Detailed Features Research Articles

Gender Characteristics of the Psychoemotional Status of Students with Different Levels of Academic Performance

The study of the health of student youth remains one of the important areas in scientific research, since the task of higher educational institutions is not only to instill professional skills, but also to form a full-fledged personality. Neuropsychiatric adaptation is one of the keys, so it is important to identify the factors that influence its formation. 120 students of the medical faculty of the Kyrgyz-Russian Slavic University were examined: 1) young men (n=30) and 2) girls (n=30) with high academic performance; 3) young men (n=30) and 4) girls (n=30) with low academic performance. We evaluated emotional intelligence according to the N. Hall scale, aggression according to L. G. Pochebut, the level of depression according to the Tsung test, emotional orientation according to B. I. Dodonov, level of self-esteem, style of behavior in conflict. Young men with high academic performance compared to similar girls are more likely to have a low level of emotional intelligence, high self-esteem, face mild depression, show more physical aggression, and have lower scores in communicative and aesthetic emotional orientation. Among those with low academic performance, girls, in comparison with young men, manage their own emotions worse, do not have high self-esteem, and have higher scores in communicative, hedonic and aesthetic emotional orientation. Sex differences in the psycho-emotional sphere are more clearly manifested among students with a high level of academic performance. The study of gender features in more detail assesses the psycho-emotional state of students, makes it possible to predict possible violations and timely carry out preventive or correctional measures, and, therefore, increase the success of adaptation.

Unsupervised learning of spatially-resolved ARPES spectra for epitaxially grown graphene via non-negative matrix factorization

This study proposed an unsupervised machine-learning approach for analyzing spatially-resolved ARPES. A combination of non-negative matrix factorization (NMF) and k-means clustering was applied to spatially-resolved ARPES spectra of the graphene epitaxially grown on a SiC substrate. The Dirac cones of graphene were decomposed and reproduced fairly well using NMF. The base and activation matrices obtained from the NMF results reflected the detailed spectral features derived from the number of graphene layers and growth directions. The spatial distribution of graphene thickness on the substrate was clearly visualized by the clustering using the activation matrices acquired via NMF. Integration with k-means clustering enables clear visualization of spatial variations. Our method efficiently handles large datasets, extracting spectral features without manual inspection. It offers broad applicability beyond graphene studies to analyze ARPES spectra in various materials.

Predictive Value of Multi-Phase CT Radiomics in Delineating Early and Advanced T Staging of Colon Cancer

This study leveraged the power of multi-phase CT radiomics, which extracts detailed features from multi-phase images, to explore the distinction between early (T1-T2) and advanced (T3-T4) colon cancer stages in each of multiple phases, providing valuable insights to healthcare professionals and enhancing their decision-making capabilities. A total of 191 patients with surgically confirmed primary colon cancer were retrospectively included, and multi-phase CT scans (non-enhanced contrast phase, arterial phase, portal venous phase, and delayed phase) were conducted within one week before surgery. Three-dimensional segmentation of colonic tumors was performed on the images of the four phases, and radiomics features of each colonic tumor were automatically extracted. Minimum redundancy maximum relevance (mRMR) was applied to features selection in each of the four phases. The least absolute shrinkage and selection operator logistic regression was conducted to determine the association between radiomics features and early (T1-T2) and advanced (T3-T4) stages of colon cancer. Additionally, diagnostic performance comparison was carried out in four phases. The mean (±SD) age of the 191 individuals was 61.87±13.137 years, with females comprising 43.5% of the cohort. The selected features for the non-enhanced, arterial, portal venous, and delayed phases numbered 7, 11, 6, and 10, respectively. In the test set, the AUC values for the on-enhanced, arterial, portal venous, and delayed phases were 0.86 (0.76-0.96), 0.84 (0.73-0.94), 0.82 (0.71-0.93), and 0.86 (0.75-0.97), with corresponding accuracies of 0.84, 0.80, 0.75, and 0.88, sensitivities of 0.73, 0.64, 0.86, and 0.68, and specificities of 0.91, 0.91, 0.68, and 1.00, respectively. DeLong's test revealed no statistically significant differences in the AUC values between the four phases within the test sets (P = 0.3233~ 0.9912). Multi-phase CT radiomics demonstrated substantial value in differentiating between early (T1-T2) and advanced (T3-T4) stages of patients with colon cancer.

