Search Results (30)

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was $7.2\%$ higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached $98.8\%$. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated. Full article

The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method’s performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions. Full article

In applications where large-order filters are needed, the computational load of adaptive filtering algorithms can become prohibitively expensive. In this paper, a comprehensive analysis of a selective partial-update least mean squares, named SPU-LMS-M-min, is developed. By employing the partial-update strategy for a non-normalized adaptive scheme, the designer can choose an appropriate number of update blocks considering a trade-off between convergence rate and computational complexity, which can result in a more than 40% reduction in the number of multiplications in some configurations compared to the traditional LMS algorithm. Based on the principle of minimum distortion, a selection criterion is proposed that is based on the input signal’s blocks with the lowest energy, whereas typical Selective Partial Update (SPU) algorithms use a selection criterion based on blocks with highest energy. Stochastic models are developed for the mean weights and mean and mean squared behaviour of the proposed algorithm, which are further extended to accommodate scenarios involving time-varying dynamics and suboptimal filter lengths. Simulation results show that the theoretical predictions are in good agreement with the experimental outcomes. Furthermore, it is demonstrated that the proposed selection criterion can be easily extended to active noise cancellation algorithms as well as algorithms utilizing variable filter length. This allows for the reduction of computational costs for these algorithms without compromising their asymptotic performance. Full article

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer’s Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer’s Disease, which is crucial for early disease detection and personalized treatment. Full article

The incidence of radiculopathy due to lumbar spinal stenosis has been on the increase in the aging population. However, patients aged ≥ 80 years hesitate to undergo conventional open surgery under general anesthesia because of the risk of postoperative morbidity and adverse events. Therefore, less invasive surgical alternatives are required for the elderly or medically handicapped patients. Transforaminal endoscopic lumbar lateral recess decompression (TELLRD) may be helpful for those patients. This study aimed to demonstrate the efficacy of TELLRD for treating radiculopathy in octogenarian patients. A total of 21 consecutive octogenarian patients with lumbar foraminal stenosis underwent TELLRD between January 2017 and January 2021. The inclusion criterion was unilateral radiculopathy, which stemmed from lumbar lateral recess stenosis. The pain source was verified using imaging studies and selective nerve blocks. Full-scale lateral canal decompression was performed using a percutaneous transforaminal endoscopic approach under local anesthesia. We found the pain scores and functional status improved significantly during the 24-month follow-up period. The clinical improvement rate was 95.24% (20 of 21 patients) with no systemic complication. In conclusion, endoscopic lateral recess decompression via the transforaminal approach is practical for octogenarian patients. Full article

This paper proposes a new framework for post-synthesis obfuscation of digital circuits using a mixture of cells combined with an Anti-SAT block. Furthermore, a novel integrated framework has been established wherein obfuscation, along with Anti-SAT and validation of the benchmarks, progress through MATLAB^®, Python, Cadence RTL Encounter^® and Cadence LEC^® to implement the proposed methodology. Area, delay, leakage power and total power are adopted as elements of the evaluation matrix. These parameters are compared between the original circuit, the circuit after obfuscation, the circuit after integration with Anti-SAT and the circuit after implementation of the proposed method of multiple-cell obfuscation with Anti-SAT. The probability of breaking a circuit is taken as the security criterion. It is mathematically proven that as the number of types of obfuscated cells used increases, then the probability of breaking the circuit decreases. The results obtained accord with the mathematical proof. The framework minimizes the delay by inserting obfuscation cells (OCs) in the non-critical paths, strengthens the security by using several types of OCs and allows the user to select a design based on justified area, leakage power and total power. However, against a Boolean SATisfiability (SAT) attack, obfuscation with multiple cells is not a sufficient defense. An Anti-SAT block performs better than obfuscation but has its own limitations. Thus, use of an Anti-SAT block in combination with multiple-cell obfuscation is proposed and implemented, giving better results against an efficient SAT attack. The number of iterations, as well as runtime to obtain the correct keys, increase significantly for the Anti-SAT block combined with multiple-cell obfuscation compared to the Anti-SAT or obfuscation block alone. Full article

Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, $d_{MFCC}$, and $d_{squence}$ is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition. Full article

The search for simple morphological predictors of oocyte quality is an important task for assisted reproduction technologies (ARTs). One such predictor may be the morphology of the oocyte nucleus, called the germinal vesicle (GV), including the level of chromatin aggregation around the atypical nucleolus (ANu)—a peculiar nuclear organelle, formerly referred to as the nucleolus-like body. A prospective cohort study allowed distinguishing three classes of GV oocytes among 135 oocytes retrieved from 64 patients: with a non-surrounded ANu and rare chromatin blocks in the nucleoplasm (Class A), with a complete peri-ANu heterochromatic rim assembling all chromatin (Class C), and intermediate variants (Class B). Comparison of the chromatin state and the ability of oocytes to complete meiosis allowed us to conclude that Class B and C oocytes are more capable of resuming meiosis in vitro and completing the first meiotic division, while Class A oocytes can resume maturation but often stop their development either at metaphase I (MI arrest) or before the onset of GV breakdown (GVBD arrest). In addition, oocytes with a low chromatin condensation demonstrated a high level of aneuploidy during the resumption of meiosis. Considering that the degree of chromatin condensation/compaction can be determined in vivo under a light microscope, this characteristic of the GV can be considered a promising criterion for selecting the best-quality GV oocytes in IVM rescue programs. Full article

