Search Results (691)

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

If $(X,\mathcal{U})$ is a Hausdorff uniform space, we define the uniform weight $w(X,\mathcal{U})$ as the smallest cardinal $\kappa$ such that $\mathcal{U}$ has a basis of cardinality $\kappa$. An important topological cardinal of a Tychonoff space X is the number of cozero sets of X, which we denote as $z\left(X\right)$. It is known that $w(X,\mathcal{U})\le z(X\times X)$ for every compatible uniformity $\mathcal{U}$ of X. We do not know if $z(X\times X)$ can be replaced by $z\left(X\right)$. We concentrate ourselves in $w(X,{\mathcal{U}}_n)$, where ${\mathcal{U}}_n$ is the fine uniformity of X, i.e., the one having the family of normal covers as a basis. We establish upper bounds for $w(X,{\mathcal{U}}_n)$ using the character and pseudocharacter in extensions of $X\times X$ or using the cardinal $z\left(X\right)$. We also find some generalizations of the equivalence: $w(X,{\mathcal{U}}_n)={\aleph }_0$ if and only if X is metrizable and the set of non-isolated points of X is compact. Full article

Achieving reliable navigation is critical for GNSS receivers subject to spoofing attacks. Utilizing the inherent sparsity and inconsistency of spoofing signals, this paper proposes an anti-spoofing framework for GNSS receivers to detect, classify, and recover positions from spoofing attacks without additional devices. A sparse decomposition algorithm with non-negative constraints limited by signal power magnitudes is proposed to achieve accurate spoofing detections while extracting key features of the received signals. In the classification stage, these features continuously refine each channel of the receiver’s code tracking loop, ensuring that it tracks either the authentic or counterfeit signal components. Moreover, by leveraging the inherent inconsistency of spoofing properties, we incorporate the Hausdorff distance to determine the most overlapped position sets, distinguishing genuine trajectories and mitigating spoofing effects. Experiments on the TEXBAT dataset show that the proposed algorithm detects 98% of spoofing attacks, ensuring stable position recovery with an average RMSE of 6.32 m across various time periods. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

This paper explores coherent upper conditional previsions, a class of nonlinear functionals that generalize expectations while preserving consistency properties. The study focuses on their integral representation using the countably additive Möbius transform, which is possible if coherent upper previsions are defined with respect to a monotone set function of bounded variation. In this work, we prove that an integral representation with respect to a countably additive measure is also possible, on the Borel $\sigma$-algebra, even when the coherent upper prevision is defined by the Choquet integral with respect to a Hausdorff measure, which is not of bounded variation. It occurs since Hausdorff outer measures are metric measures, and therefore every Borel set is measurable with respect to them. Furthermore, when the conditioning event has a Hausdorff measure in its own Hausdorff dimension equal to zero or infinity, coherent conditional probability is defined via the countably additive Möbius transform of a monotone set function of bounded variation. The paper demonstrates the continuity of coherent conditional previsions induced by Hausdorff measures. Full article

Suppose X and Y are Banach spaces, K is a compact Hausdorff space, and $C(K, X)$ is the Banach space of all continuous X-valued functions (with the supremum norm). We will study some strongly bounded operators $T:C(K, X)\to Y$ with representing measures $m:\Sigma \to L(X,Y)$, where $L(X,Y)$ is the Banach space of all operators $T:X\to Y$ and $\Sigma$ is the $\sigma$-algebra of Borel subsets of K. The classes of operators that we will discuss are the Grothendieck, p-limited, p-compact, limited, operators with completely continuous, unconditionally converging, and p-converging adjoints, compact, and absolutely summing. We give a characterization of the limited operators (resp. operators with completely continuous, unconditionally converging, p-convergent adjoints) in terms of their representing measures. Full article

The number of newly developed substances with poor water solubility continually increases. Therefore, specialized formulation strategies are required to overcome the low bioavailability often associated with this property. This review provides an overview of novel physical modification strategies discussed in the literature over the past decades and focuses on oral dosage forms. A distinction is made between ‘brick-dust’ molecules, which are characterized by high melting points due to the solid-state properties of the substances, and ‘grease-ball’ molecules with high lipophilicity. In general, the discussed strategies are divided into the following three main categories: drug nanoparticles, solid dispersions, and lipid-based formulations. Full article

In clinical practice, left ventricle hypertrophy (LVH) continues to pose a considerable challenge, highlighting the need for more reliable diagnostic approaches. This study aims to propose an automated framework for the quantification of LVH extent and the classification of myocardial segments according to hypertrophy severity using a deep learning-based algorithm. The proposed method was validated on 133 subjects, including both healthy individuals and patients with LVH. The process starts with automatic LV segmentation using U-Net and the segmentation of the left ventricle cavity based on the American Heart Association (AHA) standards, followed by the division of each segment into three equal sub-segments. Then, an automated quantification of regional wall thickness (RWT) was performed. Finally, a convolutional neural network (CNN) was developed to classify each myocardial sub-segment according to hypertrophy severity. The proposed approach demonstrates strong performance in contour segmentation, achieving a Dice Similarity Coefficient (DSC) of 98.47% and a Hausdorff Distance (HD) of 6.345 ± 3.5 mm. For thickness quantification, it reaches a minimal mean absolute error (MAE) of 1.01 ± 1.16. Regarding segment classification, it achieves competitive performance metrics compared to state-of-the-art methods with an accuracy of 98.19%, a precision of 98.27%, a recall of 99.13%, and an F1-score of 98.7%. The obtained results confirm the high performance of the proposed method and highlight its clinical utility in accurately assessing and classifying cardiac hypertrophy. This approach provides valuable insights that can guide clinical decision-making and improve patient management strategies. Full article

