Search Results (66)

Bipolar junction transistor (BJT)-based 2D temperature-sensor arrays are factory-calibrated to ±0.1 °C, but post-deployment thermal and mechanical stresses drift their per-sensor gain–offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber iteratively reweighted least squares (IRLS)) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7632 frames from a deployed 16 × 16 array exhibiting ≈5× factory-spec non-uniformity, RASC cuts the locally non-smooth fixed-pattern residual by 71 ± 5% (10-fold cross-validation (CV)), reducing this residual to a level comparable to the ±0.1 °C factory specification (as assessed by local-smoothness residual metrics, not independent absolute-temperature validation) while perturbing the calibrated field by only 0.041 °C RMSE; reduction concentrates at the edges (78% vs. 55% interior). In simulations on 8 × 8 to 32 × 32 arrays, RASC matches an oracle centralised extended Kalman filter (EKF) within 0.10 °C with ≈4× lower bandwidth. The real-data evaluation is a single-deployment proof of concept on one array and one host PCB; broader, longitudinal validation remains future work. Full article

An edge-based and curve-based rule-driven nose detection framework is designed to improve the reliability of face detection. The designed framework combines quadratic curve fitting with a calibrated scoring mechanism that fuses geometric, photometric, and structural information into a unified model. These stages jointly enforce symmetry consistency, reliable tip position, and clear wing boundaries. Candidate face regions are first refined by skin filtering and ellipse validation, from which a mid-lower facial ROI is framed for nasal candidate extraction. We further incorporate eye/mouth hints (EyeMap/MouthMap) to restrict the region of interest (ROI) to the region below the eyes, above the mouth, and between the two eyes. When a mouth is detected, this ROI refinement supersedes the chrominance-red (Cr) channel trimming; otherwise, we fall back to the Cr channel horizontal projection to detect dominant mouth peaks and trim the lower-lip band, thereby suppressing lip interference. A multi-threshold Canny procedure with histogram projection is employed to collect multiple nose rectangles by selecting various vertical and horizontal peaks under three adaptive threshold scales. Within each rectangle, edge contours are quadratically fitted and categorized into U-shape (nasal base), N-shape (nostril rim), and C-shape (nasal wings), enabling rule-based selection of the base, wings, and nostrils. The fused features are then processed by a calibrated geometric–photometric–structural scoring module that uses YCbCr contrasts and red/black penalties to suppress lip and eye confounders. Experiments with diverse faces and lighting conditions show accurate and stable nose localization, with notably reliable wing fitting and nasal base detection, improving the accuracy of face detection. Full article

This paper presents a flipped-voltage-follower low-dropout regulator (FVF-LDO) for power supply rejection enhancement and low-power operation in CMOS transimpedance amplifiers for optical receiver applications. The proposed FVF-LDO ensures high stability and reliable regulation over a wide range of load conditions by employing a flipped-voltage follower for fast local feedback and improved power supply rejection, while a super-source follower enhances the transient response through increased current-driving capability. A bandgap reference with a 3-bit trimming DAC is adopted to compensate process variations and support stable LDO operations, achieving a temperature coefficient of 19.6 ppm/°C over a wide range of −25 °C to 125 °C. The FVF-LDO exhibits a 101 mV undershoot under a 100 µA-to-10 mA load step with a 100 ns edge time. When applied to an optoelectronic inverter-based active-feedback transimpedance amplifier (TIA), the regulated supply improves the power supply rejection ratio (PSRR) from −6 dB to −38.3 dB. The proposed optoelectronic TIA realized in a 180 nm CMOS process achieves 67 dBΩ transimpedance gain, 869 MHz bandwidth, 66 dB dynamic range, 6.68 pA/√Hz input-referred noise current spectral density, and 4.68 mW power consumption from a single 1.8 V supply. The proposed TIA chip occupies a core area of 940 × 162 µm². Full article

Alzheimer’s disease (AD) is characterized by progressive cognitive decline and the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles. Beyond genetic and proteostatic mechanisms, infection- and dysbiosis-based models of AD have gained renewed attention, including the antimicrobial protection hypothesis, in which Aβ may participate in innate immune defense. Here, we reanalyzed ribosomal depleted (Ribo-Zero) RNA-seq data from dorsolateral prefrontal cortex (DLPFC) samples from the Mount Sinai Brain Bank cohort (GSE53697) to screen for non-human transcripts. Reads underwent quality control and adapter trimming, taxonomic classification with Kraken2, abundance re-estimation with Bracken, and differential abundance testing with edgeR. Across 17 samples (9 advanced AD and 8 controls), we detected low-biomass microbial signals, with Acinetobacter radioresistens showing enrichment in the AD group (FDR = 0.018). Several additional taxa showed suggestive group differences but did not remain significant after multiple testing correction, including Lactobacillus iners (FDR = 0.051). We also performed an exploratory in silico analysis of an A. radioresistens biofilm-associated protein homolog, identifying predicted amyloidogenic motifs and surface-exposed regions that may be relevant to cross-seeding hypotheses, although no mechanistic inference can be drawn without experimental validation. Given the technical challenges of inferring microbial signals from post-mortem brain RNA-seq data, including contamination risk, low microbial biomass, and overwhelming host background, these findings should be interpreted as hypothesis-generating and warrant orthogonal validation in larger, microbiome-aware cohorts. Full article

