Search Results (4,484)

Modern, electrically operated heat pumps are characterized by a high degree of efficiency and represent an attractive alternative to conventional heating systems. However, the noise emissions from heat pumps installed outside can lead to increasing noise pollution in densely populated residential areas, which represents an obstacle to widespread use. As part of a research project, a heat pump mock-up was built based on an outdoor unit in the Fraunhofer IBP. With this mock-up, investigations have now been carried out with a prototypical silencer–resonator concept. The aim was to reduce the sound power on the outlet side of the heat pump mock-up. To estimate the effect of this silencer–resonator concept for heat pumps, FEM simulations were first carried out using COMSOL Multiphysics^® with a simplified model. The simulation results validated the silencer–resonator concept for heat pumps and indicated the considerable potential for sound reduction. A measurement was then set up, with which different silencer lengths and absorber thicknesses in the silencer were tested. The measured sound attenuation was higher than the simulated values. The results showed that porous absorbers with sufficient thickness can achieve effective performance in the mid-frequency range. A maximum sound power reduction of 5.7 dB was achieved with the 0.15 m absorber. Additionally, Helmholtz resonators were implemented to attenuate the low-frequency range and tonal peaks. With these resonators sound attenuation was increased to 7.7 dB. Full article

Health systems face pressure to strengthen resilience against supply chain disruptions while maintaining cost-effective service delivery. This mixed-methods study describes a pilot project that integrated 3D printing services into a Canadian provincial health authority. Quantitative data were derived from internal clinical engineering work orders, where a scenario-based economic analysis compared original equipment manufacturer (OEM) procurement with modelled 3D-printed parts. Using conservative assumptions, selected non-electronic structural parts were assigned a fixed unit cost. Qualitative data were collected from two focus groups with clinical engineers and other end-users. Results from an exploratory scenario-based economic analysis suggest that substituting selected structurally simple clinical engineering parts with 3D-printed alternatives would be associated with modelled cost impacts ranging from a 67.4% net increase (OEM prices halved and 3D-printing costs doubled) to a 69.6% cost reduction (OEM prices increased by 10% and 3D-printing costs decreased by 20%). Demand changes affected absolute savings but not the percent difference (58.1% under ±50% quantity changes), and a pessimistic procurement scenario (OEM prices decreased by 30% and 3D-printing costs increased by 50%) reduced savings to 10.3%. Focus groups highlighted perceived benefits and implementation challenges associated with integrating additive manufacturing. Implementation was facilitated through an outsourcing model, which was perceived to shift certain responsibilities and risk-management functions to the vendor. Long-term adoption will require clearer communication and targeted education. This pilot study suggests that, under constrained regulatory scope and scenario-based assumptions, additive manufacturing may contribute to supply chain resilience and may be associated with modelled cost advantages for selected low-risk components. Full article

Poorly differentiated gastric carcinoma (PGC) is aggressive, yet subtype-specific genomics are under-characterized. We queried AACR Project GENIE^® (cBioPortal v18.0-public; 12 August 2025) for PGC and analyzed somatic alterations from targeted panels (depth ≥ 100×; variant allele frequency ≥ 5%). Mutation and copy number frequencies were summarized, co-occurrence and exclusivity were tested, and primary versus metastatic tumors were compared using chi-square with Benjamini–Hochberg correction. The cohort included 189 tumors from 188 patients (71% primary; 25% metastatic), with primary and metastatic tumor samples being collected from different patients. Recurrently mutated genes were TP53 (48.7%), CDH1 (31.2%), ARID1A (21.2%), KMT2C (8.5%), and POLD1 (7.4%); additional alterations involved ERBB3, KMT2D, KEL, CDKN2A, and FAT1 (≈1–7%). Amplifications in CCNE1 (8.2%) and FGFR2 (7.6%) were common, alongside gains in MET, MYC, KRAS, and ERBB2 and losses in CDKN2A/CDKN2B, CDH1, and PTEN. Significant co-occurrence was observed for POLD1–KMT2D (p < 0.001), POLD1–ARID1A (p < 0.001), and ARID1A–KMT2D (p < 0.001), while TP53 was mutually exclusive with ARID1A (p = 0.029) and CDH1 (p = 0.041). CDH1 (48.9% vs. 29.6%; p = 0.021) and MLH1 (8.5% vs. 1.5%; p = 0.040) were enriched in metastases, and CCNE1 alterations showed female predominance (p = 2.83 × 10⁻⁴). Several “primary-only” findings likely reflect small denominators and require replication. PGC demonstrates a mutational framework dominated by TP53, CDH1, ARID1A, and recurrent CCNE1/FGFR2 amplifications, underscoring dysregulation of cell cycle and chromatin-remodeling pathways as key drivers. Co-occurrence of POLD1, ARID1A, and KMT2D suggests coordinated disruption of DNA repair and epigenetic regulation, whereas mutual exclusivity of TP53, ARID1A, and CDH1 indicates distinct tumorigenic routes. Metastatic enrichment of CDH1 and MLH1 supports their roles in invasion and therapeutic resistance. Together, these findings highlight candidate biomarkers and actionable pathways warranting validation in larger, multi-omic cohorts to refine precision treatment strategies for this aggressive gastric cancer subtype. Full article

