Index Measurements Research Articles

Development, Content Validity, and Reliability of the Physical Therapy Social Determinants of Health Scale.

This study describes the development, content validity, and reliability of the Physical Therapy Social Determinants of Health Scale (PT-SDHS), in Doctor of Physical Therapy (DPT) students. Addressing inequities stemming from the social determinants of health (SDOH) is critical for providing effective clinical care and improving population health. Currently, no tool exists to assess DPT students' education or competence with SDOH. Recruited domain experts were licensed health professionals, had a peer-reviewed publication on SDOH, and had at least 3 years of experience with SDOH. A convenience sample of DPT students was also recruited from multiple cohorts at 2 universities in multiple geographic locations. The Association for Medical Education in Europe best practices for developing questionnaires for educational research guided development of the PT-SDHS. Eight experts established content validity through item- and scale-content validity index measures and modified Kappa statistics. Qualitative data determined clarity and comprehensiveness. Construct validity and scale reliability were determined using exploratory factor analysis and Cronbach's alpha. The PT-SDHS showed high item content validity (range: 0.86-1) and scale content validity (S-CVI/UA = 0.83; S-CVI/Ave = 0.98). After expert validation, the PT-SDHS consisted of 30 items with 5 ordinal answer choices. Factor analysis of 254 DPT student responses revealed 4 content domains with a factor loading greater than 0.4. Two items did not load onto any of the factors, and 2 items werecross-loaded onto multiple factors, so they were removed, leaving a scale of 26 items. Cronbach's alpha ranged from 0.74 (satisfactory) to 0.96 (excellent) across the 4 factors. The PT-SDHS is the first psychometrically valid and reliable instrument related to educating DPT students on 4 SDOH domains: Knowledge, Attitudes, Personal Barriers, and Beliefs About Others. This tool could aid development and assessment of SDOH educational interventions for health care students.

A dual-channel SPR sensor based on both sides polished PCF with composite films for wide-range detection of RI and temperature

Abstract A dual-channel surface plasmon resonance (SPR) fiber sensor for detecting the refractive index and temperature has been presented in this paper. The fiber is polished on both sides to form two planes, and on one side, a refractive index measurement channel is formed by coating MoS2 film on the surface of a silver film. On the other side, a polydimethylsiloxane (PDMS) layer is coated on the surface of the silver film as the temperature sensing channel. The results showed that the sensor has a maximum spectral sensitivity of 23000 nm/RIU at a liquid RI of 1.333-1.415 and a maximum spectral sensitivity of 12.3 nm/℃ at a temperature of -20 °C to 90 °C. The results show that this sensor, with high sensitivity and wide-range detection, has important applications in the fields of organic chemistry, modern medicine and environmental detection. It also has a momentous reference significance for the design of SPR sensors that can realize multiparameter sensing.

Impact of Body Mass Index and Anthropometric Measures on Cardiorespiratory Fitness in Non-obese Adult Males: A Cross-Sectional Observational Study

Impact of Body Mass Index and Anthropometric Measures on Cardiorespiratory Fitness in Non-obese Adult Males: A Cross-Sectional Observational Study

A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer's disease.

This study presents a novel multi-view learning approach for machine learning (ML)-based Alzheimer's disease (AD) diagnosis. A diffusion model is proposed to synthesize the fluorodeoxyglucose positron emission tomography (FDG PET) view from the magnetic resonance imaging T1 weighted imaging (MRI T1WI) view and incorporate two synthesis strategies: one-way synthesis and two-way synthesis. To assess the utility of the synthesized views, we use multilayer perceptron (MLP)-based classifiers with various combinations of the views. The two-way synthesis achieves state-of-the-art performance with a structural similarity index measure (SSIM) at 0.9380 and a peak-signal-to-noise ratio (PSNR) at 26.47. The one-way synthesis achieves an SSIM at 0.9282 and a PSNR at 23.83. Both synthesized FDG PET views have shown their effectiveness in improving diagnostic accuracy. This work supports the notion that ML-based cross-domain data synthesis can be a useful approach to improve AD diagnosis by providing additional synthesized disease-related views for multi-view learning. We propose a diffusion model with two strategies to synthesize fluorodeoxyglucose positron emission tomography (FDG PET) from magnetic resonance imaging T1 weighted imaging (MRI T1WI). We raise multi-view learning with MRl T1Wl and synthesized FDG PET for Alzheimer's disease (AD) diagnosis. We provide a comprehensive experimental comparison for the synthesized FDG PET view. The feasibility of synthesized FDG PET view in AD diagnosis is validated with various experiments. We demonstrate the ability of synthesized FDG PET to enhance the performance of machine learning-based AD diagnosis.

