Search Results (7,407)

V658 Car is the first known eclipsing binary system involving a classical Be star and an sdOB companion, offering a unique opportunity to study disk physics and binary interactions in unprecedented detail. From TESS data and multi-color observations from the comissão para a colaboração entre profissionais e amadores collaboration, we analyze the system’s color–magnitude diagram and compare it with radiative transfer models that include the Be star, its circumstellar disk, and the sdOB companion. While the stellar eclipses are well reproduced, two features observed in the multi-color photometry challenge the current modeling paradigm: the discrepancy between the observed reddening and the modeled blueing during the first attenuation phase and the complete lack of modeled attenuation around the second stellar eclipse. These issues highlight the need for more sophisticated modeling approaches to capture the complex interplay between disk opacity and binary dynamics. Full article

Background: Hormone-receptor-positive (HR+)/HER2-negative (HER2−breast cancer (BC) is characterized by low immunogenicity and an immunosuppressive microenvironment. These features likely contribute to the inconsistent clinical activity of immune checkpoint inhibitors (ICIs) in this BC subtype. We conducted a systematic review of clinical trials evaluating ICIs in HR+/HER2− BC patients, focusing on potential biomarkers of response and resistance to these drugs. Methods: We systematically searched in Medline via PubMed, EMBASE, and CENTRAL for phase II/III clinical trials published between 2013 and 2023, testing ICIs alone or in combination with other agents in HR+/HER2− BC patients at any stage. All the searches were performed up to 27 January 2024. Data on study characteristics, clinical outcomes, and biomarker profiles were extracted, and due to study heterogeneity, a narrative synthesis was performed, without risk-of-bias assessment or meta-analysis. Results: Twenty-five studies were included, with 3298 patients enrolled overall. Eighteen of these trials enrolled patients with advanced disease. All trials investigated ICI combination regimens, more frequently with chemotherapy, CDK4/6 inhibitors, or other immunotherapeutic agents. Most of the studies enrolling patients with advanced disease failed to show a significant clinical activity of ICIs. In the early setting, neoadjuvant chemo-immunotherapy with nivolumab or pembrolizumab increased the rate of complete responses compared to chemotherapy alone. Moreover, high programmed death-ligand 1 (PD-L1) expression, low ER (estrogen receptor), and high tumor-infiltrating lymphocyte (TIL) levels correlated with improved outcomes. Consistently, markers indicating enhanced immune activation, such as the MammaPrint High 2 (MP2) genomic signature, were associated with increased ICI sensitivity. Discussion: Despite the limited overall efficacy, ICIs may represent a viable therapeutic option for a selected subset of HR+/HER2− BC patients. However, this systematic review is limited by study heterogeneity and the inclusion of ongoing or immature trials, which prevents quantitative analysis and may affect future conclusions on ICIs in HR+/HER2− breast cancer. Finally, optimized combination strategies could enhance tumor immunogenicity, while predictive biomarkers such as PD-L1, TILs, or specific genomic signatures could identify responsive patients. Full article

