Mean Entropy Research Articles

Spectra and Thermodynamic Properties of Zig-Zag Single – Walled Carbon Nanotube with Deng–Fan–Hulthen Potential

A theoretical investigation of the spectra and thermodynamic properties of zig-zag single-walled carbon nanotubes (6,0) is carried out within the framework of the Deng–Fan–Hulthen potential. We solved the Schr¨odinger wave equation analytically in the presence of magnetic and Aharonov–Bohm (AB) flux fields using the Nikiforov–Uvarov (NU) method and obtained the energy eigenvalues and eigenfunctions. It is observed that, in the absence of both magnetic and AB fields, there is a degeneracy in the energy spectra. The presence of the magnetic and AB flux fields led to an increase in the energy eigenvalues. The combined presence of both fields removed the degeneracy noticed in the system. We calculated the partition function and used it to evaluate the thermodynamic properties such as the mean energy, free energy, and entropy in respect to the temperature, magnetic field, AB flux field and the quantum numbers.

GUP with minimal length and maximal momentum on the thermodynamics of relativistic fermi gas

This work attempts to investigate the influence of the linear and quadratic generalized uncertainty principle (LQGUP) on the thermodynamical characteristics of relativistic Fermi gas. The general thermodynamical characteristics of relativistic Fermi gas, such as mean energy, pressure, entropy, and specific heat, are obtained. According to the modified function of temperature for mean energy and specific heat of relativistic Fermi gas, we find that at high temperature limit, the value of deformation parameter in the framework of LQGUP has an important effect on thermodynamic characteristics. It appears that the deformation parameter’s value is rising at higher energy levels of science, resulting in the minimal measurable length reaching close to the Planck length scale.

Optimal Control of Underdamped Systems: An Analytic Approach

Optimal control theory deals with finding protocols to steer a system between assigned initial and final states, such that a trajectory-dependent cost function is minimized. The application of optimal control to stochastic systems is an open and challenging research frontier, with a spectrum of applications ranging from stochastic thermodynamics to biophysics and data science. Among these, the design of nanoscale electronic components motivates the study of underdamped dynamics, leading to practical and conceptual difficulties. In this work, we develop analytic techniques to determine protocols steering finite time transitions at a minimum thermodynamic cost for stochastic underdamped dynamics. As cost functions, we consider two paradigmatic thermodynamic indicators. The first is the Kullback–Leibler divergence between the probability measure of the controlled process and that of a reference process. The corresponding optimization problem is the underdamped version of the Schrödinger diffusion problem that has been widely studied in the overdamped regime. The second is the mean entropy production during the transition, corresponding to the second law of modern stochastic thermodynamics. For transitions between Gaussian states, we show that optimal protocols satisfy a Lyapunov equation, a central tool in stability analysis of dynamical systems. For transitions between states described by general Maxwell-Boltzmann distributions, we introduce an infinite-dimensional version of the Poincaré-Lindstedt multiscale perturbation theory around the overdamped limit. This technique fundamentally improves the standard multiscale expansion. Indeed, it enables the explicit computation of momentum cumulants, whose variation in time is a distinctive trait of underdamped dynamics and is directly accessible to experimental observation. Our results allow us to numerically study cost asymmetries in expansion and compression processes and make predictions for inertial corrections to optimal protocols in the Landauer erasure problem at the nanoscale.

Mass fluctuations in non-rotating BTZ black holes

We investigate the impact of oscillations of a black hole's mass around its average value on the three-dimensional black hole geometry. Drawing on a classical framework that conceptualizes fluctuations near an event horizon as mass variations, we introduce a model where the metric of a black hole, formed from the collapse of a massive null shell, exhibits oscillatory behavior in spherical modes. This dynamic is encapsulated by a non-rotating BTZ-Vaidya solution, characterized by the black hole mass fluctuating at a resonant frequency ω and a small amplitude parameter μ0. Using a perturbative approach, solutions to the null geodesic equation are determined up to the second order in μ0. The temporal fluctuations of the event horizon's location induce alterations in the thermodynamic variables' values. Upon calculating the time-averaged values, the mean Hawking temperature experiences a slight decrease due to these fluctuations, while the mean entropy exhibits an increase. Furthermore, the study explores the influence of these fluctuations on the trajectories of null rays near the horizon, which ultimately reach the anti-de Sitter boundary at late times. The analytical computation of the general solution for the perturbed rays up to the second order underscores the novel approach of this study in examining the effects of mass oscillations on black hole thermodynamics and geometry, thereby contributing a unique perspective to the field.

