Search Results (217)

Background and Objectives: The quality of recipient vessels is critical for successful microsurgical breast reconstruction, and iatrogenic damage should be minimized. Adjuvant radiotherapy (RTx) and chemotherapy (CTx) are widely used for breast cancer and may induce structural changes in recipient vessels. This study aimed to evaluate changes in recipient vessel diameters for breast reconstruction after adjuvant treatment in patients with breast cancer. Materials and Methods: A total of 167 patients with unilateral breast cancer who underwent surgical resection between 2017 and 2021 were retrospectively reviewed. Patients were classified into four groups: mastectomy only without adjuvant treatment (group A, n = 33), adjuvant RTx only (group B, n = 44), adjuvant CTx only (group C, n = 43), and combined adjuvant CTx and RTx (group D, n = 47). Preoperative and postoperative computed tomography angiography was used to measure the diameters of the thoracodorsal artery (TDA) and internal mammary artery (IMA) on the affected and unaffected sides. Differences in vessel diameters between sides and among groups were analyzed. Results: In groups B and D, the diameters of the affected TDA and IMA were significantly decreased compared with the changes observed on the unaffected side (p < 0.001). In contrast, there were no significant differences in vessel diameters between the affected and unaffected sides in groups A and C (group A: p = 0.644; group C: p = 0.367). Conclusions: Recipient vessel diameters for microsurgical breast reconstruction significantly decreased in patients who received postoperative RTx, with or without CTx. Plastic surgeons planning delayed breast reconstruction should be aware of these adjuvant therapy-related changes in recipient vessels and consider preoperative imaging assessment to accurately counsel patients regarding surgical risks and to support informed decision-making. Full article

Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data analysis (TDA) has emerged as a promising mathematical approach for exploring their structure. This scoping review maps the current landscape of research where TDA tools—such as persistent homology and Mapper—are used to examine LLM components like attention patterns, latent representations, and training dynamics. By analyzing topological features across layers and tasks, these methods provide new ways to understand how language models generalize, respond to unfamiliar inputs, and shift under fine-tuning. The review also considers how TDA-based techniques contribute to broader goals in interpretability and robustness, especially in detecting hallucinations, out-of-distribution behavior, and representational collapse. Overall, the findings suggest that TDA offers a rigorous and versatile framework for studying LLMs, helping researchers uncover deeper patterns in how these models learn and reason. Full article

UAV-based object detection faces critical challenges including extreme scale variations (targets occupy 0.1–2% image area), bird’s-eye view complexities, and all-weather operational demands. Single RGB sensors degrade under poor illumination while infrared sensors lack spatial details. We propose ATM-Net, a lightweight multimodal RGB–infrared fusion network for robust UAV vehicle detection. ATM-Net integrates three innovations: (1) Asymmetric Recurrent Fusion Module (ARFM) performs “extraction→fusion→separation” cycles across pyramid levels, balancing cross-modal collaboration and modality independence. (2) Tri-Dimensional Attention (TDA) recalibrates features through orthogonal Channel-Width, Height-Channel, and Height-Width branches, enabling comprehensive multi-dimensional feature enhancement. (3) Multi-scale Adaptive Feature Pyramid Network (MAFPN) constructs enhanced representations via bidirectional flow and multi-path aggregation. Experiments on VEDAI and DroneVehicle datasets demonstrate superior performance—92.4% mAP50 and 64.7% mAP50-95 on VEDAI, 83.7% mAP on DroneVehicle—with only 4.83M parameters. ATM-Net achieves optimal accuracy–efficiency balance for resource-constrained UAV edge platforms. Full article

Chondroitin sulfate is a glycosaminoglycan polysaccharide, playing key roles in a plethora of physiopathological processes typical of higher animals. The position of sulfate groups within CS disaccharide subunits composing the polysaccharide chain is able to encode specific functional information. In order to expand such a “sulfation code”, access to non-natural CS variants and mimics thereof can be pursued. In this context, an interesting topic concerns phosphorylated analogs of CS polysaccharides, as the replacement of sulfate groups with phosphates can lead to unreported activities of phosphorylated CS. In light of this, the phosphorylation reaction of a microbial-sourced, unsulfated chondroitin polysaccharide with phosphoric acid is reported in the present study, testing different microwave irradiation conditions and comparing them with conventional heating procedures. The obtained products were subjected to a detailed characterization, in terms of chemical structure and hydrodynamic properties, by 1D- and 2D-NMR spectroscopy and HP-SEC-TDA analysis, respectively. The characterization study showed how different reaction conditions can not only influence the regioselectivity and degree of phosphorylation but also trigger the formation of phosphate diester functionalities acting as cross-linkers between polysaccharide chains. The results from the screening presented in this work could be interesting for any research devoted to the regioselective phosphorylation of a polysaccharide. Full article

