Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start–brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors. PMSMs are crucial electromechanical energy converters that play a significant role in diverse industrial applications due to their performance advantages, including light weight, reliable operation, low noise and high efficiency ,2,3,4. TIn the feature extraction stage of traditional motor fault diagnosis research, signal processing methods, including time-domain ,13,14, fThe grayscale map coding method, while capable of partially reflecting the characteristics of vibration signals, suffers from the loss of temporal information during the coding process, leading to an absence of crucial fault characteristics. Gramian angular field (GAF) can convert the sequence signal into a 2D image, which overcomes the deficiency of missing information from gray-scale map coding, and provides a complete mapping of the signal through different features, such as colors, dots, and lines at the corresponding positions ,26. GAF In the pursuit of more accurate and stable diagnostic performance, researchers have sought to enhance their methods by combining signals from multiple sensors. Ribeiro et al.  employedImage feature extraction methods primarily rely on artificial techniques to extract features from the underlying and middle layers by considering the various features of the image locally or globally, according to its texture, shape spatial structure and other information to carry out feature extraction. The acquired features have the advantage of strong interpretability, among which representative feature extraction methods include Tamura texture features and local binary pattern (LBP), etc. They are often applied to image scene classification. The texture is a common but difficult-to-describe feature in images, which can be regarded as an attribute displaying the pixel’s spatial distribution in the image. It is often shown as locally irregular but macroscopically regular. The traditional texture feature extraction scale is relatively single. The limited information obtained from the acquired images necessitates a description of how texture primitives are combined and arranged across multiple scales. This approach enables a more comprehensive capture of the image’s structural features and its detailed information, highlighting the unique characteristics of the image across varying scales .Machine learning has become a popular technique and has been widely used in the field of motor fault detection . The extTo enhance the diagnostic method’s robustness in noisy environments and mitigate the risk of overfitting, we propose a PMSM fault diagnosis method based on the fusion of image features from multiple sensors. Firstly, the vibration acceleration signals of the PMSM at different positions under different speed and load conditions are acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method fuses the sensor signals from three different mounting positions into a single image, with each signal assigned to one RGB channel. Next, Tamura, HOG texture features and LBP features are fused to extract the features of the image. The effectiveness of the method is verified by experimental analysis. Multiple machine learning models are compared on fault feature learning and classification, and the results show that the diagnostic method has good performance and robustness, with excellent performance even in noisy environments. The rest of the paper is organized as follows. The theoretical background is described in Section 6GAF is a method of encoding one-dimensional sequences in a way that preserves their time-dependent features to the greatest extent and maintains the signals’ dependence on time. It is a density distribution map of Gramian angular field values generated by trigonometric operations after encoding in polar coordinates. GAF overcomes the limitation of one-dimensional signals, which often represent incomplete information and fail to capture detailed features effectively. The method is not only able to maintain time dependence but also includes temporal correlation.The texture, which can be thought of as an attribute reflecting the spatial distribution characteristics of the image pixels and is frequently manifested in local irregularities and macroscopic regularity, is a common and difficult-to-describe feature in the image . The texRF works by generating several classifiers that each learn and predict independently, and finally combining these results to make a prediction, which is better than the results predicted by a single classifier or model. RF consists of multiple decision trees that are independent of each other and is an integrated learning method based on these decision trees. Random Forest classification results are voted by the classification results of all the decision trees, which have a high accuracy rate .X = {x1, x2, …, x4} is scaled to the interval  by Equation (1). r. The time series is retransformed to polar coordinates by using Equation (2), where it is the timestamp, and N is a constant factor of the generated space of the regularized polar coordinate system [The GAF converts time series data into 2D images ,25. Firse system .(1)x˜i=I is the unit row vector ; X. Note from the above equation that GAF is a newly constructed operation and that form corresponds to the forms of punishment of the regular inner product [The above transformations can be used to convert the original time series into a feature map symmetric along the diagonal, which can also be used to reconstruct the time series, since the feature image contains time-related information. GAF can generate two images with different equations. Equation (3) defines the Gramian Angular Summation Field (GASF), while Equation (4) defines the Gramian Angular Difference Field (GADF). The key distinction between them is the trigonometric conversion: GASF is based on the cosine function, whereas GADF is based on the sine function. product .GADF can only transform one-dimensional time series signals, and one-dimensional signals do not provide a comprehensive enough characterization of fault features, for the signals acquired by different sensors are solved for the Gramian matrix separately and injected into the red, green and blue channels to synthesise the final MGADF image. The example of MGADF image transformation is presented in The innovation of the proposed method in this paper lies in the utilization of multiple sensor time-domain signals to construct the Gramian matrix, which corresponds to the features of the three channels of RGB. The image is transformed, which can further characterise the fault-specific features compared with the single–signal–source transformation method. The method further improves the differentiation between the different types of images, which creates the conditions for the improvement of the accuracy of the classification and recognition.Multi-scale texture feature extraction of images has developed into one of the crucial techniques for image classification and recognition to more accurately capture the comprehensive structural features of images and their detailed information as well as to demonstrate the distinctive characteristics of images at different scales . Tamura The fusion of Tamura-HOG-LBP features improves the multi-texture feature fusion extraction process of GAF images, which is shown in m represents the length of the image; and n represents the width of the image.The Tamura texture feature contains six attributes: coarseness, contrast, orientation, linearity, regularity and roughness. Coarseness indicates the granularity of the image texture pattern, the larger the granularity of the image texture pattern, the rougher the texture image, and vice versa. The coarseness calculation formula is as follows:Contrast refers to the degree of polarization between the light and dark parts of the histogram and the dynamic range of the gray level. It could indicate the image’s clarity and the depth of the texture grooves. With increasing groove depth, the visual effect of the image becomes more contrasted and clearer. The shallower the grooves, the smaller the contrast, and the blurrier the image. The contrast is calculated as:The orientation degree m × m local direction covariance matrix.Linearity refers to the degree of deviation of the pixel spacing distances in the calculation of the local covariance matrix. The degree of linearity is calculated as:r is the normalization factor; and Regularity is a measure of how regular an image’s texture is. The more regular the image, the closer the regularity value is to 1; otherwise, it is closer to zero. The regularity formula is:The idea of the HOG algorithm is to represent the profile of an image target through the distribution of edge directions . The spex, y) are obtained by calculating the gradient in the coordinate direction of the point. The specific calculation formulas are shown in Equation (13):x-axis and y-axis directions in the two-dimensional planar vertical coordinate system. The gradient magnitude and gradient direction at this pixel point are calculated as:The image is divided into two layers, and the first layer is composed of interconnected cell units. Several cells form a block, and each block can overlap. The gradient magnitude and gradient direction of a pixel point . The decimal value is the LBP value of this pixel point. Finally, by counting the number of occurrences of different LBP values, the image can be characterized.In the process of image recognition, if the high dimension of the original feature space is used for model training, it will increase the computational complexity greatly, and in the statistical properties of the sample cannot be estimated. Therefore, it is necessary to