Search Results (226)

Stationary inverse-geometry digital tomosynthesis (s-IGDT) causes truncation artifacts in reconstructed images due to its geometric characteristics. This study introduces a deep convolutional generative adversarial network (DCGAN)-based out-painting method for mitigating truncation artifacts in s-IGDT images. The proposed network employed an encoder–decoder architecture for the generator, and a dilated convolution block was added between the encoder and decoder. A dual-discriminator was used to distinguish the artificiality of generated images for truncated and non-truncated regions separately. During network training, the generator was able to selectively learn a target task for the truncated regions using binary mask images. The performance of the proposed method was compared to conventional methods in terms of signal-to-noise ratio (SNR), normalized root-mean-square error (NRMSE), peak SNR (PSNR), and structural similarity (SSIM). The results showed that the proposed method led to a substantial reduction in truncation artifacts. On average, the proposed method achieved 62.31, 16.66, and 14.94% improvements in the SNR, PSNR, and SSIM, respectively, compared to the conventional methods. Meanwhile, the NRMSE values were reduced by an average of 37.22%. In conclusion, the proposed out-painting method can offer a promising solution for mitigating truncation artifacts in s-IGDT images and improving the clinical availability of the s-IGDT. Full article

As an indispensable piece of production equipment in the industrial field, wire rope is directly related to personnel safety and the normal operation of equipment. Therefore, it is necessary to perform broken wire detection. Deep learning has powerful feature-learning capabilities and is characterized by high accuracy and efficiency, and the YOLOv8 object detection model has been adopted to detect wire breaks in electromagnetic signal images of wire rope, achieving better results. Nevertheless, the black box problem of the model brings a new trust challenge, and it is difficult to determine the correctness of the model’s decision and whether it has any potential problems, so an interpretability study needed to be carried out. In this work, a perturbation-based interpretability method—ESTC (Eliminating Splicing and Truncation Compensation)—is proposed, which distinguishes itself from other methods of the same type by targeting the signaling object instead of the ordinary object. ESTC is compared with other model-agnostic interpretable methods, LIME, RISE, and D-RISE, using the same model on the same test set. The results indicate that our proposed method is objectively superior to the others, and the interpretability analysis shows that the model predicts in a way that is consistent with the priori knowledge of the manual rope inspection. This not only increases the credibility of using the object detection model for broken wire detection but also has important implications for the practical application of using object detection model to detect wire breaks. Full article

This study introduces the truncated $(u,v)$-half-normal distribution, a novel probability model defined on the bounded interval $(u,v)$, with parameters $\sigma$ and b. This distribution is designed to model processes with restricted domains, ensuring realistic and analytically tractable outcomes. Some key properties of the proposed model, including its cumulative distribution function, probability density function, survival function, hazard rate, and moments, are derived and analyzed. Parameter estimation of $\sigma$ and b is achieved through a hybrid approach, combining maximum likelihood estimation (MLE) for $\sigma$ and a likelihood-free-inspired technique for b. A sensitivity analysis highlighting the dependence of $\sigma$ on b, and an optimal estimation algorithm is proposed. The proposed model is applied to two real-world data sets, where it demonstrates superior performance over some existing models based on goodness-of-fit criteria, such as the known AIC, BIC, CAIC, KS, AD, and CvM statistics. The results emphasize the model’s flexibility and robustness for practical applications in modeling data with bounded support. Full article

Quaternary carbonate strandplains serve as archives of land–sea interaction, including the impacts of storms and tsunamis. Incipient lithification, especially of compound beach/dune ridges within the action zone of salt spray, presents challenges to geological research, which is often limited to exposures. This study combines aerial image analysis with geophysical datasets to assess the morphostratigraphy and internal structure of the Freedom Beach Strandplain along southern Eleuthera Island, The Bahamas. Color-intensity analysis of field photographs and satellite images revealed general patterns that can be used to distinguish between areas with different grayscale parameters (sand-covered surfaces, lithified ridges, vegetation, etc.). Cross-shore (dip-section) high-resolution (800 MHz) georadar images across ten ridges (A-J) documented the internal architecture of swash-aligned ridge–swale sets. Signatures attributed to storms include truncations in shore-normal radargrams, scour features in alongshore (strike-section) images, and an extensive accumulation of large mollusk shells along one of the oldest ridges (ridge J). Preliminary radiocarbon dating yielded ages of up to 600 years, suggesting intense storms with 50–60-year periodicity as a possible mechanism for ridge formation. Full article

Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes a novel segmentation framework that integrates structure-preserving data augmentation, Boundary-aware Transformer Attention (BAT), and Geometry-aware Loss. We enhance data diversity while preserving vascular and tissue structures through truncated Gaussian-based sampling and colormap transformations. BAT strengthens boundary recognition by globally learning the inclusion relationship between the OD and OC within the skip connection paths of U-Net. Additionally, Geometry-aware Loss, which combines the normalized Hausdorff Distance with the Dice Loss, reduces fine-grained boundary errors and improves boundary precision. The proposed model outperforms existing state-of-the-art models across five public datasets—DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA—and achieves Dice scores of 0.9127 on Drishti-GS and 0.9014 on REFUGE for OC segmentation. For joint segmentation of the OD and OC, it attains high Dice scores of 0.9892 on REFUGE, 0.9782 on G1020, and 0.9879 on ORIGA. Ablation studies validate the independent contributions of each component and demonstrate their synergistic effect when combined. Furthermore, the proposed model more accurately captures the relative size and spatial alignment of the OD and OC and produces smooth and consistent boundary predictions in clinically significant regions such as the region of interest (ROI). These results support the clinical applicability of the proposed method in medical image analysis tasks requiring precise, boundary-focused segmentation. Full article

Target detection helps to identify, locate, and monitor key components and potential issues in power sensing networks. The fusion of infrared and visible light images can effectively integrate the target the indication characteristics of infrared images and the rich scene detail information of visible light images, thereby enhancing the ability for target detection in power equipment in complex environments. In order to improve the registration accuracy and feature extraction stability of traditional registration algorithms for infrared and visible light images, an image registration method based on an improved SIFT algorithm is proposed. The image is preprocessed to a certain extent, using edge detection algorithms and corner detection algorithms to extract relatively stable feature points, and the feature vectors with excessive gradient values in the normalized visible light image are truncated and normalized again to eliminate the influence of nonlinear lighting. To address the issue of insufficient deep information extraction during image fusion using a single deep learning network, a dual ResNet network is designed to extract deep level feature information from infrared and visible light images, improving the similarity of the fused images. The image fusion technology based on the dual ResNet network was applied to the target detection of sensing insulators in the power sensing network, improving the average accuracy of target detection. The experimental results show that the improved registration algorithm has increased the registration accuracy of each group of images by more than 1%, the structural similarity of image fusion in the dual ResNet network has been improved by about 0.2% compared to in the single ResNet network, and the mean Average Precision (mAP) of the fusion image obtained via the dual ResNet network has been improved by 3% and 6% compared to the infrared and visible light images, respectively. Full article

(1) Background: Flexible flatfoot is characterized by medial arch collapse, leading to musculoskeletal impairments. We examined the effects of single-arch foot orthosis (SFO) and dual-arch foot orthosis (DFO) on arch height, kinematics, and kinetics in young females during walking and jogging. (2) Methods: Healthy females (n = 19) with flexible flatfoot were tested under three conditions: regular shoes, SFO, and DFO. Motion capture and a 3D force plate gathered biomechanical data. We also used a high-speed dual fluoroscopic imaging system (DFIS) to assess dynamic foot morphology. Outcomes included normalized truncated navicular height, medial arch angle, angles and moments at the metatarsophalangeal, subtalar, ankle, knee, and hip joints. (3) Results: Both types of orthoses improved the normalized navicular height and reduced the medial arch angle, with DFO vs. SFO showing greater effects (p < 0.001). DFO vs. SFO was also more effective in limiting the range of motion (ROM) of the metatarsophalangeal joint and dorsiflexion (p < 0.001). Additionally, DFO reduced the ankle range of motion and the maximum knee flexion during walking. Both orthoses reduced subtalar plantarflexion moments during stance (p < 0.001) and modulated ankle plantarflexion moments throughout different phases of gait. DFO uniquely enhanced metatarsophalangeal plantarflexion moments during jogging (p < 0.001). (4) Conclusions: Dual vs. single transverse arch foot orthosis is more effective in improving gait biomechanics in females with flexible flatfoot. Longitudinal studies are needed to confirm these benefits. Full article

