Search Results (4,209)

This study presents a simulation-based evaluation of a wall-following and mapping framework for autonomous underwater vehicles (AUVs) equipped with a single-line laser, targeting structured environments such as rectangular tanks and dam interiors. A hardware-in-the-loop (HIL) simulation platform is developed to integrate sensor emulation, vehicle dynamics, and image-based control while preserving the onboard data formats, update rates, and communication protocols of the AUV system. Using a single camera–laser pair, the framework estimates yaw angle and lateral wall distance from laser image geometry to support real-time wall-following and frontal obstacle avoidance. Wall mapping is performed by transforming laser image features into spatial coordinates and estimating the dimensions of geometric protrusions. The framework is evaluated on simulated walls with protruding features under two navigation conditions: ideal-motion and dynamic-control operation. Simulation results show stable wall-following performance, with lateral distance errors typically below 0.1 m. Under ideal-motion conditions, mapping errors range from 1% to 13%, while under dynamic-control navigation they increase to 10–35% due to attitude fluctuations and control-induced motion. Frontal obstacle avoidance maintains a minimum clearance of 1.04 m. The results demonstrate the feasibility of using a single-line laser and a unified image stream for both real-time wall-following control and post-mission geometric mapping within the defined simulation conditions. While the evaluation is limited to simulation and assumes idealized optical conditions without modeling hydrodynamic disturbances or optical degradation effects, the framework provides a system-level reference for laser-guided inspection strategies in confined underwater environments such as tanks, reservoirs, and dams. Full article

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with reference block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

The paper presents SFPD, the new open algorithm developed by the University of Padova (PD in the acronym) for computing the steady-state regime due to any number of simultaneous faults (SF at the beginning of the acronym) both short circuits and open conductors. The algorithm does not have simplified hypotheses, since it benefits from the pre-fault regime based on PFPD_MCA (power flow by University of Padova with multiconductor cell analysis), a multiconductor power flow (developed and published by the first author) which takes into account both the active conductors (i.e., the phases subjected to the impressed voltages) and the passive conductors (i.e., the interfered metallic conductors, namely earth wires of overhead lines, metallic screens and armors of land and submarine cables, enclosures of gas insulated lines, return and earth wires of 2 × 25 kV AC high-speed railway supply system, etc.). Different types of faults are considered, and where they occur (also along the lines), by means of a suitable admittance matrix in phase frame of reference and embedded inside the overall network bus admittance matrix. Some comparisons with simplified approaches are presented in order to demonstrate the power of the method. Eventually, application to the real Italian network is comprehensively shown. Full article

Contemporary urban planning increasingly refers to pro-environmental standards, among which the 3–30–300 principle plays a significant role. One of its key assumptions is that at least three trees should be visible from every dwelling. However, a review of the existing literature reveals a substantial research gap: current analyses of green visibility are predominantly based on two-dimensional models, which fail to account for variations in the elevation of the observation point. This article introduces an original methodology for three-dimensional tree visibility analysis that explicitly incorporates building floor level as a critical factor shaping the resident’s visual perspective. The proposed approach integrates the determination of observation point locations, the modeling of architectural obstacles, and line-of-sight analysis within a 3D spatial framework. The results obtained for the selected test area indicate that only 16.3% of the analyzed windows provide a view of the required minimum of three trees. From 14.3% of windows, two trees are visible, while a single tree is visible from 27.4% of windows. Notably, as many as 42% of observation points offer no view of any trees at all. Full article

