Ordering Problem formulation. Experiments in two point-cloud environments reconstructed from real-world forests with contrasting tree densities (Environment I: 50 trunks, $\overline{n}=1.56$; Environment II: 70 trunks, $\overline{n}=2.19$; both with non-uniform spacing) show that ORACLE achieves $98.8\%$ and $99.7\%$ target coverage compared to $22.7\%$ and $25.1\%$ for the space-guided baseline, while reducing the mission overhead ratio from $202.9\%$ to $129.2\%$ (Environment I) and from $176.8\%$ to $126.6\%$ (Environment II). Ablation studies confirm that zone reactivation is the decisive factor for coverage completeness ($-18.8$ and $-17.2$ percentage points when disabled in Environments I and II, respectively) and that density weighting improves path efficiency. Full article