Reliable seed accession identification underpins germplasm conservation, traceability and breeding; however, conventional assays remain destructive, labour-intensive and difficult to scale. Here, visible–near-infrared–shortwave infrared (VIS–NIR–SWIR) hyperspectral imaging (HSI; 449.54–2399.17 nm; 563 bands) was used to classify 32 grain–legume accessions (n = 3200 seeds; 100 seeds per accession), comprising 30 common bean (Phaseolus vulgaris L.) landraces plus two outgroup legumes (Vigna angularis (Willd.) Ohwi & Ohashi and Cajanus cajan (L.) Huth). Each seed was represented by one ROI-averaged spectrum obtained from mean representative pixels within a standardised 10 × 10 pixel window at the centre of each seed. A fixed stratified 70:30 seed-level training:test partition was used, with 70 seeds per accession (n = 2240) reserved for fully independent training and 30 seeds per accession (n = 960) reserved as a fully independent test set. Principal component analysis (PCA) captured 97.42% of the spectral variance in the first three components (PC1 = 63.34%, PC2 = 23.78%, and PC3 = 10.31%). One-versus-rest wavelength association mapping revealed a maximum R² of 0.775 at 461.37 nm, and ReliefF concentrated the strongest reduced-band signal within 449.54–456.30 nm and 577.02–597.54 nm. In the original ReliefF-selected 16-band benchmark, the subspace discriminant reached 68.25% macro-F1 and 68.54% balanced accuracy; after edge-band trimming, the alternative 16-band configuration decreased to 60.67% and 60.94%, respectively. With respect to the full-spectrum sensitivity benchmark, linear discriminant analysis achieved 96.35% balanced accuracy, followed by linear SVM (94.17%). Deep learning trained directly on the full 563-band spectra reached 84.90% test accuracy, 84.47% macro-F1, 86.27% precision and 84.90% recall, with MLP_Wide outperforming the convolutional, recurrent and attention-based alternatives. Overall, under controlled laboratory conditions, this benchmark shows that accession discrimination is driven mainly by visible-domain contrasts in the most compact representations, whereas the full spectral context remains important for the most confusable accessions and for cautious future sensor design. The reduced-band findings should therefore be interpreted as exploratory guidance for sensor design rather than as a validated deployment-ready specification. Full article

This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article

Identifying robust differentially expressed genes (DEGs) in RNA-Seq data remains challenging under variable experimental conditions. To address this, we performed five independent RNA-Seq experiments using Drosophila melanogaster larvae treated with methyl lucidone—a putative juvenile hormone disruptor—and compared conventional normalization methods (relative log expression [RLE] via DESeq2 and trimmed mean of M-values [TMM] via edgeR) against our novel DEG-by-index ratio transformation (DiRT). DESeq2 identified two significant DEGs, while edgeR detected none; both methods showed limited validation across four additional independent experiments. In contrast, DiRT identified a distinct set of numerous DEGs with improved reproducibility and reliable validation. KEGG pathway analysis revealed that DiRT-derived DEGs were functionally enriched in pathways related to methyl lucidone detoxification, including the proteasome, drug metabolism, and xenobiotic metabolism mediated by cytochrome P450 and other enzymes. Although DESeq2 and edgeR remain widely used standard methods, DiRT offers a novel complementary approach to enhance DEG characterization in RNA-Seq studies affected by experimental variability. Full article

