Search Results (1,234)

In the era of VUCA, cultivating and enhancing the resilience of high-end manufacturing enterprises is critical. Based on existing research, this paper defines enterprise resilience at the beginning and constructs an enterprise resilience evaluation index system that includes three segmented capabilities: recognition and resistance, adaptation and adjustment, and recovery and rebound. Finally, the relationship between human capital investment, technological innovation, and high-end enterprise resilience is empirically studied. The research results show that human capital investment positively affects the resilience of high-end manufacturing enterprises, with breakthrough innovation and progressive innovation playing a mediating role. Digital transformation positively moderates the impact of human capital investment on the resilience of high-end manufacturing enterprises. Further, there is a higher threshold between human capital investment and technological innovation in improving the resilience of high-end manufacturing enterprises. Human capital investment has a significantly positive effect on high-end manufacturing enterprises’ ability to resist risks and adapt to adjustments but has no significant impact on recovery and rebound ability. Breakthrough and progressive innovation partially mediate the impact of human capital investment on the ability to resist risks and adapt to adjustments, while breakthrough innovation has no significant impact on the recovery of the rebound ability; however, progressive innovation completely mediates the relationship between human capital investment and the recovery of rebound ability. Compared with Chinese non-state-owned enterprises, state-owned enterprises’ efforts to increase investment in human capital only positively impact their ability to resist risks. Compared with large-scale enterprises, the increase in human capital investment in small-scale enterprises only has a significant positive impact on the ability to resist risks. Based on the above, this paper suggests that high-end manufacturing enterprises should enhance their strategic focus and constantly strengthen their investment in human capital and technological innovation; at the same time, they should further optimize the structure of human capital investment and introduce and cultivate cutting-edge talents. Full article

The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model. The models were evaluated using data from 360 lifting trials performed by 10 subjects (5 males and 5 females) with an average age of 51.50 (±9.83) years. The accuracy of the two models was compared against data collected by a laboratory-based motion capture system as a function of 12 ACGIH lifting risk zones and 3 grouped risk zones (low, medium, and high). Results showed that only the ratio + length model provides acceptable estimates of lifting risk with an average of 69% accuracy level for predicting one of the 3 grouped zones and a higher rate of 92% for predicting the high lifting zone. Full article

Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate. Full article

The manuscript conducts a comparative analysis to assess the impact of noise on medical images using a proposed threshold value estimation approach. It applies an innovative method for edge detection on images of varying complexity, considering different noise types and concentrations of noise. Five edges are evaluated on images with low, medium, and high detail levels. This study focuses on medical images from three distinct datasets: retinal images, brain tumor segmentation, and lung segmentation from CT scans. The importance of noise analysis is heightened in medical imaging, as noise can significantly obscure the critical features and potentially lead to misdiagnoses. Images are categorized based on the complexity, providing a multidimensional view of noise’s effect on edge detection. The algorithm utilized the grid search (GS) method and random search with nine values (RS9). The results demonstrate the effectiveness of the proposed approach, especially when using the Canny operator, across diverse noise types and intensities. Laplace operators are most affected by noise, yet significant improvements are observed with the new approach, particularly when using the grid search method. The obtained results are compared with the most popular techniques for edge detection using deep learning like AlexNet, ResNet, VGGNet, MobileNetv2, and Inceptionv3. The paper presents the results via graphs and edge images, along with a detailed analysis of each operator’s performance with noisy images using the proposed approach. Full article

Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. Full article

Color is a key indicator in evaluating seed cotton quality. Accurate and rapid detection of seed cotton color is essential for its storage, processing, and trade. In this study, an RGB imaging and semantic segmentation-based method was proposed for seed cotton color detection. First, a color detection system utilizing machine vision technology was developed to capture seed cotton images. Next, a Color-Unet model, incorporating convolutional block attention and improved inception E modules based on Unet, was applied to effectively remove impurities and shadows from the images, resolving the over-segmentation issue commonly encountered in traditional threshold segmentation. The results demonstrated that the pixel accuracy of segmentation reached 97.20%, the mean intersection over union was 91.81%, and the average segmentation speed was 322.3 ms per image. The Color-Unet model effectively addressed the over-segmentation problem. Subsequently, seed cotton color indexes were calculated using Hunter color formulas based on the segmented images. To evaluate the accuracy of color measurement obtained with the proposed method, a regression analysis was performed, comparing the results of those from the HX-410 measurement. The coefficient of determination of yellowness was 0.883, with a root mean square error of 0.150 and a mean relative error of 2.61%. The coefficient of determination of reflectance degree was 0.832, with a root mean square error of 1.56% and a mean relative error of 1.84%. The proposed method allows for the rapid and accurate assessment of seed cotton color from RGB images, providing a valuable technical reference for seed cotton color evaluation. Full article