Assessing the Histological Malignancy Grade of Olfactory Neuroblastoma Using the Apparent Diffusion Coefficient Histogram Analysis.

Introduction Olfactory neuroblastoma (ONB) is a rare malignant tumor of the upper nasal cavity. The Hyams classification is an important histological grading system for diagnosing recurrence and predicting survival in ONB. This study aimed to evaluate the utility of apparent diffusion coefficient (ADC) histogram analysis in distinguishing between high-grade and low-grade ONB based on the Hyams classification system. Methods This retrospective study included 17 patients (11 males, six females; mean age 54 years, range 29-84) diagnosed with ONB who underwent pretreatment magnetic resonance imaging (MRI) including diffusion-weighted imaging between December 2017 and September 2022. Two board-certified radiologists outlined the regions of interest on ADC maps of the tumors. Mean, minimum, maximum ADC, standard deviation, skewness, kurtosis, and entropy were calculated from the ADC histograms. Patients were divided into low-grade (Hyams I-II) and high-grade (Hyams III-IV) groups based on histopathological evaluation by a board-certified pathologist. ADC histogram parameters were compared between the two groups using Mann-Whitney U tests. Two-sided p-values of < 0.05 were considered statistically significant. Results The study included 10 low-grade (two grade I, eight grade II) and seven high-grade (five grade III, one grade III/IV, one grade IV) ONB cases. Comparison between the low-grade and high-grade groups showed no statistically significant differences in any of the ADC histogram parameters analyzed: mean ADC (median 1.02 vs 0.95; p = 0.591), minimum ADC (0.84 vs 0.78; p = 0.494), maximum ADC (1.06 vs 1.19; p = 0.625), standard deviation (0.09 vs 0.14; p = 0.433), skewness (-0.48 vs -0.75; p = 0.133), kurtosis (2.79 vs 3.12; p = 0.161), and entropy (4.69 vs 5.06; p = 0.315). Conclusion This study demonstrated that ADC histogram analysis was unable to differentiate between high-grade and low-grade ONB based on the Hyams classification. The findings suggest that preoperative grading of ONB malignancy using ADC histogram parameters is challenging. Thus, grading based on preoperative imaging evaluation is difficult.

General theory for localizing the where and when of entropy production meets single-molecule experiments

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

Transformer based deep learning hybrid architecture for phase unwrapping

A deep learning Hybrid architecture for phase unwrapping has been proposed. The hybrid architecture is based on integration of Convolutional Neural Networks (CNN) with Vision Transformer. The performance of Hybrid architecture/network in phase unwrapping is compared against CNN based standard UNET network. Structural Similarity Index (SSIM) and Root Mean Square Error (RMSE) have been used as performance metrics to assess the performance of these deep learning networks for phase unwrapping. To train and test the networks, dataset with high mean Entropy has been generated using Gaussian filtering of random noise in Fourier plane. The Hybrid architecture is tested on test dataset and is found to have superior performance metrics against the UNET network. Their performance is also tested in noisy environment with various noise levels and Hybrid architecture demonstrated better anti-noise capability than UNET network. Hybrid architecture was successfully validated in real world scenario using experimental data from custom built Digital Holographic Microscope. With the advent of newer architectures and hardware, Deep learning networks can further improve the performance in solving inverse problems.

Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables.