LFIR-YOLO: Lightweight Model for Infrared Vehicle and Pedestrian Detection

The complexity of urban road scenes at night and the inadequacy of visible light imaging in such conditions pose significant challenges. To address the issues of insufficient color information, texture detail, and low spatial resolution in infrared imagery, we propose an enhanced infrared detection model called LFIR-YOLO, which is built upon the YOLOv8 architecture. The primary goal is to improve the accuracy of infrared target detection in nighttime traffic scenarios while meeting practical deployment requirements. First, to address challenges such as limited contrast and occlusion noise in infrared images, the C2f module in the high-level backbone network is augmented with a Dilation-wise Residual (DWR) module, incorporating multi-scale infrared contextual information to enhance feature extraction capabilities. Secondly, at the neck of the network, a Content-guided Attention (CGA) mechanism is applied to fuse features and re-modulate both initial and advanced features, catering to the low signal-to-noise ratio and sparse detail features characteristic of infrared images. Third, a shared convolution strategy is employed in the detection head, replacing the decoupled head strategy and utilizing shared Detail Enhancement Convolution (DEConv) and Group Norm (GN) operations to achieve lightweight yet precise improvements. Finally, loss functions, PIoU v2 and Adaptive Threshold Focal Loss (ATFL), are integrated into the model to better decouple infrared targets from the background and to enhance convergence speed. The experimental results on the FLIR and multispectral datasets show that the proposed LFIR-YOLO model achieves an improvement in detection accuracy of 4.3% and 2.6%, respectively, compared to the YOLOv8 model. Furthermore, the model demonstrates a reduction in parameters and computational complexity by 15.5% and 34%, respectively, enhancing its suitability for real-time deployment on resource-constrained edge devices.

A Person Re-Identification Method for Fusing Convolutional Attention and Transformer Architecture

Rapid and accurate localization of target person via person re-identification is one of the important methods in intelligent security systems,but faces great challenges due to complex environmental changes. In order to build a model applicable to various complex scenes, deep learning-based person re-identification models have become a key research direction for researchers. The existing convolutional neural network-based or transformer-based deep learning models are still have certain shortcomings in the extraction of local details and are not able to cope well with realistic complex scenes. To address this problem, a method fusing convolutional neural network and transformer (FCAT) is proposed to enhance the transformer's attention to local detail information. This method mainly improves the transformer's ability to extract local detail features indirectly by embedding convolutional space attention and channel attention respectively to enhance the attention to important regions and important channel features in the image. Comparative ablation experiments on three publicly available person re-identification datasets demonstrate that FCAT achieves comparable results to existing methods on non-occluded datasets, and significantly improves performance on occluded datasets. The proposed model is also has a lightweight effect, and the inference speed is improved without increasing the amount of outer computation and model parameters.

ACGAN for Addressing the Security Challenges in IoT-Based Healthcare System

The continuous evolution of the IoT paradigm has been extensively applied across various application domains, including air traffic control, education, healthcare, agriculture, transportation, smart home appliances, and others. Our primary focus revolves around exploring the applications of IoT, particularly within healthcare, where it assumes a pivotal role in facilitating secure and real-time remote patient-monitoring systems. This innovation aims to enhance the quality of service and ultimately improve people’s lives. A key component in this ecosystem is the Healthcare Monitoring System (HMS), a technology-based framework designed to continuously monitor and manage patient and healthcare provider data in real time. This system integrates various components, such as software, medical devices, and processes, aimed at improvi1g patient care and supporting healthcare providers in making well-informed decisions. This fosters proactive healthcare management and enables timely interventions when needed. However, data transmission in these systems poses significant security threats during the transfer process, as malicious actors may attempt to breach security protocols.This jeopardizes the integrity of the Internet of Medical Things (IoMT) and ultimately endangers patient safety. Two feature sets—biometric and network flow metric—have been incorporated to enhance detection in healthcare systems. Another major challenge lies in the scarcity of publicly available balanced datasets for analyzing diverse IoMT attack patterns. To address this, the Auxiliary Classifier Generative Adversarial Network (ACGAN) was employed to generate synthetic samples that resemble minority class samples. ACGAN operates with two objectives: the discriminator differentiates between real and synthetic samples while also predicting the correct class labels. This dual functionality ensures that the discriminator learns detailed features for both tasks. Meanwhile, the generator produces high-quality samples that are classified as real by the discriminator and correctly labeled by the auxiliary classifier. The performance of this approach, evaluated using the IoMT dataset, consistently outperforms the existing baseline model across key metrics, including accuracy, precision, recall, F1-score, area under curve (AUC), and confusion matrix results.

