Intensity Characteristics Research Articles

Research progress of MRI-based radiomics in hepatocellular carcinoma

BackgroundPrimary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC).ObjectiveThis study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment.MethodsA systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence.ResultsA comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction.ConclusionMRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.

A pooled analysis of the side effects of non-invasive Transcutaneous Auricular Vagus Nerve Stimulation (taVNS)

IntroductionTranscutaneous auricular vagus nerve stimulation (taVNS) is a promising technique for modulating vagal afferent fibers non-invasively and has shown therapeutic potential in neurological, cognitive, and affective disorders. While previous research highlights its efficacy, the safety profile of taVNS has been less extensively examined.MethodsThis study therefore aimed to systematically investigate side effects of taVNS in a large pooled dataset consisting of n = 488 participants, utilizing a standardized questionnaire to assess ten reported side effects. Analyses included effects of stimulation type (interval vs. continuous), stimulation duration, stimulation intensity and participant characteristics (age and gender) as potential modulators.ResultsThe findings support the safety of taVNS, with minimal and mild side effects reported across participants (M = 1.86, SD = 1.36). Although participants receiving sham stimulation were 32.4% less likely to report unpleasant feelings compared to participants receiving taVNS, this effect was driven primarily by low-end ratings (specifically, a rating of 1, indicating not at all when experiencing the corresponding side effect), thus suggesting limited clinical relevance. Interval stimulation notably reduced the likelihood of some side effects, particularly for neck pain, dizziness and unpleasant feelings, suggesting potential for optimizing taVNS protocols. Stimulation intensity and duration showed few statistically significant, but clinically minimal (i.e., very small) effects.ConclusionOverall, these findings demonstrate a favorable safety profile of taVNS, with mostly mild and transient effects, supporting its use as a suitable non-invasive tool in both research and clinical applications.

CT and MRI features of adrenal hemangioma: A study of 21 cases from two centers

PurposeTo retrospectively analyze the CT and MR imaging presentations of adrenal hemangioma (AH) and to strengthen the recognition for such tumors. Materials and methodsThis retrospective study enrolled 21 patients with 22 lesions histologically proven AH from two centers between October 2010 and November 2023. The clinical presentation and preoperative diagnosis were recorded. Two radiologists reviewed the CT and MR imaging features in consensus, including number, size, shape, boundary, attenuation, signal intensity, and dynamic enhancement pattern. ResultsThe study included nine men and twelve women (mean age 55.6 ± 12.5 years, range, 35–77 years) without hormone production. AH was unilateral in 19 cases, bilateral in 2 cases. The maximum diameter was more than 3 cm in 19/22(86 %). AHs had oval (10/22,45 %), round (5/22, 23 %), or (7/22, 32 %) multilocular shape, and well-defined boundary (18/22,82 %). On unenhanced CT, 11/20 (55 %) displayed peripheral iso-density and central hypo-intensity, 3/20 (15 %) heterogeneously hyper-density, 6/20 (30 %) hypodensity, and 14/20 (70 %) contained speckled calcification. On T2-weighted images, 7/12 (58 %) exhibited nodular hyper-intensity peripherally, markedly hyper-intensity centrally, and hypo-intense fibrotic scar in between, 5/12 (42 %) hyperintensity. On T1-weighted images, 6/12 (50 %) displayed center hyper-intensity surrounded by peripheral hypo-intensity,5/12 hypo-intensity, 1/11 (9 %) hyperintensity. On DWI, 7/12 (58 %) demonstrated peripheral hyper-intensity and central hypo-intensity, 5/12 (42 %) hyper-intensity. CT and MR imaging findings were suggestive of cystic change and necrosis (19/22,86 %, 4 cystic tumors), hemorrhage (15/22,68 %), fat (7/22, 33 %) within the tumors. CT and MR enhanced images showed peripheral nodular enhancement with (3/22,14 %) or without (13/22, 59 %) delayed central filling, nodular peripheral and center enhancement with progressive partial fill-in (2/22,9%), capsular and/or septal mild enhancement (4/22, 18 %), and capsule (16/22,73 %). The solid part was significantly enhanced, similar to the abdominal aorta (AA) with no statistical difference between the two (P > 0.05). ConclusionsSmall AHs (<3cm) have the typical imaging features of hemangioma. The imaging findings of large AHs were various, the combination of T2-hyperintense signal with inner hypo-intense fibrotic scar, speckled calcification, central extensive necrosis and hemorrhage, and nodular peripheral enhancement with persistently slight further contrast accumulation may contribute to suggest the possibility. Cystic AH should be considered as a differential diagnosis for cystic adrenal masses.