To investigate the pattern recognition of complex defect types in XLPE (cross-linked polyethylene) cable partial discharges and analyze the effectiveness of identifying partial discharge signal patterns, this study employs the variational mode decomposition (VMD) algorithm alongside entropy theories such as power spectrum entropy, fuzzy entropy, and permutation entropy for feature extraction from partial discharge signals of composite insulation defects. The mean power spectrum entropy (PS), mean fuzzy entropy (FU), mean permutation entropy (PE), as well as the permutation entropy values of IMF2 and IMF13 (Pe) are selected as the characteristic quantities for four categories of partial discharge signals associated with composite defects. Six hundred samples are selected from the partial discharge signals of each type of compound defect, amounting to a total of 2400 samples for the four types of compound defects combined. Each sample comprises five feature values, which are compiled into a dataset. A Snake Optimization Algorithm-optimized Support Vector Machine (SO-SVM) model is designed and trained, using the extracted features from cable partial discharge datasets as case examples for recognizing cable partial discharge signals. The identification outcomes from the SO-SVM model are then compared with those from conventional learning models. The results demonstrate that for partial discharge signals of XLPE cable composite insulation defects, the SO-SVM model yields better identification results than traditional learning models. In terms of recognition accuracy, for scratch and water ingress defects, SO-SVM improves by 14.00% over BP (Back Propagation) neural networks, by 5.66% over GA-BP (Genetic Algorithm-Back Propagation), and by 12.50% over SVM (support vector machine). For defects involving metal impurities and scratches, SO-SVM improves by 13.39% over BP, 9.34% over GA-BP, and 12.56% over SVM. For defects with metal impurities and water ingress, SO-SVM shows enhancements of 13.80% over BP, 9.47% over GA-BP, and 13.97% over SVM. Lastly, for defects combining metal impurities, water ingress, and scratches, SO-SVM registers increases of 11.90% over BP, 9.59% over GA-BP, and 12.05% over SVM.

Rotating effects on the thermophysical properties of a two-dimensional GaAs quantum ring

In this work, we have investigated the rotating effect on the thermodynamic properties of a 2D quantum ring. Accordingly, we have considered the radial potential of a 2D quantum ring and solved the Schrödinger equation in the presence of the Aharonov–Bohm effect and a uniform magnetic field for the considered potential. According to the solution of the equation, we calculated the eigenvalues and eigenfunctions of the considered system. Using the calculated energy spectrum, we obtained the partition function and thermodynamic properties of the system, such as the mean energy, specific heat, entropy and free energy. Our results show that the rotating effect has a significant influence on the thermophysical properties of a 2D quantum ring. We also study other effects of the rotating term: (1) the effect of different values of rotating parameters, and (2) the effect of negative rotation on the thermodynamic properties of the system. Our results are discussed in detail.

Motor Imagery Classification Using Effective Channel Selection of Multichannel EEG.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain-computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain-computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III-IV(A) and BCI competition IV-I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

Abstract PO5-07-06: Impact of patient-specific factors on quantitative breast parenchymal texture features