High-performance nylon 6,6 webbings used in critical applications degrade under solar exposure, necessitating reliable methods to assess their residual strength non-destructively. This study investigates the feasibility of using instrumental color change as a predictive indicator for the loss of breaking strength. Four colors of nylon 6,6 webbings were subjected to accelerated xenon-arc solar weathering for up to 15 days. The resulting color change was quantified using both the CIELab and CIEDE2000 formulas, and residual breaking strength was measured following ASTM D6775. A regression analysis was performed to correlate these properties. The results demonstrate that a strong predictive relationship exists, but its efficacy is highly color-dependent. Webbing with high initial chroma, namely tan (R² = 0.889) and navy (R² = 0.817), showed a strong correlation between color change and strength loss. In contrast, the models for low-chroma black and white webbings were weak and unreliable. Furthermore, the simpler CIELab (ΔE*_ab) formula provided slightly more accurate predictions than the more complex CIEDE2000 (ΔE*₀₀) metric. It is concluded that colorimetry can be a viable non-destructive tool for predicting mechanical degradation, but its application is limited to specific high-chroma materials, precluding a universal model based entirely on colorimetry. Full article

In recent years, new methods to reuse, repurpose, recycle, and recover decommissioned wind turbine blades (dWTBs) have actively been developed in the wind industry. In this study, the authors address the scientific challenge of repurposing decommissioned wind turbine blades for earthwork applications, particularly as part of retaining structures. A gravity retaining structure made entirely from recycled materials is introduced, consisting of glass fibre-reinforced polymer (GFRP) composite modular units derived from dWTBs. To improve the structure’s sustainability, a mixture of typical sand and lightweight waste materials is considered for filling and backfilling of the GFRP units. In particular, two waste materials are examined—a polymer foil derived from recycled laminated glass and tyre-derived aggregate (TDA) in the form of rubber powder—which are incorporated into the sand matrix in typical dry mass proportions ranging from 2% to 32% and 5% to 20%, respectively, reflecting practical ranges considered in geotechnical backfill applications. The research involved material testing of all recyclates and their mixtures with standard sand, as well as two-dimensional finite-element (2D FE) analysis of a retaining structure using the determined material properties. To facilitate the real-world implementation of this novel technology, a structure was designed to account for ground conditions at a specific site to protect against an existing landslide. In summary, this study presents the concept of a sustainable retaining structure along with results from material tests and an initial design for implementation, supported by FE analysis of overall stability. Full article

This article explores various applications and advancements in the fields of energy management (EM), cybersecurity (CS), and automation across multiple sectors, including smart grids (SGs), the Internet of things (IoT), trading, e-commerce, and autonomous systems. A variety of innovative solutions and methodologies are discussed, such as enhanced impedance methods for simulation stability, decision support systems for resource allocation, and advanced algorithms for detecting cyber-physical threats. The integration of artificial intelligence (AI) and machine learning (ML) techniques is highlighted, particularly in addressing challenges such as fault tolerance, economic distribution in cyber-physical systems (CPSs), and protection coordination in complex environments. Additionally, the development of robust algorithms for real-time monitoring and control demonstrates significant potential for improving system efficiency and resilience against various types of attacks. Full article