This review provides an overview of the cross-sectional imaging features of gastrointestinal (GI) metastases presenting with a linitis plastica (LP) pattern and illustrates these findings through a series of cases from various primary tumors. It also addresses key diagnostic challenges, with particular attention to differential diagnosis. The term linitis plastica (LP) refers to the macroscopic appearance of a hollow organ with diffuse mural tumor infiltration, leading to loss of parietal distensibility. Although rare, primary LP can occur throughout the gastrointestinal (GI) tract. First described in the stomach—the most common site—it is typically associated with undifferentiated adenocarcinoma composed of poorly cohesive cells, often with signet ring morphology. Beyond primary GI tumors, LP-like metastases may also arise from extragastrointestinal primaries, most notably breast carcinoma (particularly the lobular subtype), as well as urinary bladder and prostate carcinomas. LP-like GI metastases typically manifest as circumferential, enhancing wall thickenings with exaggerated zonal anatomy and luminal narrowing. Due to diffuse parietal tumor infiltration—often with mucosal preservation—the submucosa and serosa appear disproportionately thickened and show greater enhancement relative to the muscularis propria (MP). This specific imaging appearance is known as the malignant target sign, which must be distinguished from the benign target sign, where the most prominent low-density layer corresponds to edematous submucosa. Additional key features include homogeneous enhancement with loss of layer differentiation on delayed-phase imaging and a concentric ring pattern on MR. Secondary findings may also be present, such as intestinal obstruction and concomitant peritoneal carcinomatosis (PC). Gastrointestinal metastases with an LP pattern present a significant diagnostic challenge, as they can mimic both primary tumors and benign inflammatory or infectious conditions. Accurate diagnosis is critical because management strategies differ substantially. Since the mucosa is often spared, endoscopy and superficial biopsies may yield false-negative results. Therefore, while immunohistochemistry (IHC) remains essential for confirmation, radiologists play a pivotal role in raising suspicion for LP-like GI metastases and recommending deep, extensive biopsies to obtain adequate representative tissue. Furthermore, in cases of an unknown primary tumor, recognition of the LP pattern can provide important clues to the potential site of origin. Full article

To tackle the challenges of complex construction environment interference (e.g., lighting variations, occlusion, and marker contamination) and the demand for high-precision alignment during the hoisting process of bridge-erecting machines, this paper presents a two-stage marker detection–localization network tailored to hoisting alignment. The proposed network adopts a “coarse detection–fine estimation” phased framework; the first stage employs a lightweight detection module, which integrates a dynamic hybrid backbone (DHB) and dynamic switching mechanism to efficiently filter background noise and generate coarse localization boxes of marker regions. Specifically, the DHB dynamically switches between convolutional and Transformer branches to handle features of varying complexity (using depthwise separable convolutions from MobileNetV3 for low-level geometric features and lightweight Transformer blocks for high-level semantic features). The second stage constructs a Transformer-based homography estimation module, which leverages multi-head self-attention to capture long-range dependencies between marker keypoints and the scene context. By integrating enhanced multi-scale feature interaction and position encoding (combining the absolute position and marker geometric priors), this module achieves the end-to-end learning of precise homography matrices between markers and hoisting equipment from the coarse localization boxes. To address data scarcity in construction scenes, a multi-dimensional data augmentation strategy is developed, including random homography transformation (simulating viewpoint changes), photometric augmentation (adjusting brightness, saturation, and contrast), and background blending with bounding box extraction. Experiments on a real bridge-erecting machine dataset demonstrate that the network achieves detection accuracy (mAP) of 97.8%, a homography estimation reprojection error of less than 1.2 mm, and a processing frame rate of 32 FPS. Compared with traditional single-stage CNN-based methods, it significantly improves the alignment precision and robustness in complex environments, offering reliable technical support for the precise control of automated hoisting in bridge-erecting machines. Full article

This paper discusses a special type of complement of perception verbs in Serbian, introduced by kako (‘how’). Via a parallel corpus analysis, I compare the distribution of Serbian kako-clauses and English -ing forms. I show that two types of non-interrogative kako-clauses can be used in translations of English -ing forms, distinguished based on their formal and interpretive properties: ‘eventive’ and propositional kako-clauses. Eventive clauses focus on directly perceived events and cannot be negated or combined with epistemic verbs, while propositional clauses express beliefs or judgments and have a truth value. At a formal level, eventive clauses feature a null subject, while propositional clauses feature an overt nominative subject. I argue that this distinction is captured syntactically through the notion of phasehood, with only propositional clauses merging a full CP domain. Adopting the Form-Copy operation, I propose that eventive clauses lack a phase boundary, allowing for the deletion of a lower subject copy and yielding the observed case alternation and null embedded subject. This analysis offers a unified syntactic account of kako-complements and contributes to the typology of perception-based clause embedding. Full article

