Search Results (31,782)

A non-contact laser polishing method for additively manufactured polymer components with complex three-dimensional geometries is presented, employing a 6-axis robotic system. Robot-guided sample orientation, a quasi-top-hat scanning strategy, and closed-loop temperature control are combined to address curved geometries. On Selective Laser Sintering (SLS)-manufactured Polyamide 12 (PA12) tensile samples with three build orientations and two thicknesses, laser polishing yields up to a 15% increase in tensile strength (Rm) and a 50% increase in elongation at break (A). For 45°-built 5 mm samples, Rm increases from 31.53 MPa to 36.33 MPa and A from 6.52% to 9.8%, approaching the tensile strength reported for optimally oriented SLS-printed PA12 Smooth samples of the same grade. For convex–concave PA12 demonstrators, areal roughness (Sa) on convex surfaces is reduced from 33.6 µm to 2.7 µm (approximately 92%) and the high-pass-filtered micro-roughness (Sa_HP) on concave surfaces by 98.2% to 0.15 µm. For Fused Deposition Modeling (FDM)-printed Polyaryletherketone (PAEK) samples, Sa is reduced from 28.35 µm to 4.1 µm and Sa_HP from 15.98 µm to 0.23 µm (98.6%), despite the high melting temperature and anisotropic raster topography. These results demonstrate that robotic laser polishing constitutes a viable post-processing approach for functionally demanding polymer applications. Full article

Blade Tip Timing (BTT) is an essential non-contact technique for monitoring vibrations in rotating machinery, but its practical accuracy is often degraded by noise, undersampling, and spectral leakage. This paper proposes a multi-stage robust Bayesian high-resolution identification framework that systematically addresses these challenges. A recursive digital algorithm based on Kalman filtering estimates the rotational speed without requiring once-per-revolution probes, effectively suppressing sensor noise. An attention-enhanced dynamic convolutional autoencoder then generates channel-specific window functions to minimize spectral leakage. The core identification algorithm extracts phases via all-phase FFT and employs sub-bin interpolation to overcome the resolution limitation of conventional FFT. A Tukey-biweight-based robust aggregation strategy is used to suppress the influence of abnormal or unequal-quality sensor channels during multi-channel phase fusion. A Bayesian prior distribution over the vibration order guides the estimation toward physically plausible values under noisy conditions. Finally, a coarse-to-fine multi-stage search strategy drastically reduces computational burden while preserving accuracy. Experiments on a rotor-blade test bench at constant and variable speeds show that the method reduces the noise floor by about 60 dB, achieves a maximum frequency identification error of 7.84%, and accelerates the search by approximately 48.6% compared to exhaustive search. The proposed method provides a reliable and efficient solution for blade health monitoring. Full article

Cardiac auscultation remains a widely used non-invasive method for assessing cardiac function; however, conventional acoustic stethoscopes are limited by subjective interpretation and lack of digital signal-handling capabilities. This study presents the design and fabrication of a chitosan-based diaphragm digital stethoscope using a biopolymer-derived acoustic interface. Chitosan was extracted from mealworm larvae shells through sequential chemical processing and subsequently processed into a glycerol-plasticized film via solution casting to obtain a flexible diaphragm. The mechanical properties of the diaphragm were evaluated to assess its suitability for acoustic applications. The diaphragm was mechanically coupled to a piezoelectric sensor and integrated into a custom 3D-printed chest piece connected to a microcontroller-based acquisition system. Heart sound signals were acquired from four conventional auscultation sites (aortic, pulmonic, tricuspid, and mitral regions). The recorded signals were processed using band-pass filtering, envelope extraction, and time–frequency analysis to visualize waveform morphology and frequency content. The signals obtained exhibited temporal and spectral features consistent with reported phonocardiography characteristics, including identifiable S1 and S2 components. These results demonstrate the feasibility of using chitosan-based diaphragm materials for heart sound acquisition in a digital stethoscope configuration, providing a low-complexity platform for further development of biopolymer-based acoustic sensing devices. Full article

