Journal of Radiation Research and Applied Sciences最新文献

As photovoltaic (PV) systems continue to grow in both industrial and residential applications, the need for accurate and reliable testing methodologies become increasingly important. Solar simulators are crucial tools for testing and analyzing the performance of PV systems under different conditions. In recent years, solar simulators have emerged as promising tools for understanding PV systems, offering advantages over traditional methods. This paper proposes the modeling of a single-ended primary inductance converter (SEPIC) based solar simulator that replicates the current-voltage (I–V) characteristics of an actual PV module. The circuit model was developed and tested under varying environmental conditions, including rapidly changing irradiance levels (600 W/m², 1000 W/m², 800 W/m²) at constant temperature (25 °C) with a modified Perturb and Observe (P&O) maximum power point tracker (MPPT) algorithm in Matlab/Simulink. Unlike the traditional P&O MPPT, the modified P&O simplifies the algorithm steps and the computing stage; therefore, it requires fewer lines of code to execute the tracking process. To demonstrate the effectiveness, the simulation results are compared to a real PV module, the Kyocera KC-60. According to the simulation results, the proposed SEPIC converter based solar simulator is an effective tool for developing and testing PV systems with 97.3% average power conversion efficiency, accurately replicating the module behavior under various conditions, enhancing reliability and efficiency.

Introduction

We observed unexpected gating window discrepancies in asymmetric respiratory patterns. Therefore, the present study was conducted to test the hypothesis that asymmetric breathing causes this discrepancy.

Materials and methods

Six regular respiratory patterns were generated using the QUASAR software. 4-dimensional computed tomography (4DCT) was acquired with each breathing pattern using a QUASAR respiratory motion phantom with a 1.2 cm spherical acrylic ball embedded in a cedar insert. A Real-time Position Management system was used for respiratory signal acquisition during the 4DCT. Each treatment plan was created using gating windows ranging from 30% to 70%. TrueBeam STx was used for gated beam delivery, and the stored waveform data files and gated kV images were analyzed after irradiation. Two different concepts of phase were used to analyze the waveform data files. The respiratory phase defined prospectively by the TrueBeam gating system during treatment was called “phase_pro”, and the phase defined retrospectively by waveform data analysis after treatment was called “phase_retro".

Results

The phase_pro values were close to the planned gating window (30% or 70%) for in all breathing patterns. However, the phase_retro values differed from the planned gating window in the asymmetric patterns. The gating window was either pulled forward or pushed back from the treatment plan depending on the direction of the asymmetry. Furthermore, this discrepancy increased as the asymmetry of the respiratory pattern increased. In some asymmetric breathing models, the tumor location on the gated kV image was outside the internal target volume contour.

Conclusion

This study confirmed that the gating window discrepancy between 4DCT and the TrueBeam gating system occurs with asymmetric respiration pattern, even with regular breathing. Therefore, if a patient's breathing is not exactly symmetrical, appropriate measures should be taken against this unexpected systematic error.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder with abnormal cardiac trophy-1 (ANP) production and insulin resistance. In recent years, the application of low radioactive terahertz waves as a new therapeutic method in ovarian syndrome has attracted wide attention. The observation group received traditional Chinese medicine combined with low radiation terahertz wave treatment, and the control group received traditional treatment. Plasma ANP levels and insulin resistance indexes were compared between the two groups before and after treatment. The results showed that Chinese medicine combined with Western medicine had a good effect in the treatment of PCOS, which could effectively relieve symptoms, improve the levels of sex hormone, plasma cardiac fact-1 and insulin resistance, and did not increase adverse reactions compared with Western medicine alone, and could improve the pregnancy outcome, with good efficacy and safety. Terahertz waves are feasible in the detection of Chinese herbs. Traditional Chinese medicine combined with terahertz wave is a new choice for the treatment of PCOS, which is expected to be widely used in clinic.

Thoracoscopic assisted cardiac surgery, as a new surgical technique, has been widely used in the field of cardiac surgery. However, the study on the effect of thoracoscopic assisted nursing intervention after cardiac surgery is not sufficient. The aim of this study was to evaluate the effect of imaging based thoracoscopic assisted nursing intervention after cardiac surgery and explore the associated factors. The study included patients undergoing thoracoscopic assisted cardiac surgery and were grouped according to post-operative care interventions. For the intervention group, individualized nursing plan was developed according to the imaging results. For the control group, we used the usual nursing measures. Then the nursing effect of the two groups of patients was compared. The intervention group with individualized care based on imaging findings showed a significant advantage over the control group. Patients in the intervention group recovered more quickly and had a lower incidence of postoperative complications. In the formulation of nursing plan, combined with the patient's imaging results, the patient's condition and recovery can be more accurately assessed, and more effective nursing measures can be provided.