Abstract Breast parenchymal texture characterizes the spatial and structural patterns of breast tissue and is an independent predictor of breast cancer risk. Although it has conceptual similarities with another breast composition measure, breast density, parenchymal texture is more complex and is represented by multiple quantitative measures. The extent to which the epidemiology and biological basis of parenchymal texture and breast density differ has not been well described. In this cross-sectional study, we evaluated quantitative measures of parenchymal texture among 308 postmenopausal women aged ≥ 45 years who participated in a routine breast cancer screening at an academic medical center between 2020 and 2022. After providing informed consent, participants completed a short interview-based questionnaire. Additional demographic and breast cancer risk factor information was obtained from the medical record. Parenchymal texture features were measured from craniocaudal views of full-field digital mammograms. Measured texture values were standardized and averaged across left and right breasts. For this analysis, we evaluated associations between features previously validated as being associated with breast cancer risk (fractal dimension, grey-level mean, co-occurrence entropy and inverse difference moment) and patient personal and clinical characteristics reported at the time of screening. Associations between texture features and participant characteristics were evaluated using Pearson correlations (continuous variables) or the Kruskal-Wallis test (categorical variables). Generalized linear models were used to adjust for technical factors associated with image acquisition, including mammography machine, compression force, and software version. P-values less than 0.05 were considered statistically significant. Among the 308 participants, 70% were White, 24% were Black, 1% were Asian, 1% were American Indian, and 4% were another race that was not specified. 4% of participants reported Hispanic ethnicity. The mean age was 64.7 years (range 45 – 84 years). Fewer than half of the women were reported by a radiologist to have dense breasts (heterogeneously dense – 24%, extremely dense – 5%, vs. almost entirely fatty – 21%, scattered fibroglandular densities – 50%). The mean body mass index (BMI) was 29.4 kg/m2 (range 19.4 to 53.1 kg/m2) and 77% of the women were parous. BMI was strongly correlated with fractal dimension (r=0.62, P&amp;lt; 0.01) and grey-level mean (r=-0.48, P&amp;lt; 0.01), and had a weaker correlation with co-occurrence entropy (r=-0.19, P&amp;lt; 0.01). Age was weakly correlated with co-occurrence inverse difference moment (r=-0.12, P=0.03). BI-RADS breast density was inversely associated with fractal dimension, grey-level mean, and co-occurrence entropy (all P&amp;lt; 0.01). Adjustment for technical factors attenuated some associations, but BMI associations with fractal dimension (P&amp;lt; 0.01) and gray-level mean (P&amp;lt; 0.01), and breast density associations with fractal dimension, grey-level mean, and co-occurrence entropy (all P&amp;lt; 0.01) all remained statistically significant. Parity was not associated with any of the features evaluated in unadjusted or adjusted analyses (all P &amp;gt;0.05). These data suggest that, in postmenopausal women, risk-associated parenchymal texture features are associated with measures affected by body size and adiposity, but not with measures related to age or reproductive status. Longitudinal studies are needed to understand the temporality of these associations. Additionally, future studies will address the joint effects of texture features and the impact of endogenous estrogen levels on the features, which will provide insight into the biological mechanisms that influence texture feature associations with personal characteristics and breast cancer risk. Citation Format: Sarah Nyante, Yukie Kajita, Walter Mankowski, Ley Killeya, Despina Kontos, Xianming Tan, Eric Cohen, Cherie Kuzmiak. Impact of patient-specific factors on quantitative breast parenchymal texture features [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-06.

A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

AbstractWithin the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications.

Exploration of structural, magnetic, and magnetocaloric characteristics of double perovskites HoRCoMnO6 (R=Ho, Gd, Eu or Nd)

A conventional solid-state process was used to synthesize the double perovskite materials HoRCoMnO6 (R=Ho, Gd, Eu, Nd). The structural properties of the compounds were investigated using X-ray powder diffraction (XRD). The results revealed that Ho₂CoMnO₆ crystallizes in a monoclinic structure with the P21/n space group. In contrast, the other compounds HoRCoMnO6 (R=Gd, Eu, or Nd) exhibit an orthorhombic structure with the Pnma space group. As a result, the average crystallite size also changes as a function of rare-earth element doping. This investigation reveals that the magnetic properties of the compounds studied are significantly dependent on the doping elements. The Curie temperature TC, for example, increases from 80 to 118 °C with the ionic radii of rare earths increasing. Furthermore, the study of the magnetocaloric effect (MCE) shows that the maximum of the entropy variation (−ΔSMmax) increases from 4.97 to 6.06 J/(kg∙K) under a magnetic field of 5 T with substitution by rare-earth ions. To examine the efficiency of MCE materials, the relative cooling power (RCP) was evaluated and is found to increase with increment of rare-earth radius till 406.69 J/kg for Nd. The mean entropy variation with temperature (TEC) was also studied. Due to their significant magnetocaloric performance, HoRCoMnO6 (noted as HRCMO) compounds (with R=Ho, Gd, Eu or Nd) could be good candidates for low-temperature magnetic cooling applications