The high-resolution global shipping emission inventory by the Shipping Emission Inventory Model (SEIM)

Abstract. The high-resolution ship emission inventory serves as a crucial dataset for various disciplines including atmospheric science, marine science, and environmental management. Here, we present a global high-spatiotemporal-resolution ship emission inventory at a resolution of 0.1° × 0.1° for the years 2013 and 2016–2021, generated by the state-of-the-art Shipping Emission Inventory Model (SEIMv2.2). Initially, the annual 30 billion Automatic Identification System (AIS) data underwent extensive cleaning to ensure data validity and accuracy in temporal and spatial distribution. Subsequently, integrating real-time vessel positions and speeds from AIS data with static technical parameters, emission factors, and other computational parameters, SEIM simulated ship emissions on a ship-by-ship, signal-by-signal basis. Finally, the results were aggregated and analyzed. In 2021, the ship activity dataset established based on AIS data covered 109 300 vessels globally (101 400 vessels reported by the United Nations Conference on Trade and Development). Concerning the major air pollutants and greenhouse gases, global ships emitted 847.2×106 t of CO2, 2.3×106 t of SO2, 16.1×106 t of NOx, 791.2 kt of CO, 737.3 kt of HC (hydrocarbon), 415.5 kt of primary PM2.5, 61.6 kt of BC (black carbon), 210.3 kt of CH4, and 45.1 kt of N2O in 2021, accounting for 3.2 % of SO2, 14.2 % of NOx, and 2.3 % of CO2 emissions from all global anthropogenic sources, based on the Community Emissions Data System (CEDS). Due to the implementation of fuel-switching policies, global ship emissions of SO2 and primary PM2.5 saw a significant reduction of 81.3 % and 76.5 % in 2021 compared to 2019, respectively. According to the inventory results, the composition of vessel types contributing to global ship emissions remained relatively stable through the years, with container ships consistently contributing ∼ 30 % of global ship emissions. Regarding vessel age distribution, the emission contribution of vessels built before 2000 (without Tier standards) has been declining, dropping to 10.2 % in 2021, suggesting that even a complete phase-out of these vessels would have limited potential for reducing NOx emissions in the short term. On the other hand, the emission contribution of vessels built after 2016 (meeting Tier III standard) kept increasing, reaching 13.3 % in 2021. Temporally, global ship emissions exhibited minimal daily fluctuations. Spatially, high-resolution emission characteristics of different vessel types were delineated. Patterns of ship emission contributions by different types of vessels vary among maritime regions, with container ships predominant in the North and South Pacific, bulk carriers predominant in the South Atlantic, and oil tankers prevalent in the Arabian Sea. The distribution characteristics of ship emissions and intensity also vary significantly across different maritime regions. Our dataset, which is accessible at https://doi.org/10.5281/zenodo.10869014 (Wen et al., 2024), provides a daily breakdown by vessel type and age; it is available for broad research purposes, and it will provide a solid data foundation for fine-scale scientific research and shipping emission mitigation.

Improving the understanding of rainfall-runoff processes: Temporal dynamic of event runoff response in Loess Plateau, China.