In the fabrication process of pipelines for petrochemical and other industries, weld reinforcement is often excessive and adversely affects subsequent processes such as anticorrosion treatment and surface coating. Weld reinforcement must be removed through a grinding process. Welding deformation and fit-up errors often lead to highly irregular weld geometries, which makes robotic grinding difficult and causes the task to still heavily rely on manual operation. To address this issue, this study proposes an automatic weld recognition and grinding trajectory planning method based on 3D visualization and deep learning. A weld recognition network, termed WSR-Net, has been developed based on an improved PointNet++ architecture with a cross-attention mechanism, achieving a segmentation accuracy of 98.87% and a mean intersection over union of 90.71% on the test set. An intrinsic shape signature (ISS) key point selection algorithm with orthogonal slicing-based pruning optimization is developed to robustly extract key weld ridge points that characterize the weld trend on rugged weld surfaces. According to the height differences between the weld and the adjacent base metal surfaces, the grinding reference surface is fitted using the weld contour through the moving least-squares method. The ridge line points are projected onto the grinding reference surface along the local normal to generate the expected grinding trajectory points. The grinding trajectory that meets the process constraints is generated through reverse layer slicing. Grinding experiments demonstrate that the proposed WSR-Net achieves robust segmentation performance for both planar and curved surface welds. With the reverse layered trajectory planning method, the proposed method enables high-precision automatic grinding of complex spatially curved surface welds. The results show that the final grinding mean error is 0.316 mm, which satisfies the preprocessing requirements for subsequent processes. The proposed method provides a feasible technical method for the intelligent grinding of spatially curved surface welds. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

Background: Surgical management of adult patients with post-dysplastic coxarthrosis using total hip arthroplasty is technically demanding and carries an increased risk of complications. In cases of high iliac dislocation classified as Crowe type IV, restoring the acetabular component to the anatomical hip centre often requires femoral shortening osteotomy to enable safe reduction in the prosthetic joint. Nevertheless, long-term evidence on functional outcomes and prosthesis survival with this approach is limited. Methods: A retrospective cohort study included 19 patients with 22 cases of Crowe type IV post-dysplastic hip osteoarthritis treated with uncemented total hip arthroplasty (Pinnacle/S-ROM, DePuy, Warsaw, IN, USA) combined with transverse subtrochanteric femoral shortening osteotomy. Patients underwent serial clinical follow-up, including assessment of range of motion, measurement of limb-length discrepancy, and functional evaluation using the Harris Hip Score and the WOMAC questionnaire. Radiological assessment included evaluation of osteotomy union, implant positioning, and osteolysis on standardized radiographs. Vertical distances of the centre of rotation (CR), the tip of the greater trochanter (GT), and the tip of the lesser trochanter (LT) from both reference lines were measured bilaterally, and inter-side differences were calculated. The reference lines consisted of the line connecting the inferior margins of the ischial bones and the teardrop (TD) line. Results: All osteotomies united at a mean of 5.57 months, with a mean follow-up of 129 months. Mean limb-length discrepancy decreased from 5.27 cm to 1.5 cm, and mean hip flexion improved from 82.9° to 106°. Functional outcomes improved significantly, with mean WOMAC increasing from 55.4 to 80.1 (p < 0.001) and mean Harris Hip Score from 49.8 to 84.66 at up to 3 years of follow-up (p < 0.001). Osteotomy length correlated strongly with lesser trochanter–teardrop distance (p = 0.00000048). Complications included distal femoral fissure (27.3%) and revision (18%), with no infection or permanent neurological deficit. Conclusions: Total hip arthroplasty combined with subtrochanteric femoral shortening osteotomy for Crowe type IV post-dysplastic hip osteoarthritis appears to be a feasible and effective procedure in an experienced centre, providing reliable osteotomy healing and significant early functional improvement that is sustained over time. Limb-length discrepancy was reduced and satisfactory biomechanical restoration was achieved, with an acceptable complication profile and implant survival of 81.3% at long-term follow-up. The LT–TD parameter was identified as a potential predictor of osteotomy length, enabling the proposal of a preoperative planning equation. However, given the limited sample size and lack of validation, these findings should be interpreted cautiously. Further studies are needed to confirm their broader applicability. Full article