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has become an intermittent yet economically significant disease of wheat in the Upper Midwest during the last decade. Because chemical and cultural controls remain ineffective, breeders rely on developing resistant varieties, yet visual ratings in inoculated nurseries are labor-intensive, subjective, and time-consuming. To accelerate this process, we combined unmanned-aerial-vehicle hyperspectral imaging (UAV-HSI) with a carefully tuned chemometric workflow that delivers rapid, objective estimates of disease severity. Principal component analysis cleanly separated BLS, leaf rust, and Fusarium head blight, with the first component explaining 97.76% of the spectral variance, demonstrating in-field pathogen discrimination. Pre-processing of the hyperspectral cubes, followed by robust Partial Least Squares (RPLS) regression, improved model reliability by managing outliers and heteroscedastic noise. Four variable-selection strategies—Variable Importance in Projection (VIP), Interval PLS (iPLS), Recursive Weighted PLS (rPLS), and Genetic Algorithm (GA)—were evaluated; rPLS provided the best balance between parsimony and accuracy, trimming the predictor set from 244 to 29 bands. Informative wavelengths clustered in the near-infrared and red-edge regions, which are linked to chlorophyll loss and canopy water stress. The best model, RPLS with optimal preprocessing and variable selection based on the rPLS method, showed high predictive accuracy, achieving a cross-validated R² of 0.823 and cross-validated RMSE of 7.452, demonstrating its effectiveness for detecting and quantifying BLS. We also explored the spectral overlap with Sentinel-2 bands, showing how UAV-derived maps can nest within satellite mosaics to link plot-level scouting to landscape-scale surveillance. Together, these results lay a practical foundation for breeders to speed the selection of resistant lines and for agronomists to monitor BLS dynamics across multiple spatial scales. Full article

As a commonly used configuration for advanced unmanned aerial vehicles (UAVs), the flying-wing configuration suffers from pitching moment trimming issues due to the lack of horizontal tail. The UAV either needs to unload lift at the trailing edge or needs to increase the wingtip twist angle at the cost of losing the lift-to-drag ratio. The commonly used methods for solving pitching moment trimming issues are compared and analyzed in this paper, and it is found that the method of trailing-edge twist has advantages under cruising lift coefficient. Furthermore, a trailing-edge twist deformation parameterized model that can deform multiple critical sections is designed with relevant grids. The multi-objective genetic algorithm is used to optimize the parameterized model and obtain the optimized results. Through comparative analysis, it is found that the optimized trailing-edge twist model has an advantage in distributing the pitching moment. By optimizing the distribution of aerodynamic forces and moments, cruise trim is achieved with only a 1.43% cost to the cruise lift-to-drag ratio compared to the initial model. Full article

The use of UHP for food processing includes many applications such as cutting, peeling, pasteurization, and pumping through the orifice to affect food rheology. This paper focuses on food cutting applications using UHP waterjets. State-of-the-art food cutting systems are described including pumps, manipulators, sensors, cutting heads, and software. While UHP technology is commercially available at 621 MPa of pressure, most food cutting systems’ pressure is below 400 MPa. Highly focused waterjets are important for efficient slicing of food and thus diamond orifices with sharp entry edges are used in specially designed cutting using fast acting on/off valves. Automation is at an advanced level for fish, pin bone removal, poultry, meat, and vegetable processing systems where upstream sensor data are used with CNC controllers to determine the paths of the cutting jet(s) at relatively high production rates for portioning or trimming to tight specifications. Harvesting lettuce proved to be highly successful in improving the overall productivity and working environment ergonomics. An important advantage of the waterjet in increasing the shelf life of trimmed food is presented. For example, celery and lettuce shelf life increases by days over mechanical cutting. The use of salt as an abrasive material in abrasive waterjet cutting nozzles was found to be impractical for cutting meat with bone and more work is needed in this area. Bakery, cake, and sandwich cutting applications are utilized in actual plants in the USA and Europe. For example, small envelop cake cutting machines using relatively low-power jets are used for cutting cake into different shapes. Full article

Cold mix asphalt (CMA) pothole repair is extensively utilized in time-sensitive highway maintenance due to its rapid deployment and all-weather applicability. However, premature failures caused by suboptimal construction practices under operational constraints (e.g., emergency repairs and adverse weather) necessitate frequent reworks, inadvertently escalating material consumption and associated environmental burdens. To address this challenge, this study proposes a quality-driven optimization framework integrating enhanced Analytic Hierarchy Process (AHP) and Grey Relational Analysis (GRA). The methodology systematically evaluates 18 technical parameters across six critical construction phases—grooving/molding, cleaning/drying, bonding layer application, material paving, compaction, and edge trimming—to identify dominant quality determinants. The analysis highlights material placement and compaction as the most significant phases in the repair process, with specific technical parameters such as compaction standardization, paving uniformity, compactor dimension selection, and material application emerging as key quality drivers. To assess the feasibility of the optimized process, a grey relational analysis was adopted to compare the proposed protocol with the cold-patch practices currently adopted by two municipal maintenance agencies in Shanghai, demonstrating superior alignment with an ideal repair benchmark. The developed model empowers highway agencies to achieve dual operational–environmental gains: maintaining urgent repair efficiency while mitigating secondary resource depletion through reduced repetitive interventions. Full article