Enhancing the understanding of the rainfall-runoff temporal dynamics in semi-arid and semi-humid regions is crucial for flood disaster mitigation. Loess Plateau is a unique environment within semi-arid and semi-humid regions, characterized by its deep loess soil, prevalent short-duration intense rainfall, and changes in underlying surface conditions. In this research, 25 catchments from the Loess Plateau were chosen to examine the temporal variations in event runoff responses across different time scales. The study analyzed event rainfall characteristics, catchment wetness conditions, and runoff responses to identify the factors influencing event-to-event variability, seasonal patterns, and long-term shifts in runoff behavior. The results show that the event runoff response exhibits distinct patterns across different ecological regions of Loess Plateau. In the Loess Hilly and Gully region, the event runoff response is primarily influenced by rainfall intensity characteristics and occurs due to infiltration excess. In the River Valley Plain and Earth-rocky Mountainous regions characterized by a humid climate, event runoff response is influenced by pre-event soil moisture and shows a strong correlation with rainfall volume. In the Loess Hilly and Gully region, catchments experience a noticeable transition between saturated and unsaturated states over long-term changes, driven by the combined impacts of land use and climate changes. The study provides insights into the mechanisms behind runoff generation at the catchment scale and offers valuable information for flood forecasting in semi-arid and semi-humid regions.

Advancing Seabed Bedform Mapping in the Kuźnica Deep: Leveraging Multibeam Echosounders and Machine Learning for Enhanced Underwater Landscape Analysis

The ocean, covering 71% of Earth’s surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Kuźnica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Kuźnica Deep’s underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation.

Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs.

The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image. As these models should fail to reconstruct unhealthy structures, the reconstruction errors indicate anomalies. However, a significant challenge is to balance the accurate reconstruction of healthy anatomy and the undesired replication of abnormal structures. While diffusion models have shown promising results with detailed and accurate reconstructions, they face challenges in preserving intensity characteristics, resulting in false positives. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image. We demonstrate that this conditioning allows for accurate and local adaptation to the general input intensity distribution while avoiding the replication of unhealthy structures. We compare the novel approach to different state-of-the-art methods and for different data sets. Our results show substantial improvements in the segmentation performance, with the Dice score improved by 11.9%, 20.0%, and 44.6%, for the BraTS, ATLAS and MSLUB data sets, respectively, while maintaining competitive performance on the WMH data set. Furthermore, our results indicate effective domain adaptation across different MRI acquisitions and simulated contrasts, an important attribute for general anomaly detection methods. The code for our work is available at https://github.com/FinnBehrendt/Conditioned-Diffusion-Models-UAD.

Hybrid Statistical-Textural and Intensity Feature-Based Approach for Accurate Discrimination of Retinal Diseases

Retinal imaging features such as fundus lesions are crucial signs of several ocular diseases, such as diabetic retinopathy and age-related macular degeneration. The early and accurate detection of the aforementioned abnormalities is essential for correct diagnosis and therapeutic management. In this study, we propose a new hybrid statistical-textural and intensity feature-based method for the improvement of the discrimination between retinal diseases with machine learning classifiers. By incorporating statistical texture analysis to measure changes in pixel intensity, this method not only extracts features based on intensities of fundus images but also serves as the comparison tool for a complete evaluation of the retina. A robust feature set is developed by merging intensity-based features with several texture descriptors including GLCM, LBP etc. We extract these features and then use them to train and test the performance of a number of classifiers, such as Support Vector Machines (SVM) and Random Forests for more accurate detection. Experiments have been conducted on a public dataset of retinal images and with the support vector machine classifier, encouraging results compared to purely texture-based or intensity-based methods in terms of classification accuracy are obtained. The overall findings of this study underscore the promise of integrating both statistical-textural and intensity features for improved retinal disease detection process, which will serve as an important stepping stone for future investigations and production of automated screening tools in retinal. The method also holds potential to enable early intervention in the development of vision-related diseases and, ultimately, decrease their impact.

Design of multi-stage spectrum sensing via layer customized convolutional neural network for cooperative communication

ABSTRACT Cognitive radios make extensive use of cooperative spectrum sensing to make optimum use of the accessible spectrum. Multi-stage spectrum sensing in cooperative communication refers to a technique used in cognitive radio systems where multiple stages of sensing are employed to improve the accuracy spectrum sensing. Accordingly, this paper proposes a unique design of ISDP-AL-CNN-based multi-stage spectrum sensing for cooperative communication. The multi-stage spectrum sensing is performed under two stages: (i) Determining the presence of PU and (ii) Detect the availability of unused radio spectrum. In stage 1, the energy detection approach is performed for each received signal and then compare the energy detected signal with threshold to determine whether PU is present or not. In stage 2, the features such as SNR and PSD are extracted from the received signal and subjected to obtain feature vector and fed the formulated feature into Fusion centre to extract intense features. Further, the retrieved features are given to the ISDP-AL-CNN model to train the features. The ISDP-AL-CNN model adopts ISDP-based attention layer for better performance of the training network. Moreover, it uses SVD to set the data label in the ISDP-AL-CNN model and proficiently detects the spectrum availability in the CR-IoT network.