Passive Wi-Fi localization for critical-infrastructure security operations centers (SOCs) faces three interconnected limitations. First, many existing methods produce single-point coordinate estimates without calibrated uncertainty, making them unsuitable for automated SOC response. Second, localization pipelines often lack an evidentiary chain of custody, limiting reliable post-incident auditability. Third, SOC automation cannot safely rely on uncalibrated confidence values because erroneous high-impact actions and missed escalations carry asymmetric operational costs. This study presents a Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization framework for heterogeneous sensor networks composed of stationary sensors, mobile receivers, and UAV-assisted collection nodes. Instead of producing a single coordinate estimate, the method derives a posterior spatial distribution with calibrated uncertainty from monitor-mode observations, including RSSI aggregates, management/control frame features, channel occupancy indicators, and receiver logs. The framework combines three tightly coupled components: (i) Bayesian coordinate estimation with robust loss functions and range-dependent error modeling; (ii) uncertainty calibration that converts posterior confidence into operational SOC response modes (AUTO, VERIFY, and OBSERVE) via empirical coverage metrics and reliability diagrams; and (iii) a permissioned evidentiary logging layer that anchors integrity-relevant metadata and policy labels on-chain while keeping raw telemetry off-chain for tamper-evident auditability and scalability. The coupling between layers is explicit: calibrated confidence scores govern smart-contract gating conditions, and smart-contract policy thresholds feed back into the calibration stage. Field validation shows that localization performance degrades markedly beyond approximately 40 m, indicating a practical boundary for confident automated action. The proposed framework integrates passive sensing, uncertainty-aware localization, and blockchain-based evidentiary trust for secure critical-infrastructure sensor networks. Its key contributions are: (1) a posterior-distribution-based passive localization pipeline; (2) empirical coverage metrics for calibrating SOC response thresholds; (3) a hybrid on-chain/off-chain architecture linking localization outputs to a permissioned ledger; and (4) field validation establishing the 40 m operational validity boundary. Full article

Background/Objectives: A disadvantage of Descemet stripping automated endothelial keratoplasty (DSAEK) in eyes with prior glaucoma filtration surgery is the difficulty in maintaining air tamponade during the procedure. Herein, we report the use of bleb compressive sutures in managing air tamponade in the anterior chamber during DSAEK in eyes with blebs following trabeculectomy. Methods: This retrospective case series included 34 eyes of 33 patients that developed bullous keratopathy following trabeculectomy. Bleb compression suturing was performed using a 10-0 nylon suture in eyes with an intraocular pressure (IOP) < 10 mmHg or a fragile ischemic bleb. Postoperative IOP, air ingress into the bleb, rebubbling, bleb leakage, and bleb damage were evaluated. Results: Of the 34 eyes, 13 underwent bleb compression suturing before DSAEK (suture group), whereas 21 eyes did not (non-suture group). The mean preoperative IOP was 7.5 ± 2.5 mmHg and 11.2 ± 4.2 mmHg in the suture and the non-suture groups, respectively. The IOP was measured 2 h postoperatively in 14 eyes, increasing by 18 ± 9.3 and 11.7 ± 3.1 mmHg in the suture and non-suture groups, respectively, compared to the preoperative IOP, with no significant differences. At 2 h postoperatively, two eyes in the suture group and one eye in the non-suture group exhibited an IOP spike (≥30 mmHg). One eye in the non-suture group required rebubbling owing to air ingress into the bleb. The mean IOP was 7.1 ± 3.2 and 9.4 ± 4.6 mmHg in the suture and non-suture groups, respectively, 1–2 weeks postoperatively. Preoperative and postoperative IOPs did not significantly differ in either group, and no suture-related complications were observed. Conclusions: For eyes with blebs, bleb compression suturing in DSAEK provides effective air tamponade during graft adhesion. Full article