The discovery of profound differences in the brain microbiota of Alzheimer’s disease (AD) patients and age-matched controls (AMCs) raised questions of postmortem contamination and bacterial transport processes which could be informed by microspatial heterogeneities. We performed semiquantitative species-specific bacterial analyses on multiple micro biopsies from each of the 30 brain specimens (AD and controls). We trimmed ~1 mm of each specimen’s edges for surface contaminants and made multiple sterile biopsy punches of the resultant core of each specimen. To identify species-specific abundances, we used our validated, semiquantitative, full-length 16S rRNA gene pan-domain amplification protocol followed by high-fidelity circular consensus sequencing performed on a Pacific Biosciences Sequel IIe instrument. Statistical analyses showed no significant increase in bacterial abundance on trimmed surfaces compared to core specimens, including C. acnes, the most abundant species previously identified in AD. We did find evidence of substantial bacterial species abundance differences among micro-biopsies obtained from within individual tissue blocks supporting our hypothesis of microspatial heterogeneities. The autopsy brain specimens used in our analyses in this study and our previous publication were not contaminated prior to or postharvesting but we suggest that future microbiological analyses of brain specimens include similar types of edge-core comparison analyses. Further, the species-level bacterial abundance heterogeneities among specimens of the same tissue suggest that multiple symbiotic processes may be occurring. Full article

To reveal the scribing mechanism of ultra-thin glass, single-factor scribing tests were carried out. The effects of the scribing wheel angle θ, scribing force F, and scribing speed v on the lateral cracks width w, scribing depth d, median cracks size l, and cross-section deflection angle α were analyzed to present the scribing quality. The results show that w increases with an increase in θ and F. Further, l and d increase with an increase in F. However, d shows an increasing trend with the increase in θ, and l shows a decreasing trend. In the range of 120–140°, α shows a trend of increasing first and then decreasing with an increase in F. The 120° scribing wheel angle, 20 N scribing force, and 100–400 mm/s scribing speed show the best scribing quality, which limits micro-cracks at the initiation stage without any damage or chipping. Under this condition, the breaking surface edges were free of debris and cracks. A smooth and trim Wallner ripple was obtained from the median cracks with a minimum deflection angle. Full article

Virtual simulation of high-resolution multi-axis machining processes nowadays plays an important role in the production of complex parts in various industries. In order to improve the surface quality and productivity, process parameters, such as spindle speed, feedrate, and depth of cut, need to be optimized by using an accurate process model of milling, which requires both the fast virtual prototyping of machined part geometry for tool path verification and accurate determination of cutter–workpiece engagement for cutting force predictions. Under these circumstances, this paper presents an effective volumetric method that can accurately provide the required geometric information with high and stable computational efficiency under the condition of high grid resolution. The proposed method is built on a tri-level grid, which applies two levels of adaptive refinement in space decomposition to abolish the adverse effect of a large fine-level branching factor on its efficiency. Since hierarchical space decomposition is used, this multi-level representation enables the batch processing of affected voxels and minimal intersection calculations, achieving fast and accurate modeling results. To calculate the instantaneous engagement region, the immersion angles are obtained by fusing the intersection points between the bottom-level voxel edges and the cutter surface, which are then trimmed by feasible contact arcs determined using envelope theory. In a series of test cases, the proposed method shows higher efficiency than the tri-dexel model and stronger applicability in high-precision machining than the two-level grid. Full article

Several challenges arise during edge trimming of carbon fibre-reinforced polymer (CFRP) composites, such as the formation of machining-induced burrs and delamination. In a recent development, appropriate-quality geometric features in CFRPs can be machined using special cutting tools and optimised machining parameters. However, these suitable technologies quickly become inappropriate due to the accelerated tool wear. Therefore, the main aim of our research was to find a novel solution for maintaining the machined edge quality even if the tool condition changed significantly. We developed a novel mechanical edge-trimming technology inspired by wobble milling, i.e., the composite plate compression is governed by the proper tool tilting. The effectiveness of the novel technology was tested through mechanical machining experiments and compared with that of conventional edge-trimming technology. Furthermore, the influences of the tool tilting angle and the permanent chamfer size on the burr characteristics were also investigated. A one-fluted solid carbide end mill with a helix angle of 0° was applied for the experiments. The machined edges were examined trough stereomicroscopy and scanning electron microscopy. The images were evaluated through digital image processing. Our results show that multi-axis edge-trimming technology produces less extensive machining-induced burrs than conventional edge trimming by an average of 50%. Furthermore, we found that the tool tilting angle has a significant impact on burr size, while permanent chamfer does not influence it. These findings suggest that multi-axis edge trimming offers a strong alternative to conventional methods, especially when using end-of-life cutting tools, and highlight the importance of selecting the optimal tool tilting angle to minimize machining-induced burrs. Full article