Background: Homocystinuria caused by cystathionine β-synthase (CBS) deficiency is the most common congenital disorder related to sulfur amino acid metabolism, manifested by neurological, vascular, and connective tissue involvement. Methods: This study analyzed the pathogenic gene and molecular mechanism of two classic homocystinuria families through whole exome sequencing and in vitro experiments including minigene assay and expression analysis. Results: Both probands presented with ectopia lentis, high myopia, and abnormally elevated homocysteine level, but one of them had more severe clinical manifestations, including general growth retardation, mild intellectual disability, and severe pectus excavatum. Their family members were phenotypically normal but presented slightly higher levels of homocysteine in plasma. Whole exome sequencing revealed that the two probands carried c.833T>C (p.Ile278Thr) and c.1359-1G>C, and c.919G>A (p.Gly307Ser) and c.131delT (p.Tle44Thrfs*38) compound heterozygous mutations in the CBS gene, respectively. Bioinformatics and in vitro functional analysis showed that the c.1359-1G>C mutation affects the normal splicing of CBS gene, resulting in the production of two abnormal transcripts and the production of two truncated proteins. One of the c.1359-1G>C splicing events (c.1359_1467del) and c.131delT (p.Tle44Thrfs*38) both lead to a significant decrease in CBS mRNA and protein levels. Conclusions: Accurate diagnosis of patients with homocystinuria is of great importance for timely and effective treatment, as well as for the provision of appropriate genetic counseling and prenatal diagnosis guidance to the affected families. Full article

The Manufacturing Message Specification (MMS) protocol is frequently used to automate processes in IEC 61850-based substations and smart-grid systems. However, it may be susceptible to a variety of cyber-attacks. A frequently used protection strategy is to deploy intrusion detection systems to monitor network traffic for anomalies. Conventional approaches to detecting anomalies require a large number of labeled samples and are therefore incompatible with high-dimensional time series data. This work proposes an anomaly detection method for high-dimensional sequences based on a bidirectional LSTM autoencoder. Additionally, a text-mining strategy based on a TF-IDF vectorizer and truncated SVD is presented for data preparation and feature extraction. The proposed data representation approach outperformed word embeddings (Doc2Vec) by better preserving critical domain-specific keywords in MMS traffic while reducing the complexity of model training. Unlike embeddings, which attempt to capture semantic relationships that may not exist in structured network protocols, TF-IDF focuses on token frequency and importance, making it more suitable for anomaly detection in MMS communications. To address the limitations of existing approaches that rely on labeled samples, the proposed model learns the properties and patterns of a large number of normal samples in an unsupervised manner. The results demonstrate that the proposed approach can learn potential features from high-dimensional time series data while maintaining a high True Positive Rate. Full article