Indoor space segmentation is essential for indoor navigation, 3D reconstruction, and Building Information Modeling (BIM). However, reliable segmentation from unstructured 3D point clouds remains challenging due to structural voids caused by occlusion and noise, as well as the difficulty of distinguishing permanent structural elements from dense non-structural clutter. To address these issues, this paper proposes a semantic-grid structural completion method for indoor space segmentation from 3D point clouds. The method first integrates RandLA-Net-based semantic segmentation with geometric similarity correction to improve structural consistency. Subsequently, a semantic-grid structural completion algorithm detects and fills structural voids under height constraints; this process employs dual-grid structural marking with a 2D semantic occupancy grid and a 3D voxel grid to identify missing observations and generates synthetic points with inherited semantic labels to restore structural integrity within the scene. A density-aware height difference filtering method is then applied to remove non-structural clutter and clearly separate structural elements from the rest of the scene. Finally, indoor spaces are delineated through connectivity-based segmentation and inverse distance-weighted label propagation. Experiments on public datasets, including S3DIS, UZH and Structured3D, demonstrate that the proposed method consistently outperforms existing approaches, achieving a mean F1 Score of 0.99, an Intersection over Union (IoU) of 0.98, and a Segmentation Error Rate (SER) of 0 in most scenarios, particularly in occlusion-affected and structurally complex indoor environments. Full article

Predicting long-term anaerobic digestion (AD) performance for food waste remains challenging because of substrate variability, process disturbance, and limited routine monitoring data. This study developed a practical framework that combines biomethane potential (BMP) test results with time-series analyses to estimate methane production during steady-state long-term AD operation. Ten paired batch and long-term datasets from three research groups were analysed. Among four BMP kinetic models, the Cone model gave the best fit in eight of 10 datasets. For long-term prediction, a 3-day sliding-window method and two Kalman filter approaches were compared. The one-dimensional Kalman filter achieved the best overall predictive accuracy, while the two-dimensional Kalman filter, which incorporated substrate conversion efficiency, provided clearer identification of persistent abnormal deviations associated with potential inhibition. The proposed framework offers a simple and localised decision support tool for methane forecasting, noise reduction, and early warning of instability when only BMP data and routine methane measurements are available. Full article

Anti-collision detection technologies primarily rely on optical, radar, or laser sensors; however, their performance often deteriorates severely under adverse weather conditions (e.g., rain, snow, fog) or in scenarios involving visual occlusion. By contrast, magnetic anomaly detection leverages perturbations in the geomagnetic field induced by target objects (e.g., vehicles, metallic obstacles), exhibiting intrinsic all-weather operability and strong anti-interference capability. Nevertheless, conventional magnetic anomaly detection methods suffer from the limited applicability of the magnetic dipole model, which only affords coarse positioning accuracy and is predominantly suited for long-range targets. To address this limitation, this paper proposes an Extended N-th-Pole Magnetic Dipole (E-NMD) model that improves accuracy by analyzing the Lagrangian cosine term and rigorously constraining truncation errors under specific operational conditions. Experimental results demonstrate that, for steel with a relative permeability of 200, the model achieves a fitting variance of 99.87%. Furthermore, to overcome the inversion difficulties arising when the strength of short-range magnetic anomalies is comparable to sensor noise, an Adaptive Iterative Extended Kalman Filter (AI-EKF) is developed to enable robust noise suppression and precise distance estimation. Results indicate that E-NMD outperforms the traditional N-th-Pole Magnetic Dipole (NMD) model in proximal state estimation, achieving a 39.62% reduction in Root Mean Square Error (RMSE). Finally, in light of parameter uncertainty in magnetic anomaly targets under real-world conditions, a Dual-Mode Pairwise Iterative Extended Kalman Filter (DI-EKF) is introduced to jointly estimate parameters and system states, yielding an 89% reduction in RMSE compared to AI-EKF. Full article