Pentagonal two-dimensional allotropes—penta-graphene (PG) and penta-SiC₂—are promising but experimentally elusive materials whose identification requires spectroscopic fingerprints that extend beyond ground-state descriptors. Using density functional theory within a core-hole formalism and polarisation-resolved cross sections, we compute element- and site-resolved K-edge spectra for pristine H- and OH-terminated PG, Si-substituted PG, and pristine/H-passivated penta-SiC₂. In PG, the C K-edge shows a ${\pi }^{*}$ onset at 285 eV from three-coordinated C and ${\sigma }^{*}$ bands at 293–303 eV, yielding three plateaus and a strong low-energy z-polarised response. The H/OH functionalisation suppresses the 283–288 eV plateau and weakens the polarisation anisotropy, which can be rationalised by PDOS changes at the two non-equivalent C sites. Si substitution generates a polarisation-dependent Si K-edge doublet (∼1844/1857 eV). In penta-SiC₂, the high-energy Si feature broadens (1850–1860 eV) and the C K-edge becomes strongly anisotropic; H-passivation yields a sharp, almost polarisation-independent C K-edge at 290 eV. The presence of clearly resolved, system-dependent spectral features enables unambiguous experimental discrimination between phases and terminations, facilitating spectroscopic discovery and supporting device development in 2D pentagonal materials. Full article

Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land features can lead to low accuracy or even failure when matching results. To address these challenges, in this study, a remote sensing image matching framework is proposed based on multi-perception and enhanced feature description. Specifically, the framework consists of two core components: a feature extraction network that integrates multiple perceptions and a feature descriptor enhancement module. The designed feature extraction network effectively focuses on key regions while leveraging depthwise separable convolutions to capture local features at different scales, thereby improving the detection capabilities of feature points. Furthermore, the feature descriptor enhancement module optimizes feature point descriptors through self-enhancement and cross-enhancement phases. The enhanced descriptors not only extract the geometric information of the feature points but also integrate global contextual information. Experimental results demonstrate that, compared to existing remote sensing image matching methods, our approach maintains a strong matching performance under conditions of angular and scale variation. Full article

This work presents a novel time-to-digital converter based on the analog sampling of dual-phase periodic signals generated from a free-running oscillator. A proof-of-concept ASIC, implemented in 130 nm CMOS technology, achieves an average single-shot precision of 0.9 ps-rms for time intervals up to 3 ns, with a best performance of 0.79 ps-rms. It maintains a precision below 3.7 ps-rms for intervals up to 25 ns. The design demonstrates excellent linearity, with a peak-to-peak differential nonlinearity of 0.56 LSB and a peak-to-peak integral nonlinearity of 1.43 LSB. The free-running oscillator is shareable across multiple channels, enabling power consumption of approximately 4.1 mW per channel and efficient area utilization. These features make the design highly suitable for detection systems requiring picosecond-level precision and high channel density, such as silicon pixel sensors, SPADs, LiDARs, and time-correlated single-photon counting systems. Furthermore, the architecture shows strong potential for use in high-count-rate applications, reaching up to 22 Mcps. Full article

Background: Differentiating pediatric pneumonia from acute bronchitis remains a persistent clinical challenge due to overlapping symptoms, often leading to diagnostic uncertainty and inappropriate antibiotic use. Methods: This study introduces DAPLEX, a structured ensemble learning framework designed to enhance diagnostic accuracy and reliability. A retrospective cohort of 868 pediatric patients was analyzed. DAPLEX was developed in three phases: (i) deployment of diverse base learners from multiple learning paradigms; (ii) multi-criteria evaluation and pruning based on generalization stability to retain a subset of well-generalized and stable learners; and (iii) complementarity-driven knowledge fusion. In the final phase, out-of-fold predicted probabilities from the retained base learners were combined with a consensus-based feature importance profile to construct a hybrid meta-input for a Multilayer Perceptron (MLP) meta-learner. Results: DAPLEX achieved a balanced accuracy of 95.3%, an F1-score of ~0.96, and a ROC-AUC of ~0.99 on an independent holdout test. Compared to the range of performance from the weakest to the strongest base learner, DAPLEX improved balanced accuracy by 3.5–5.2%, enhanced the F1-score by 4.4–5.6%, and increased sensitivity by a substantial 8.2–13.6%. Crucially, DAPLEX’s performance remained robust and consistent across all evaluated demographic subgroups, confirming its fairness and potential for broad clinical. Conclusions: The DAPLEX framework offers a robust and transparent pipeline for diagnostic decision support. By systematically integrating diverse predictive models and synthesizing both outcome predictions and key feature insights, DAPLEX substantially reduces diagnostic uncertainty in differentiating pediatric pneumonia and acute bronchitis and demonstrates strong potential for clinical application. Full article