This study explores the impact of structural modifications on the stress distribution and flow characteristics of a nozzle box in a steam turbine, specifically targeting the components made from high-strength StE460 steel. Using Computational Fluid Dynamics (CFDs) and Thermal–Fluid–Structure Interaction (Thermal–FSI) simulations, we examine the effects of shortening the nozzle guide vanes by 7 mm. This novel approach significantly reduces the stress levels within the nozzle box segments, bringing them below the critical thresholds and thus enhancing component durability. Moreover, the modification leads to improved flow efficiency, evidenced by the higher outlet velocities, temperatures, and mass flow rates, all of which contribute to increased turbine power output without negatively impacting the downstream flow dynamics. This balance between durability and flow performance underscores the value of targeted structural innovations in high-temperature, high-stress environments. This study’s findings suggest that such modifications can substantially improve turbine efficiency and operational longevity, marking an important advancement in industrial applications where reliability and efficiency are paramount. Future work will assess the long-term effects under variable operational conditions to further optimize these benefits. Full article

Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to detect Leishmania spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. Starting with acquiring and labeling 500 images, an experimental design was implemented, including preprocessing and segmentation techniques such as Otsu, local thresholding, and Iterative Global Minimum Search (IGMS) to improve parasite detection. The phenotypic features of the parasites were extracted, focusing on morphology, texture, and color. Machine learning models (ANN, SVM, and RF) optimized through Grid Search were applied for classification. The model with the best results was a Support Vector Machine (SVM), achieving a sensitivity of 91.87% and a specificity of 89.21% at the crop level. Compared with previous studies, these results highlight the relevance and consistency of the methodology used, supporting the initial hypothesis. This suggests that machine learning techniques offer a promising path toward improving the diagnosis of cutaneous leishmaniasis. Full article

Tonal powertrain noise can have a strong negative impact on passengers’ quality and comfort perception in the interior of electric vehicles. Therefore, in the vehicle development process, the assessment of the perceptibility of tonal powertrain noise is essential. As wind and tire noise can possibly mask tonal noises, engineers use modern masking models to determine the masking threshold of tonal powertrain noise from vehicle interior measurements. In the presently used method, the masking threshold is mostly generated with torque-free deceleration measurements. However, the influence of torque on masking tire noise must be considered. As this requires time-consuming and costly road measurements, an extension of the method is being developed, which will also enable the use of roller dynamometer measurements for the assessment. For the extension of the method, however, the influence of the torque must also be considered. This paper presents a novel calculation method that quantifies the influence of torque on the masking threshold and converts masking thresholds from an arbitrary torque level to another. By identifying the frequency and speed range that is mainly affected by the torque-dependent tire noise, a regression model with respect to the tractive force on the tires can be used to calculate a torque-dependent correction factor. The developed method can significantly improve the validity of masking thresholds and quantitatively, the method generalizes well across different vehicle segments. The error can be reduced to below 2 dB above 2000 rpm and to below 1 dB above 4000 rpm. By using this method, more valid target level settings for tonal powertrain noise can be derived. Full article