Diffusion equation quantification: selective enhancement algorithm for bone metastasis lesions in CT images

Objective.Diffusion equation (DE) imaging processing is promising to enhance images showing lesions of bone metastasis (LBM). The Perona-Malik diffusion (PMD) model, which has been widely used and studied, is an anisotropic diffusion processing method to denoise or extract objects from an image effectively. However, the smoothing characteristics of PMD or its related method hinder extraction and enhancement of soft tissue regions of medical image such as computed tomography (CT), typically leaving an indistinct region with ambient tissues. Moreover, PMD expands the border region of the objects. A novel diffusion methodology must be used to enhance the LBM region effectively.Approach.For this study, we originally developed a DE quantification (DEQ) method that uses a filter function to selectively provide modulated diffusion according to the original locations of objects in an image. The structural similarity index measure (SSIM) and Lie derivative image analysisL-value map were used to evaluate image quality and processing.Main results.We determined superellipse function with its ordern=4as a better performing filter for the LBM region. DEQ was found to be more effective at contrasting LBM for various LBM CT images than PMD or its improved models when the filter was a positive exponential similar function. DEQ yields enhancement agreeing with the indications of positron emission tomography despite complex LBM comprising osteoblastic, osteoclastic, mixed tissues, and metal artifacts, which is innovative. Moreover, DEQ retained high quality of image (SSIM> 0.95), and achieved a low mean value of theL-value (<0.001), indicative of our intended selective diffusion compared to other PMD models.Significance.Our method improved the visibility of mixed tissue lesions, which can assist computer visional framework and can help radiologists to produce accurate diagnose of LBM regions which are frequently overlooked in radiology findings because of the various degrees of visibility in CT images.

Satellite-Based and Street-View Green Space and Adiposity in US Children

Green space may be associated with obesity outcomes in children by promoting physical activity and reducing stress. However, previous studies have mostly used a single measure of green space and body mass index (BMI) as the sole indicator of adiposity. To examine associations of the satellite-based normalized difference vegetation index (NDVI) and street-view imagery-based green space metrics with comprehensive adiposity measures in children. The Project Viva cohort study observed participants from mid-childhood (mean [SD] age, 8.0 [0.8] years) and early adolescence (mean [SD] age, 13.3 [1.0] years) through late adolescence (mean [SD] age, 17.8 [0.7] years) between January 1, 2007, and December 31, 2021. This analysis included children with data on green space and adiposity measures. Data were analyzed from January 1 to June 30, 2024. Green space metrics (ie, percentage of trees, grass, and other green space) using street-view imagery and deep learning algorithms along with the NDVI were linked with participants' home addresses during mid-childhood and early adolescence. Body mass index (calculated as weight in kilograms divided by height in meters squared) and waist circumference z scores were calculated from anthropometric measurements during early and late adolescence. The total fat mass index (FMI; calculated as fat mass in kilograms divided by height in meters squared) and trunk FMI were obtained using dual-energy x-ray absorptiometry. Separate linear regression models adjusted for a child's age, sex, individual and neighborhood socioeconomic status, and population density estimated prospective associations of the NDVI and street-view green space (in mid-childhood or early adolescence) with 4 adiposity measures (in early or late adolescence). The sample included 843 children (mean [SD] age at mid-childhood, 7.9 [0.8] years; 423 girls [50.2%]) at baseline. After adjustment for confounders, a higher NDVI (per 1-SD increase) within 270 m in early adolescence was associated with lower z scores for BMI (-0.16 [95% CI, -0.27 to -0.04]) and waist circumference (-0.13 [95% CI, -0.23 to -0.03]), as well as a lower total FMI (-0.62 [95% CI, -1.02 to -0.23]) and trunk FMI (-0.33 [95% CI, -0.55 to -0.12]) in late adolescence. Higher percentages of street-view other green space (flowers, plants, and fields), but not street-view trees and grass, within 500 m in early adolescence were associated with lower BMI z scores (-0.09 [95% CI, -0.18 to -0.01) in late adolescence. The NDVI and street-view green space exposure at mid-childhood were not associated with adiposity measures in early adolescence. This cohort study of US children suggests that higher levels of satellite-based NDVI greenness and percentages of street-level green space components (flowers, plants, and fields) were associated with lower adiposity. The findings support the exploration of increasing residential green space levels and adding specific green space components as an urban planning and public health intervention strategy to combat the prevalence of childhood obesity in the US.

Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object recognition accurate and invincible, we propose a hybrid digital–optical correlator specifically designed to perform under adverse weather conditions. This approach integrates the dark channel prior (DCP) with the fringe-adjusted joint transform correlator (FJTC), promising significant potential to enhance the robustness of the object recognition process under challenging environmental conditions. The proposed scheme presents a unique and alternative approach for object recognition under bad weather conditions. The incoming input scenes are processed with the DCP, enabling the FJTC to perform optical correlation on the refined images. The effectiveness of the proposed method is evaluated using several performance metrics like the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), correlation peak intensity (CPI), processing time, and recognition accuracy. To validate the performance of the proposed study, numerical simulation along with hybrid digital–optical demonstrations have been conducted.

Advances and clinical Implications of neuroanatomical biomarkers in schizophrenia by optimizing brain tissue segmentation

Schizophrenia (SZ) is a complex mental disorder marked by structural abnormalities in brain regions such as gray matter (GM), white matter (WM), ventricles and the cerebellum. Accurate segmentation of these brain subregions is crucial for diagnosing SZ, but traditional methods often struggle with the complexity of the disorder. To address this, the study employs advanced optimization techniques, specifically Cuckoo Search (CS), Grey Wolf Optimization (GWO) and Improved Grey Wolf Optimization (IGWO), to segment regions extracted from T1-weighted MR images of SZ patients and HC/NC. The IGWO method consistently outperforms others, achieving Structural Similarity Index Measures (SSIM) of 0.98 for ventricles and 0.96 for the cerebellum, with segmentation accuracies of 99% for both regions. Overlapping measures also reflect the effectiveness of proposed method, with DICE coefficients of 0.89 (SZ) and 0.94 (healthy controls) for ventricles and JACCARD indices of 0.80 and 0.95, respectively. For the cerebellum, DICE coefficients were 0.89 (SZ) and 0.93 (healthy controls), with JACCARD indices of 0.79 and 0.88. Subsequent classification using the ResNet50 deep learning model achieved high accuracy rates, particularly for the cerebellum (94%) and ventricles (93%). Performance metrics including recall, specificity, precision and F1-score further validate the robustness of the proposed method. These results highlight the significant role of the cerebellum and ventricles in SZ progression and offer an enhanced approach to improving diagnostic accuracy.

Comparative study of generalized-sampling-theorem-based digital super-resolution for 2D data

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

RADD-CycleGAN: unsupervised reconstruction of high-quality ultrasound image based on CycleGAN with residual attention and dual-domain discrimination.

Plane wave imaging (PWI) is fast, but limited by poor imaging quality. Coherent plane wave compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images. To enhance the ability to reconstruct image details, we specially design a process of hybrid dynamic and static channel selection followed by the frequency domain discriminator. The low-quality images are generated by the 3-angle CPWC, while the high-quality images are generated as real images (ground truth) by the 75-angle CPWC. The training set includes unpaired images, whereas the images in the test set are paired to verify the validity and superiority of the proposed model. Finally, we respectively design ablation and comparison experiments to evaluate the model performance. Compared with the basic CycleGAN, our proposed method reaches a better performance, with a 7.8% increase in the peak signal-to-noise ratio (PSNR) and a 22.2% increase in the structural similarity index measure (SSIM). The experimental results show that our method achieves the best unsupervised reconstruction from low quality images in comparison with several state-of-the-art methods.