Background: Patients with cancer often develop a prothrombotic state that can evolve into venous and arterial thrombosis, which is associated with poor clinical outcomes. Glioblastoma multiforme (GBM) is the most frequently associated with thrombosis, but the underlying causes of this prothrombotic state are poorly defined. Objectives: We designed a study to characterize the expression of coagulation factor X (FX) and its alternatively spliced transcripts in glioblastoma tissues surgically removed from patients and in clonal cell lines. Methods: The F10 mRNA and FX protein were quantified in tissues surgically removed from seven patients with glioblastoma (glioma grade 3–4) and those from non-tumor patients. Glioblastoma cells from three human clonal lines were examined for the expression and secretion of FX at baseline and after the cells were stimulated with lipopolysaccharide (LPS) or subjected to oxygen/glucose starvation in culture. PCR products were subjected to Sanger sequencing and amplicon sequencing to identify F10 isoforms and their ratios. A chromogenic assay was performed to assess FX activity. Results: Glioblastoma tissue and cell lines expressed high levels of the full-length and an alternatively spliced F10 mRNA. The latter produced a C-terminal truncated FX. The ratio of full-length to truncated F10 transcripts was significantly higher in normal brain tissues than in glioblastoma tissue. In cultured cells from the glioblastoma cell lines, FX was secreted to the conditioned medium and was active in cleaving a chemical substrate. The FX expression and secretion were upregulated in cells stimulated with LPS or subjected to oxygen/glucose starvation. Discussion: Glioblastoma cells synthesize and secrete FX that was active in promoting thrombin generation. These findings provide a new underlying mechanism to explain why glioblastoma patients are prone to developing thrombosis. Full article

The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the realm of the meteorological drought. The time series of the SHI can be used as a platform for deriving the longest duration, L_T, and the largest magnitude, M_T (in standardized form), of a hydrological drought over a desired return period of T time units (year, month, or week). These parameters are predicted based on the SHI series derived from the annual, monthly, and weekly flow sequences of Canadian rivers. An important point to be reckoned with is that the monthly and weekly sequences are non-stationary compared to the annual sequences, which fulfil the conditions of stochastic stationarity. The parameters, such as the mean, standard deviation (or coefficient of variation), lag 1 autocorrelation, and conditional probabilities from SHI sequences, when used in Markov chain-based relationships, are able to predict the longest duration, L_T, and the largest magnitude, M_T. The product moment and L-moment ratio analyses indicate that the monthly and weekly flows in the Canadian rivers fit the gamma probability distribution function (pdf) reasonably well, whereas annual flows can be regarded to follow the normal pdf. The threshold level chosen in the analysis is the long-term median of SHI sequences for the annual flows. For the monthly and weekly flows, the threshold level represents the median of the respective month or week and hence is time varying. The runs of deficit in the SHI sequences are treated as drought episodes and thus the theory of runs formed an essential tool for analysis. This paper indicates that the Markov chain-based methodology works well for predicting L_T on annual, monthly, and weekly SHI sequences. Markov chains of zero order (MC0), first order (MC1), and second order (MC2) turned out to be satisfactory on annual, monthly, and weekly scales, respectively. The drought magnitude, M_T, was predicted satisfactorily via the model M_T = I_d × L_c, where I_d stands for drought intensity and L_c is a characteristic drought length related to L_T through a scaling parameter, ɸ (= 0.5). The I_d can be deemed to follow a truncated normal pdf, whose mean and variance when combined implicitly with L_c proved prudent for predicting M_T at all time scales in the aforesaid relationship. Full article

Determining the ‘best-fitting’ distribution for data is an important problem in data analysis. Specifically, observing how the distribution of data changes as values below (or above) a threshold are omitted from analyses can be of use in various applications, from animal movement to the modelling of natural phenomena. Such truncated distributions, known as hazard functions, are widely studied and well understood in survival analysis, although rarely widely used in data analysis. Here, by considering the hazard and reverse-hazard functions, we demonstrate a qualitative assessment of the ‘best-fit’ distribution of data. Specifically, we highlight the potential advantages of this method when determining whether power-law behaviour may or may not be present in data. Finally, we demonstrate this approach using some real-world datasets. Full article