Practical applications such as solar power energy systems, electronic cooling, and the convective drying of vented enclosures require continuous developments to enhance fluid and heat flow. Numerous studies have investigated the enhancement of heat transfer in L-formed vented cavities by inserting heat-generating components, filling the cavity with nanofluids, providing an inner rotating cylinder and a phase-change packed system, etc. Contemporary work has examined the thermal performance of L-shaped porous vented enclosures, which can be augmented by using metal foam, using nanofluids as a saturated fluid, and increasing the wall surface area by corrugating the cavity’s heating wall. These features are not discussed in published articles, and their exploration can be considered a novelty point in this work. In this study, a vented cavity was occupied by a copper metal foam with PPI = 10 and saturated with a copper–water nanofluid. The cavity walls were well insulated except for the left wall, which was kept at a hot isothermal temperature and was either non-corrugated or corrugated with rectangular waves. The Darcy–Brinkman–Forchheimer model and local thermal non-equilibrium models were adopted in momentum and energy-governing equations and solved numerically by utilizing commercial software. The influences of various effective parameters, including the Reynolds number (20 ≤ Re ≤ 1000), the nanoparticle volume fraction (0% ≤ φ ≤ 20%), the inflow and outflow vent aspect ratios (0.1 ≤ D/H ≤ 0.4), the rectangular wave corrugation number (N = 5 and N = 10), and the corrugation dimension ratio (CR = 1 and CR = 0.5) were determined. The results indicate that the flow field and heat transfer were affected mainly by variations in Re,  D/H, and φ for a non-corrugated left wall; they were additionally influenced by N and CR when the wall was corrugated. The fluid- and solid-phase temperatures of the metal foam increased with an increase in Re and D/H. The fluid-phase Nusselt number near the hot left sidewall increased with an increase in φ by (25–60)%, while the solid-phase Nusselt number decreased by (10–30)%, and these numbers rose by around 3.5 times when the Reynolds number increased from 20 to 1000. For the corrugated hot wall, the Nusselt numbers of the two metal foam phases increased with an increase in Re and decreased with an increase in D/H, CR, or N by 10%, 19%, and 37%. The original aspect of this study is its use of a thermal, non-equilibrium, nanofluid-saturated metal foam in a corrugated L-shaped vented cavity. We aimed to investigate the thermal performance of this system in order to reinforce the viability of applying this material in thermal engineering systems. Full article

As emergent material candidates for extreme environments, refractory high-entropy alloys (HEAs) or refractory multi-principal-element alloys (RMPEAs) comprising refractory metals feature qualities such as high radiation tolerance, corrosion resistance, and mechanical strength. A set of MoNbTi-based RMPEA samples with Al, Cr, V, and Zr additions are prepared by spark plasma sintering and investigated for their response to irradiation using 10 MeV Si⁺ ions with a dose of $1.43\times {10}^{15}$ ions/cm². Positron annihilation spectroscopy and transmission electron microscopy are employed as atomic- and meso- scale techniques to reveal how chemical complexity, nanotwinning, and phase fractions play an important role in radiation-induced defect accumulation and damage tolerance. The study provides experimental evidence of nanotwinning acting as an effective sink for radiation-induced point defects. Full article