In geographic information data processing, the matching of road data at different scales is crucial. Due to scale differences, road features can change, posing a challenge to multi-scale matching. Spatial relationship is the key to matching because it remains stable at different scales. In this paper, we propose an improved summation product of direction and distance (ISOD) descriptor, which combines features such as included angle chain and camber variance with similarity features such as length, direction, and Hausdorff distance to construct an integrated similarity metric model for multi-scale road matching. The experiments proved that the model achieved 94.75% and 93.34% precision and recall in 1:50,000 and 1:10,000 scale road data matching and 86.39% and 94.06% in 1:250,000 and 1:50,000 scale road data matching, respectively. This proves the effectiveness and practicality of the method. The ISOD descriptor and integrated similarity metric model in this paper provide an effective method for multi-scale road data matching, which helps the integration and fusion of geographic information data, and has an important application value in the fields of intelligent transport and urban planning. Full article

Background: 1,2,3,4,5-pentathiepines (PTEs) are compounds originally identified in marine ascidians and are currently under investigation for their promising pharmacological properties, particularly as potential antineoplastic agents. Objectives: In this study, we investigated the antineoplastic properties of a series of ten indolizine-based PTEs, comprising eight previously reported compounds and two newly synthesized derivatives. Methods: These compounds were evaluated against a panel of human cancer cell lines of diverse tissue origins, as well as, for the first time, on non-cancerous CR9 fibroblasts to assess their cytotoxic selectivity. In addition, their effects were tested on 3D spheroid models, providing preliminary insights into their potential in vivo efficacy. Initial screening focused on cell viability, followed by a more detailed characterization of the most active compounds in terms of their ability to induce apoptosis, necrosis, cell cycle arrest, and reactive oxygen species (ROS) generation. The anti-migratory activity of PTEs and a newly adapted assay to confirm sulfur species release in the cells were also performed for the first time. Results and Conclusions: Our findings reveal that four PTEs bearing hydrophilic, hydrogen-bonding functional groups, particularly the two inspired by natural analogs, exhibited the most potent anticancer activity. Full article

Artificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively. Full article

This study introduces a new metric structure called the Graphical Fuzzy Cone Metric Space (GFCMS) and explores its essential properties in detail. We examine its topological aspects in detail and introduce the notion of Hausdorff distance within this setting—an advancement not previously explored in any graphical structure. Furthermore, a fixed-point result is proven within the framework of GFCMS, accompanied by examples that demonstrate the applicability of the theoretical results. As a significant application, we construct fractals within GFCMS, marking the first instance of fractal generation in a graphical structure. This pioneering work opens new avenues for research in graph theory, fuzzy metric spaces, topology, and fractal geometry, with promising implications for diverse scientific and computational domains. Full article

Canopy gaps are vital microhabitats for forest carbon cycling and species regeneration, whose accurate extraction is crucial for ecological modeling and smart forestry. However, traditional monitoring methods have notable limitations: ground-based measurements are inefficient; remote-sensing interpretation is susceptible to terrain and spectral interference; and traditional algorithms exhibit an insufficient feature representation capability. Aiming at overcoming the bottleneck issues of canopy gap identification in mountainous forest regions, we constructed a multi-task deep learning model (ES-Net) integrating an edge–semantic collaborative perception mechanism. First, a refined sample library containing multi-scale interference features was constructed, which included 2808 annotated UAV images. Based on this, a dual-branch feature interaction architecture was designed. A cross-layer attention mechanism was embedded in the semantic segmentation module (SSM) to enhance the discriminative ability for heterogeneous features. Meanwhile, an edge detection module (EDM) was built to strengthen geometric constraints. Results from selected areas in Yunnan Province (China) demonstrate that ES-Net outperforms U-Net, boosting the Intersection over Union (IoU) by 0.86% (95.41% vs. 94.55%), improving the edge coverage rate by 3.14% (85.32% vs. 82.18%), and reducing the Hausdorff Distance by 38.6% (28.26 pixels vs. 46.02 pixels). Ablation studies further verify that the synergy between SSM and EDM yields a 13.0% IoU gain over the baseline, highlighting the effectiveness of joint semantic–edge optimization. This study provides a terrain-adaptive intelligent interpretation method for forest disturbance monitoring and holds significant practical value for advancing smart forestry construction and ecosystem sustainable management. Full article

Introduction: The escalating scarcity of skilled healthcare professionals is particularly pronounced within surgical specialties, where the prospect of attracting prospective medical practitioners poses formidable challenges. Throughout their academic journey, students exhibit diminishing enthusiasm and motivation to pursue careers in surgery, including trauma surgery. It is postulated that the caliber of teaching plays a pivotal role in influencing students’ subsequent specialization choices. Methods: This prospective observational study was conducted among a cohort of third-year medical students at the German University Medicine Greifswald. The methodology encompassed the utilization of a self-administered questionnaire to procure data. Results: The study encompassed 177 participants, of whom 34.7% expressed an inclination toward a career in surgery (22.7% in trauma surgery). Participants who reported a favorable impact from the examination course displayed a significantly heightened interest in clinical clerkships within trauma surgery (p < 0.001), and even expressed a contemplation of specializing in orthopedics and trauma surgery (p = 0.001). Logistic regression analysis highlighted that the convergence of practical training and positive role modeling emerged as the most influential factors augmenting the allure of trauma surgery. Conclusions: Evidently, students who gleaned substantial benefits from high-quality practical instruction in trauma surgery exhibited a significantly heightened likelihood of pursuing this domain in their future endeavors. Surgical academic institutions stand to leverage this insight in their strategic planning for attracting and retaining potential residents. Cultivating a positive affinity for trauma surgery should be instilled early in the curriculum, subsequently sustained through ongoing immersive engagement that encompasses professional as well as interpersonal dimensions. Full article