In this paper, we study self-normalized moderate deviations for degenerate U-statistics of order 2. Let $\{X_i,i\ge 1\}$ be i.i.d. random variables and consider symmetric and degenerate kernel functions in the form $h(x,y)={\sum }_{l=1}^{\infty }{\lambda }_l{g}_l\left(x\right)g_l\left(y\right)$, where ${\lambda }_l>0$, $E{g}_l\left(X_1\right)=0$, and $g_l\left(X_1\right)$ is in the domain of attraction of a normal law for all $l\ge 1$. Under the condition ${\sum }_{l=1}^{\infty }{\lambda }_l<\infty$ and some truncated conditions for $\{g_l\left(X_1\right):l\ge 1\}$, we show that $\log P({\displaystyle \frac{{\sum }_{1\le i\ne j\le n}h(X_i,X_j)}{{\max }_{1\le l<\infty }{\lambda }_l{V}_{n,l}^2}}\ge x_n^2)\sim -{\displaystyle \frac{x_n^2}{2}}$ for $x_n\to \infty$ and $x_n=o\left(\sqrt{n}\right)$, where $V_{n,l}^2={\sum }_{i=1}^n{g}_l^2\left(X_i\right)$. As application, a law of the iterated logarithm is also obtained. Full article

Situs anomalies, including situs inversus and situs ambiguous (SAMB), are rare congenital conditions typically noted in pediatric populations, with SAMB being particularly uncommon in adults. This case study addresses the incidental discovery of situs ambiguous with polysplenia in a 65-year-old man evaluated for suspected adrenal adenoma. The patient’s medical history included benign prostatic hyperplasia and tuberculous pleurisy. Methods included a thorough physical examination and laboratory tests, which showed normal cortisol levels and ACTH rhythm. Contrast-enhanced CT imaging revealed multiple spleens near the right adrenal region, altered liver positioning, a truncated pancreas, and a right-sided stomach, while the right adrenal gland was not visualized. Notably, the patient exhibited minimal symptoms despite these significant anatomical anomalies. The findings underscore the rarity of situs ambiguous in adults and its unexpected association with endocrine pathology. This case highlights the importance of comprehensive imaging and a multidisciplinary approach in managing patients with unusual anatomical presentations. It suggests that situs anomalies may be more prevalent in adult populations than previously recognized and emphasizes the need for increased clinical awareness and evaluation in similar cases. Full article

The symmetric Kullback–Leibler centroid, also called the Jeffreys centroid, of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks, including information retrieval, information fusion, and clustering. However, the Jeffreys centroid is not available in closed form for sets of categorical or multivariate normal distributions, two widely used statistical models, and thus needs to be approximated numerically in practice. In this paper, we first propose the new Jeffreys–Fisher–Rao center defined as the Fisher–Rao midpoint of the sided Kullback–Leibler centroids as a plug-in replacement of the Jeffreys centroid. This Jeffreys–Fisher–Rao center admits a generic formula for uni-parameter exponential family distributions and a closed-form formula for categorical and multivariate normal distributions; it matches exactly the Jeffreys centroid for same-mean normal distributions and is experimentally observed in practice to be close to the Jeffreys centroid. Second, we define a new type of inductive center generalizing the principle of the Gauss arithmetic–geometric double sequence mean for pairs of densities of any given exponential family. This new Gauss–Bregman center is shown experimentally to approximate very well the Jeffreys centroid and is suggested to be used as a replacement for the Jeffreys centroid when the Jeffreys–Fisher–Rao center is not available in closed form. Furthermore, this inductive center always converges and matches the Jeffreys centroid for sets of same-mean normal distributions. We report on our experiments, which first demonstrate how well the closed-form formula of the Jeffreys–Fisher–Rao center for categorical distributions approximates the costly numerical Jeffreys centroid, which relies on the Lambert W function, and second show the fast convergence of the Gauss–Bregman double sequences, which can approximate closely the Jeffreys centroid when truncated to a first few iterations. Finally, we conclude this work by reinterpreting these fast proxy Jeffreys–Fisher–Rao and Gauss–Bregman centers of Jeffreys centroids under the lens of dually flat spaces in information geometry. Full article