reduce the dimensionality of the original features. In this paper, we use the Principal Component Analysis (PCA) method to achieve feature extraction, which reduces the number of dimensions, so as to improve the speed of image recognition.x has no redundant information. The PCA algorithm can be expressed as Equation (17):x for the first k terms is:Ideally, the feature space of the sample The resulting mean square error is:S is the covariance matrix of x, and According to the Lagrange multiplier, the expression for the extreme value of the mean square error is obtained:The mean square error when representing x in terms of k eigenvectors is:From Equation (21), it can be concluded that, when the value of In fault classification recognition, the classifiers’ role is to determine the type of fault to which the test sample belongs based on well-labeled training data with different fault types. Linear regression and SVM are commonly used as binary classifiers and are not suitable for classifying a wide range of faults. Neural network classifiers can affect the speed of fault identification due to their slow convergence rate. Therefore, random forest is chosen as the method for classifying and identifying motor ITSC faults.S have m categories i = 1, 2, …, m); s and A of a sample, its expected entropy is:k denotes the total number of sample features and i-th dimensional feature of the sample belonging to the category whereThe basic structure of a random forest is a decision tree, and the main mathematical description of a decision tree is as follows: let the sample set A can be obtained:The entropy gain of feature The entropy gain rate The random forest consists of several decision tree structures, and the main process of its classification is shown in I(.) denotes the transformation function, j are the positive and negative categories determined by the random forest, respectively, Let a random forest consist of RF has a great advantage in dealing with high-dimensional data and having high adaptability to the dataset. Secondly, the advantage of RF is that the training speed is quick. The importance of variables is sorted according to certain rules. The implementation is relatively simple. The idea of the RF algorithm is that a new training set is generated by randomly selecting N sample subsets from the original sample training set with put-back repetitions, and then the RF consisting of N decision trees is generated. The new classification result is obtained by judging the selection result of each class by the decision tree. The subset of elements used to decide the ideal node splits approves weaker elements’ representation in RF . The lowb and the minimum leaf point tree m under the condition of satisfying the optimal solution.Dung beetle optimizer (DBO) is a population intelligent optimisation algorithm . This apM1 represents the feature attributes of the original samples; and N1 is the upper limit of the size of the decision tree-based classifier.When using DBO to optimise RF parameters, it is necessary to choose a suitable objective function to evaluate the advantages and disadvantages of each set of parameters. In this paper, when using RF to train each decision tree-based classifier, about 1/3 of the texture fusion feature parameters are not extracted for training, which is called Out Of Bag data. Out Of Bag data can replace the test set to estimate the generalization error of the RF model, so this paper chooses the error score rate of Out Of Bag data as the objective function of DBO for searching the optimal parameter adapted to the fault diagnosis model of the electric motor, which is calculated by the formula:The RF and its parameter optimization process can constitute the original sample set of GAF image texture feature parameters of different states of the PMSM, proportionally divided into training and test sets, and the diagnosis of PMSM faults can be carried out based on the constructed model, which is shown in The general framework of the proposed method is shown in Step 1: Obtain Acquire time series data of PMSMs for different fault types. Measure the axial, radial and seat vibration signals of the motor in different states at different speeds and load conditions.Step 2: Compute the Gramian matrix for each of the three signals, corresponding to the individual RGB channels, for the MGADF image transformation.Step 3: The features of the image are solved by Tamura-HOG-LBP features.Step 4: PCA feature space dimensionality reduction is performed on the solved features.Step 5: The number of decision trees b and minimum leaf point tree number m under optimal solution conditions are satisfied by the DBO algorithm and fed into the RF classifier.Step 6: Learn and generate an RF classifier to classify and identify the input features.The length of the dataset to be coded is usually 2n, e.g., 64, 128, 256, and 512. As shown in The performance of PMSMs gradually degrades as the components’ performance deteriorates, which can lead to safety hazards and, in severe cases, result in downtime accidents, causing substantial economic losses. The common types of faults in PMSM are mechanical failure, winding short circuit and demagnetization faults, etc., among which the fault characteristics of inter-turn short circuit faults, local demagnetization faults, and eccentricity faults are more similar. There has been limited research on differentiating between the three types of faults mentioned above. Therefore, this paper focuses on investigating these three fault types.To verify the reasonableness of the proposed method in practical applications, a PMSM fault simulation platform is constructed, as shown in The number of specimens for each working condition is described in The vibration signals, color features and MGADF images are shown in The MGADF images of the steady state dataset under different load conditions at the speed of 1000 r/min are shown in The MGADF images under different speed conditions with a load of rated load are shown in Based on the dataset, feature vectors are extracted by the fusion of Tamura-HOG-LBP texture features. High-dimensional data visualization is made possible by t-SNE. When we apply t-SNE to n-dimensional data, it will intelligently map n-dimensional data to 3D or even 2D data, effectively preserving the relative similarity of the original data. Because t-SNE follows nonlinearity rather than linearity, it is able to capture the intricate flow structure of high-dimensional data. In To verify the superiority of the deep networks in this paper, Support Vector Machines (SVM), BP Neural Networks (BPNN), Radial Basis Function Neural Networks (RBFNN), CNN, RF and DBO-RF are compared. The input data for both this method and other deep learning methods are fused texture features extracted from MGADF images, and the primary focus of the comparative experiments lies in accuracy, precision, recall, and Fl-score. In tables concerning diagnostic performance, bold text highlights the highest diagnostic results.This section compares various signal-to-image methods, including GADF, GASF, Markov Transition Fields(MTF), and Recurrence Plot(RP). In The diagnostic results are presented in The average generation time for each image is listed in In order to avoid overfitting, which leads to high algorithm accuracy, the noise immunity of this paper’s algorithm is also investigated . To simuA Gaussian white noise with a signal-to-noise ratio of 5 dB is introduced to the current signal to demonstrate the impact of noise on the vibration signal. A comparison is depicted in Gaussian white noise at signal-to-noise ratios of 5 dB, 10 dB, and 20 dB was introduced to the original test dataset to evaluate the overall test accuracy. All methods were assessed at these three noise levels. As illustrated in To tackle the challenges associated with the reliability and stability of fault diagnosis methods in industrial manufacturing PMSMs, a fault diagnosis method based on multi-sensor fusion of image features is proposed. For different types of motor faults, vibration acceleration signals of the PMSM under varying speed and load conditions were collected by sensors placed at different positions. The Gramian matrix is solved separately and the red, green and blue channels are injected to synthesise the final MSDP image. Based on the extraction of image feature vectors through the fusion of Tamura-HOG-LBP texture features, several machine learning methods are compared for the tasks of fault feature learning and classification. The results show that the proposed diagnostic method has the best diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54%. This technique maximizes the utilization of data collected in industrial settings and enhances its robustness against various environmental conditions by amalgamating multiple sensor signals into an input image. It exhibits outstanding performance, even in noisy environments. The method is non-intrusive and can be extended to condition monitoring and diagnosis of industrial motors, offering prospects for practical industrial applications in motor fault diagnosis. In future work, improvements will be made in the following two areas for better industrial applications. (1) Since PMSMs can encounter load or speed variations during operation, while this paper primarily examines scenarios with constant load and speed, the accuracy of the proposed method may be impacted when applied to situations with load or speed fluctuations. Future research will delve into fault diagnosis for PMSMs in transient load or speed change scenarios. (2) In this study, vibration signals are employed for fusion in fault diagnosis. In the future, the fusion of current, vibration, and temperature signals will be explored to achieve enhanced fault diagnosis capabilities.