Background: Recent studies on bronchiectasis have revealed significant structural abnormalities and pathophysiological changes. However, there is limited research focused on pulmonary venous variability and congenital variation. Through our surgical observations, we noted that coarctation of pulmonary veins and atrophied lung volume are relatively common in bronchiectasis patients. Therefore, we conducted a retrospective study to explore pulmonary venous variation and secondary manifestations in bronchiectasis cases, utilizing 3D reconstruction software (Mimics Innovation Suite 21.0, Materialise Dental, Leuven, Belgium) to draw conclusions supported by statistical evidence. Method: This retrospective study included patients with bronchiectasis and healthy individuals who underwent CT examinations at Beijing Chao-Yang Hospital between January 2017 and July 2023. Chest CT data were reconstructed using Materialise Mimics. Pulmonary veins and lung lobes were segmented from surrounding tissue based on an appropriate threshold determined by local grey values and image gradients. Subsequently, venous cross-sectional areas and lung volumes were measured for statistical analysis. Result: CT data from 174 inpatients with bronchiectasis and 75 cases from the health examination center were included. Three-dimensional reconstruction data revealed a significant reduction in cross-sectional areas of pulmonary veins in the left lower lobe (p < 0.001), the right lower lobe (p = 0.030), and the right middle lobe (p = 0.009) of bronchiectasis patients. Subgroup analyses indicated that approximately 73.5% of localized cases of the left lower lobe exhibited pulmonary vein stenosis, while in the diffuse group, this proportion was only 52.6%. Furthermore, the cross-sectional area of pulmonary veins had a gradually decreasing trend, based on a small sample. Lung function tests showed significant reductions in FEV1, FVC, and FEV1% in bronchiectasis patients, attributed to the loss of lung volume in the left lower lobe, which accounted for 60.9% of the included sample. Conclusions: Our recent findings suggest that pulmonary venous stenosis is a common variation in bronchiectasis and is often observed concurrently with reduced lung volume, particularly affecting the left lower lobe. Moreover, localized cases are more likely to suffer from pulmonary venous stenosis, with an ambiguous downtrend as the disease progresses. In conclusion, increased attention to pulmonary venous variation in bronchiectasis is warranted, and exploring new therapies to intervene in the early stages or alleviate obstruction may be beneficial. Full article

In order to solve the problems of traditional high-voltage power supply ignition protection circuits, such as non-essential start–stop power supply, a slow response speed, the system needing to be restarted manually, and so on, a high-voltage power supply system self-recovery ignition protection circuit was designed using an IGWO (improved grey wolf optimization) and PID control strategy designed to speed up the response speed, and improve the reliability and stability of the system. In high-voltage power supply operation, the firing discharge phenomenon occurs. Current transformers fire signal into a current signal through the firing voltage value and Zener diode voltage comparison to set the safety threshold; when the threshold is exceeded, the fire protection mechanism is activated, reducing the power supply voltage output to protect the high-voltage power supply system. When the ignition signal disappears, based on the IGWO-PID control of the ignition self-recovery circuit according to the feedback voltage, the DC supply voltage of the high-voltage power supply is adjusted, inhibiting the ignition discharge and, according to the ignition signal, “segmented” to restore the output of the initial voltage. MATLAB/Simulink was used to establish a system simulation model and physical platform test. The results show that the protection effect of the designed scheme is an improvement, in line with the needs of practical work. Full article

In a complex urban environment, the impact of building demolitions by blasting on the structural integrity of nearby metro tunnels is critical. This study systematically analyzed the blasting and demolition process of a building adjacent to a metro tunnel using various monitoring methods, including blasting vibration, dynamic strain, deformation and settlement, pore water pressure, and displacement. The results indicate that the metro tunnel’s vibration response can be divided into four stages: notch blasting, notch closure, overall collapse impact, and auxiliary notch blasting. The most significant impact on the tunnel segments occurred during the building’s ground impact phase, with a peak particle velocity of 0.57 cm/s. The maximum tensile and compressive stresses induced in the tunnel segments did not exceed 0.4 MPa, well within the safety limits. Displacement and settlement changes in the tunnel structure were less than 1 mm, far below the warning threshold. Additionally, blasting vibrations significantly affected the pore water pressure in the surrounding soil. However, fluctuations caused by ground impact vibrations were minimal, and the pore water pressure quickly returned to its initial level after the blasting concluded. Throughout the process, no adverse effects on the metro tunnel structure were observed. Full article