Due to the complex structure of high-canopy-density forests, the traditional individual tree segmentation (ITS) algorithms based on ALS point cloud, which set segmentation threshold manually, is difficult to adequately cover a variety of complex situations, so the ITS accuracy is unsatisfactory. In this paper, a top-down segmentation strategy is adopted to propose an adaptive segmentation method based on extreme offset deep learning, and the ITS set aggregation strategy based on gradient change criterion is designed for the over-segmentation generated by random offset, and the precise ITS is realized. Firstly, the segmentation sub-plot is set as 25 m × 25 m, the regional point cloud and its treetop are marked, and the offset network is trained. Secondly, the extreme offset network is designed to carry out spatial transformation of the original point cloud, and each point is offset to the position near the treetop to obtain the offset point cloud with a high density at the treetop, which enhances the discrimination among individual trees. Thirdly, the self-adaptive mean shift algorithm based on average neighboring distance is designed to divide and mark the offset point cloud. Fourthly, the offset point cloud, after clustering, is mapped back to the original space to complete the preliminary segmentation. Finally, according to the gradient change among different canopies, the ITS aggregation method is designed to aggregate adjacent canopies with a gentle gradient change. In order to investigate the universality of the proposed method on different stand structures, two coniferous forest plots (U1, U2) in the Blue Ridge area of Washington, USA, and two mixed forest plots (G1, G2) in Bretten, Germany, are selected in the experiment. The learning rate of the deep network is set as 0.001, the sampled point number of the sub-plot is 900, the transformer dimension is 512 × 512, the neighboring search number of points is 16, and the number of up-sampling blocks is 3. Experimental results show that in mixed forests (G1, G2) with complex structures, the F-score of the proposed method reaches 0.89, which is about 4% and 7% higher than the classical SHDR and improved DK, respectively. In high-canopy-density areas (U2, G2), the F-score of the proposed method reaches 0.89, which is about 3% and 4% higher than the SHDR and improved DK, respectively. The results show that the proposed method has high universality and accuracy, even in a complex stand environment with high canopy density. Full article

Medium-density rigid polyurethane (PU) foams are often produced in sealed molds; therefore, the processes inside the mold and structure of the produced foam blocks need to be understood. The structural and mechanical anisotropy is shown to be the third variable along with (1) concentration of the nanoclay filler and (2) density, to determine the mechanical properties of the filled PU foam composites produced in a sealed mold. The varying anisotropy of the specimens hinders the accurate evaluation of the filling effect. The methodology for the estimation of the anisotropy characteristics of specimens from different locations within the nanoclay filled PU foam blocks is elaborated. A criterion, based on analysis of Poisson’s ratios, is formulated for the selection of specimens with similar anisotropy characteristics. The shear and bulk moduli are estimated theoretically, dependent on the filler’s concentration, using the experimentally determined constants. Full article

As multi-factory production models are more widespread in modern manufacturing systems, a distributed blocking flowshop scheduling problem (DBFSP) is studied in which no buffer between adjacent machines and setup time constraints are considered. To address the above problem, a mixed integer linear programming (MILP) model is first constructed, and its correctness is verified. Then, an iterated greedy-algorithm-blending multi-factory collaboration mechanism (mIG) is presented to optimize the makespan criterion. In the mIG algorithm, a rapid evaluation method is designed to reduce the time complexity, and two different iterative processes are selected by a certain probability. In addition, collaborative interactions between cross-factory and inner-factory are considered to further improve the exploitation and exploration of mIG. Finally, the 270 tests showed that the average makespan and RPI values of mIG are 1.93% and 78.35% better than the five comparison algorithms on average, respectively. Therefore, mIG is more suitable to solve the studied DBFSP_SDST. Full article

There is a large amount of drilling core data in the Mackay River oil sands block in Canada, and the accurate identification of facies from the cores is important and necessary for the understanding of the subsurface reservoir. The traditional recognition method of facies from cores is by human work and is very time consuming. Furthermore, the results are different according to different geologists because of the subjective judgment criterion. An efficient and objective method is important to solve the above problem. In this paper, the deep learning image-recognition algorithm is used to automatically and intelligently recognize the facies type from the core image. Through a series of high-reliability preprocessing operations, such as cropping, segmentation, rotation transformation, and noise removal of the original core image, that have been manually identified, the key feature information in the images is extracted based on the ResNet50 convolutional neural network. On the dataset of about 200 core images from 13 facies, an intelligent identification system of facies from core images is constructed, which realizes automatic facies identification from core images. Comparing this method with traditional convolutional neural networks and support vector machines (SVM), the results show that the recognition accuracy of this model is as high as 91.12%, which is higher than the other two models. It is also shown that for a relatively special dataset, such as core images, it is necessary to rely on their global features in order to classify them, and, with a large similarity between some of the categories, it is extremely difficult to classify them. The selection of a suitable neural network model can have a great impact on the accuracy of recognition results. Then, the recognized facies are input as hard data to construct the three-dimensional facies model, which reveals the complex heterogeneity and distribution of the subsurface reservoir for further exploration and development. Full article

Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques. Full article

Wellbore instability resulting from deep mudstone hydration severely restricts the development of oil and gas resources from deep reservoir in western China. Accurate evaluation of drilling fluid inhibition properties plays an important role in selecting drilling fluid that can control deep mudstone hydration and then sustain wellbore stability. The previous evaluations are conducted by qualitative analysis and cannot consider the influence of complex hydration conditions of deep mudstone (high temperature, high pressure and flushing action). The study proposes a quantitative method to evaluate drilling fluid’s inhibition property for deep mudstone under natural drilling conditions. In this method, the cohesive strength of mudstone after hydration is adopted as the inhibition index of the tested drilling fluid. An experimental platform containing a newly designed HPHT (High pressure and high temperature) hydration experiment apparatus and mechanics characterization of mudstone after hydration based on scratch test is proposed to obtain the current inhibition index of tested drilling fluid under deep well drilling environments. Based on the mechanical–chemical wellbore stability model considering strength weakening characteristics of deep mudstone after hydration, a cross-correlation between drilling fluid density (collapse pressure) and required inhibition index (cohesive strength) for deep mudstone is provided as the quantitative evaluation criterion. Once the density of tested mud is known, one can confirm whether the inhibition property of tested mud is sufficient. In this study, the JDK mudstone of a K block in western China is selected as the application object of the proposed evaluation method. Firstly, the evaluation chart, which can demonstrate the required inhibition indexes of the tested fluids quantitatively with various densities for JDK mudstone, is constructed. Furthermore, the experimental evaluations of inhibition indexes of drilling fluids taken from two wells in K block are conducted under ambient and deep-well drilling conditions, respectively. In order to show the validity and advantage of the proposed method, a comparison between the laboratory evaluation results and field data is made. Results show that the laboratory evaluation results under deep-well drilling conditions are consistent with the field data. However, the evaluation under ambient conditions overestimates the inhibition property of the tested fluid and brings a risk of wellbore instability. The developed quantitative method can be a new way to evaluate and optimize the inhibition property of drilling fluid for deep mudstone. Full article

Background: The gastrointestinal symptom score (GIS) is used in a standardized form to ascertain dyspeptic symptoms in patients with functional dyspepsia in clinical practice. As a criterion for evaluating the effectiveness of a treatment, the change in the summed total point value is used. The total score ranges from 0 to 40 points, in which a higher score represents a more serious manifestation of the disease. Each symptom is included with equal importance in the overall evaluation. The objective of this study was to test this assumption from a patients’ perspective. Our aim was to measure the priorities of patients for the ten gastrointestinal symptoms by using best–worst scaling. Method: A best–worst scaling (BWS) object scaling (Case 1) was applied. Therefore, the symptoms of the GIS were included in a questionnaire using a fractional factorial design (BIBD—balanced incomplete block design). In each choice set, the patients selected the component that had the most and the least impact on their well-being. The BIB design generated a total of 15 choice sets, which each included four attributes. Results: In this study, 1096 affected patients were asked for their priorities regarding a treatment of functional dyspepsia and motility disorder. Based on the data analysis, the symptoms abdominal cramps (SQRT (B/W): −1.27), vomiting (SQRT (B/W): −1.07) and epigastric pain (SQRT (B/W): −0.76) were most important and thus have the greatest influence on the well-being of patients with functional dyspepsia and motility disorders. In the middle range are the symptoms nausea (SQRT (B/W): −0.69), acid reflux/indigestion (SQRT (B/W): −0.29), sickness (SQRT (B/W): −0.26) and retrosternal discomfort (SQRT (B/W): 0.26), whereas the symptoms causing the least impact are the feeling of fullness (SQRT (B/W): 0.80), early satiety (SQRT (B/W): 1.54) and loss of appetite (SQRT(B/W): 1.95). Discussion: Unlike the underlying assumption of the GIS, the BWS indicated that patients did not weight the 10 symptoms equally. The results of the survey show that the three symptoms of vomiting, abdominal cramps and epigastric pain are weighted considerably higher than symptoms such as early satiety, loss of appetite and the feeling of fullness. The evaluation of the BWS data has illustrated, however, that the restrictive assumption of GIS does not reflect the reality of dyspeptic patients. Conclusions: In conclusion, a preference-based GIS is necessary to make valid information about the real burden of illness and to improve the burden of symptoms in the indication of gastrointestinal conditions. The findings of the BWS demonstrate that the common GIS is not applicable to represent the real burden of disease. The results suggest the potential modification of the established GIS by future research using a stated preference study. Full article