The method can accurately extract continuous and reliable surface signals for both strong and weak beams among different terrains and land cover types.Laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) contain a lot of noise, which necessitates the requirement for a signal photon extraction method. In this study, we propose a density clustering method, which combines slope and elevation information from optical stereo images and adaptively adjusts the neighborhood search direction in the along-track direction. The local classification density threshold was calculated adaptively according to the uneven spatial distribution of noise and signal density, and reliable surface signal points were extracted. The performance of the algorithm was validated for strong and weak beam laser altimetry data using optical stereo images with different resolutions and positioning accuracies. The results were compared qualitatively and quantitatively with those obtained using the ATL08 algorithm. The signal extraction quality was better than that of the ATL08 algorithm for steep slope and low signal-to-noise ratio (SNR) regions. The proposed method can better balance the relationship between recall and precision, and its F Light detection and ranging (LiDAR), together with spectral imaging technology and synthetic aperture radar technology, is regarded as a core information acquisition technology for Earth observation . The IceSeveral methods, such as raster image processing ,21, locaIn general, four key parameters—neighborhood search shape, size, search direction, and density threshold—need to be improved, regardless of the density-based method used, with the search direction being particularly difficult to determine accurately. The existing methods adaptively adjust the search direction by estimating the surface slope using the rough denoising results  or estimIn this study, we propose an adaptive signal photon extraction method that integrates slope information with the elevation information extracted from optical stereo images, named the Slope, Elevation, and Density Based Spatial Clustering of Applications with Noise (SE-DBSCAN). First, a mathematical model of the along-track terrain slope was established and extracted from the dense matching point cloud of the optical stereo images according to the track position information of the laser altimetry data. Then, according to the terrain similarity theory, a registration strategy based on elevation differences registered the dense matching point cloud and laser altimetry data to solve the problem of inconsistent positioning accuracy between the optical satellite images and the laser altimetry data. Finally, a slope optimization strategy adaptively adjusted the search direction of the clustering algorithm, with the local threshold used to reduce the influence of unevenly distributed photon data density in the along-track direction.Zhengzhou in central China was selected as the study area . The terrain in this area varies considerably, with mountains, hills, plains, and an appropriate amount of vegetation and buildings distributed around the study area. The surface slopes differ appreciably, meaning that the performance of the algorithm can be fully verified. The method proposed in this study was verified and analyzed using optical stereo images from Gaofen-7, Ziyuan-3, and Tianhui-1, as well as data from the ATL03  and ATL0To minimize the influence that changes in the terrain and land cover types had on the experiment, the laser altimetry data for the study area were screened with the condition of being as consistent as possible in terms of acquisition time or season. Laser altimetry data from different seasons were also selected to fully verify the accuracy of the slope extraction from different types of optical stereo images. The spatial distribution of the optical stereo images and laser altimetry data in the study area is shown in First, dense matching of the optical stereo images was performed to obtain point clouds with which to invert the surface slope and elevation at the location of the laser altimetry data. The dense matching point cloud was then matched with the laser altimetry data. Finally, the optimal terrain matching slope was selected from the terrain slope estimated using the dense matching point cloud as reference data for adaptive signal and noise identification. A flowchart of the overall proposed method is shown in The ATL03 product generates terrain surface profile data with a large amount of noise; thus, the terrain slope in the along-track direction can be used as reference data to extract signal photons from the ATL03 data. However, it is necessary to first obtain information about the elevation along the track. The semi-global matching (SGM)  method iA fitting plane-based along-track terrain slope estimation method was therefore proposed to accurately estimate the along-track terrain slope in the ATL03 data. Three main steps were involved in developing the algorithm. First, the ATL03 data were divided into segments at intervals of a regular distance, with some overlap. The corresponding latitudes and longitudes and the centerline of a segment could then be obtained according to the photon data of the segment. Next, a buffer zone extending a certain length from the centerline was created for each segment. The three-dimensional coordinates of the dense matching point cloud inside the buffer zone were acquired, and the fitting plane was calculated. Finally, the intersection line between the vertical plane on which the fitting centerline of the segment was located and the fitting plane of the dense matching point cloud was calculated. The slope of the line was thus the along-track terrain slope in the ATL03 data within the segment. In The point cloud data generated by the dense matching of optical satellite stereo images using the initial orientation parameters may be less accurate in terms of horizontal and vertical positioning. Although laser altimetry data have relatively high elevation accuracy, the horizontal positioning accuracy is comparatively low ,36. The Step 1: The elevation of the point at which the densest laser altimetry point cloud was observed was calculated for each segment.Theoretically, when the density of the potential laser altimetry point cloud is estimated along the terrain slope, the highest density is obtained. Therefore, neighborhood search along the terrain slope is critical for accurate estimation of the highest point cloud density in each segment. Nevertheless, spatial distribution differences were observed between the segment-wise derived slopes and elevations in Step 2: The elevation of the dense matching point cloud data at the center point of each segment was extracted using the segment length and step size selected in Step 1. The elevation at the center of a segment was interpolated using the fitting terrain line based on the dense matching point cloud data.σ ranges were discarded.Step 3: Outliers from the terrain elevations derived from the laser altimetry data and dense matching point clouds were eliminated. Standard deviations for the elevation differences at the center of the segments were calculated. Data that lay outside the 3Step 4: The registration parameters were obtained.The elevation data obtained were utilized to generate fitting spline curves, which were then translated using the fixed along-track distance moving step size and vertical moving step size, with a new central elevation interpolated from the curve after each translation. The standard deviations for the differences in the center elevations obtained from the laser altimetry data and the dense matching point cloud process were then calculated for all segments. The along-track translation distance and vertical moving distance with the smallest standard deviation were selected as the registration parameters, meaning that the horizontal registration parameter could be considered the along-track horizontal translation parameter. Next, according to the orbit orientation, the horizontal translation parameter was decomposed into The DBSCAN algorithm, which was first proposed by Easter , is a clThe semi-major and semi-minor lengths of the elliptical neighborhood are related to factors such as the laser spot size, sampling frequency, laser energy, and the densities of the signal and noise points. Appropriate semi-major and semi-minor lengths should conform to the continuity of the surface data points. Detectors can sense reflected photons anywhere along the track. In addition, it has previously been pointed out that terrain elevations obtained via 50-beam pulse inversion can better reflect the detailed characteristics of the ground surface . BecauseThe search direction, which is the angle of rotation of the elliptical neighborhood, could be expressed by the along-track slope estimation based on the dense matching point cloud according to the method described in The density threshold is that at which the noise and signal points are classified. The value of this threshold is related to the densities of the noise and signal points and the range of the search neighborhood and can be set according to the estimated noise and signal point densities and the size of the previously determined neighborhood. The density threshold should lie between the noise and signal density for a potential signal region, and to better preserve the ground surface signal points, the density threshold should be closer to the maximum noise density. Furthermore, due to differences in factors such as surface reflectance, atmospheric environment, altitude, and sun incidence angle, the noise density varies significantly along the track direction  and the n denotes the total number of photons in each segment; t reaches the optimal threshold.The Otsu method was