Background: In this study, we aimed to comparatively evaluate the morphology of internal carotid artery (ICA) bifurcations with and without aneurysms and identify risk factors for aneurysm development that are associated with the bifurcation geometry. Method: In this two-center study, the computerized tomography angiography data of 1512 patients were evaluated. The study included 64 (4.2%) patients with ICA bifurcation aneurysms (ICAbifAn) and patients with anterior circulation aneurysms (non-ICAbifAn). ICA (P1) was defined as the parent artery, and the middle (M1) and anterior (A1) cerebral artery segments were defined as daughter arteries. We measured the diameters of the P1, M1, and A1 and their ratios (BifSR) to identify the risk factors. In addition, we calculated the bifurcation angle in two ways by measuring all angles between the P1 and daughter arteries and compared these two methods. The first method was the angle between the M1 and A1 (α), and the second was the sum of the angles between the P1 and daughter arteries (BifA). Result: A total of 163 patients who met the inclusion criteria were included in this study: 58 patients in the ICAbifAn group and 105 patients in the non-ICAbifAn group. A univariate logistic regression analysis revealed that the P1, BifSR, α, and BifA measurements were significant predictors of aneurysm formation. However, after a multivariate analysis, only the BifA angle retained its significance (OR, 0.911 (0.877–0.946), p < 0.001). In the ROC curve, the optimal BifA threshold for accurately differentiating between an ICAbifAn and non-aneurysmal bifurcation was 210° (area under the curve (AUC), 0.81; sensitivity, 69%; and specificity, 87%). The α angle had an AUC of 0.68. Conclusions: These results suggest that bifurcation geometry plays a significant role in the likelihood of aneurysm formation. We also showed that the BifA was more predictive of aneurysm formation than the α angle. Full article

Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within target images, CVAPS can generate a uniformly scaled change-magnitude map that is suitable for a global threshold. However, vigorous user intervention is required to achieve optimal performance. Therefore, to eliminate user intervention and retain the merit of CVAPS, an unsupervised CVAPS (UCVAPS) CD method, RFCC, which does not require rigorous user training, is proposed in this study. In the RFCC, we propose an unsupervised remote sensing image segmentation algorithm based on the Mamba model, i.e., RVMamba differentiable feature clustering, which introduces two loss functions as constraints to ensure that RVMamba achieves accurate segmentation results and to supply the CSBN module with high-quality training samples. In the CD module, the fuzzy C-means clustering (FCM) algorithm decomposes mixed pixels into multiple signal classes, thereby alleviating cumulative clustering errors. Then, a context-sensitive Bayesian network (CSBN) model is introduced to incorporate spatial information at the pixel level to estimate the corresponding posterior probability vector. Thus, it is suitable for high-resolution remote sensing (HRRS) imagery. Finally, the UCVAPS framework can generate a uniformly scaled change-magnitude map that is suitable for the global threshold and can produce accurate CD results. The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods. Full article

Canine hip dysplasia (CHD) screening relies on accurate positioning in the ventrodorsal hip extended (VDHE) view, as even mild pelvic rotation can affect CHD scoring and impact breeding decisions. This study aimed to assess the association between pelvic rotation and asymmetry in obturator foramina areas (AOFAs) and to develop a computer vision model for automated AOFA measurement. In the first part, 203 radiographs were analyzed to examine the relationship between pelvic rotation, assessed through asymmetry in iliac wing and obturator foramina widths (AOFWs), and AOFAs. A significant association was found between pelvic rotation and AOFA, with AOFW showing a stronger correlation (R² = 0.92, p < 0.01). AOFW rotation values were categorized into minimal (n = 71), moderate (n = 41), marked (n = 37), and extreme (n = 54) groups, corresponding to mean AOFA ± standard deviation values of 33.28 ± 27.25, 54.73 ± 27.98, 85.85 ± 41.31, and 160.68 ± 64.20 mm², respectively. ANOVA and post hoc testing confirmed significant differences in AOFA across these groups (p < 0.01). In part two, the dataset was expanded to 312 images to develop the automated AOFA model, with 80% allocated for training, 10% for validation, and 10% for testing. On the 32 test images, the model achieved high segmentation accuracy (Dice score = 0.96; Intersection over Union = 0.93), closely aligning with examiner measurements. Paired t-tests indicated no significant differences between the examiner and model’s outputs (p > 0.05), though the Bland–Altman analysis identified occasional discrepancies. The model demonstrated excellent reliability (ICC = 0.99) with a standard error of 17.18 mm². A threshold of 50.46 mm² enabled effective differentiation between acceptable and excessive pelvic rotation. With additional training data, further improvements in precision are expected, enhancing the model’s clinical utility. Full article