High-resolution transport-of-intensity quantitative phase microscopy with annular illumination

Transport-of-intensity phase imaging using commercially available confocal microscope

Significance

Confocal microscopy is an indispensable tool for biologists to observe samples and is useful for fluorescence imaging of living cells with high spatial resolution. Recently, phase information induced by the sample has been attracting attention because of its applicability such as the measurability of physical parameters and wavefront compensation. However, commercially available confocal microscopy has no phase imaging function.

Aim

We reborn an off-the-shelf confocal microscope as a phase measurement microscope. This is a milestone in changing the perspective of researchers in this field. We would meet the demand of biologists if only they had measured the phase with their handheld microscopes.

Approach

We proposed phase imaging based on the transport of intensity equation (TIE) in commercially available confocal microscopy. The proposed method requires no modification using a bright field imaging module of a commercially available confocal microscope.

Results

The feasibility of the proposed method is confirmed by evaluating the phase difference of a microlens array and living cells of the moss Physcomitrium patens and living mammalian cultured cells. In addition, multi-modal imaging of fluorescence and phase information is demonstrated.

Conclusions

TIE-based quantitative phase imaging (QPI) using commercially available confocal microscopy is proposed. We evaluated the feasibility of the proposed method by measuring the microlens array, plant, and mammalian cultured cells. The experimental result indicates that QPI can be realized in commercially available confocal microscopy using the TIE technique. This method will be useful for measuring dry mass, viscosity, and temperature of cells and for correcting phase fluctuation to cancel aberration and scattering caused by an object in the future.

Roadmap on computational methods in optical imaging and holography [invited]

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00340-024-08280-3.

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.

Exploring a novel HE image segmentation technique for glioblastoma: A hybrid slime mould and differential evolution approach

Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing. In recent years, many scholars have combined different metaheuristic algorithms with MIS to improve the quality of Image Segmentation (IS). Slime Mould Algorithm (SMA) is a metaheuristic approach inspired by the foraging behavior of slime mould populations in nature. In this investigation, we introduce a hybridized variant named BDSMA, aimed at overcoming the inherent limitations of the original algorithm. These limitations encompass inadequate exploitation capacity and a tendency to converge prematurely towards local optima when dealing with complex multidimensional problems. To bolster the algorithm's optimization prowess, we integrate the original algorithm with a robust exploitative operator called Differential Evolution (DE). Additionally, we introduce a strategy for handling solutions that surpass boundaries. The incorporation of an advanced cooperative mixing model accelerates the convergence of BDSMA, refining its precision and preventing it from becoming trapped in local optima. To substantiate the effectiveness of our proposed approach, we conduct a comprehensive series of comparative experiments involving 30 benchmark functions. The results of these experiments demonstrate the superiority of our method in terms of both convergence speed and precision. Moreover, within this study, we propose a MIS technique. This technique is subsequently employed to conduct experiments on IS at both low and high threshold levels. The effectiveness of the BDSMA-based MIS technique is further showcased through its successful application to the medical image of brain glioblastoma. The evaluation of these experimental outcomes, utilizing image quality metrics, conclusively underscores the exceptional efficacy of the algorithm we have put forth.

Structured illumination contrast transfer function for high resolution quantitative phase imaging

We report a sub-diffraction resolution imaging of non-fluorescent samples through quantitative phase imaging. This is achieved through a novel application of structured illumination microscopy (SIM), a super-resolution imaging technique established primarily for fluorescence microscopy. Utilizing our contrast transfer function formalism with SIM, we extract the high spatial frequency components of the phase profile from the defocused intensity images, enabling the reconstruction of a quantitative phase image with a frequency spectrum that surpasses the diffraction limit imposed by the imaging system. Our approach offers several advantages including a deterministic, phase-unwrapping-free algorithm and an easily implementable, non-interferometric setup. We validate the proposed technique for high-resolution phase imaging through both simulation and experimental results, demonstrating a two-fold enhancement in resolution. A lateral resolution of 0.814 µm is achieved for the phase imaging of human cheek cells using a 0.42 NA objective lens and an illumination wavelength of 660 nm, highlighting the efficacy of our approach for high-resolution quantitative phase imaging.