Ensuring the quality and structural stability of industrial steel buildings requires precise geometric control during the execution stage, in accordance with assembly standards defined by EN 1090-2:2020. In this context, this work proposes a methodology that enables the automatic detection of geometric deviations by comparing the intended design with the actual as-built structure using a Terrestrial Laser Scanner. The integrated pipeline processes the 3D point cloud of the asset by projecting it into 2D images, on which a YOLOv8 segmentation model is trained to detect, classify and segment commercial steel cross-sections. Its application demonstrated improved identification and geometric representation of cross-sections, even in cases of incomplete or partially occluded geometries. To enhance generalisation, synthetic 3D data augmentation was applied, yielding promising results with segmentation metrics measured by mAp@50-95 reaching 70.20%. The methodology includes a systematic segmentation-based filtering step, followed by the computation of Oriented Bounding Boxes to quantify both positional and angular displacements. The effectiveness of the methodology was demonstrated in two field applications during the assembly of industrial steel structures. The results confirm the method’s effectiveness, achieving up to 94% of structural elements assessed in real assemblies, with 97% valid segmentations enabling reliable geometric verification under the standards. Full article

Conventional object detectors represent each object by a deterministic bounding box, regressing its center and size from RGB images. However, such discrete parameterization ignores the inherent uncertainty in object appearance and geometric projection, which can be more naturally modeled as a probabilistic density field. Recent works have introduced Gaussian-based formulations that treat objects as distributions rather than boxes, yet they remain limited to 2D images or require late fusion between image and depth modalities. In this paper, we propose a unified Gaussian-based framework for direct 3D object detection from RGB-D inputs. Our method is built upon a vision transformer backbone to effectively capture global context. Instead of separately embedding RGB and depth features or refining depth within region proposals, our method takes a full four-channel RGB-D tensor and predicts the mean and covariance of a 3D Gaussian distribution for each object in a single forward pass. We extend a pretrained vision transformer to accept four-channel inputs by augmenting the patch embedding layer while preserving ImageNet-learned representations. This formulation allows the detector to represent both object location and geometric uncertainty in 3D space. By optimizing divergence metrics such as the Kullback–Leibler or Bhattacharyya distances between predicted and target distributions, the network learns a physically consistent probabilistic representation of objects. Experimental results on the SUN RGB-D benchmark demonstrate that our approach achieves competitive performance compared to state-of-the-art point-cloud-based methods while offering uncertainty-aware and geometrically interpretable 3D detections. Full article

Karst aquifers provide critical water resources in the Mediterranean region, yet climate change threatens their sustainability. This study integrates stable isotope analysis (δ²H, δ¹⁸O), hydrochemistry, and hydrological time series to characterize precipitation–groundwater dynamics in the Mt. Učka karst system (Croatia). Precipitation samples collected across an altitudinal gradient of approximately 1400 m and groundwater from three major groundwater sources were analyzed over a 2.5-year period. Precipitation exhibits pronounced isotopic variability with d-excess values indicating mixed Atlantic–Mediterranean moisture sources. Groundwater is primarily recharged by precipitation from the cold part of the hydrological year. It exhibits substantial attenuation of isotopic signals, which indicates extensive mixing processes but prevents quantitative estimation of mean residence time. Groundwater is predominantly recharged from elevations above 900 m a.s.l., with one spring showing evidence of higher-elevation recharge. Analysis confirms the system’s dual porosity: a rapid, conduit-dominated response indicates high vulnerability to surface contamination, while a sustained, matrix-dominated response provides greater buffering capacity. These findings highlight the vulnerability of karst systems to projected reductions in autumn precipitation, the critical recharge season, and demonstrate the necessity of multi-tracer approaches for comprehensive aquifer characterization. Full article