Infrared and Visible Image Fusion via Sparse Representation and Guided Filtering in Laplacian Pyramid Domain

The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.

Detection of tea leaf blight in UAV remote sensing images by integrating super-resolution and detection networks.

Tea leaf blight (TLB) is a common disease of tea plants and is widely distributed in tea gardens. Although the use of unmanned aerial vehicle (UAV) remote sensing can help to achieve a wider scale for TLB detection, the blurring of UAV images, overlapping of tea leaves, and small size of TLB spots pose significant challenges to the task of detection. This study proposes a method of detecting TLB in UAV remote sensing images by integrating super-resolution (SR) and detection networks. We use an SR network called SERB-Swin2sr to reconstruct the detailed features of UAV images and solve the problem of detail loss caused by the blurring in UAV images. In SERB-Swin2sr, a squeeze-and-excitation ResNet block (SERB) is introduced to enhance the models' ability to extract the target details in the images, and the convolution stem replaces the convolution block in order to increase the convergence rate and stability of the network. A detection network called SDDA-YOLO is applied to achieve precise detection of TLB in UAV remote sensing images. In SDDA-YOLO, a shuffle dual-dimensional attention (SDDA) module is introduced to enhance the feature fusion capability of the network, and an Xsmall-scale detection layer is used to enhance the detection ability of small lesions. Experimental results show that the proposed method is superior to current detection methods. Compared with a baseline YOLOv8 model, the precision, mAP@0.5, and mAP@0.5:0.95 of the proposed method are improved by 4.2%, 1.6%, and 1.8%, and the size of our model is only 4.6MB.

Ninth Version of the AJCC and UICC Nasopharyngeal Cancer TNM Staging Classification

Accurate staging is a fundamental step in treating patients with nasopharyngeal carcinoma (NPC) worldwide; this is crucial not only for prognostication, but also for guiding treatment decisions. The American Joint Committee on Cancer (AJCC)/Union for International Cancer Control (UICC) tumor-node-metastasis (TNM) system is the global language for clinicians, researchers, and cancer registries. Continual improvement that aligns with contemporary pattern of care is essential. To improve the prognostic accuracy and clinical applicability of the eighth edition (TNM-8) for NPC. This multicenter study analyzed patients with NPC with detailed tumor features during January 2014 and December 2015 and was reviewed by experienced radiologists. The data analysis was completed in December 2023. The findings were further confirmed with internal and external validation. Statistical analyses and clinical considerations were reviewed by the AJCC/UICC multidisciplinary head and neck panels and attained consensus. The recommendations were evaluated by the AJCC Evidence-Based Medicine Committee before final endorsement as the ninth version (TNM-9). The primary end point was overall survival. Adjusted hazard ratios of different subgroups were then assessed for confirmation of optimal stage grouping. Of the 4914 patients analyzed, 1264 (25.7%) were female and 3650 (74.3%) were male; the median (SD) age was 48.1 (12.0) years. Advanced radiological extranodal extension (with involvement of adjacent muscles, skin, and/or neurovascular bundles) was identified as an independent adverse factor for all end points: this was added as a criterion for N3. Patients with nonmetastatic disease were regrouped into stages I to III instead of TNM-8 stages I to IVA. Significant hazard discrimination was achieved by grouping T1-2N0-1 as stage I, T3/N2 as stage II, and T4/N3 as stage III. Although the T1-2N0-1 subgroups had comparable 5-year overall survival, subdivisions into IA (T1-T2N0) and IB (T1-T2N1) were recommended due to the distinction in adjusted hazard ratios following adjustment for chemotherapy use. Metastatic disease was exclusively classified as stage IV, and prognostication was further refined by subdivision into IVA (M1a, ≤3 lesions) and IVB (M1b, >3 lesions). TNM-9 demonstrated superiority compared with TNM-8 in major statistical aspects. The results of this diagnostic study suggest that the ninth version of TNM staging for NPC, based on robust analyses and a comprehensive review by the AJCC/UICC staging committees, provides an improved staging system for global application and a framework for future incorporation of nonanatomical factors. This will be launched for global application in January 2025.