With the increasing density of transmission lines, line crossings and spans have become more common, and the electromagnetic environment of transmission lines has attracted increasing attention. Investigating the electromagnetic field distribution in transmission line crossing regions is therefore of great significance for line layout and preliminary design. In this study, the parameters of transmission lines in crossing regions are first obtained by parsing the GIM (Grid Information Model) file. A three-dimensional electromagnetic field model of a double-circuit transmission line on the same tower is then established using the finite element method, and the accuracy of the proposed approach is validated by comparison with field measurement data. Based on the developed model, the electric and magnetic field distributions of both the double-circuit transmission line and the crossing region are calculated. Furthermore, the effects of different crossing angles, phase sequence combinations, and voltage levels on the electromagnetic field distribution are systematically investigated. By comparing the electromagnetic field characteristics under different phase sequence schemes, an optimized phase sequence configuration for double-circuit transmission lines and crossing regions is proposed. The results provide a theoretical basis and technical reference for electromagnetic environment assessment and design optimization of transmission lines in crossing regions. Full article

Autonomous navigation is essential for orchard transportation robots to support automated operations and precision orchard management. However, in trellis orchards, dense vegetation and complex canopy structures often degrade the stability of GNSS-based navigation in in-row environments. To address this issue, this study proposes a GNSS–vision integrated navigation framework for orchard transportation robots. The performance of GNSS-based navigation in out-of-row environments and vision-based navigation in in-row environments was experimentally evaluated under representative orchard operating conditions. In out-of-row areas, the robot employs GNSS-based path planning and trajectory tracking to achieve reliable navigation in relatively open, lightly occluded environments. During in-row navigation, a deep learning-based real-time object detection approach is used to detect tree trunks and trellis supporting structures. By integrating corner-point selection with temporal RANSAC-based line fitting, a stable orchard row structure is constructed to generate robust navigation references. The visual perception module serves as the front-end sensing component of the navigation system and is designed to be independent of specific object detection architectures, allowing flexible integration with different real-time detection models. Field experiments were conducted under various orchard layouts and growth stages. The average lateral deviation of GNSS-based navigation in out-of-row scenarios ranged from 0.093 to 0.221 m, while the average heading deviation of in-row visual navigation was approximately 5.23° at a robot speed of 0.6 m/s. These results indicate that the proposed perception and navigation methods can maintain stable navigation performance within their respective applicable scenarios in trellis orchard environments. The experimental findings provide a practical and engineering-oriented basis for future research on automatic navigation mode switching and system-level integration of orchard transportation robots. Full article

Additive manufacturing enables the rapid fabrication of radiographic phantoms for X-ray and CT imaging, supporting applications such as patient simulation, dosimetry, imaging protocol optimization, and quality assurance. Polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS) are among the most widely used printing polymers in phantoms; however, their X-ray attenuation properties can vary substantially among manufacturers, product lines within manufacturers, and even between colors of the same product. Cylindrical samples of 34 PLA filaments from 11 manufacturers and 13 ABS filaments from 9 manufacturers were evaluated for X-ray attenuation and energy dependence between 70 and 140 kV using a clinical CT scanner. Measured mass densities ranged from 1.17 to 1.34 g/cm³ for PLA and 1.03–1.11 g/cm³ for ABS. At 120 kV, Hounsfield unit (HU) values spanned 109 to 424 HU for PLA and −34 to 40 HU for ABS. Energy dependence, quantified as the HU at 70 kV minus HU at 140 kV, ranged from −29 to +172 HU for PLA filaments and −52 to −4 HU for ABS filaments. Identical products differing only in color showed HU variations from <2 HU to >90 HU at 120 kV, with no consistent pattern linking specific colors to highest or lowest attenuation. These findings demonstrate that 3D printing materials require individual characterization, as base polymer designation alone does not predict X-ray behavior accurately. The observed variability, however, enables the design of phantoms with tailored attenuation and energy-dependent contrast. Referring only to base polymers when specifying 3D printing materials for radiographic phantoms or suggesting printing materials as radiographic substitutes to mimic a specified tissue or reference material without naming the actual product, including color, is, thus, insufficient. Full article

Advanced Driver Assistance Systems (ADAS) are rapidly evolving to improve driving safety and comfort, in line with the European Community’s Vision Zero objectives. However, current validation methods mainly focus on steady-state conditions and regulatory compliance, while standardized KPIs capable of capturing vehicle dynamics during transient maneuvers and relating them to human driving behavior are still lacking. This study proposes a KPI-based methodology to evaluate lane centering systems in both steady-state and transient conditions, using a human driver as a reference. New parameters—aggressiveness and smoothness indices—are introduced to quantify the dynamic response. Simulator and constant-speed vehicle tests highlight the differences between ADAS strategies and human behavior, providing insights into comfort and acceptance. Full article