Boltzmann's Mean Entropy Formula for Unbounded Spin Systems

Boltzmann's Mean Entropy Formula for Unbounded Spin Systems

The Prognostic Value of Left Ventricular Entropy From T1 Mapping in Patients With Hypertrophic Cardiomyopathy

BackgroundThe prognostic value of left ventricular (LV) entropy in hypertrophic cardiomyopathy (HCM) is unclear. ObjectivesThis study aimed to assess the prognostic value of LV entropy from T1 mapping in HCM. MethodsA total of 748 participants with HCM, who underwent cardiovascular magnetic resonance (CMR), were consecutively enrolled. LV entropy was quantified by native T1 mapping. A competing risk analysis and a Cox proportional hazards regression analysis were performed to identify potential associations of LV entropy with sudden cardiac death (SCD) and cardiovascular death (CVD), respectively. ResultsA total of 40 patients with HCM experienced SCD, and 65 experienced CVD during a median follow-up of 43 months. Participants with increased LV entropy (≥4.06) were more likely to experience SCD and CVD (all P < 0.05) in the entire study cohort or the subgroup with low late gadolinium enhancement (LGE) extent (<15%). After adjustment for the European Society of Cardiology predictors and the presence of high LGE extent (≥15%), LV mean entropy was an independent predictor for SCD (HR: 1.03; all P < 0.05) by the multivariable competing risk analysis and CVD (HR: 1.06; 95% CI: 1.03-1.09; P < 0.001) by multivariable Cox regression analysis. ConclusionsLV mean entropy derived from native T1 mapping, reflecting myocardial tissue heterogeneity, was an independent predictor of SCD and CVD in participants with HCM. (Cardiac Magnetic Resonance Imaging Clinical Application Registration Study; ChiCTR1900024094)

Enthalpy, mean energy, entropy, and Gibbs free energy of lithium dimer under magnetic field

It is well known that the lithium diatomic molecule has important potential application in engineering, and industry. For this purpose, in this research, the thermal properties of the lithium diatomic molecule are theoretically calculated taking into account both an external magnetic field and Aharonov-Bohm (AB) flux. To this end, the interaction in the diatomic molecule is proposed as an improved Scarf ΙΙ potential model (ISPM). The Schrödinger equation is solved using an appropriate Pekeris-like approximation form for the centrifugal barrier and the energy spectrum and wave functions are obtained. Then, the partition function is obtained to study the thermal properties of the lithium dimer. The calculated thermal properties of 7Li2 (2 3Πg) in this work are mean energy, entropy, specific heat, enthalpy, and Gibbs free energy. Our theoretical results are also compared with the experimental data. The findings show that the obtained results are in good agreement with experimental data. Without considering the magnetic field and AB flux, the deviation of specific heat, Gibbs free energy, and enthalpy are 3.73%, 4.96%, and 5.31%, respectively. By considering the magnetic field and AB flux, we find that the deviation of the aforementioned properties are 1.91%, 2.04%, and 2.32%, respectively. Therefore, it is found that the external magnetic field and AB flux have great influences on the thermal properties of lithium.

Medical application with an extended Ailamujia inverted Weibull model: Properties, estimation and simulation

In this article, we propose a new lifetime model called Kavya-Manoharan Ailanujia inverted Weibull distribution, which is obtained from the Kavya-Manoharan (KM) transformation family and Ailanujia inverted Weibull distribution. Various statistical properties are obtained, involving moments, incomplete moments, conditional moments, mean deviations, mean residual lifetime and mean past lifetime and entropy. Moreover, the newly proposed distribution, characterized by two parameters, proves to be remarkably versatile, as demonstrated in the application results. Several statistical properties of the KMAIW distribution are deduced, and the estimation of its parameters is accomplished through three distinct techniques. To assess the effectiveness of these approaches, a comprehensive simulation study is conducted. The significance and applicability of the proposed model are underscored through the analysis of a real-world dataset. In comparative evaluations against well-known distributions featuring two, three, and four parameters, the suggested model consistently outperforms them all. This highlights the superior fitting capability and adaptability of the proposed model in comparison to established distributions with varying parameter complexities.