Projections of Heavy Precipitation Characteristics Over the Greater Alpine Region Using a Kilometer–Scale Climate Model Ensemble

AbstractThis study presents a detailed analysis of the CORDEX‐FPS multi‐model ensemble of convection‐permitting climate simulations over the greater Alpine region. These simulations cover 10‐year time slices and were obtained by downscaling global climate model (GCM) projections, using regional climate models (RCMs) and kilometer‐scale convection‐permitting models (CPMs). Our analysis over the Alpine area agrees with previous studies in terms of projected summer precipitation changes for the end of the century, in particular regarding a decrease in mean precipitation and increases in hourly precipitation intensities. In addition, we assess projected changes over different subregions, provide analyses at monthly and seasonal basis for temporal aggregations ranging from 1 hr to 5 days, address different extreme precipitation indices, and present validation against an Alpine‐scale daily precipitation data set and an hourly precipitation product based on 3 Doppler radars. The evaluation reveals that CPMs show a refinement of spatial patterns, reduce the overestimation of precipitation frequency, and better capture intense precipitation characteristics. The improvements are especially apparent on the sub‐daily scale and in the summer season. Convection‐Permitting Model climate projections show an increase in precipitation intensity for all seasons and across all temporal aggregations in all regions, except for the Mediterranean in summer. The projections from different CPMs qualitatively agree, despite significant differences in the GCMs circulation changes, suggesting that the increase in heavy events is primarily due to thermodynamic effects. We also present a hypothesis explaining why projections of relative changes in hourly precipitation percentiles are similar between CPMs and RCMs, despite large biases in RCMs.

Influencing Mechanism of Precipitation Pollution Intensity in Qinhuai River Basin Based on Random Forest

Analyzing the distribution characteristics of precipitation pollution intensity in the basin and identifying the main factors affecting the precipitation pollution intensity are the important basis for realizing the accurate management of diffused pollution. Based on the surface water quality data from four typical sections of the main stream of Qinhuai River Basin and rainfall data collected from 2021 to 2022, the distribution characteristics of precipitation pollution intensity in the basin were analyzed, and representative natural and social factors were selected to construct models of the precipitation pollution intensity of ammonia nitrogen （NH4+-N）, permanganate index, and total phosphorus （TP） based on random forest algorithm. Additionally, the main driving factors of precipitation pollution intensity were identified, and the influencing mechanism was analyzed. The results showed that rainfall was the main driving factor for the water quality exceeding the standard in the Qinhuai River Basin, and the proportion of rainfall events within 3 days of when the water quality of the sections in the study area exceeded the standard was between 61.4% and 97.4%. The precipitation pollution intensity of NH4+-N and TP showed a gradual increase from upstream to downstream. NH4+-N was the primary pollutant in the midstream Qiqiaoweng section and the downstream Sanchahekou section, with the precipitation pollution intensity ranging from 0.12 to 2.98 and from 0.31 to 3.84, respectively. The precipitation pollution intensity of permanganate index in the upstream Yangqiao section was the largest, ranging from 0.41 to 1.35, and the precipitation pollution intensity of the Jiangjundadao section, which is located on the channel of diverted water, was at a lower level compared with that of the other three sections. Water diversion was an important influencing factor for the reduction of precipitation pollution intensity of NH4+-N and permanganate index （P&lt;0.01）； the proportion of built-up area was significantly positively correlated with the precipitation pollution intensity of NH4+-N and TP； the proportion of cultivated area was significantly positively correlated with the precipitation pollution intensity of permanganate index, and the proportion of forest land area was significantly negatively correlated with the precipitation pollution intensity of these three indicators. Under the conditions of light rain, moderate rain, and heavy rain, the rainfall was significantly positively correlated with the precipitation pollution intensity of each indicator, and the precipitation pollution intensity remained high under the condition of torrential rain. The post-rain pollution of NH4+-N and TP mainly originated from the middle and lower reaches of the study area, which was significantly affected by regional characteristics such as the percentage of built-up area and water diversion （P&lt;0.01）, and the post-rain pollution of permanganate index mainly originated from the upstream area with a wide distribution of cultivated land area and was significantly affected by flow （P&lt;0.01）.