Garment Animation NeRF with Color Editing

AbstractGenerating high‐fidelity garment animations through traditional workflows, from modeling to rendering, is both tedious and expensive. These workflows often require repetitive steps in response to updates in character motion, rendering viewpoint changes, or appearance edits. Although recent neural rendering offers an efficient solution for computationally intensive processes, it struggles with rendering complex garment animations containing fine wrinkle details and realistic garment‐and‐body occlusions, while maintaining structural consistency across frames and dense view rendering. In this paper, we propose a novel approach to directly synthesize garment animations from body motion sequences without the need for an explicit garment proxy. Our approach infers garment dynamic features from body motion, providing a preliminary overview of garment structure. Simultaneously, we capture detailed features from synthesized reference images of the garment's front and back, generated by a pre‐trained image model. These features are then used to construct a neural radiance field that renders the garment animation video. Additionally, our technique enables garment recoloring by decomposing its visual elements. We demonstrate the generalizability of our method across unseen body motions and camera views, ensuring detailed structural consistency. Furthermore, we showcase its applicability to color editing on both real and synthetic garment data. Compared to existing neural rendering techniques, our method exhibits qualitative and quantitative improvements in garment dynamics and wrinkle detail modeling. Code is available at https://github.com/wrk226/GarmentAnimationNeRF.

An Algorithm for Simplifying 3D Building Models with Consideration for Detailed Features and Topological Structure

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse algorithm, the method defines the region formed by the first-order neighboring triangles of the endpoints of the edge to be collapsed as the simplification unit. It incorporates the centroid displacement of the simplification unit, significance level, and approximate curvature of the edge as influencing factors for the collapse cost to control the edge collapse sequence and preserve model details. Additionally, considering the unique properties of 3D building models, boundary edge detection and face overlay are added as constraints to maintain the model’s topological structure. The experimental results show that the algorithm is superior to the classic QEM algorithm in terms of preserving the topological structure and detailed features of the model. Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. Moreover, the advantages of this algorithm become more pronounced as the simplification rate increases. The research findings can be applied to the simplification of 3D building models.

Generation of NIR Spectral Band from RGB Image with Wavelet Domain Spectral Extrapolation Generative Adversarial Network

Near-infrared (NIR) imaging exhibits outstanding penetration capabilities and robust anti-interference characteristics. However, acquiring high-resolution and high-fidelity NIR images is difficult due to the limited mobility, high cost, and low resolution of NIR imaging hardware. In this paper, we proposed a new end-to-end Wavelet Domain Spectral Extrapolation Generative Adversarial Network (WSEGAN) to generate highly realistic NIR images from RGB images. Since RGB and NIR images have different types of noise and artifacts, which affects the quality of NIR images generation. We design a generator with a discrete wavelet transform and an attention mechanism to capture multi-resolution contextual information, and a multi-scale discriminator to capture detailed and global features. Then, the proposed approach is evaluated by three different datasets to achieve optimal results regarding both visual effects and quantitative evaluation. The normalized difference vegetation index (NDVI) is utilized to validate the effectiveness of the generated NIR images. The result demonstrates a strong correlation between the generated images and the actual vegetation distribution. More importantly, the proposed network is testified to generate NIR images to be fused with the RGB image source for agricultural target detection tasks. In dark conditions, results show that using multi-modal data instead of RGB images improves mAP0.5 detection accuracy by 4% on the CAPSICUM dataset and by 8% on the KIWI dataset across five object detection methods. This follows the physical significance of the NIR imaging and demonstrates the potential use of the NIR images generated by the proposed method.

Multi-stage cascade GAN for synthesis of contrast enhancement CT aorta images from non-contrast CT

Recently in diagnosis of Aortic dissection (AD), the synthesis of contrast enhanced CT (CE-CT) images from non-contrast CT (NC-CT) images is an important topic. Existing methods have achieved some results but are unable to synthesize a continuous and clear intimal flap on NC-CT images. In this paper, we propose a multi-stage cascade generative adversarial network (MCGAN) to explicitly capture the features of the intimal flap for a better synthesis of aortic dissection images. For the intimal flap with variable shapes and more detailed features, we extract features in two ways: dense residual attention blocks (DRAB) are integrated to extract shallow features and UNet is employed to extract deep features; then deep features and shallow features are cascaded and fused. For incomplete flaps or lack of details, we use spatial attention and channel attention to extract key features and locations. At the same time, multi-scale fusion is used to ensure the continuity of the intimal flap. We perform the experiment on a set of 124 patients (62 with AD and 62 without AD). The evaluation results show that the synthesized images have the same characteristics as the real images and achieves better results than the popular methods.