Flexible dynamic quantitative phase imaging based on division of focal plane polarization imaging technique

This paper proposes a flexible and accurate dynamic quantitative phase imaging (QPI) method using single-shot transport of intensity equation (TIE) phase retrieval achieved by division of focal plane (DoFP) polarization imaging technique. By exploiting the polarization property of the liquid crystal spatial light modulator (LC-SLM), two intensity images of different defocus distances contained in orthogonal polarization directions can be generated simultaneously. Then, with the help of the DoFP polarization imaging, these images can be captured with single exposure, enabling accurate dynamic QPI by solving the TIE. In addition, our approach gains great flexibility in defocus distance adjustment by adjusting the pattern loaded on the LC-SLM. Experiments on microlens array, phase plate, and living human gastric cancer cells demonstrate the accuracy, flexibility, and dynamic measurement performance for various objects. The proposed method provides a simple, flexible, and accurate approach for real-time QPI without sacrificing the field of view.

Target-surface multiplexed quantitative dynamic phase microscopic imaging based on the transport-of-intensity equation

Microscopic phase digital imaging based on the transport of intensity equation, known as TIE, is widely used in optical measurement and biomedical imaging since it can dispense with the dependence of traditional phase imaging systems on mechanical rotational scanning and interferometry devices. In this work, we provide a single exposure target-surface multiplexed phase reconstruction (SETMPR) structure based on TIE, which is remarkably easy to construct since it directly combines a conventional bright-field inverted microscope with a special image plane transmission structure that is capable of wavefront shaping and amplification. In practice, the SETMPR is able to achieve dynamic, non-interferometric, quantitative refractive index distribution of both static optical samples and dynamic biological samples in only one shot, meaning that the only limitation of measuring frequency is the frame rate. By comparing the measurement results of a microlens array and a grating with a standard instrument, the quantitative measurement capability and accuracy are demonstrated. Subsequently, both in situ static and long-term dynamic quantitative imaging of HT22 cells were performed, while automatic image segmentation was completed by introducing machine learning methods, which verified the application prospect of this work in dynamic observation of cellular in the biomedical field.

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining

Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.

Research on partially coherent spatial light interference microscopy

Based on partial coherence theory, this study rigorously deduces the principle of spatial light interference microscopy (SLIM) and improves the calculation method of SLIM. The main problem we found with SLIM is that it simply defaults the phase of the direct light to 0. To address this problem, we propose and experimentally demonstrate a double four-step phase shift method. Simulation results show that this method can reduce the relative error of oil-immersed microsphere reconstruction to about 3.7%, and for red blood cell reconstruction, the relative error can be reduced to about 13%.

Physics-informed neural network for phase imaging based on transport of intensity equation

Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by the illposedness of inversion at the origin of the spectrum. There are also retrieval ambiguities resulting from the lack of sensitivity to the curl component of the Poynting vector occurring with strong absorption. Here, we establish a physics-informed neural network (PINN) to address these issues, by integrating the forward and inverse physics models into a cascaded deep neural network. We demonstrate that the proposed PINN is efficiently trained using a small set of sample data, enabling the conversion of noise-corrupted 2-shot TIE phase retrievals to high quality phase images under partially coherent LED illumination. The efficacy of the proposed approach is demonstrated by both simulation using a standard image database and experiment using human buccal epitehlial cells. In particular, high image quality (SSIM = 0.919) is achieved experimentally using a reduced size of labeled data (140 image pairs). We discuss the robustness of the proposed approach against insufficient training data, and demonstrate that the parallel architecture of PINN is efficient for transfer learning.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data

Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly obtain with traditional optical microscopy. In addition, there are trade-offs between the parameters of traditional optical microscopes. Generally, a higher resolution results in a smaller field of view (FOV) and narrower depth of field (DOF). To overcome these drawbacks, we report a novel semi-supervised deep learning-based hybrid network framework, termed ContransGAN, which can be used in traditional optical microscopes with different magnifications to obtain high-quality quantitative phase images. This network framework uses a combination of convolutional operation and multiheaded self-attention mechanism to improve feature extraction, and only needs a few unpaired microscopic images to train. The ContransGAN retains the ability of the convolutional neural network (CNN) to extract local features and borrows the ability of the Swin-Transformer network to extract global features. The trained network can output the quantitative phase images, which are similar to those restored by the transport of intensity equation (TIE) under high-power microscopes, according to the amplitude images obtained by low-power microscopes. Biological and abiotic specimens were tested. The experiments show that the proposed deep learning algorithm is suitable for microscopic images with different resolutions and FOVs. Accurate and quick reconstruction of the corresponding high-resolution (HR) phase images from low-resolution (LR) bright-field microscopic intensity images was realized, which were obtained under traditional optical microscopes with different magnifications.

Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy

We present a new label-free three-dimensional (3D) microscopy technique, termed transport of intensity diffraction tomography with non-interferometric synthetic aperture (TIDT-NSA). Without resorting to interferometric detection, TIDT-NSA retrieves the 3D refractive index (RI) distribution of biological specimens from 3D intensity-only measurements at various illumination angles, allowing incoherent-diffraction-limited quantitative 3D phase-contrast imaging. The unique combination of z-scanning the sample with illumination angle diversity in TIDT-NSA provides strong defocus phase contrast and better optical sectioning capabilities suitable for high-resolution tomography of thick biological samples. Based on an off-the-shelf bright-field microscope with a programmable light-emitting-diode (LED) illumination source, TIDT-NSA achieves an imaging resolution of 206 nm laterally and 520 nm axially with a high-NA oil immersion objective. We validate the 3D RI tomographic imaging performance on various unlabeled fixed and live samples, including human breast cancer cell lines MCF-7, human hepatocyte carcinoma cell lines HepG2, mouse macrophage cell lines RAW 264.7, Caenorhabditis elegans (C. elegans), and live Henrietta Lacks (HeLa) cells. These results establish TIDT-NSA as a new non-interferometric approach to optical diffraction tomography and 3D label-free microscopy, permitting quantitative characterization of cell morphology and time-dependent subcellular changes for widespread biological and medical applications.

Annular illumination in 2D quantitative phase imaging: a systematic evaluation

Quantitative phase imaging (QPI) is an invaluable microscopic technology for definitively imaging phase objects such as biological cells and optical fibers. Traditionally, the condenser lens in QPI produces disk illumination of the object. However, it has been realized by numerous investigators that annular illumination can produce higher-resolution images. Although this performance improvement is impressive and well documented, the evidence presented has invariably been qualitative in nature. Recently, a theoretical basis for annular illumination was presented by Bao et al. [Appl. Opt.58, 137 (2019)APOPAI0003-693510.1364/AO.58.000137]. In our current work, systematic experimental QPI measurements are made with a reference phase mask to rigorously document the performance of annular illumination. In both theory and experiment, three spatial-frequency regions are identified: low, mid, and high. The low spatial-frequency region response is very similar for disk and annular illumination, both theoretically and experimentally. Theoretically, the high spatial-frequency region response is predicted to be much better for the annular illumination compared to the disk illumination--and is experimentally confirmed. In addition, the mid-spatial-frequency region response is theoretically predicted to be less for annular illumination than for disk illumination. This theoretical degradation of the mid-spatial-frequency region is only slightly experimentally observed. This bonus, although not well understood, further elevates the performance of annular illumination over disk illumination.

Artificial Intelligence-Based Human-Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human-computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

Accelerated Fourier ptychographic diffraction tomography with sparse annular LED illuminations

Fourier ptychographic diffraction tomography (FPDT) is a recently developed label-free computational microscopy technique that retrieves high-resolution and large-field three-dimensional (3D) tomograms by synthesizing a set of low-resolution intensity images obtained with a low numerical aperture (NA) objective. However, in order to ensure sufficient overlap of Ewald spheres in 3D Fourier space, conventional FPDT requires thousands of intensity measurements and consumes a significant amount of time for stable convergence of the iterative algorithm. Herein, we present accelerated Fourier ptychographic diffraction tomography (aFPDT), which combines sparse annular light-emitting diode (LED) illuminations and multiplexing illumination to significantly decrease data amount and achieve computational acceleration of 3D refractive index (RI) tomography. Compared with existing FPDT technique, the equivalent high-resolution 3D RI results are obtained using aFPDT with reducing data requirement by more than 40 times. The validity of the proposed method is experimentally demonstrated on control samples and various biological cells, including polystyrene beads, unicellular algae and clustered HeLa cells in a large field of view. With the capability of high-resolution and high-throughput 3D imaging using small amounts of data, aFPDT has the potential to further advance its widespread applications in biomedicine.