Surface reconstruction from 3D point clouds has a wide range of applications. In this paper, we focus on the reconstruction from raw, sparse point clouds. Although some existing methods work on this topic, the results often suffer from geometric defects. To solve this problem, we propose a novel method that optimizes a neural network (referred to as signed distance function) to fit the Signed Distance Field (SDF) from sparse point clouds. The signed distance function is optimized by projecting query points to its iso-surface accordingly. Our key idea is to encourage both the direction and distance of projection to be correct through the supervision provided by a nearest neighbor prior. In addition, we mitigate the error propagated from the prior function by augmenting the low-frequency components in the input. In our implementation, the nearest neighbor prior is trained with a large-scale local geometry dataset, and the positional encoding with a specified spectrum is used as a regularization for the optimization process. Experiments on the ShapeNetCore dataset demonstrate that our method achieves better accuracy than SDF-based methods while preserving smoothness. Full article

To investigate the complex interaction in multi-structure systems, this study establishes a refined 3D numerical model based on a transportation hub project in Tianjin to analyze the asymmetric coupling deformation mechanism of a deep excavation adjacent to a shared-wall metro station and elevated bridge piles. This study highlights the transition from soil-mediated interaction mechanisms to those dominated by structures under shared-wall constraints. Results show that the existing station acts as a high-stiffness boundary, effectively suppressing lateral-wall deflection and basal heave on the proximal side. A critical finding is the reversal of the station’s deformation mode: while stations with a soil buffer typically tilt toward the excavation, the shared-wall station exhibits a clockwise rotation away from the excavation; this phenomenon is driven by excavation-induced basal rebound directly transferred through the common diaphragm wall. Furthermore, the station exerts a significant “shielding effect” on adjacent bridge piles, shifting their maximum lateral displacement from the pile head to the toe and reducing overall deformation. Parametric analyses reveal that optimizing shared-wall thickness is more effective for controlling lateral deformation, whereas increasing wall depth primarily mediates vertical heave. This study concludes that, for shared-wall systems, design priorities must shift from settlement control to anti-heave measures, and pile monitoring should extend to the deeper critical zones identified in this study. Full article

Path planning, as a key technology in unmanned aerial vehicle (UAV) systems, affects the overall efficiency of task completion and is often limited by energy consumption, obstacles, and maneuverability in complex application environments. Traditional algorithms have insufficient performance in nonlinear, multimodal, and multiconstraints problems. Based on this, this paper proposes an improved exponential-trigonometric optimization (ETO) to solve a 3D smooth path planning model based on a spherical Bezier curve. Firstly, a fixed arc length resampling strategy is proposed to address the issue of the insufficient adaptability of existing path smoothing methods to dynamic threats. Generate a uniformly distributed set of reference points along the Bezier curve and combine it with spherical projection to improve the safety and efficiency of the flight path. On this basis, establish a total cost function that includes four types of costs. Secondly, a new ETO variant called IETO is proposed by introducing the alpha evolution strategy, noise and physical attack strategy, and opposition-based cross teaching strategy into ETO. Then, the effectiveness of IETO for addressing various optimization problems is showcased through population diversity analysis, ablation analysis, and benchmark experiments. Finally, the results of the simulation experiment indicate that IETO stably provides shorter and smoother safe paths for UAVs in three elevation maps with different terrain features. Full article

A method utilizing quaternion principal component analysis (QPCA) for three-dimensional fluorescence spectral (3D FS) feature extraction is employed to identify frying oil in edible oil. Particle swarm optimization partial least squares support vector machine (PSO-LSSVR) is utilized for detecting frying oil concentration. The study includes rapeseed oil, soybean oil, peanut oil, blending oil, and corn oil samples. Adulteration involves adding frying oil to these edible oils at concentrations of 0%, 5%, 10%, 30%, 50%, 70%, and 100%. Firstly, the F7000 fluorescence spectrometer is employed to measure the 3D FS of the adulterated edible oil samples, resulting in the generation of contour maps and 3D FS projections. The excitation wavelengths utilized in these measurements are 360 nm, 380 nm, and 400 nm, while the emission wavelengths span from 220 nm to 900 nm. Secondly, leveraging the automatic peak-finding function of the spectrometer, a quaternion parallel representation model of the 3D FS data for frying oil in edible oil is established using the emission spectra data corresponding to the aforementioned excitation wavelengths. Subsequently, in conjunction with the K-nearest neighbor classification (KNN), three feature extraction methods—summation, modulus, and multiplication quaternion feature extraction—are compared to identify the optimal approach. Thirdly, the extracted features are input into KNN, particle swarm optimization support vector machine (PSO-SVM), and genetic algorithm support vector machine (GA-SVM) classifiers to ascertain the most effective discriminant model for adulterated edible oil. Ultimately, a quantitative model for adulterated edible oil is developed based on partial least squares regression, PSO-SVR and PSO-LSSVR. The results indicate that the classification accuracy of QPCA features combined with PSO-SVM achieved 100%. Furthermore, the PSO-LSSVR quantitative model exhibited the best performance. Full article