ADT: Person re-identification based on efficient attention mechanism and single-channel dual-channel fusion with transformer features aggregation

In person re-identification(Re-ID) tasks, using only convolutional neural network(CNNs) makes it challenging to fully exploit the global key information in person images, while using only the Transformer structure fails to adequately capture the multi-scale local detail information in person images. Therefore, to address the more challenging person Re-ID tasks, effectively integrating CNN and Transformer into an algorithmic framework capable of fusing multi-dimensional features has become one of the current focal points of research. Due to the ability of the attention mechanism to suppress the extraction of non-salient features by convolutional neural networks, and the capability of the dual-channel multi-scale mechanism to facilitate the extraction of finer-grained deep information by the Transformer, we propose a network Based on Efficient Attention Mechanism and Single-channel Dual-channel Fusion with Transformer Features Aggregation (ADT). First, we introduce the Convolutional Block Attention Module(CBAM) and Spatial Reconstruction Unit (SRU) into the CNN network to construct a Multi-dimensional Attention Features Re-fusion Network(MCF). By weighting the local features extracted in the convolutional network, attention to person detail features in the model is enhanced. Then, we propose the Single and Dual-channel Integrated Transformer Features Aggregator(SDT) and overlap it in MCF. This integration is designed to more effectively aggregate the multi-scale high-dimensional global features from each layer of the CNN network. Experimental results on the Market1501, DukeMTMC, MSMT17, and CUHK03 datasets demonstrate that our model significantly enriches features representation compared to most methods.

Characterization of the in-situ degradation process of P3HT:PCBM based on hyperspectral and neural networks

In situ online observation of surface morphology during degradation processes is of paramount importance for exploring the stability of organic photovoltaic materials. In this study, we designed an in situ online characterization system based on hyperspectral and neural network technologies, and observed the degradation processes of P3HT:PCBM thin film materials. The system is capable of collecting hyperspectral image data from 101 channels within the 400–700 nm wavelength range for characterizing detailed surface features of materials. Additionally, to automate the processing of hyperspectral image data, we designed a spectral image segmentation algorithm based on neural networks and proposed a foreground attention mechanism to improve the segmentation accuracy of the algorithm. The experimental results indicate that the system can achieve high spectral characterization of P3HT:PCBM thin film materials and automate image data processing through artificial intelligence algorithms, with an image segmentation accuracy of 99.62 %. Furthermore, owing to the higher spectral resolution of this system and its computer-assisted analysis capabilities for material image data, not only are the in-situ variations in size, density, and formation rate of aggregates formed during the thermal degradation process of P3HT:PCBM thin film materials experimentally analyzed, but also the fluorescence changes at the edges of aggregates during the photodegradation process are revealed. The reliable code can be found at the following link: https://github.com/HyperSystemAndImageProc/IONFMDP-UHHNN.

Contrast-Enhanced Micro-CT Imaging of Murine Mandibles: A Multi-Method Approach for Simultaneous Hard and Soft Tissue Analysis.

To develop and evaluate a novel multi-method micro-computed tomography (μCT) imaging protocol for enhanced visualization of both hard and soft tissues in murine mandibles, addressing the limitations of traditional imaging techniques in dental research. We employed a contrast-enhanced (CE) μCT imaging technique using Lugol's iodine as a contrast agent to visualize the intricate structures of murine mandibles. The protocol involved the combination of conventional μCT imaging as well as CE-μCT, including decalcification with EDTA, allowing for simultaneous assessment of hard and soft tissues. The method is compared with standard imaging modalities, and the ability to visualize detailed anatomical features is discussed. The CE-μCT imaging technique provided superior visualization of murine mandibular structures, including dental pulp, periodontal ligaments and the surrounding soft tissues, along with conventional μCT imaging of alveolar bone and teeth. This method revealed detailed anatomical features with high specificity and contrast, surpassing traditional imaging approaches. Our findings demonstrate the potential of CE-μCT imaging with Lugol's iodine as a powerful tool for dental research. This technique offers a comprehensive view of the murine mandible, facilitating advanced studies in tissue engineering, dental pathology and the development of dental materials.