Calls of the little auk (Alle alle) chicks reflect their behavioural contexts.

Animal vocalisations can often inform conspecifics about the behavioural context of production and the underlying affective states, hence revealing whether a situation should be approached or avoided. While this is particularly important for socially complex species, little is known about affective expression in wild colonial animals, and even less to about their young. We studied vocalisations of the little auk (Alle alle) chicks in the Hornsund breeding colony, Svalbard. Little auks are highly colonial seabirds, and adults convey complex behavioural contexts through their calls. We recorded chick calls during two contexts of opposite affective valence: handing by a human, and while they interact with their parents inside the nest. Using permuted discriminant function analysis and a series of linear mixed models, we examined the effect of the production context/associated affective valence on the acoustic parameters of those calls. Calls were reliably classified to their context, with over 97% accuracy. Calls uttered during handling had higher mean entropy, fundamental frequency, as well as lower spectral centre of gravity and a less steep spectral slope compared to calls produced during interactions with a parent inside the nest. The individuality of handling calls, assessed by information content, was lower than the individuality of calls uttered in the nest. These findings suggest that seabird chicks can effectively communicate behavioural/affective contexts through calls, conveying socially important messages early in development. Our results are mostly in line with emotional expression patterns observed across taxa, supporting their evolutionary continuity.

HER2 in Gastric Cancer: A Comprehensive Analysis Combining Meta-Analysis and DCE-MRI Radiomics.

Advanced gastric cancer (AGC) is a severe malignant tumor, and overexpression of HER2/ERBB2 may play a crucial role in its development. The purpose of this study is to investigate the overexpression of HER2/ERBB2 in gastric cancer through a meta-analysis and examine its relationship with perfusion parameters using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technology. We conducted an extensive literature search and collected relevant studies for the meta-analysis. We used a random-effects model and assigned weights using the "inverse variance" method. Additionally, we included 95 AGC patients diagnosed pathologically between April 2018 and October 2021. They all underwent DCE-MRI scans, and the data were subsequently analyzed using the Omni kinetic software. HER2 expression was assessed using immunohistochemistry. The meta-analysis revealed an overall odds ratio (OR) of .21 for HER2/ERBB2 overexpression in gastric cancer, with a 95% confidence interval of [.14, .30]. DCE-MRI results showed a significant association between high HER2 expression and poor tumor differentiation (P < .005). The extracellular volume fraction (Ecv) quantile, mean, relative deviation, median intensity, and difference entropy were significantly higher in the low HER2 expression group compared to the high HER2 expression group. Receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of DCE-MRI radiomic parameters with significant differences were close to .7. Overexpression of HER2/ERBB2 in gastric cancer is significantly associated with certain radiomic parameters of DCE-MRI, providing a valuable diagnostic tool for clinical practice. Furthermore, the meta-analysis further confirmed the critical role of HER2/ERBB2 in the development of gastric cancer.

Estimation of the Xrama Distribution Parameter Under Complete and Progressive Type-II Censored Schemes

In this paper, a two-component mixture of Exponential and Rama distribution named Xrama distribution is introduced and discussed. The distributional properties such as moment and the related measures including other characteristics namely quantile function, mean residual life function, Bonferroni curve, Lorenz curve, order statistics, stochastic ordering, and entropy were derived. Simulation study for both complete and progressive type-2 censored schemes is used to illustrate and analyze the behavior of the model. The proposed model’s performance is assessed through a comparison with various other lifetime distributions using negative log-likelihood, Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Hannan–Quinn information criterion on an actual dataset. The results reveal that, when modeling real lifetime data, the suggested model exhibits greater flexibility than the competing models examined in this study.