Corner detection is a fundamental task in computer vision that plays a critical role in applications such as image registration, 3D reconstruction, and object tracking. In biological visual systems, simple cells in the primary visual cortex exhibit high selectivity to edge stimuli of specific orientations, while end-stopped cells can detect geometric singular structures such as line segment endpoints and corners. Existing corner detection methods based on visual neural computation typically employ a strategy of densely distributed end-stopped cells for corner localization, which suffers from significant localization deviation under small angle conditions due to mutual interference between responses of adjacent neurons. To address this problem, this paper proposes a bionic corner detection method based on cooperative processing of simple cells and end-stopped cells. The method constructs a two-stage cooperative processing framework: the edge filtering stage employs a Gabor filter bank to simulate the orientation selectivity of simple cells, extracting edge positions and orientation information; the dynamic construction stage builds unilateral end-stopped cells only at filtered edge positions based on local orientation information, fundamentally avoiding computational redundancy and response interference caused by global dense distribution; the corner localization stage determines precise corner coordinates through hierarchical clustering and dual-cluster centroid fusion strategies. Experimental results demonstrate that, in the 15° acute-angle regime where dense end-stopped schemes are most severely affected by response interference, the proposed method reduces the mean localization error from 8.76 to 2.34 pixels, corresponding to a 73.3% improvement; averaged across the eight tested angle levels from 15° to 165°, the improvement is approximately 40.9%, and all per-angle differences are statistically significant (paired t-test, p < 0.01 or below, N = 10 independent runs). On standard test images, the method attains the lowest mean localization error among the eight compared detectors (1.58 pixels, versus 1.68–3.42 pixels for Harris, FAST, COSFIRE, KAZE, SuperPoint, Deep Corner, and Wei et al.), while maintaining competitive detection rate, false-alarm rate, and runtime. Physiological plausibility validation experiments show that the correlation coefficient between the detection deviation of this method and human perceptual deviation reaches 0.923, indicating that the output of the framework aligns with previously reported human perceptual bias patterns and supporting its biological plausibility as a biologically inspired—rather than mechanistic—model of corner perception. The source code, dataset, and experimental results are publicly available (see Data Availability Statement). Full article

Hydraulic fracture geometry is of great importance for evaluating stimulation effectiveness and supporting the efficient development of unconventional oil and gas reservoirs, and it can be estimated from field shut-in water hammer signals. However, field signals are commonly characterized by strong noise, pronounced non-stationarity, strong dependence on manual extraction of effective response segments, and limited automation in inversion analysis. To address these issues, this study develops an integrated automated interpretation framework for shut-in water hammer analysis, which combines an adaptive shape-preserving Kalman filter for non-stationary signal denoising, an automatic response segment identification method, and a particle swarm optimization-based inversion strategy for fracture geometry estimation. The framework is validated using field high-frequency pressure data from hydraulically fractured wells. The results show that the proposed denoising method improves the signal-to-noise ratio from 11.99 dB to 25.05 dB while preserving key transient features. The response segments can be extracted efficiently, with runtimes of 0.84–1.22 s and onset errors within 0–5 s. For a representative fracturing stage, the relative errors of the inverted fracture half-length and fracture height are 6.21% and 3.04%, respectively. The proposed framework provides a low-cost and field-applicable tool for fracture evaluation and engineering decision-making. Full article

Purpose: Obesity, characterized by abnormal fat accumulation with comorbidities, continues to increase dramatically, particularly in the pediatric population. Identifying the environmental and genetic causes underlying the development of obesity during early childhood is crucial for establishing preventive and protective treatments for this complex disease. We aimed to investigate genetic variants related to non-syndromic early-onset childhood obesity. Methods: Whole-exome sequencing was performed in three independent consanguineous families with obesity, including three index cases and two additional affected siblings. Non-synonymous variants with minor allele frequency < 0.01 in all normal populations were filtered using the Genomize-SEQ Platform. Variant confirmations and familial segregations were analyzed by Sanger sequencing. Results: WES revealed a shared ATXN3 gene variant and two known variants of the SH2B1 and ADIPOQ genes, which were reported to be associated with obesity. Additionally, five heterozygous novel gene variants of the ANKK1, NEGR1, OGDH, ABCB1, and GSK3B genes were identified, which are predicted to cause excessive fat accumulation and disruption of energy balance in individuals. Conclusions: We suggest that the cumulative effects of all obesity-associated detected variants lead to the early-onset obesity phenotype observed in individuals. Hence, periodic follow-up and treatment opportunities are recommended for index cases, alongside the adoption of a more active lifestyle and healthy nutrition practices. Full article