Soil spatial variability is an inherent feature of natural strata, and random field theory provides an effective framework for quantifying it, aiding accurate deformation prediction. This study focuses on the tunnel section between Kepugongyuan and Gangduhuayuan Stations on Wuhan Metro Line 12. Its novelty focuses on analyzing dual-line shield-induced ground response with explicit consideration of multi-layer soil spatial variability. It examines the effects of the coefficient of variation and the horizontal/vertical spatial correlation distances of cohesion, internal friction angle, and elastic modulus—considering multilayer soil variability—on ground disturbance induced by twin-tunnel shield construction. The main findings include the following: (1) In cross-section, the settlement trough transitions from a “W”-shaped double trough to a “V”-shaped single trough as excavation advances, with the settlement center moving toward the midpoint between the tunnels. Longitudinally, soil heaves ahead of the shield and settles behind. (2) Ignoring spatial variability results in underestimated deformations; nearly 80% of stochastic simulations produced larger maximum surface settlements compared to deterministic analysis. (3) Ground loss and shield thrust disturbance are categorized into four zones based on tunnel diameter (D): Disturbance Zone, Secondary Zone, Transition Zone, and Undisturbed Zone. These findings provide practical guidance for predicting ground deformation and managing settlement-related risks in urban dual-line shield projects. Full article

The automated localization of the flange interface in LNG tanker loading and unloading imposes stringent requirements for accuracy and illumination robustness. Traditional monocular vision methods are prone to localization failure under extreme illumination conditions, such as intense glare or low light, while LiDAR, despite being unaffected by illumination, suffers from limitations like a lack of texture information. This paper proposes an illumination-robust localization method for LNG tanker flange interfaces by fusing monocular vision and LiDAR, with three scenario-specific innovations beyond generic multi-sensor fusion frameworks. First, an illumination-adaptive fusion framework is designed to dynamically adjust detection parameters via grayscale mean evaluation, addressing extreme illumination (e.g., glare, low light with water film). Second, a multi-constraint flange detection strategy is developed by integrating physical dimension constraints, K-means clustering, and weighted fitting to eliminate background interference and distinguish dual flanges. Third, a customized fusion pipeline (ROI extraction-plane fitting-3D circle center solving) is established to compensate for monocular depth errors and sparse LiDAR point cloud limitations using flange radius prior. High-precision localization is achieved via four key steps: multi-modal data preprocessing, LiDAR-camera spatial projection, fusion-based flange circle detection, and 3D circle center fitting. While basic techniques such as LiDAR-camera spatiotemporal synchronization and K-means clustering are adapted from prior works, their integration with flange-specific constraints and illumination-adaptive design forms the core novelty of this study. Comparative experiments between the proposed fusion method and the monocular vision-only localization method are conducted under four typical illumination scenarios: uniform illumination, local strong illumination, uniform low illumination, and low illumination with water film. The experimental results based on 20 samples per illumination scenario (80 valid data sets in total) show that, compared with the monocular vision method, the proposed fusion method reduces the Mean Absolute Error (MAE) of localization accuracy by 33.08%, 30.57%, and 75.91% in the X, Y, and Z dimensions, respectively, with the overall 3D MAE reduced by 61.69%. Meanwhile, the Root Mean Square Error (RMSE) in the X, Y, and Z dimensions is decreased by 33.65%, 32.71%, and 79.88%, respectively, and the overall 3D RMSE is reduced by 64.79%. The expanded sample size verifies the statistical reliability of the proposed method, which exhibits significantly superior robustness to extreme illumination conditions. Full article