Non-recursive transport of intensity phase retrieval with the transport of phase

The transport of intensity equation (TIE) is a non-interferometric phase retrieval method that originates from the imaginary part of the Helmholtz equation and is equivalent to the law of conservation of energy. From the real part of the Helmholtz equation, the transport of phase equation (TPE), which represents the Eikonal equation in the presence of diffraction, can be derived. The amplitude and phase for an arbitrary optical field should satisfy these coupled equations simultaneously during propagation. In this work, the coupling between the TIE and TPE is exploited to improve the phase retrieval solutions from the TIE. Specifically, a non-recursive fast Fourier transform (FFT)-based phase retrieval method using both the TIE and TPE is demonstrated. Based on the FFT-based TIE solution, a correction factor calculated by the TPE is introduced to improve the phase retrieval results.

Study the Adsorption of Letrozole Drug on the Silicon Doped Graphdiyne Monolayer: a DFT Investigation

In the current study, by employing first-principles computations, the adsorption behavior of letrozole (LET) was investigated on the pristine graphdiyne nanosheet (GDY) as well as Si-doped graphdiyne (SiGDY). According to the adsorption energy, charge transfer value, and the change in the bang gap energy, the tendency of the pristine GDY towards LET is insignificant. However, the interaction of LET with SiGDY was strong and the adsorption energy was approximately − 19.20 kcal/mol. In addition, the associated electrical conductivity with SiGDY increased by approximately 23.53 % following the adsorption of LET. The results show that SiGDY can be employed as an electronic sensor to detect LET. Furthermore, LET is detected by SiGDY in the water phase based on the magnitude of solvation energy. Finally, a considerable charge-transfer between LET and SiGDY is a precondition for the adsorption of the LET molecule with proper binding energies, which delivers the Si atoms with a significant positive charge.

Single-shot phase contrast microscopy using polarisation-resolved differential phase contrast

We present a robust, low-cost single-shot implementation of differential phase microscopy utilising a polarisation-sensitive camera to simultaneously acquire four images from which phase contrast images can be calculated. This polarisation-resolved differential phase contrast (pDPC) microscopy technique can be easily integrated with fluorescence microscopy.

Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method's capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT's procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.

Management of human resources in the green economy: Does green labour productivity matter in low-carbon development in China

Environmentally friendly economic development has become a global concern, whereas the existing literature has ignored the human resources management in the green economy. This study utilizes the basic Cobb-Douglas production function and examines the nonlinear effect of labour productivity on the environment in China. Non-linear findings infer that a positive change in labour productivity has a positive and negative change in labour productivity, and has a negative effect on CO2 emissions in the short run, while results persisted and stable in the long run in China. The crux of this study is that labour productivity is vital for understanding the evolution of a green economy. Conventionally, capital productivity and energy consumption also tend to follow dirty productivity growth and thus, increased environmental pollution. Indeed, research and development is a forceful input to environmental quality. Based on findings, policymakers should need to focus on human resource productivity, green business, and ecosystem protection.

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com.

Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

Industry 4.0: technology spillover impact on digital manufacturing industry

Purpose

Under the guidance of the concept of openness and development, the paper grasps the mechanism of technology spillover in developed countries and analyzes how to better absorb advanced manufacturing technology based on empirical analysis so as to point out the path for the transformation and development of China’s digital manufacturing industry.

Design/methodology/approach

The paper constructs the panel data model and further analyzes the impact of international technology spillovers on the transformation and development of the digital manufacturing industry.

Findings

This paper measures the level of technology spillover in the Yangtze River Delta region and finds that foreign direct investment (FDI) technology spillover and import trade technology spillover among four provinces and cities show a growth trend from 2010 to 2017. But after 2017, there is a certain degree of decline.

Originality/value

With the advent of industry 4.0, the digital manufacturing industry of all countries in the world is developing with a new attitude, the global technology spillover methods are diverse and the spillover channels have changed greatly, which will affect the transformation and upgrading of China's digital manufacturing industry.