Sub-diffraction-limited single-photon 3D imaging based on domain features extraction network at kilometer-scale distance

Single-photon imaging holds extensive application value, yet its imaging quality is often constrained by low signal-to-background ratios (SBRs). In this paper, we designed a network based on the statistical priors of photons, corresponding to the Gaussian statistical prior along the photon time axis, the Poisson statistical prior of photon counts, the varying receptive field requirements for different SBRs, and the deconvolution for solving the photon point spread function (PSF). We designed multiple modules to handle different photon statistical priors and introduced Selective Kernel Network (SKNet) as the feature extraction unit. This enables our method to accurately restore the true spatial distribution of photons. Compared to other methods, we place greater emphasis on the impact of neighborhood characteristics on photons and feature extraction under low SBR. In addition, we designed FoV Module to do deconvolution for PSF at sub-pixel scanning scenarios for sub-diffraction imaging. Experimental results demonstrate our methods, in particularly, achieved the best implementation results to date at low SBR data, which can achieve denoising while preserving more detail features and our methods also achieved twofold sub-diffraction imaging at a distance of 1.5 km using collected real-world data, thereby showing its immense potential to improve the performance of single-photon imaging systems for kilometer-scale imaging.

Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction.

The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model's effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.

Clinical, histopathological, and immunophenotypic spectrum of hepatic epithelioid hemangioendothelioma: Eight years’ data of a tertiary care center from North India

BackgroundEpithelioid hemangioendothelioma (EHE) is an uncommon vascular tumor that commonly affects the liver. Hepatic EHE (HEHE) presents with variable clinical and histopathological features. We describe detailed clinico-histopathological features, differential diagnosis, and treatment outcomes of the cases of HEHE. Materials and methodsAll cases of HEHE diagnosed in our institute in the last eight years (2016-2023) were reviewed (n=8) (total 36 cases of EHE; 22.2%). The clinical features, radiology, histopathology, immunophenotype, molecular features, and treatment outcomes of all cases were evaluated. ResultsThe median age of presentation was 49.5 years with a female: male ratio of 7:1. Abdominal pain was the commonest presentation. Approximately two-third of the patients had multifocal lesions. Histopathology showed purely epithelioid, predominantly epithelioid, and predominantly spindle-cell morphology in 50%, 25%, and 25% respectively. All cases showed typical myxohyaline/ fibrous stroma and organized thrombi of the portal/ central veins. CD31 was the most commonly used immunostain with positivity in all cases. CAMTA1 break-apart FISH was positive in 75% of cases while none showed TFE3 immunopositivity. Chemotherapy was the most commonly employed therapy (n=5) followed by surgery (n=2). The median duration of follow-up was 26 months. Five patients were alive with disease (two patients ≥3 years), one patient died of sudden cardiac death, and two patients were lost to follow-up. Two patients developed metastatic disease at follow-up. ConclusionWe describe the clinico-histopathological features and differential diagnosis of HEHE. This appears to be the largest case series of HEHE from India.

Including detailed chemistry features in the modeling of emerging low-temperature reactive flows: A review on the application to diluted and MILD combustion systems

Developing and optimizing new reactive systems with carbon-neutral fuels like biofuels, e-fuel, hydrogen, or ammonia is crucial for sustainable energy. This requires advanced technologies capable of fuel flexibility, high efficiency, and minimal pollutant emissions. However, these energy carriers still produce pollutants, especially NOx. To address this, engineers aim to lower combustion process temperatures by adopting different strategies such as burned gas recirculation, staging or increasing the air-to-fuel ratio. Yet, lean flames, though effective at emission reduction, are prone to instability and extinction, posing safety and mechanical risks. Emerging technologies like MILD Combustion, based on burned gas recirculation and reactant dilutions offer interesting solutions. The review article begins by synthesizing experimental studies and numerical simulations of MILD turbulent combustion. It then explores fundamental phenomena specific to diluted combustion (where MILD regimes are included as sub-sets), including autoignition and flame propagation. Using high-fidelity simulations and advanced experiments, it examines flow and mixing roles in reactive zones stabilization. Moving forward, the review paper addresses the inclusion of detailed chemical properties in modeling turbulent combustion systems. Scientific challenges revolve around modeling the intricate interactions between combustion chemistry and flow turbulence while maintaining computational efficiency compatible with industrial constraints. To address this, various simplified chemistry methods – such as reduced, tabulated, or optimized chemistry – have been developed. Additionally, turbulence/chemistry coupling modeling remains unresolved in simulations, with three main routes – geometrical, statistical, or reactor-based approaches – available for turbulent combustion modeling. The state-of-the-art in simplified chemistry and turbulent combustion modeling for low-temperature regimes is then focused on capturing MILD regimes, where there is a crucial impact of dilution by burnt gases, heat transfer, and turbulence mixing on the chemical flame structure. Recent advancements enabled by machine learning and deep learning algorithms are also highlighted. Lastly, the article underscores the critical need for data to validate models, emphasizing the importance of scale-bridging experiments.