employed to calculate the local density threshold of each segment. Based on the point cloud density for a segment, the optimal division threshold was calculated, allowing the variance between signal and noise photons to be maximized. The local density threshold of each segment was then calculated using Equations (6)–(8):It should be noted that, in the case of complex terrains or a low SNR, the signal point densities of some segments were low, and the noise densities were high. The local threshold calculated using the Otsu method may be higher than that obtained for the point densities in the signal regions. It is therefore necessary to define upper and lower limits for the density threshold. The maximum density of each segment was taken as the upper limit, and the lower limit was the maximum noise density for each segment. When the density threshold calculated using the Otsu method was higher than the maximum density within a segment, the latter was chosen as the density threshold for the segment. Meanwhile, if the calculated threshold was smaller than the maximum noise density, the average was adopted as the density threshold so as to include as many signal points as possible.In this situation, some high-density noise may be retained as signal points. Hence, more noise must be removed. In this study, the residual noise was eliminated based on the absolute elevation difference and the 31-score calculated to evaluate the performance of the algorithm [R, P, and F1 denote the recall, precision, and F1-score, respectively; true positive (TP) is the number of correctly identified signal photons; false negative (FN) represents the number of photons identified as noise photons that are, in fact, signal photons; false positive (FP) is the number of photons identified as signal photons that are, in fact, noise photons. TP means the signal photons belong to the terrain surface in the real world and the extraction result is correct, FN means that the signal photons belong to the terrain surface but the extraction result is wrong, and FP means that the noise photons not in the terrain surface area are extracted as a signal.To examine the denoising accuracy of the proposed algorithm and compare the resulting data with the ATL08 data, optical images were combined with manual inspection and the ATL08 classification results to locate the actual ground surface points associated with the data. These points were then used as reference data for algorithm evaluation. Laser altimetry data are discrete point cloud data that describe surface profiles. Hence, in evaluating the target points, those in the neighborhood of the target point were extracted from the reference data and the point closest to the target identified. Its class  was then compared with that of the target and the recall, precision, and lgorithm  using EqOptical images obtained through different sensors have different resolutions and position accuracies. To validate the accuracy of the terrain slopes obtained from images taken by different sensors as auxiliary data for filtering, optical stereo images from the satellites Gaofen-7, Ziyuan-3, and Tianhui-1 were adopted to invert the terrain slopes and the correlation between the resulting slopes, and the inversion results obtained using the laser altimetry data  were calculated. Previous analysis suggested that positioning errors can be associated with both optical stereo images and laser altimetry data. Moreover, the actual terrain in the study area is complex, with a wide range of land cover types. Before comparing the terrain slopes obtained using the two datasets, registration of the dense matching point clouds to laser altimetry data according to the terrain similarity theory was required to verify the correspondence between the derived slopes. The laser altimetry data describing the 4.5–22.5 km segment in dataset 1 were selected for analysis . The derAccording to the designed algorithm described in The laser altimetry data in the overlapping areas covered by Gaofen-7, Ziyuan-3, and Tianhui-1 (dataset 2) were adopted to evaluate the performance of the optical stereo images at different resolutions, as well as the positioning accuracy in assisting the extraction of surface signal photons. Various terrain types, including mountains and plains, with relatively significant variations are present, together with a moderate amount of vegetation and buildings, in the study area. Hence, the algorithms’ performance can be comprehensively assessed. In this study, the semi-major length of the elliptical neighborhood in the clustering algorithm was set equal to the laser spot diameter (17.5 m), and the semi-minor length was set to 3 m. With these values, the fitting accuracy of slopes from dense matching point clouds and the terrain variations could be sufficiently considered and excess noise avoided, meaning that the local densities were not underestimated. The same values were set for these two parameters in all subsequent experiments.The signal photon extraction results of dataset 2 are shown in F1-score .The performance of the proposed algorithm and ATL08 was quantitatively analyzed using recall, precision, and the F1-score . A recalThe ATL08 algorithm did not generate satisfactory results for steep slopes because the pulse width was stretched on steep slopes and there were fewer photons per unit area compared to flat terrains. However, the proposed algorithm used local density thresholds and best-fitting slope estimation based on dense matching point clouds to effectively determine the optimal direction for the neighborhood search, allowing better signal photon extraction results to be attained. The weak beams of the laser altimeter systems had fewer measurement points in areas with sparse vegetation and low shrubs, meaning that the overall density of the measurement points was low. To obtain reliable ground surface points, the proposed algorithm did not identify photons close to the ground surface as signal points, striking a balance between recall and precision; thus, the proposed algorithm was able to successfully obtain continuous terrains from the weak beam ICESat-2 data in mountainous areas even when the SNR was typically very low.Due to the revisited period and cloud cover, it was difficult to obtain high-quality optical stereo images that are completely consistent with the acquisition time of laser altimetry data in the study area. Cloud cover in images may result in holes during dense matching, and some measures need to be taken to reduce the impact of the cloud, such as atmospheric correction or filling the holes with dense matching point clouds generated from other optical stereo images. Moreover, if the acquisition time of optical stereo images and laser altimetry data is different, the ground surface may undergo some changes, and the derived slopes may be different from those of the current ground surface. Although our proposed optimal slope selection method can help reduce the effect of slope estimation error, some uncertainty may still exist. Thus, the acquisition time of optical stereo images and laser altimetry data should be as close as possible.This study proposed an adaptive signal photon extraction method that combines slope and elevation information derived from optical stereo images. Signal photon extraction was performed for laser altimetry data in cases with different land cover types and terrains. The experimental results demonstrated that the proposed algorithm could adjust the search direction and the density threshold for classification according to the along-track slope and the signal photon density. The proposed algorithm was found to be highly adaptive to laser altimetry data of various terrains taken at different times or beam energy levels. It could effectively and accurately extract ground surface signal photons using data from both strong and weak beam photon-counting laser altimeter systems, allowing the generation of continuous ground surface profiles. Even in cases where the SNR was low, i.e., when weak beam data acquired in the daytime were used, the proposed algorithm could extract relatively reliable and continuous signal photons from the ground surface. This is the first study in which slope and elevation information obtained from optical stereo images was adopted to assist in extracting ground surface signal photons from ICESat-2/ATLAS data. This research proves the valuable support and important role of information from optical stereo images in ICESat-2/ATLAS signal extraction. Effective solutions are available for extracting ground surface signal photons from ICESat-2/ATLAS data. In this study, a plane fitting-based strategy to extract the along-track slope, a registration strategy based on initial laser altimetry data, and an optimal slope selection strategy were designed, which successfully solve issues associated with the low positioning accuracy of optical stereo images, undulating terrains, and complex and diverse land cover types where optical images are adopted to assist in signal photon extraction from laser altimetry data. Hence, accurate directions were attained for signal search and clustering. This was further combined with local thresholding to significantly reduce the dependence of the algorithm on the included parameters and increase its robustness. Furthermore, the present study also achieved composite processing of optical images and laser altimetry data to lay a good research foundation for improving the positioning of optical images. Future research may focus on extracting reliable information using optical stereo images from different sources to assist in the effective extraction of ground surface signal photons from ICESat-2/ATLAS data for complicated surface environments and, in turn, improve the positioning of optical stereo images.