Optical fiber multimode interference sensors using spatial multiplexing

Multimode fiber (MMF) sensors based on intermodal interferences have been widely studied due to their advantages of easy manufacture and high sensitivity. We introduced the scheme of spatial multiplexing to MMF sensors for improvement of resolution. A seven-core fiber (SCF) was fused with the NCF sensing head with a slight misalignment. Therefore, each core of the SCF has different coupling coefficients with the NCF due to its particular location at the cross section of the fusion point. The NCF sensor head was then shared by multiple cores and thus can be considered to be multiplexing. In addition, traditional demodulation method based on transmission spectrum mainly depends on the tracking spectral peaks or dips, where only a portion of spectrum data were utilized. To address this limitation, we further proposed a deep convolutional network to interrogate the spectrum that can exploit the whole spectrum data. The measurements of temperature and liquid refractive index were demonstrated in experiments. With the help of spatial multiplexing and deep learning algorithm, the resolution of temperature and refractive index of 0.0054°C and 2.25×10-5 RIU were obtained, about ten times improvement of the traditional NCF sensor. Our proposed spatial multiplexing offers a simple way to enhance the resolution without extra processing of the NCF.

Structural similarity of lithospheric velocity models of Chinese mainland

Existing lithospheric velocity models exhibit similar structures typically associated with the first-order tectonic features, with dissimilarities due to different data and methods used in model generation. The quantification of model structural similarity can help in interpreting the geophysical properties of Earth’s interior and establishing unified models crucial in natural hazard assessment and resource exploration. Here we employ the complex wavelet structural similarity index measure (CW-SSIM) active in computer image processing to analyze the structural similarity of four lithospheric velocity models of Chinese mainland published in the past decade. We take advantage of this method in its multiscale definition and insensitivity to slight geometrical distortion like translation and scaling, which is particularly crucial in the structural similarity analysis of velocity models accounting for uncertainty and resolution. Our results show that the CW-SSIM values vary in different model pairs, horizontal locations, and depths. While variations in the inter-model CW-SSIM are partly owing to different databases in the model generation, the difference of tomography methods may significantly impact the similar structural features of models, such as the low similarities between the full-wave based FWEA18 and other three models in northeastern China. We finally suggest potential solutions for the next generation of tomographic modeling in different areas according to corresponding structural similarities of existing models.

Super-resolution reconstruction of wind fields with a swin-transformer-based deep learning framework

Deep learning approaches that allow for the rapid simulation of high-resolution atmospheric turbulence are expected by using the super-resolution (SR) technique. Recently, the shifted window attention mechanism in Swin-Transformer offers a significant improvement compared with the vanilla attention mechanism. This method restricts the attention computation to a local neighborhood, reducing the computational load to a linear relationship with sequence length. However, its original architecture is unsuitable for the SR in turbulence due to the mismatch with classification, detection, and segmentation tasks. In this study, the hierarchical structure is redesigned, and a new relative position representing approach is introduced to facilitate the SR procedures of turbulent wind. The channel-shuffled perceptual loss is integrated into the loss function to guide the update of weight parameters. The experimental cases of idealized two-dimensional turbulent flow and realistic boundary layer wind are employed to validate the performance. The results suggest that the proposed approach remarkably outperformed the previous Super-Resolution Convolutional Neural Network, Deep Statistical Downscaling, and Regional Climate Model Emulator in wind vectors. It exhibits lower values than the other three networks whether in terms of point-wise metrics like mean square error, mean absolute error, mean absolute percentage error, or perceptual metrics, including structural similarity index measure and probability density functions. The reconstructed wind vectors closely match the target high-resolution results. This study will help advance the application of shifted window attention mechanisms in wind field SR.

Enhancing road image clarity with residual neural network dehazing model

Lane markers, or road markers, are the painted lines on a roadway that separate different lanes of traffic. Lane markers guide drivers and ensure orderly vehicle flow. They are essential for advanced driver assistance systems (ADAS), providing reference points for vehicle positioning on the road. These markers enable ADAS to give warnings, assistance, and automation features that enhance driver safety and convenience. However, unpredictable illumination, such as a foggy environment, can suppress marker visibility, impacting ADAS's performance. Deep learning-based methods are well-known for their superiority in handling various haze patterns. This paper presents a residual network (ResNet)-based deep learning model to improve road image clarity impacted by fog. The residual neural network dehaze model (RNN-D) utilises a joint loss function to produce haze-free images with improved lighting conditions and enhanced details. The model was trained, validated, and fine-tuned using hazy and corresponding non-hazy datasets to ensure that the model is quantitatively superior in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). RNN-D achieved an average PSNR of 27.98 and SSIM of 0.8 on multiple open sourced datasets. The proposed algorithm's superior performance and visually appealing results make it a powerful tool for real-world image dehazing applications.