In top-N recommendation, incorporating item-side information can improve ranking quality under sparse user–item interactions; however, common flat concatenation strategies may weaken the structural correspondence between user ratings and item attributes while simultaneously increasing model size. To address this issue, this study proposes DAERec-GCA, a deep autoencoder-based collaborative filtering framework that organizes rating signals and genre information in a genre-channel-aligned two-dimensional representation. The model applies shared weights across genre channels and aggregates channel outputs to generate item scores, enabling side-information integration without the parameter growth associated with flattened genre-aware formulations. The framework was evaluated on MovieLens-100K, 1M, and 10M under a warm-start five-fold cross-validation protocol using ranking-based metrics. In addition, a structured ablation study was conducted against ROnly, Flat1D, GenreProfile, GenreEmbed, and GenreGated, together with a controlled train-side sparsity analysis and a computational profiling analysis covering trainable parameters, epoch time, inference latency, and peak GPU memory. The results show that DAERec-GCA remains competitive across all three datasets and exhibits its clearest advantage under sparse and moderately sparse training conditions. The findings suggest that genre-channel alignment provides a practical trade-off between structural expressiveness, parameter efficiency, and recommendation quality in sparse recommendation settings. Full article

Accurate short-term load forecasting at disaggregated levels is critical for energy management in microgrids and institutional environments, yet it remains a challenge due to high consumption variability and limited contextual information. This paper proposes a hybrid model that combines Fuzzy ARTMAP neural networks with K-means clustering to improve hourly load forecasting using real data from a university microgrid. The methodology includes key preprocessing steps such as filtering low-load records, removing holidays, interpolating missing values, and applying cyclic encoding to standardize the data into 96 time intervals per day (15-min resolution). For each prediction, the average load profile of the five most recent weekdays is computed and compared to cluster centroids to identify the most similar group, which is then used to train the neural network. Results demonstrate consistent improvements in MAPE, RMSE, and MAE compared to the non-clustered baseline. The model showed robustness to non-stationary behavior and atypical patterns, even when relying solely on timestamp and load data. The proposed strategy outperformed conventional approaches and proved suitable for complex, data-limited environments. Full article

In the field of drone navigation systems, a high-precision positioning solution can be provided by an integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS). But when satellite signals are interfered with or blocked by tall buildings, the errors of SINS will disperse rapidly due to the complex air and mechanical vibrations, leading to a serious degradation of navigation accuracy. To enhance the positioning performance in this situation, this paper proposes a hybrid information fusion method based on attention-augmented temporal convolutional network (TCN) for drone SINS/GNSS navigation system. A feature integration and prediction model is constructed to provide a pseudo-positioning reference for the integrated navigation filter during GNSS-denied periods, in which TCN is used to establish a predictive positioning error correction model based on inertial measurements and SINS data, while a self-attention model is incorporated to extract complex global drone motion features. The performance of the proposed method has been experimentally verified using Global Positioning System (GPS) and SINS data collected from real drone flight test. Comparison results among the proposed model, SINS with TCN, SINS with convergent Kalman filter (KF) prediction section and SINS-only indicate that the proposed method can effectively improve the drone positioning accuracy in specific GNSS-denied environments. Full article

Background/Objective: Sesame (Sesamum indicum L.) is a nutritionally and economically important oilseed crop that is grown predominantly by smallholder farmers in Mozambique. However, its breeding process is constrained by a limited understanding of the genetic diversity in sesame germplasm. Therefore, this study determined the genetic diversity and population structure of a panel of 109 sesame accessions from Instituto de Investigação Agrária de Mocambique (IIAM) using DArTseq SNPs. Methods: The generated 14,763 SNPs were filtered, retaining 11,502 high-quality SNPs for this study. Results: Overall genetic diversity was moderate (mean He = 0.30, Ho = 0.30, MAF = 0.21, PIC = 0.25). Population structure analysis using sparse non-negative matrix factorization identified eight subpopulations, consistent with principal component analysis implemented via the Latent factor mixed model. Discriminant analysis of principal components (DAPC) and Ward’s hierarchical clustering based on Nei’s distance resolved the same eight clusters, although DAPC revealed overlap among clusters, consistent with extensive admixture. Analysis of molecular variance showed that 85.85% of total molecular variation was within subpopulations and 14.15% among the subpopulations. Pairwise fixation indices (ranging from 0.02 to 0.10) identified divergent subpopulations 7 and 1 as suitable candidates for hybridization. Within subpopulations, observed heterozygosity exceeded expected heterozygosity, likely reflecting residual heterozygosity in sesame landraces, admixture, reverse Wahlund effect and scoring of paralogs as heterozygous SNPs. Conclusions: Overall, this study provided insights into sesame’s genetic diversity in Mozambique, contributing to germplasm conservation and informed parental selection. Full article