The assessment protocol included near- and far-field measurements, considering the extruder as the primary emission source, and portable measuring devices for evaluating particulate emissions reaching the inhalable zone of the lab workers. A Failure Modes and Effects Analysis (FMEA) study for the extrusion process line was defined along with a Failure Tree Analysis (FTA) process in which the process deviations, their sources and the relations between them were documented. FTA allowed the identification of events that should take place in parallel (simultaneously) or in series for the failure modes to take place and the respective corrective actions to be proposed .Nanoparticles are being used in novel applications of the thermoplastics industry, including automotive parts, the sports industry and leisure and consumer goods, which can be produced nowadays through additive manufacturing. However, there is limited information on the health and safety aspects during the production of these new materials, mainly from recycled sources. This study covers the exposure assessment to nano- and micro-size particles emitted from the nanocomposites during the production of filaments for 3D printing through a compounding and extrusion pilot line using recycled  thermoplastic polyurethane (TPU) and recycled polyamide 12 (PA12), which have been also upcycled through reinforcement with iron oxide nanoparticles (Fe Extensi3O4) , nanocla3O4)  and nano3O4) . Convent3O4) . AdditioTechnical challenges within nanocomposite manufacturing include the achievement of proper dispersion and distribution of nanoparticles in the matrix, the avoidance of nanoparticle agglomeration and the optimization of the bond between matrix and filler in the interface layer . Among tExposure to nano-size and micro-size particles is greatly discussed in the literature due to the potential adverse effects on human health. Nanomaterials present higher toxicity potential than bulk materials of identical chemical composition, due to their smaller size and higher surface area, allowing chemical interactions to occur . NanomatPotential routes of exposure are inhalation, dermal, ingestion and ocular penetration. The respiratory tract is the main exposure pathway to nanomaterials, and particle deposition in the lungs depends on the nanomaterial’s aerodynamic diameter. Particles above >1 μm in aerodynamic diameter tend to deposit in the inhalable zone and can be easily removed, while smaller size particles (<100 nm) tend to deposit in the tracheobronchial and alveolar regions ; thus, they cannot be removed and block the alveoli . DepositThe field of nanosafety has displayed rapid research growth within the last decades . Nano-spIt is quite important to note that nanosafety approaches focus on identifying and analysing hazards related to nanomaterial exposures; however, personnel will undoubtedly be exposed to various other occupational hazards within the context of nanocomposite manufacturing. These consist of laboratory or industrial setting safety concerns, such as high temperatures, noise and physical hazards (moving parts and sharp tools). They are investigated and addressed on the basis of a Plan-Do-Check-Act (PDCA) approach, through the principles of the extensively adopted ISO 45001:2018 standard .While the literature is expanding on both occupational safety in polymer manufacturing and nanosafety as distinct fields, research is lacking regarding an integrated approach to evaluate both nano-specific and process hazards. The objective of this work is to conduct a comprehensive risk evaluation for the hazards present within a nanocomposite 3D printing filament manufacturing process through twin-screw extrusion. An airborne (nano)particle exposure investigation through on-site measurements is presented, complemented by safety-oriented Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) studies which elaborate on the various physical hazards present in the manufacturing process.3O4 nanoparticles (NPs)–Fe3O4–110 iron oxide nanoparticles 99.5% purity, 20 nm in diameter, 1% PVP-coated, supplied by GetNanoMaterials . Afterwards, dilutions with pure recycled polymers resulted in different concentrations, i.e., 2.5, 5, 7.5 and 10 wt%, and 5 m length filaments were produced through extrusion for each concentration. The addition of susceptor materials for induction heating, such as iron oxide nanoparticles, due to their strong magnetic permeability [3O4 nanoparticles, including TPU and PA12 under study in the present work, have been investigated for their induced heating and healing ability [Four different feedstocks were used to produce filaments for Fused Filament Fabrication (FFF) 3D printing through polymer extrusion. The two polymer materials that were studied were post-industrial polymer waste streams that have been mechanically recycled. Specifically, recycled thermoplastic polyurethane (TPU), originated from Elastollan 1164D–TPU , and polyamide (PA), originated from Grilamid L 20A HL NZ natural , were used as feedstock materials, to be enhanced with the nanoparticles. Initially, a concentrated masterbatch was prepared through compounding for each case, at a concentration of 10 wt%. with Feeability , introdueability  and on-deability . Once exeability . Local m ability . All theThe production line included all steps from feedstock pre-processing, i.e., pelletizing, sieving, extrusion and filament winding. The process flow diagram is shown below . The masAn exposure assessment was performed for the evaluation of real-time particle number concentrations in the near- and far-field using condensation particle counters (CPC)  and optical particle counters (OPC) , while personal aerosol monitoring equipment  allowed the evaluation of mass-based concentrations of particles near the breathing zone of the laboratory operators. Two persons were equipped with the portable devices: one laboratory operator and one visitor/observer. Five different exposure measuring equipment types were applied, resulting in a total of eight devices covering near- and far-field emission measurements from the extruder as well as the emissions reaching the breathing zone of the laboratory personnel present. The placement of CPC and OPC instruments in both the near- and far-field enabled the qualitative evaluation of the dispersion and removal of particles emitted due to the process. It also allowed the identification of emission events caused by adjacent activities. The positioning of the instruments is illustrated in Failure Modes and Effect Analysis (FMEA) methodology was used for hazard analysis and risk assessment in the extrusion pilot line, as described in SAE J 1739-2009 Standard . ThroughSeverity (S) was ranked from one to five, describing the effect per failure mode .Occurrence (O) was ranked from one to five and was associated with the likelihood of each failure mode to take place .Detection (D) was ranked from one to five and was associated with the difficulty of a failure mode being detected and prevented .Risk prioritisation was based on the calculation of the Risk Priority Number (RPN) as the product of the three elements  of each potential failure mode shown in :SeverityThe higher the RPN, the higher the associated risk of the studied mode and the higher is the priority in taking action . In thisThe top event, which includes the description of the critical system;The basic events, which include the low-level identified causes;The logic gates , which display the logic connection between the basic events and the top event.A qualitative Failure Tree Analysis (FTA) was conducted—as a supportive study to the FMEA analysis—to analyse the relationship between a failure event during a process and the potential causes in the extrusion pilot line without focusing on their likelihood of occurrence (as described in IEC 61025:2006 Standard). Integrating the FMEA study with FTA analysis has been examined to enhance comprehension of the relation between the events that lead to the investigated failure modes . The FTAFailure Tree Analysis is based on Boolean logic, where the events are arranged in sequences of series relationships (the “ORs”) or parallel relationships (the “ANDs”). Results for each event are presented in a tree-like diagram using logic symbols to show dependencies among events. In other words, the FTA assessment concerns the identification and analysis of conditions and factors that cause or may potentially cause or contribute to the occurrence of a defined top event.3 [This section contains the analysis of the main emission/exposure events that were reported during the campaign. References are made to the corresponding graph, for each discussed exposure/emission event. As a point of reference for interpreting results, the Short-Term Exposure Limits (STEL) are used. For CPC measurements, the STEL is defined as 80,000 particles/cm3 , and botDuring the day on which the extrusion process was performed, several emission/exposure events were recorded and connected to various activities that were taking place in the extrusion workroom. Each event is marked on all relevant graphs . Activit3, were only observed in the near-field measurements, leading to the remark that these emissions were not dispersed in the far-field sections of the workplace due to the installed ventilation system and the mobile arm hood. Similar trends were observed during event one for the OPC, for particles up to 2.5 μm, and personal monitoring instruments  and tetrahydrofuran (THF))—resulting in various instantaneous peaks recorded on the number of sub-micron particles as noted in the events one and three . The peatruments , reflectConcentration upsurges were also observed during the use of a vacuum cleaner (event two) mainly for particles larger than 0.3 μm. This finding can be attributed to the limited particle retention properties of the vacuum cleaner filter and the resuspension of previously collected particles. Across all stationary instruments, these peaks were lower in the near-field measurements, since the vacuum cleaner exhaust was facing the far-field measuring instruments, and therefore are not considered a direct result of the extrusion line. Upsurges were more pronounced for particle number concentrations of particles larger than 0.3 μm  as well 3O4 NPs) were added to the extrusion hopper at the predefined concentrations (3 (3 for 0.3–20 μm particles) (3). Emissions of larger particles can also be attributed to cleaning and maintenance activities, onset by