As deep learning-based Anomaly Detection (AD) transitions from theoretical research to industrial application, the focus is shifting towards operational efficiency and economic viability on edge devices. While recent studies have achieved remarkable detection accuracy on standard benchmarks, they often rely on heavy memory banks or complex backbones, which pose challenges for deployment in resource-constrained manufacturing environments. Furthermore, real-world inspection lines often present distinct challenges—such as environmental noise and strict latency requirements—that are not fully addressed by accuracy-centric metrics. To bridge the gap between high-performance research models and practical edge deployment, we introduce WEDGE-Net. Our approach is designed to balance structural precision with extreme memory efficiency. We decouple anomaly detection into two specialized streams: (1) a Frequency Stream (DWT) that physically filters out environmental noise to isolate structural defects, and (2) a Context Stream where a Semantic Module explicitly guides feature extraction to enforce object consistency. By synthesizing these two modalities, WEDGE-Net effectively suppresses high-frequency noise while enhancing structural-feature compactness. To validate operational stability, we conducted a robustness analysis of the ‘Tile’ category, which poses a challenging task for distinguishing defects from high-frequency textures. In this stress test, WEDGE-Net demonstrated superior resistance to environmental noise compared to conventional methods. Experimental results on the MVTec AD dataset demonstrate that WEDGE-Net achieves a mean image-level AUROC of 97.82% and an inference speed of 686.5 FPS (measured on an RTX 4090 GPU) under an extreme 1% memory-compression setting. Notably, our method demonstrates superior efficiency, achieving a 2.1× inference speedup over the widely adopted comparative model (PatchCore-10%) while maintaining competitive detection accuracy (e.g., 100% AUROC on Transistor). We hope this work serves as a practical reference for implementing real-time industrial inspection on resource-constrained edge devices. Full article

To explore the stress-bearing characteristics of the “inner tensioned steel ring–segment–surrounding rock” composite structure in TBM (Tunnel Boring Machine) pressurized water conveyance tunnels, a 3D refined finite element model for this composite structure was established, with the Class V surrounding rock section of the TBM pressurized water conveyance tunnel in the Rongjiang-Guanbu water diversion project selected as the research subject. The effects of the internal water pressure, surrounding rock type and tunnel burial depth on the mechanical properties of the composite structures are studied. The findings demonstrate that reinforcing the tunnel structure with an inner tensile steel ring can effectively constrain tunnel deformation, diminish the tensile stress of segments and the extent of tensile zones, and enhance the bearing capacity of the composite structure. Under the effect of internal water pressure, the compressive stress of segments, vertical deformation, joint opening degree, stress of connecting bolts, stress of the inner tension ring, and stress of anchor rods all exhibit a reduction compared to the scenario without internal water pressure. Under the combined action of external water–soil pressure and internal water pressure, variations in surrounding rock types lead to respective increases of 37.16%, 15.75%, and 15.12% in the stress of connecting bolts, segment joint misalignment, and anchor bolt stress. As the tunnel burial depth increases, the stress of connecting bolts and the vertical deformation of segment and the joint misalignment of the pipe segment increase by 140%, 107% and 60.61%, respectively. In addition, under the combined action of external water and soil pressure and internal water pressure, the load-sharing ratios of the surrounding rock, pipe segment, inner tension ring and anchor rod are 34.87%, 34.59%, 21.59% and 8.95%, respectively, and the load-sharing ratio of the inner tensioned ring is 85.80% higher than that observed in the absence of internal water pressure, indicating that internal water pressure effectively enhances the load-sharing performance of the inner tensioned steel ring. In the composite structure, the load-sharing ratio of surrounding rock decreases as the surrounding rock class increases (from Class III to Class V). Under the same load condition, the load-sharing ratio of Class III surrounding rock is 7.14% higher than that of Class V. As the tunnel burial depth increases, the inner tensioned steel ring and anchor rods function more prominently as reserve-bearing components. When the tunnel burial depth reaches 71 m, the load-sharing ratio of the inner tension steel ring and anchor rod increases by 19.91% and 55.72%, respectively, compared with that of the buried depth of 31 m. The research results can provide a theoretical reference for the lining design and late reinforcement measures of similar tunnel projects. Full article