In response to the challenges of low recycling rates of waste tires and their underutilization in loess subgrades, this study systematically investigates the compression deformation characteristics of tire particle (4–6 mm)-modified loess through comprehensive laboratory testing. Using one-dimensional compression tests and cyclic loading–unloading tests, the effects of different tire particle contents (0% to 100%) on pore structure evolution, compression parameters—including the compression coefficient, compression modulus, and volumetric compression coefficient—and deformation mechanisms were thoroughly analyzed. The study reveals critical state characteristics and deformation mechanisms of tire-derived aggregate–loess mixtures (TDA-LMs) and establishes a predictive model for their compression behavior. The research results indicate the following: (1) The compression behavior of TDA-LM exhibits a distinct dosage threshold and stress dependence: the critical blending ratio is 30% under stresses below 100 kPa, increasing to 40% at higher stresses (≥100 kPa); (2) Mixtures with medium to low tire content display strain hardening, whereas pure tire specimens show approximately 10% modulus softening within the 200–300 kPa range. Stress- and content-dependent models for the compression modulus and volumetric compression coefficient were developed with high accuracy (R² > 0.96); (3) The dominant deformation mechanism shifts from soil skeleton plastic yielding (at tire contents < 40%) to rubber-dominated elastic deformation (at contents > 50%). Over 85% of cumulative deformation occurs during the initial loading phase, indicating that particle–soil interface restructuring primarily takes place early in the loading process. This study provides a theoretical basis and practical design parameters for the application of waste tires in loess subgrade engineering, supporting the sustainable reuse of solid waste in environmentally friendly geotechnical construction. Full article

We propose a geometry topological framework to analyze storm dynamics by coupling persistent homology with Anti-de Sitter (AdS)-inspired metrics. On radar images of a bow echo event, we compare Euclidean distance with three compressive AdS metrics ($\alpha$ = 0.01, 0.1, 0.3) via time-resolved $H_1$ persistence diagrams for the arc and its internal cells. The moderate curvature setting ($\alpha =0.1$) offers the best trade-off: it suppresses spurious cycles, preserves salient features, and stabilizes lifetime distributions. Consistently, the arc exhibits longer, more dispersed cycles (large-scale organizer), while cells show shorter, localized patterns (confined convection). Cross-correlations of $H_1$ lifetimes reveal a temporal asymmetry: arc activation precedes cell activation. A differential indicator $\Delta \left(t\right)$ based on Wasserstein distances quantifies this divergence and aligns with the visual onset in radar, suggesting early warning potential. Results are demonstrated on a rapid Corsica bow echo; broader validation remains future work. Full article

Traditional deep learning methods for histopathological analysis suffer from a lack of interpretability, which limits their use in the clinic despite their high accuracy. This paper proposes a Topological Data Analysis (TDA) framework for interpretable colorectal cancer tissue classification. We used persistent homology to extract topological features from 5000 histological images representing eight tissue classes, combining persistence landscapes with Support Vector Machine (SVM) classification. This method achieved an overall accuracy rate of 82.70%, while providing biologically interpretable features that are directly related to tissue morphology. Topological features successfully represented cellular connectivity as well as structural patterns, enabling perfect classification of morphologically distinct tissue pairs. This research demonstrates that topological data analysis (TDA) represents a promising alternative to non-transparent methods, offering competitive efficiency while ensuring interpretability, a crucial aspect for its clinical integration in computational pathology. Full article

We propose a nested ensemble learning framework that utilizes Topological Data Analysis (TDA) to extract and integrate topological features from graph data, with the goal of improving performance on classification and regression tasks. Our approach computes persistence diagrams (PDs) using lower-star filtrations induced by three filter functions: closeness, betweenness, and degree 2 centrality. To overcome the limitation of relying on a single filter, these PDs are integrated through a data-driven, three-level architecture. At Level-0, diverse base models are independently trained on the topological features extracted for each filter function. At Level-1, a meta-learner combines the predictions of these base models for each filter to form filter-specific ensembles. Finally, at Level-2, a meta-learner integrates the outputs of these filter-specific ensembles to produce the final prediction. We evaluate our method on both simulated and real-world graph datasets. Experimental results demonstrate that our framework consistently outperforms base models and standard stacking methods, achieving higher classification accuracy and lower regression error. It also surpasses existing state-of-the-art approaches, ranking among the top three models across all benchmarks. Full article

This work presents the HX-responsiveness of the following heteroleptic donor–M–acceptor dithiolene complexes: Bu4N[MII(L1)(L2)] [M = Ni(1), Pd(2), Pt(3)], where L1 is the chiral acceptor ligand [(R)-α-MBAdto = chiral (R)-(+)α-methylbenzyldithio-oxamidate] and L2 is the donor ligand (tdas = 1,2,5-thiadiazole-3,4-dithiolato). Addition of hydrohalic acids induces a strong bathochromic shift and visible color change, which is fully reversed by ammonia (NH₃). Moreover, the sensing capability of 1 was further evaluated by deposition on a cellulose substrate. Exposure to HCl vapors induces an evident color change from purple to green, whereas successive exposure to NH₃ vapors fully restores the purple color. Remarkably, cellulose films of 1 were revealed to be excellent optical sensors against the response to triethylamine, which is a toxic volatile amine. Moreover, the HCl-responsiveness of the nonlinear optical properties of complexes 1, 2, and 3 embedded into a poly(methyl methacrylate) poled matrix was demonstrated. Reversible chemical second harmonic generation (SHG) switching is achieved by exposing the poled films to HCl vapors and then to NH₃ vapors. The SHG response ratio HCl–adduct/complex is significant (around 1.5). Remarkably, the coefficients of the susceptibility tensor for the HCl–adduct films are always larger than those of the respective free-complex films. Density Functional Theory (DFT) and time-dependent DFT calculations help in highlighting the structure–properties relationship. Full article