material failure and disruption of the extrusion process.After the first two hours of cleaning the setup, there was a significant concentration peak for the UFPs both in the near- and far-field measurements  due to ttrations , after 3tions (3  for CPC rticles) a. The ob3O4 NPs/PA masterbatch (event ten), a very short peak is observed on the near-field CPC readings. This is limited to only the smallest particles that do not manage to reach the far-field at this point.A peak in the values of particle number concentrations and TVOC concentration was recorded shortly after the 4 h point (event seven) . This prIn addition to the near-field/far-field configuration, differences in exposure potential depending on the operator’s position are also displayed through the result of the personal exposure monitoring . During While there is a lack of available literature on exposure assessment during polymer extrusion, the potential for emission of UFPs and VOCs during thermal processing of thermoplastics has been documented extensively through studies on fused filament fabrication ,41,42. IRegarding the increased concentrations observed during vacuum cleaning, similar results have been demonstrated across the literature ,44,45. TA total of seven failure modes (FM) were identified by the operators and assigned to the studies nodes. In detail, FM1 is related to the heating up of the extruder, and during the heating up step at the targeted temperatures (and during extrusion where the equipment is heated up at or above 190 °C), there is a high chance of employees coming in contact with the hot surfaces resulting in severe burns. In this case, the identified causes of the failure mode are the lack of safety guards on the extruder to prevent the operator from coming into contact with the equipment, failure of the current personal protective equipment (PPE) used and the potential operator error during use. The current process safety controls comprise the best practices in place based on the current standard operation procedure, the safety training of lab personnel and the use of PPE required, resulting in a high-risk level (RPN was 32), and remedial actions should be given high priority. A similar set of safety measures, including work instructions and personnel training, are also described in the work of Russkih  for the The recommended actions considered to mitigate the risk were (i) the use of ergonomic heat-resistant gloves to be used throughout the process, which would allow the user to operate the equipment freely as the current heat-resistant PPE in place may discomfort the operator, (ii) lab coats with knit cuffs or other equivalents to be used throughout the task in the extruder, as the lab coat sleeves would prevent the arm from coming in contact with the hot surfaces, and (iii) visible warning signs in multiple positions to be used to alert the operator from coming in contact with the hot surfaces of the equipment. It is worth noting that the use of signs could also lower the risk of operator error. Petretta et al.  have revAdditional physical hazards were identified in failure modes FM2, FM3, FM4 and FM6 related to the operation of the extruder and pelletizer instruments. FM2 is related to the water bath where—during the extrusion process—the produced filament passes through water at room temperature to cool down its temperature before the binding step at the spool. However, water spillage is likely as the water tank is exposed due to lack of top cover leading to operator harm  as well as equipment hazards such as short-circuiting damaging the equipment resulting in process disruption. The lack of safety guards on the water tank to prevent water spillage and the potential operator error during use has led to an RPN of 30, constituting a high-risk failure, despite the current process controls comprising of the water vessel structure as the water level is below the tank edges, best practices in place and grounding all equipment components, following the existing safety regulations and protocols. The FMEA assessment showed that during the extrusion process, clogging of the twin-screw extruder or other parts of the extrusion is likely to occur either due to material built-up as a result of partial solidification/reduced flowability inside the twin-screw extruder or due to extrusion of large diameter filaments causing errors in the binding process. This can result in high pressure inside the extruder (<300 bar) leading to stall in order to prevent equipment failure; hence, process disruption requiring immediate maintenance action may take place as well as high pressure built-up in the extruder. The identified cause of the failure mode is the contamination of operating materials with previously extruded material of different properties  and inappropriate maintenance. Therefore, current process controls comprise the proper cleaning of the extruder before the extrusion process initiation. Additionally, the prolonged residence times of materials inside the extruder may result in the release of hazardous fumes and airborne hazardous substances  as the materials can start decomposing at operating temperatures. Should this happen, it would be reflected in the particle concentrations in At the same time, another physical hazard is the noise levels during operation due to the compressor of the air blow dryer. As part of the extrusion process, an air blow dryer is installed in series after the water bath for filament drying before binding. However, during the air compression, noise levels exceeded 80 dB which is the lower risk value for a busy workplace. Directive 2003/10/EC  describeAs part of the OECD “Harmonized Tiered protocol for exposure assessment” , a folloAs discussed in the TVOC measurements analysis, the instrumentation limitations lead to difficulties in interpreting the results obtained, since identification of VOC species is not possible. The specific substances emitted could be identified through analytical techniques such as Gas Chromatography–Mass Spectrometry (GC–MS). GC–MS has been consistently and successfully applied to study the VOC emissions within a similar field of study, the FFF 3D printing emission research . It can An additional limitation encountered is that the measurements were conducted in one day, with various process phases taking place in sequence . This led to the processes not being totally isolated from each other and introduced the possibility of the emissions of each phase interfering with the next phases. However, it can be argued that the purpose of such studies is to study exposure potential within a given workplace under typical conditions in terms of workload setup and work intensity. Therefore, since this process workflow is representative of the workplace examined, the study presents value for the exposure assessment under the particular conditions, although showing limitations in terms of how much this information can be generalized. An important point to note is that this limitation is ingrained in the concept of exposure assessment studies in occupational processes since the reproducibility of exact conditions within the work environment is unrealistically challenging in most cases. Waters et al. reviewed occupational exposure assessment methods and argued that since a multitude of factors is in play within the assessment of exposures, variability in different days of measurements is unavoidable. The authors also highlight that while variability cannot be changed, a structured scheme of repeated measurements can assist in defining the levels of variability present and thus support exposure characterization . TherefoUndoubtedly, dedicated and isolated studies on specific materials and processes would be pivotal in clarifying details regarding the emission profiles. Further research can be focused on such “targeted” approaches, such as examining the extrusion of only one material in different process settings. This would assist in understanding the emission potential of the various materials as well as the determinant parameters that may influence the magnitude of emissions for each case .An important element to note is that an indicative reference exposure threshold was used to interpret the nanoparticle exposure levels. As discussed previously, this does not constitute a health-based limit and is not specific to materials applied in this study. However, despite the uncertainties introduced, it remains a pragmatic approach to understand exposure levels, given the absence of health-based occupational exposure limits for nanoparticles and engineered nanomaterials. Furthermore, given the uncertainty on the presence or not of engineered nanomaterials as discussed previously, it would be invalid to apply a species-specific nanomaterial-based exposure threshold.Regarding the process safety part of the study, it should be noted that the data for the FMEA and FTA examinations were obtained through bilateral interviews with the process operators, and no prior event occurrence data was available. Documentation of near-miss events or accidents that may have occurred in the context of the nanocomposite manufacturing line across the spectrum of its operation  could have supported the assessment with historical data, in the sense that some of the events may have occurred in the past, indicating a confirmation of their occurrence probability. Such an endeavour was presented in a study by Gopalaswami et al. , whereinThis study presents an extended methodology for the evaluation of occupational risk and safety, applied for a nanocomposite production line. Measurements of airborne contaminant concentrations were coupled with process hazard approaches (FMEA and FTA). Measurements showed an increased exposure potential at the start of the extrusion process, consistent with what is reported in the literature for similar processes. Additionally, emissions of particles were observed during the cleaning activities. Seven failure modes were identified and were further analysed through FTA. The currently applied control systems were documented, and additional mitigation actions were proposed. The estimated RPN after the implementation of the suggested controls provides an understanding of their impact and enables the prioritization of the most effective mitigation measures. Further studies on other recycling processes can provide a wider understanding of the risk and safety aspects and allow for more effective implementation of a control system in the early stages of process design.