Fast measurement of group index variation with optimum precision using Hong–Ou–Mandel interferometry

Hong–Ou–Mandel (HOM) interferometry has emerged as a valuable means for quantum sensing applications, particularly in measuring physical parameters that influence the relative optical delay between photon pairs. Unlike classical techniques, HOM-based quantum sensors offer higher resolution due to the intrinsic dispersion cancellation property of correlated photon pairs. Due to the use of single photons, HOM-based quantum sensors typically involve a large integration time to acquire the signal and subsequent post-processing for high-resolution measurements, restricting their use for real-time operations. Based on our understanding of the relationship between measurement resolution and the gain medium length that produces photon pairs, we report here on the development of an HOM-based quantum sensor for high-precision group index measurements. Using a 1 mm long periodically poled KTP (PPKTP) crystal for photon-pair generation, we have measured the group index with a precision of ∼6.75×10−6 per centimeter of sample length at an integration time of 100 ms, surpassing the previous reports by 400%. Typically, the measurement range reduces with the increase in the resolution. However, using a novel scheme compensating photon delay due to group index changes stepwise with an optical delay stage, we have measured the group index variation of PPKTP crystal over a range of 3.5 × 10−3 for a temperature change from 25 to 200 °C, corresponding to an optical delay adjustment of ∼200 μm while maintaining the same precision (∼6.75×10−6 per centimeter of sample length). The current results establish the usefulness of HOM-interferometer-based quantum sensors for fast, precise, and long-range measurements in various applications, including quantum optical coherence tomography.

Deterministic construction of unimodular tight frames consisting orthogonal blocks via block preserving operators

A highly redundant incoherent unimodular tight frame (UMTF) finds desirability across various fields, including quantum communications and signal processing. Incorporating a larger number of orthogonal blocks into such a UMTF yields advantages, especially in domains like image compression and wireless communications. Despite their valuable utility, constructions of these frames are exceedingly rare. Moreover, in most of such existing UMTFs, the number of orthogonal blocks is in the order of the frame vectors dimension. This paper focuses on a deterministic construction of incoherent UMTFs consisting of a large number of orthogonal blocks using combinatorial design. Further, we illustrate that the computational complexity for constructing the stated frame is lower compared to the existing methods. Additionally, we show that our proposed construction yields comparable results in sparse and block sparse signal recovery compared to the existing methods and outperforms the partial discrete Fourier transform matrix. Furthermore, empirical findings highlight that images reconstructed using our construction exhibit higher structural similarity index measure values in comparison with other existing methods.

An adaptive enhanced human memory algorithm for multi-level image segmentation for pathological lung cancer images

Lung cancer is a critical health issue that demands swift and accurate diagnosis for effective treatment. In medical imaging, segmentation is crucial for identifying and isolating regions of interest, which is essential for precise diagnosis and treatment planning. Traditional metaheuristic-based segmentation methods often struggle with slow convergence speed, poor optimized thresholds results, balancing exploration and exploitation, leading to suboptimal performance in the multi-thresholding segmenting of lung cancer images. This study presents ASG-HMO, an enhanced variant of the Human Memory Optimization (HMO) algorithm, selected for its simplicity, versatility, and minimal parameters. Although HMO has never been applied to multi-thresholding image segmentation, its characteristics make it ideal to improve pathology lung cancer image segmentation.The ASG-HMO incorporating four innovative strategies that address key challenges in the segmentation process. Firstly, the enhanced adaptive mutualism phase is proposed to balance exploration and exploitation to accurately delineate tumor boundaries without getting trapped in suboptimal solutions. Second, the spiral motion strategy is utilized to adaptively refines segmentation solutions by focusing on both the overall lung structure and the intricate tumor details. Third, the gaussian mutation strategy introduces diversity in the search process, enabling the exploration of a broader range of segmentation thresholds to enhance the accuracy of segmented regions. Finally, the adaptive t-distribution disturbance strategy is proposed to help the algorithm avoid local optima and refine segmentation in later stages.The effectiveness of ASG-HMO is validated through rigorous testing on the IEEE CEC'17 and CEC'20 benchmark suites, followed by its application to multilevel thresholding segmentation in nine histopathology lung cancer images. In these experiments, six different segmentation thresholds were tested, and the algorithm was compared to several classical, recent, and advanced segmentation algorithms. In addition, the proposed ASG-HMO leverages 2D Renyi entropy and 2D histograms to enhance the precision of the segmentation process.Quantitative result analysis in pathological lung cancer segmentation showed that ASG-HMO achieved superior maximum Peak Signal-to-Noise Ratio (PSNR) of 31.924, Structural Similarity Index Measure (SSIM) of 0.919, Feature Similarity Index Measure (FSIM) of 0.990, and Probability Rand Index (PRI) of 0.924. These results indicate that ASG-HMO significantly outperforms existing algorithms in both convergence speed and segmentation accuracy. This demonstrates the robustness of ASG-HMO as a framework for precise segmentation of pathological lung cancer images, offering substantial potential for improving clinical diagnostic processes.