In contemporary democracies, social media platforms are widely used for political campaigning, with political figures seeking to connect with diverse segments of the public. This study aims to illuminate the implementation of online political campaigning in Greece by analysing the content shared on four social media platforms (Facebook, X, Instagram, and TikTok) by the leaders of the three principal political parties during the 2023 pre-election phase. We employ content analysis to evaluate primarily the textual and secondarily the visual elements of the posts (N = 1.222) found on the relevant accounts, using a coding framework that comprises 32 variables. The research draws on concepts pertinent to the platformised era, such as “virality,” “personalisation,” and “network media logic,” to highlight how contemporary politicians favour direct and easily comprehensible communication patterns with citizens. The results suggest that political leaders in Greece utilise social media as a means of emotionally mobilising voters. X and Facebook emerged as the primary platforms for distributing political messages, while Instagram and TikTok focused more on personalised and non-political content. The elements of personalisation were particularly prominent in the posts made by the leader of the Conservative Party (New Democracy), who demonstrated greater proficiency in utilising TikTok’s features. Full article

Industrial solid waste fly ash has been widely applied in various fields as a resource for waste repurposing. The use of fly ash can significantly reduce production costs and at the same time reduce environmental pollution to achieve sustainability. This study explores the feasibility of using fly ash as a raw material to formulate high-temperature ceramic glazes, examining the composition, surface phases, and texture patterns of the resultant glazes. This study systematically assesses the impact of formulation modifications on glazing qualities by XRF, XRD, and SEM testing methods. The results show that 1. in high-temperature glazes, the element that determines the degree of transparency in the surface phase is the Ti content; 2. Zinc and Ferrum are important factors that can fine-tune the color shade and crystal mention; and 3. controlling the fly ash content in the glaze can change its color and texture. The novelty of this paper lies in utilizing fly ash to create high-performance, high-value-added ceramic products that feature unique aesthetics and artistic effects. In the future, we can investigate the influence of fly ash on glaze coloration, and the formation of different texture effects, as well as achieve specific color mixing. Full article

Mineral identification technology is a key technology in the construction of intelligent mines. In ore classification and detection, mining scenarios present challenges, such as diverse ore types, significant scale variations, and complex surface textures. Traditional detection models often suffer from insufficient multi-scale feature representation and weak dynamic adaptability, leading to the missed detection of small targets and misclassification of similar minerals. To address these issues, this paper proposes an efficient multi-scale ore classification and detection model, YOLO-EDH. To begin, standard convolution is replaced with deformable convolution, which efficiently captures irregular defect patterns, significantly boosting the model’s robustness and generalization ability. The C3k2 module is then combined with a modified dynamic convolution module, which avoids unnecessary computational overhead while enhancing the flexibility and feature representation. Additionally, a content-guided attention fusion (HGAF) module is introduced before the detection phase, ensuring that the model assigns the correct importance to various feature maps, thereby highlighting the most relevant object details. Experimental results indicate that YOLO-EDH surpasses YOLOv11, improving the precision, recall, and mAP50 by 0.9%, 1.7%, and 1.6%, respectively. In conclusion, YOLO-EDH offers an efficient solution for ore detection in practical applications, with considerable potential for industries like intelligent mine resource sorting and safety production monitoring, showing notable commercial value. Full article

To address the challenges of dynamic coupling interference and time-frequency feature degradation in current approaches to Ultra-High-Frequency Radio-Frequency Identification (UHF RFID) behavior recognition, this study proposes a novel behavior recognition method integrating multi-feature analysis with a dual-path residual network. The proposed method mitigates interference by using phase difference methods to eliminate signal errors and cross-correlation, as well as adaptive equalization algorithms to decouple interfering signals. To identify the target tags participating in behavioral interactions, we construct a three-dimensional feature space and apply an improved weighted isolated forest algorithm to detect active tags during interactions. Subsequently, Doppler shift analysis extracts behavioral features, and multiscale wavelet-packet decomposition generates time-frequency representations. The dual-path residual network then fuses global and local features from these time-frequency representations for behavioral classification, thereby identifying interaction behaviors such as ‘taking away’, ‘putting back’, and ‘hesitation’ (characterized by picking up, then putting back). Experimental results demonstrate that the proposed scheme achieves behavioral recognition accuracy of 94% in complex scenarios, significantly enhancing the overall robustness of interaction behavior recognition. Full article