Histopathological image analysis can be used for the detection of lung and colon cancer by the investigation of microscopic images of tissue samples. Since manual diagnosis takes a long time and is subjected to differing opinions of doctors, automated lung and colon cancer diagnosis becomes necessary. Therefore, the purpose of the study is to develop a transfer learning approach for lung and colon cancer detection on histopathological image analysis. It involves leveraging pre-trained model to analyze histopathological images. In addition, the proposed model uses improved ShuffleNet with deep convolutional recurrent neural network for feature extraction and classification, respectively. Besides, Al-Biruni Earth Radius Optimization and coati optimization algorithm are employed for hyperparameter tuning process. The experimental result analysis of the proposed model on the LC25000 database shows its promising performance on lung and colon cancer diagnosis.An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models. Cancer can affect any organ of the human body; the foremost areas to be commonly affected are the brain, colon, skin, breasts, stomach, rectum, liver, prostate, and lungs. The common tumors to cause death in females and males are lung and colon cancer (LCC) . When luArtificial intelligence (AI) methods are utilized in the medical domain, such as the initial detection of biomedical images, health disasters, forecasts of diseases, etc. . Deep leThis manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. In the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER algorithm. For the effectual detection of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. At the final stage, the coati optimization algorithm (COA) is exploited for the parameter selection of the DCRNN approach. The design of BER and COA for the hyperparameter tuning of the improved ShuffleNet and DCRNN models demonstrates the novelty of the work. To examine the result of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images.In , the autThe authors of  presenteMangal et al.  aimed toIn , a cliniAlthough several LCC classification models are available in the literature, it is still required to enhance the detection performance. Most of the existing works did not focus on the hyperparameter optimization process. Generally, hyperparameter optimization helps in the identification of an optimal combination of hyperparameters for a given model architecture and dataset. It proficiently searches the hyperparameter space for the detection of the optimal configuration, which saves time and computational resources by automatically exploring different combinations rather than relying on manual trial-and-error approaches. It helps in quickly finding a good set of hyperparameters without exhaustively evaluating every possible combination. Therefore, in this work, the BER and COA are used for the hyperparameter tuning process.In this manuscript, we developed a novel LCC detection model named the BERTL-HIALCCD technique. The study aims to find LCC effectually on histopathological images. To achieve this, the BERTL-HIALCCD approach follows a series of subprocesses, namely improved ShuffleNet feature extraction, BER-based parameter optimization, DCRNN-based classification, and COA-based hyperparameter tuning. https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images (accessed on 12 June 2023). Every image is 768 × 768 pixels and in .jpeg file format. The 5 class labels can be lung squamous cell carcinomas, colon adenocarcinomas, lung adenocarcinomas, and benign lung and benign colonic tissues.The LC25000 dataset comprises 25,000 color images with 5 class labels of 5000 images each. The dataset is available at In this work, the improved ShuffleNet method extracts features from histopathological images. Depthwise convolution (DW-Conv) is a special case of gathering convolutions in general and then evaluating a typical convolution with rocedure :(1)Gi,j,In Equation (1), The channel shuffle method is a significant innovation point developed by ShuffleNet that realizes the data interchange of channels from the extracted feature method with smaller computation costs. In this work, effective channel attention (ECA) was established to suppress the relevant attributes and accomplish the weighting method of the features for succeeding the classifier models. However, when retaining the lightness, the LSR loss function can be performed when considering the multi-dimensional loss computation and enhances the model noise immunity.The visual attention modules are drawn from human visual features to emphasize crucial data in images that are advantageous for enhancing the performance of the model. Visual attention mechanism brings accuracy improvements to CNNs by weighting the outcome features but mainly at the cost of enhancing the difficulty, namely convolution block attention mechanism (CBAM) SE. ECA refers to a lightweight attention model that borrowed the idea from SE to construct a channel attention module that is established in CNN to have participated during the end-to-end trained model. ECA exploits 1D convolutions for an extracted feature that prevents feature downscaling and efficiently captures cross-channel data interactions.Assume the input feature matrix is In Equation (5), In Equation (6), For the effectual identification of the hyperparameter values of the improved ShuffleNet method, the BER algorithm is used. The optimizer technique aims to find the optimum solution for the problem with a set of constraints . In thisIn the proposed AER algorithm, the population is split into subgroups. The number of individuals in every group can be adapted to enhance the balance between the exploration and exploitation processes. Moreover, to assure the convergence of optimization techniques for the population, the elitism approach can be applied by keeping the leading solution when no best solution is found. If the fitness of the solution does not increase dramatically for 3 iterations, this is the local optima, and subsequently, other exploration individuals are produced by using the mutation process.Exploration avoids local optimal stagnation by movement towards a better solution and is responsible for finding exciting locations in the search space.Heading towards the best solution strategy is used to search for prospective regions around its existing location in the search space. This could be obtained by repetitively searching amongst near promising alternatives for a better option with respect to the fitness value:The exploitation team is responsible for improving the present solution. The BER evaluates every individual fitness value at all the cycles and differentiates the optimal individual. The BER applies two dissimilar strategies to accomplish exploitation, as follows.The following equation is used for moving the searching agent towards the better solution:The most potential is the area surrounding the lead (better solution). Consequently, some individuals hunt in the surrounding area for a better solution with the possibility of finding the best solution. The BER exploits the subsequent formula to realize these operations.From the expression, A mutation is an alternative approach applied by the BER. It can be a genetic operator used for creating and sustaining population diversity. It avoids an earlier convergence by assisting in avoiding the local optima, such as a modification of the searching space as a springboard to other interesting topics. The mutation is crucial for the remarkable exploration of the BER.The BER selects the better performance for the following iteration to guarantee the quality of the defined solution. Even though the elitism technique improves the efficacy, it causes an early convergence when using a multi-modal function. Note that the BER gives impressive exploration abilities by applying a mutation method and searching nearby individuals to the exploration group. Due to its strong exploration abilities, the BER could prevent early convergence.At this stage, the features are passed to the DCRNN model for classification. Recently, a CNN was investigated and proved efficient in high-dimensional and large-scale data learning . AdditioThe BN layer standardizes the mini-batch via the whole network, which reduces the internal covariate shift caused by progressive transform, and the dropout layer might prevent overfitting and decrease the amount of neurons. Therefore, for all the input samples, a feature map is attained. After the max-pooling and convolutional layers, an LSTM layer is exploited for learning the temporal characteristics amongst the attained mapping features, and an FC layer with sixty-four neurons is effectuated to learn the global features. Eventually, a softmax layer is executed for deriving the probability distribution through two classes respective to normal and abnormal heart sound signals. (i)Coatis’ escape strategy from the predator.(ii)Coatis’ strategy while attacking iguanas.To improve the detection rate, the COA is used for hyperparameter tuning. The updating method of a candidate solution (coatis place) from the COA dependent upon modeling 2 basic performances of coatis are given below :(i)CoatiConsequently, the COA population was upgraded 2 stages.The primary stage of updating the coatis’ population is modeled by simulating the strategy while attacking the iguanas. Here, a set of coatis climb trees to attain an iguana and scare it. This approach leads the coatis to move toward dissimilar locations in the searching space, which illustrates their exploration capability in a global searching space. Here, the location of the fittest member of the population is considered the location of the iguana. Thus, the rise in the coatis’ location in the tree can be a mathematical formula:Once the iguana falls to the ground, it can be positioned at an arbitrary location from the searching space. As per the arbitrary location, the coatis on the ground moves from the searching space, which can be given as follows:The new location evaluated for all the coatis is acceptable for the updating procedure once it enhances the value of the main function or the coatis remain in the prior location.Now, The second stage is to update the location of the coatis from the searching space using a mathematical process according to the natural behaviors of the coatis while encountering predators and escaping from them. Once the predator attacks a coati, the animal escapes during the locating. The coatis’ moves during these strategies result in a safer location closer towards the existing location that represents the exploitation capability during a local search.To simulate these behaviors, a random location is produced nearby the location where every coati is located as follows:A recently estimated location is suitable once it enhances the value of the main function as follows:In Equation (21), The COA iteration is completed after the location of the coatis when the decision variable is updated. The updating process is based on Equations (15)–(21) and reiterated until it attains the maximum iteration. The COA method uses a fitness function (FF) to obtain a superior efficiency of the classifier. It describes a positive integer to suggest the finest outcome of the candidate solutions. The decline of the classifier rate of errors is assumed in the FF.In this section, the cancer detection outcomes of the BERTL-HIALCC technique are validated using the LC25000 database . The datThe confusion matrices of the BERTL-HIALCC method in the LCC detection results are depicted in In In In In The loss analysis of the BERTL-HIALCC algorithm at the time of training and validation is validated on 80:20 of TRP/TSP in A detailed precision–recall (PR) curve of the BERTL-HIALCC method is shown on 80:20 of TRP/TSP in In To illustrate the improved cancer recognition results of the BERTL-HIALCC technique, a brief comparison study is carried out in Additionally, based on In this study, we developed a novel LCC detection model named the BERTL-HIALCCD technique. This study aimed to identify LCC effectually on HSI. To achieve this, the BERTL-HIALCCD method followed a series of subprocesses, namely improved ShuffleNet feature extraction, BER-based parameter optimization, DCRNN-based classification, and COA-based hyperparameter tuning. Finally, a COA-based parameter selection process was carried out for the DCRNN model. To examine the performance of the BERTL-HIALCCD technique, a detailed set of experiments was conducted on a large dataset of HSI. The experimental results demonstrated that the combination of AER and COA algorithms attained an improved performance for cancer detection compared to the other models. In the future, the performance of the BERTL-HIALCDD method can be improved with multimodal fusion approaches. In addition, a histopathological image analysis can be integrated with clinical data, such as patient demographics, medical history, and genetic information, which can offer a better holistic understanding of cancer detection. It can significantly enhance the accuracy and reliability of the detection system and enable personalized medicine approaches. In addition, methods such as attention mechanisms, saliency maps, or feature visualization can assist in the identification of the important regions in histopathological images and provide insights into the decision-making process.