Many people use various methods to treat inflammation. Inflammation treatment is primarily and widely managed with medications, but there are ongoing efforts to use lasers for treating conditions such as peri-implantitis. Recent studies have been conducted using various lasers, such as diodes, Er:YAG, and CO₂ lasers, to sterilize and purify implant surfaces. In this study, the therapeutic effects of photothermal therapy, a laser treatment method for inflammation around implants (peri-implantitis), were analyzed through numerical analysis based on the theory of heat transfer. Peri-implantitis locates between the gingiva and implant, requiring the laser to be angled during irradiation. The numerical analysis conditions were selected as follows: laser power in the range of 0.0–4.0 W, laser irradiation angles between 50° and 80°, and laser irradiation times of 100–500 s. The Arrhenius damage integral was used to quantitatively verify the therapeutic effects of photothermal therapy (irreversible inflammatory damage). Finally, the optimal conditions for each laser irradiation angle were derived using the Arrhenius thermal damage ratio, which indicates the proportion of the total inflammatory volume where the Arrhenius damage integral exceeds 1.

In the field of biomedical research, data characteristics often exhibit significant variability, challenging the applicability of classical Gumbel distribution for biomedical data modeling. To address this, this paper introduces a novel extension of the Gumbel model known as the odd beta prime Gumbel (OBP-Gum) model. Derived from the odd beta prime family, the new distribution exhibits greater kurtosis compared to the traditional Gumbel distribution. Importantly, the proposed distribution is designed to capture right-skewed, left-skewed, and nearly symmetric density functions, as well as increasing, decreasing, constant, and upside-down bathtub shapes for its hazard rate function, providing excellent curvature features for creating flexible statistical models for biomedical research. We derive the fundamental features of the OBP-Gum model, such as the quantile function, linear representations, moment generating function, moments, skewness, kurtosis, incomplete moments, and Rényi and Tsallis entropies. Parameter estimation for this new model is conducted using the maximum likelihood estimation method. A simulation study demonstrates the performance of the model parameters. The empirical findings, based on applications to two biomedical datasets, suggest that the OBP-Gum distribution outperforms existing models, particularly in handling extreme observations. Instead of relying on conventional models for decision-making, this research provides relevant stakeholders with an improved statistical distribution for more accurate biomedical data modeling.

Accurate segmentation of liver blood vessels in magnetic resonance imaging (MRI) images is a challenging task due to the complex tree-like structure and anisotropic diffusion properties of blood vessels. To solve this problem, we propose a new Dual-Path Diffusion Model (DPDM) framework. The framework consists of two collaborative diffusion paths: a local feature learning path based on convolution operations and a global context modeling path based on transform blocks. Local path encodes rich shape priors and preserve spatial details, while global path captures long-distance dependencies and enhance representation. In the decoding phase, the boundary features from the boundary path are fused with the features of the ordinary path decoding, which further enhances the shape sensitivity. In addition, we leverage a multi-task learning scheme to jointly optimize vascular segmentation and boundary prediction tasks in an end-to-end manner. Experiments on retrospective clinical dataset demonstrate that the proposed DPDM framework achieves excellent performance on the liver vessel segmentation task. Compared with state-of-the-art methods, our approach achieved a 6.0% and 7.3% performance improvement in Dice coefficient and IoU index, respectively. Our approach offers a promising solution for automated blood vessel segmentation in precision medicine.

This study introduces a novel three-parameter time distribution called Type II Topp-Leone exponentiated gamma (TIITL-EG) distribution. This new model offers excellent flexibility in handling skewed data. We analyze various mathematical properties such as ordinary moments, moment-generating functions, and conditional moments. Mean residual life, different entropy measures, and order statistics are computed. Model parameters are estimated using the maximum likelihood method. Finally, we demonstrate the use of this model through real data application, highlighting its versatility.

Background

Computer algorithms are extensively utilized in hospitals for the purpose of achieving expedited and precise disease identification from medical data. The objective of this study is to propose a deep-learning (DL) framework to classify the chosen digital skin image (DSI) database into psoriasis and molluscum with better accuracy.

Aims and objectives

Aims to develop a DL-tool using the pre-trained ResNetV2 DL-models and verify the performance of the developed tool using a chosen DSI database. Further, confirm the merit of the ResNetV2-based tool against other chosen DL-models.

Methods and results

This study initially examines the performance of chosen pre-trained DL (PDL) methods using the DSI database using conventional and fused features. Proposed DL-tool consist the following stages; (i) image collection and resizing it 224x224x3 pixels, (ii) deep-features extraction using the selected PDL, (iii) feature reduction and serial features concatenation to get a new features vector, and (iv) the performance evaluation through three-fold cross validation and confirmation. The feature reduction and serial features fusion of this study is performed initially using feature sorting based on its rank and 50% dropout and Particle Swarm Optimization (PSO) based feature reduction. The reduced features of two chosen PDL-models are then considered to obtain the Fused Deep Features (FDF) and the classification task is executed on the DSI data to verify the performance of the developed DL-tool. The experimental outcome of this study confirms that the proposed scheme helps to provide a detection accuracy of >97%, when the K-Nearest Neighbour based classification is executed.

Conclusion

Investigational outcome of this study confirms that the proposed DL-tool helps to achieve better detection accuracy when the features of ResNetV2 models are considered to generate the FDF. Further, the accuracy achieved with the PSO based FDF is better compared to the conventional method generated FDF.