Change points caused by extreme events in global economic markets have been widely studied in the literature. However, existing techniques to identify change points rely on subjective judgments and lack robust methodologies. The objective of this paper is to generalize a novel approach that leverages topological data analysis (TDA) to extract topological features from time series data using persistent homology. In this approach, we use Taken’s embedding and sliding window techniques to transform the initial time series data into a high-dimensional topological space. Then, in this topological space, persistent homology is used to extract topological features which can give important information related to change points. As a case study, we analyzed 26 stocks over the last 12 years by using this method and found that there were two financial market volatility indicators derived from our method, denoted as $L_1$ and $L_2$. They serve as effective indicators of long-term and short-term financial market fluctuations, respectively. Moreover, significant differences are observed across markets in different regions and sectors by using these indicators. By setting a significance threshold of 98 % for the two indicators, we found that the detected change points correspond exactly to four major financial extreme events in the past twelve years: the intensification of the European debt crisis in 2011, Brexit in 2016, the outbreak of the COVID-19 pandemic in 2020, and the energy crisis triggered by the Russia–Ukraine war in 2022. Furthermore, benchmark comparisons with established univariate and multivariate CPD methods confirm that the TDA-based indicators consistently achieve superior F1 scores across different tolerance windows, particularly in capturing widely recognized consensus events. Full article

Heart transplantation represents a pivotal intervention for end-stage heart failure, extending survival. However, it imposes profound physical, psychological, and social challenges that often undermine recipients’ quality of life (QoL). These challenges are especially pronounced in collectivist cultural contexts like China, where familial obligations and stigma surrounding chronic illness intensify existential burdens. Grounded in theoretical frameworks including Coping Theory, Self-Determination Theory, Socioemotional Selectivity Theory, and Terror Management Theory, this cross-sectional study explored the interplay between social support and QoL among Chinese heart transplant recipients, elucidating the mediating roles of self-esteem and death anxiety, as well as their sequential chain-mediating pathway. Employing validated psychometric instruments, including the Social Support Rating Scale (SSRS), Rosenberg Self-Esteem Scale (RSES), Templer Death Anxiety Scale (T-DAS) and SF-36 Health Survey, along with chain-mediation modeling, the analysis revealed that social support exerts a direct positive influence on QoL, supplemented by indirect effects through enhanced self-esteem, reduced death anxiety, and a chained cognitive-existential mechanism linking these factors. These insights highlight the complex psychosocial dynamics of post-transplant adaptation, advocating for targeted and culturally attuned interventions. These interventions include family-based support programs, self-esteem enhancement strategies, and death anxiety counseling. The aim is to promote holistic rehabilitation and sustained well-being among heart transplant recipients in China’s context. Full article

Grey cast iron with spheroidal graphite has been known and widely used since the 20th century (since 1947). Numerous methods have been developed for the secondary metallurgy process to produce nodular graphite. Spontaneous crystallization of nodular graphite is known in foundry practice and other fields. Examples of cast iron with spheroidal graphite include pure alloys with low sulfur content and natural samples containing nodular graphite, formed by natural forces (meteorites and combustion ash). This article presents the results of two industrial experiments that led to the formation of nodular graphite precipitates without the addition of elements that promote spheroidization. Studies were carried out on high-silicon cast iron intended for corrosion-resistant castings. TDA, chemical composition analysis, light and scanning microscopy, EDS, X-ray spectroscopy, and digital image analysis were used to identify the nodular precipitates. The analyses confirmed the presence of nodular graphite precipitates, and known growth mechanisms were assigned to them. It is likely that deoxidation of the metal bath during the metallurgical process contributed to the spontaneous crystallization of graphite spheroids. Full article