Magnetic resonance imaging (MRI) super-resolution (SR) enables high-resolution reconstruction from low-resolution acquisitions, reducing scan time and easing hardware demands. However, most deep learning-based SR models are large and computationally heavy, limiting deployment in clinical workstations, real-time pipelines, and resource-restricted platforms such as low-field and portable MRI. We introduce CHARMS, a lightweight convolutional–Transformer hybrid with attention regularization optimized for MRI SR. CHARMS employs a Reverse Residual Attention Fusion backbone for hierarchical local feature extraction, Pixel–Channel and Enhanced Spatial Attention for fine-grained feature calibration, and a Multi-Depthwise Dilated Transformer Attention block for efficient long-range dependency modeling. Novel attention regularization suppresses redundant activations, stabilizes training, and enhances generalization across contrasts and field strengths. Across IXI, Human Connectome Project Young Adult, and paired 3T/7T datasets, CHARMS (~1.9M parameters; ~30 GFLOPs for 256 × 256) surpasses leading lightweight and hybrid baselines (EDSR, PAN, W2AMSN-S, and FMEN) by 0.1–0.6 dB PSNR and up to 1% SSIM at ×2/×4 upscaling, while reducing inference time ~40%. Cross-field fine-tuning yields 7T-like reconstructions from 3T inputs with ~6 dB PSNR and 0.12 SSIM gains over native 3T. With near-real-time performance (~11 ms/slice, ~1.6–1.9 s per 3D volume on RTX 4090), CHARMS offers a compelling fidelity–efficiency balance for clinical workflows, accelerated protocols, and portable MRI. Full article

Dense and complex underground structures impose stringent requirements on shield tunneling. In the close-proximity construction of super-large-diameter shield tunnels, challenges may arise, including adverse impacts on the normal operation of existing structures, as well as difficulties in ensuring the bearing capacity and deformation control of these structures during excavation. This study, based on the stratigraphic conditions of the Chengdu area, employs FLAC3D 7.0 version software to simulate the section where the Shuanghua Road Tunnel underpasses both Metro Line 10 and the Chengdu-Guiyang High-Speed Railway. The main conclusions are as follows: (1) Tunnel underpassing induces uneven settlement in the metro tunnel, with a maximum settlement reaching 47.7 mm. The settlement trough exhibits a twin-peak morphology during dual-line construction. When a single super-large-diameter tunnel line crosses the existing structural cluster, the maximum settlement is located directly above the crossing point. During dual-line crossing, the maximum settlement shifts towards the midpoint between the two new tunnel lines. (2) As the left line of the new tunnel approaches the existing structure, the cross-sectional deformation of the existing structure is “pulled” towards the direction of the excavated new tunnel. After the new left line moves away, the cross-sectional deformation gradually recovers to a bilaterally symmetrical state. (3) The tunnel cross-section undergoes dynamic “compression-tension” convergence changes during the construction process, with a maximum longitudinal tensile convergence of −1.28 mm. (4) During the underpassing of the existing structural cluster by the super-large-diameter tunnel, the maximum torsion angle is approximately −0.016°, occurring at the moment when the shield machine head first passes directly beneath, located directly above the new tunnel. The torsion angle of the existing structure is greatest during the first underpassing event, and the maximum torsion angle during the second underpassing is lower than that during the first. This study reveals the composite deformation mode of “settlement-convergence-torsion” during the underpassing of existing structural clusters by super-large-diameter shield tunnels, providing a theoretical basis for risk control in similar adjacent engineering projects. Full article

Effectively fusing the rich semantic information from camera images with the high-precision geometric measurements provided by LiDAR point clouds is a key challenge in mobile robot environmental perception. To address this problem, this paper proposes a highly extensible instance-aware fusion framework designed to achieve efficient alignment and unified modeling of heterogeneous sensory data. The proposed approach adopts a modular processing pipeline. First, semantic instance masks are extracted from RGB images using an instance segmentation network, and a projection mechanism is employed to establish spatial correspondences between image pixels and LiDAR point cloud measurements. Subsequently, three-dimensional bounding boxes are reconstructed through point cloud clustering and geometric fitting, and a reprojection-based validation mechanism is introduced to ensure consistency across modalities. Building upon this representation, the system integrates a data association module with a Kalman filter-based state estimator to form a closed-loop multi-object tracking framework. Experimental results on the KITTI dataset demonstrate that the proposed system achieves strong 2D and 3D detection performance across different difficulty levels. In multi-object tracking evaluation, the method attains a MOTA score of 47.8 and an IDF1 score of 71.93, validating the stability of the association strategy and the continuity of object trajectories in complex scenes. Furthermore, real-world experiments on a mobile computing platform show an average end-to-end latency of only 173.9 ms, while ablation studies further confirm the effectiveness of individual system components. Overall, the proposed framework exhibits strong performance in terms of geometric reconstruction accuracy and tracking robustness, and its lightweight design and low latency satisfy the stringent requirements of practical robotic deployment. Full article