Investigating Distribution Characteristics of Electromagnetic Reflection Intensity and Intrapulse Doppler Frequency Coupling Mechanism of Plasma-Sheath-Covered Target

Investigating Distribution Characteristics of Electromagnetic Reflection Intensity and Intrapulse Doppler Frequency Coupling Mechanism of Plasma-Sheath-Covered Target

Molecular Characteristics of High-Grade Glioma in Relation to 5-Aminolevulinic Acid (5-ALA) Fluorescence Intensity.

5-aminolevulinic acid (5-ALA) fluorescence used in glioma surgery has different intensities within tumors and amongdifferent patients, some molecular and external factors have been implicated, but there is no clear evidence analyzing the difference of fluorescence according to glioma molecular characteristics. This study aimed to compare molecular factors of glioma samples with fluorescence intensity to identify potential cofounders and associations with clinically relevant tumor features. Tumor samples of high-grade glioma patients operated using 5-ALA for guided resection were included for comparative analysis of fluorescence intensity and molecular features. All the samples were processed under the same conditions. The power for fluorescent stimulation and acquisition time was the same between samples. An inverted fluorescence microscope compared the mean fluorescence for each molecular variation. p53, ATRX and Ki67 expression and IDH1 mutation were assessed by immunohistochemistry. Follow-up of the patients for progression-free survival and overall survival was made. We found that the fluorescence intensity for each specific tumor was independent of the methylation of the methylguanine-DNA-methyltransferase (MGMT) promoter region assessed by pyrosequencing, there was no association of fluorescence with p53, ATRX, IDH1 mutation as assessed by immunochemistry. Also, fluorescence intensity has no relation with time of tumor recurrence or overall survival. With the results, we argue that many factors are involved in fluorescence intensity that may be related to the specific metabolic status of the glioma cells analyzed, which is more likely to be responsible for the variation of fluorescence.

Impact of pH on the Physicochemical, Structural, and Techno-Functional Properties of Sesame Protein Isolate.

Sesame protein isolate (SPI) is a highly nutritious plant protein distinguished by its essential amino acid profile. This study investigates the influence of pH on SPI's physicochemical, structural, and techno-functional properties, highlighting its potential as a sustainable protein source for various food applications. Our findings revealed that SPI had a protein content of 90.60% and a protein extraction yield of 77.2%. The density is measured at 0.72 g/mL, with a critical compressibility index of 19.22, indicating excellent flowability for weaning foods. Notably, the ζ-potential shifts from +39 mV at pH 3.0 to 0 at the isoelectric point (pI, 5-5.5) and becomes negative at higher pH levels. We observed a direct correlation between solubility, fluorescence intensity, and functional characteristics of SPI, with peak solubility and functional properties at acidic and alkaline pH levels and lowest values at the pI. Structural analyses confirmed the relationship between electrical charge, hydrophobicity, and functional attributes, with the highest surface hydrophobicity observed at pH 2.0. In conclusion, our findings underscore the critical role of pH in modulating the physicochemical properties of sesame protein isolate, enhancing its applicability in food formulations. SPI demonstrates significant potential as a versatile ingredient in the functional food product development.

Exercise-driven cellular autophagy: A bridge to systematic wellness.