A multi-frame fusion video deraining neural network based on depth and luminance features

Rainy weather is a common natural occurrence, but capturing clear and accurate images in rainy conditions can be challenging. Consequently, addressing the effects of rain on video imagery is essential for enhancing visual quality and the accuracy and reliability of computer vision applications. In this work, we introduce a novel rain model accompanied by a multi-frame fusion video rain removal algorithm that exploits depth and luminance features. We present an innovative three-stage video deraining methodology that synergizes multi-frame fusion with advanced neural network design to achieve highly realistic and clear rain removal. Our Option-Flow-Depth-Luminance (OFDL) Derain algorithm propels the research forward by offering a new perspective on rain removal. Incorporating depth and luminance features into image processing, we devise a novel rain model that allows the algorithm to handle complex environments and a variety of rain patterns effectively. Additionally, our model takes into account degradation factors such as rain streaks, accumulation, runoff, and occlusion, leading to the generation of more authentic rain-affected images that improve the training and performance evaluation of the model. Experimental tests conducted on the RainSynLight25, RainSynComplex25, and NTURain datasets demonstrate that our method outperforms current state-of-the-art techniques, achieving increases of 4.3 dB, 2.2 dB, and 2.2 dB in PSNR, and enhancements of 0.062, 0.109, and 0.008 in Structural Similarity Index Measure (SSIM), respectively. Moreover, our approach exhibits superior processing speed, further underscoring its practical advantages.

A multi-level thresholding image segmentation algorithm based on equilibrium optimizer

Multi-level thresholding for image segmentation is one of the key techniques in image processing. Although numerous methods have been introduced, it remains challenging to achieve stable and satisfactory thresholds when segmenting images with various unknown properties. This paper proposes an equilibrium optimizer algorithm to find the optimal multi-level thresholds for grayscale images. The proposed algorithm AEO (advanced equilibrium optimizer) uses two sub-populations to balance exploration and exploitation during the multi-level threshold search process. Two mutation schemes are proposed for the sub-populations to prevent them from being trapped in local optima. AEO offers a repair function to avoid generating duplicate thresholds. The performance of AEO is evaluated on multiple benchmark images. Experimental results demonstrate that AEO has an outstanding ability for multi-level threshold image segmentation in terms of cross-entropy, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM).

Luminescence and scintillation characteristics of Ce3 + and Tb3+ co-doped SiO2 fiber for low-dose gamma radiation

Ce3+ and Tb3+ co-doped SiO2 optical fibers (SCTF) were prepared using the modified chemical vapor deposition method. The optical and structural properties of the SCTF samples were investigated using refractive index measurements, energy dispersive X-ray spectroscopy, photoluminescence (PL), transmission loss, and fluorescence lifetime. The SCTF was fabricated into a radiation dosimeter, and its gamma radioluminescence (RL) response was studied over a dose rate range of 424.27–2095.16 mGy/min and a total dose range of 424.27–2095.16 mGy. The radiation response exhibited good linearity. To verify the repeatability of the dosimeter, repeated experiments were conducted at a fixed dose rate of 2095.16 mGy/min. The results demonstrated that the dosimeter has good repeatability. An energy transfer model in SCTF was established, and Ce/Tb-doped SiO2 was prepared using a sol-gel method. The energy transfer model was verified by measuring its PL spectrum. The above results verify that the SCTF shifts the RL to a longer wavelength due to the energy transfer of radioluminescence, which is more conducive to the remote sensing and transmission of SiO2 optical fiber in an irradiated environment.