Objective: This study aimed to determine if a disproportionate number of radiologists practice in high-income versus low-income counties in New Jersey (NJ), identify which vulnerable populations are most in need of more radiologists, and discuss how these relative differences can ultimately influence health outcomes.Methods: The NJ Health Care Profile, a database overseen and maintained by the Division of Consumer Affairs, was queried for all actively practicing radiologists within the state of NJ. These results were grouped into diagnostic and interventional radiologists followed by further stratification of physicians based on the counties where they currently practice. The median household income and population size of each county for 2021 were obtained from the US Census database. The ratio of the population size of each county over the number of radiologists in that county was used as a surrogate marker for disparities in patient care within the state and was compared between counties grouped by levels of income.Results: Of the 1,186 board-certified radiologists actively practicing within the state of NJ, 86% are solely diagnostic radiologists and 14% are interventional radiologists. About 44% of radiologists practice within counties that are within the top one-third of median household income in NJ, 25% practice within counties in the middle one-third, and 31% practice within counties in the bottom one-third.Conclusions: There is a disproportionate number of radiologists practicing in high-income counties as opposed to lower-income counties. A contradiction to this trend was noted in three low-income counties: Essex, Camden, and Atlantic County, all of which exhibited low numbers of individuals per radiologist that rivaled those of higher-income counties. This finding is a concrete measure of successful radiologist recruitment efforts within these counties during the past few years to combat the increased prevalence of disease and associated complications that historically marginalized communities tend to disproportionately exhibit. Other low-income counties should look to what Essex, Camden, and Atlantic County have done to increase radiologist recruitment to levels that rival those of high-income areas. The growing shortage of physicians in the United States and the implications this trend has on overall patient health has been well documented. Patients who are residents of areas deemed to have a shortage of primary care physicians, which are typically low-income or rural areas, have already been shown to have increased morbidity and mortality due to heart disease, cancer, and diabetes . The COVPrevious county-level analysis of the US radiologist workforce utilized public datasets from 2014 and earlier which showed negative associations between county income and radiologist supply . There aThis surge is seen especially after the resolution of the COVID-19 pandemic and more specifically in underserved communities where an uptick in imaging studies like breast cancer screenings was observed. Essex County, New Jersey (NJ), is home to a large underserved community that observed a significant increase in the number of screening mammograms following the COVID-19 shutdown . WithoutThis article was previously presented in the form of an electronic poster presentation at the 2023 American College of Radiology Annual Meeting on May 6, 2023.To ultimately evaluate disparities in radiologist densities between different counties, a list of currently active and practicing radiologists had to be generated. This study chose to utilize and query the NJ Health Care Profile to generate the aforementioned list. The database’s maintenance and oversight by the Division of Consumer Affairs made it a highly reliable option for the purposes of this study .The advanced search option setting was used to generate alphabetized lists of actively practicing radiologists within NJ. Each radiologist was then searched online to determine all the locations, and ultimately the counties, in which they were practicing. Multiple locations within a single county were only listed as one county location due to the focus of the study pertaining to the actual counties themselves rather than the towns comprising the county. Any physician practicing outside of NJ was disregarded. Once a physician was found to be practicing in at least one location within the state of NJ, they were further stratified on the basis of being either a diagnostic radiologist or an interventional radiologist.Using the US Census database, the median household income and population size for each county was recorded in the year 2021. All of the counties were then ordered by their median household so that the counties could be stratified into the following three different income categories: counties in the top one-third of median household income, counties in the middle one-third of median household income, and counties in the bottom one-third of median household income.Using the substratifications by radiology specialty type, household income, population size, and counties, Microsoft Excel  was used to carry out chi-squared analyses and descriptive statistics on these different subgroups, including many proportions and ratios using the stratified dataset. This allowed the study to use the ratio of the population size of each county to the number of radiologists in each county to serve as a surrogate marker for disparities following a standardization to account for differing population sizes between all of the different counties within the state. This ratio was subsequently used to compare disparities in the amount of practicing radiologists within a county on the basis of the income level within which the county fell into. A p-value less than 0.05 was considered statistically significant.After querying the NJ Health Care Profile for all actively practicing radiologists within the state of New Jersey, the study identified 1,186 board-certified radiologists. Of these 1,186 radiologists, 86%  are solely diagnostic radiologists, while 14% (148 physicians) are interventional radiologists. As a result of many radiologists covering locations in multiple counties, the study found 1,555 county-based coverages.Subsequent to stratifying counties by median household income in 2021, three groupings of the counties were created by splitting the counties into the following: in the top one-third of median household income, in the middle one-third of median household income, and in the bottom one-third of median household income. The study found that of the 1,555 county-based coverages, 44.37% (690 coverages) of them were in counties in the top one-third of median household income. About 25.15% (391 coverages) were in counties in the middle one-third of median household income, while 30.48% (474 coverages) were in counties in the bottom one-third of median household income  were in counties that fell into the top one-third of median household income relative to all counties within the state. Statistical analyses demonstrated a significant difference in county-based coverage by radiologists depending on county income stratification. Therefore, from a public health standpoint that underscores health equity, this is an extremely alarming trend especially given the already apparent disparity in the rates of certain radiologic procedures, such as diagnostic and screening mammograms, within low-income communities . An ineqThe number of patients per radiologist within a certain categorical stratification was used as a surrogate measure for entities such as healthcare resource allocations and the burden on medical professionals, both of which are critical factors that ultimately determine the quality of care that a patient receives . With deDespite the importance and significant implications of the results of this study, it is also important to acknowledge a few limitations of the study. The data for the study regarding the populations of each county within the state of NJ was limited by the most recent US Census Bureau report which was in 2021. Therefore, any systemic changes that may have been instituted to remedy statewide health disparities after this year may not be accurately reflected. Additionally, querying the NJ Health Care Profile did not allow for the investigators of this study to account for teleradiology within counties. Despite these limitations, the results of this study demonstrated clear significance in the disparities pertaining to radiology within the state. Further longitudinal studies to assess radiologist recruitment efforts are needed to determine which counties are successfully recruiting more radiologists.Many public health efforts and initiatives to streamline equitable healthcare have displayed the need for greater medical resource allocation in low-income communities, particularly due to the greater need present within these communities. However, the current study shows that the field of radiology specifically within the state of NJ has yet to address this concern.It is apparent that the highest income communities have less patient burden for radiologists, a surrogate for patient outcomes and health within the community as a whole. There needs to be a statewide re-evaluation of current policies and initiatives to redirect the geographic distribution of radiologists to better reflect the number of patients in a given geographic location rather than the current trend of following geographic income levels. NJ is particularly vulnerable to systemic community-based health disparities rooted in socioeconomic disparities due to having many counties (5 out of 21) that are even below the national poverty level.As we continue to evaluate what can be done to improve community health both statewide and nationally, it is imperative that we explore the avenue of redirecting radiologists geographically to create uniformity in the number of patients per radiologist in a given geographic county and area.