Exercise enhances health by supporting homeostasis, bolstering defenses, and aiding disease recovery. It activates autophagy, a conserved cellular process essential for maintaining balance, while dysregulated autophagy contributes to disease progression. Despite extensive research on exercise and autophagy independently, their interplay remains insufficiently understood. This review explores the molecular mechanisms of exercise-induced autophagy in various tissues, focusing on key transduction pathways. It examines how different types of exercise trigger specific autophagic responses, supporting cellular balance and addressing systemic dysfunctions. The review also highlights the signaling pathways involved, their roles in protecting organ function, reducing disease risk, and promoting longevity, offering a clear understanding of the link between exercise and autophagy. Exercise-induced autophagy is governed by highly coordinated and dynamic pathways integrating direct and indirect mechanical forces and biochemical signals, linking physical activity to cellular and systemic health across multiple organ systems. Its activation is influenced by exercise modality, intensity, duration, and individual biological characteristics, including age, sex, and muscle fiber composition. Aerobic exercises primarily engage AMPK and mTOR pathways, supporting mitochondrial quality and cellular homeostasis. Anaerobic training activates PI3K/Akt signaling, modulating molecules like FOXO3a and Beclin1 to drive muscle autophagy and repair. In pathological contexts, exercise-induced autophagy enhances mitochondrial function, proteostasis, and tissue regeneration, benefiting conditions like sarcopenia, neurodegeneration, myocardial ischemia, metabolic disorders, and cancer. However, excessive exercise may lead to autophagic overactivation, leading to muscle atrophy or pathological cardiac remodeling. This underscores the critical need for balanced exercise regimens to maximize therapeutic efficacy while minimizing risks. Future research should prioritize identifying reliable biomarkers, optimizing exercise protocols, and integrating exercise with pharmacological strategies to enhance therapeutic outcomes.

Investigation of the steady regimes in a cross-shaped reactor by particle image velocimetry

In the chemical engineering area, impinging flow plays a significant role in process intensification and energy consumption reduction. Thoroughly revealing the formation and evolution of vortices within the reactor has emerged as a crucial scientific issue. This paper systematically studies the steady-state flow at low Re (1 ≤ Re ≤ 200, where Re is the Reynolds number) in a cross-shaped reactor by particle image velocimetry technology. The evolution, distribution, and intensity characteristics of vortices in the reactor chamber are focused on. We show that at 55 ≤ Re ≤ 120, the distribution of vorticity and shear rate in the chamber show unimodal and bimodal patterns, respectively, and the center of the chamber is a local area with high vorticity and low shear. In contrast, for 120 &amp;lt; Re ≤ 200, the distribution of vorticity turns into a bimodal pattern, and the shear rate develops into a trimodal pattern. The center of the chamber constitutes a local area characterized by low vorticity and high shear. Additionally, based on the modified monopole vortex model, the distributions of vorticity and velocity of vortices in the steady engulfment flow are accurately depicted.

Atypical (symplastic) uterine leiomyoma masquerading as endometrial malignancy in a 78-year-old post-menopausal Nigerian woman

Abstract Background: The atypical (symplastic) uterine leiomyoma is uncommon and is often characterized by similar macroscopic features as typical uterine fibroid but differs microscopically by having intense cytological features such as enlargement of the nucleus, hyperchromasia, and prominent nucleoli. It lacks histopathological features usually seen in leiomyosarcoma but harbours a low potential of transformation into leiomyosarcoma. Atypical leiomyoma very rarely occurs in post-menopausal women. Case presentation: A 78-year-old postmenopausal woman presented to our gynaecology clinic with an 8-month history of post-menopausal bleeding and abdominal swelling. Physical examination revealed abdominal swelling but blood chemistry was normal. An initial diagnosis of endometrial carcinoma was made and she subsequently had a total abdominal hysterectomy with bilateral salpingo-oophorectomy. Following histopathological evaluations, a confirmatory diagnosis of atypical uterine leiomyoma was made. She has been symptom-free for two years following surgical intervention. Conclusion: Despite their rarity in postmenopausal women, atypical fibroids should be considered in the differential diagnosis for postmenopausal bleeding. Considering the malignant transformation potential, recurrence risk of this pathology, and the completion of family size in this case, hysterectomy is the most preferred treatment option even though myomectomy can be offered to patients with plans of conception in the future.