Background/Objectives: Among cotton species, Gossypium barbadense produces the strongest fibers. Examining cytoskeletal dynamics in single epidermal cells of G. barbadense ovules offers a direct approach to investigating fiber quality. Microtubules are major cytoskeletal components whose organization and dynamics are precisely regulated by microtubule-associated proteins (MAPs). However, information on the TPX2 family remains limited, and characterizing its features in G. barbadense is critical to clarifying the role of TPX2 family members in fiber strength formation. Methods: Using the Arabidopsis thaliana TPX2 sequence as a reference, 40, 49, 26, and 26 TPX2 family members were identified in the genomes of G. barbadense, Gossypium hirsutum, Gossypium arboreum, and Gossypium raimondii, respectively. We further analyzed the expression pattern of GbTPX2-35 and validated its function via virus-induced gene silencing (VIGS). Results: In G. barbadense, GbTPX2-35 (Gbar_D11G59825.1) was significantly upregulated in fiber samples of the parental lines at 25 days post-anthesis, and this expression pattern was further validated in G. barbadense lines with extreme fiber strength phenotypes. Next, VIGS-mediated silencing of GbTPX2-35 downregulated the transcript levels of cellulose synthase and microtubule-related protein genes, a finding further validated by mature fiber strength phenotypic data. Conclusions: This study preliminarily validated a pathway in which GbTPX2-35 regulates fiber strength by coordinating cellulose biosynthesis with microtubule cytoskeleton dynamics, providing valuable candidate genes and theoretical support for molecular breeding of high-strength cotton fibers. Full article

The management of hazardous municipal solid waste incineration (MSWI) residues represents a critical challenge for sustainable development due to their increasing generation and environmental risk. At the same time, the cement industry faces urgent pressure to reduce CO₂ emissions associated with clinker production, creating a demand for alternative supplementary cementitious materials. The aim of this study was to evaluate the feasibility of valorising hazardous municipal solid waste incineration (MSWI) air pollution control fly ash (EWC 19 01 07*) as a constituent of Portland composite cement, in line with circular economy principles and the need to reduce CO₂ emissions associated with clinker production. The investigated fly ash, originating from flue gas cleaning processes, is characterised by high alkalinity and elevated concentrations of heavy metals, which currently necessitate controlled landfilling. To enable its safe reuse, the ash was subjected to high-temperature thermal treatment following granulation and subsequently incorporated into cement formulations under semi-industrial conditions. Two Portland composite cements were produced with different ash contents, corresponding to CEM II/A-07 and CEM II/B-07, while a Portland cement manufactured from the same clinker was used as a reference material. The chemical and phase composition of the ash before and after thermal treatment was analysed using XRF and XRD, supported by SEM/EDS observations. The results demonstrate that thermal treatment at 1150 °C induces partial phase stabilisation of APC fly ash without full vitrification, allowing its integration into cement systems under semi-industrial conditions. The incorporation of ash significantly alters hydration behaviour through increased water demand governed by particle porosity, CaO-rich phase composition, and early ionic interactions in the pore solution, leading to reduced workability and mechanical performance. While immobilisation efficiencies exceeding 99.5% were achieved for most heavy metals due to precipitation and incorporation into hydration products, barium exhibited persistent leaching controlled by its solubility under highly alkaline conditions and limited incorporation into C–S–H phases. These findings define both the technological feasibility and the key environmental constraints of APC fly ash utilisation in Portland composite cement. From a sustainability perspective, the proposed approach contributes to the reduction in hazardous waste landfilling and supports clinker substitution in cement production. The results demonstrate the potential of integrating waste management and low-carbon material design within a circular economy framework while highlighting current environmental limitations related to barium leaching. Full article