Melodic Movements: The Role of Music in Shaping Sport Performance and Psychological Responses

The application of music in sport can enhance training and competition performance. This study explored the effects of different music genres on students' emotional, psychological, and athletic performance. Motivational music (e.g., blues, jazz, classical) has reflective and complex qualities, with a tempo of 60-80 beats per minute; high-tempo music (e.g., rock, heavy metal, alternative) has intense and rebellious characteristics, with a tempo of 120-140 beats per minute; upbeat music (e.g., pop music, New German Wave), film music, and popular German music have cheerful and traditional qualities, with a tempo of 100-130 beats per minute; rhythmic music (e.g., soul/R&amp;B, rap/hip-hop, and electronic music), have strong rhythmic features, with a tempo of 80-100 beats per minute; and self-selected music is chosen by the participants based on preference. Using a structured questionnaire, data were collected from 400 students at Shandong Sport University (50.8% male, 49.3% female, ages 18-21) to assess demographics, music preferences (Short Test of Music Preferences), psychological responses (Sport Emotions Questionnaire (SEQ) and Attention Questionnaire (AQ-RARC), sport motivation (adapted from Gill et al., 1983), and physical performance (International Fitness Inventory). Results from the PLS-SEM analysis indicated a composite reliability (CR ≥0.7) and an average variance extracted (AVE ≥0.5), meeting the Fornell-Larcker criteria for the model's reliability and validity. Model fit indices (SRMR, RMSEA &lt; 0.08; CFI &gt; 0.90) and path coefficient (t-value &gt; 1.96), confirmed significance, with an R² value &gt; 0.3 and a Q² value &gt; 0, demonstrating the model's explanatory and predictive power. Findings suggest that different music genres significantly enhance students' athletic performance and psychological well-being.

Study on Splitting Intensity and Acoustic Emission Characteristics of Brazilian Deep Gray Sandstone at Different Scales

In order to determine the influence of size on the mechanical properties of sandstone and acoustic emission damage characteristics, the Brazilian splitting test of 6 sandstone samples with diameters of 25mm, 37mm, 50mm, 69mm, 80mm and 100mm was carried out by using the rock weak surface direct shear instrument and acoustic emission monitoring system The effects of size change on the splitting strength, deformation, acoustic emission activity and damage characteristics of rock samples are discussed. The results show that the larger the specimen size is, the more original defects are contained in the specimen, which has a great influence on the development trend of stress-strain curve, peak strength and peak strain during loading. With the increase of sample size, the peak strength and peak strain become smaller and smaller, and the ratio of peak compressive strain to peak tensile strain remains in the range of 2~4 times. According to the development characteristics of acoustic emission signals, the whole process of rock damage is divided into four stages: micro-crack compaction stage, new crack generation and expansion stage, crack rapid expansion stage and failure stage. The diameter sample of 69mm~100mm showed almost no AE signal at the beginning of test loading, and the activity of acoustic emission signal is intense at the moment of failure.The larger the specimen size, the larger the stress value and b value corresponding to the fracture entering the instability expansion stage. Tensile microcracks are the main types of cracks in the splitting test, and with the increase of the size, the shear microcracks gradually decrease, and the tensile microcracks increase.

Comprehensive Improvement of Binocular Structured Light Calibration Method Based on Radical-Conservative Cooperative Particle Swarm.

To achieve high-precision 3D reconstruction, a comprehensive improvement has been made to the binocular structured light calibration method. During the calibration process, the calibration object's imaging quality and the camera parameters' nonlinear optimization effect directly affect the caibration accuracy. Firstly, to address the issue of poor imaging quality of the calibration object under tilted conditions, a pixel-level adaptive fill light method was designed using the programmable light intensity feature of the structured light projector, allowing the calibration object to receive uniform lighting and thus improve the quality of the captured images. Then, collaborative Particle Swarm Optimization was studied to optimize the camera parameters. Compared with other optimization algorithms, this algorithm has higher global search capability and can obtain more accurate camera parameters. Under comprehensive improvement, the 3D reconstruction accuracy of binocular structured light is 0.053 mm, showing a 36.33% improvement in reconstruction accuracy compared to mainstream calibration methods.