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This research develops an elastoplastic damage constitutive model incorporating the strain softening response of common engineering soil materials in southeastern Xizang to evaluate and optimize reinforcement solutions for highway-traversing landslide accumulations. Grounded in deterioration mechanics theory, the model characterizes the progressive strength loss and failure evolution of the soils. Verified and calibrated, it is numerically implemented in FLAC3D to simulate the stability conditions of a landslide affecting planned highway infrastructure in southeastern Xizang. Safety factors of 1.25, 1.07, and 1.02 under normal operation, rainfall, and seismic excitation loads, respectively, reveal the inadequacy of intrinsic stability. Consequently, dynamic compaction and chemical grouting techniques are assessed via simulation. An optimal strategy, entailing 6-m-deep densification at the highway location with 10% silica fume enhancement of 66.3% of the landslide area and 50.8% of the soil-bedrock interface, results in safety factors of 1.70, 1.49, and 1.23 for the three scenarios. The improved area is minimized to streamline construction practicality and economics while preserving geotechnical integrity. The integrated modeling outcomes demonstrate the model's capability in capturing localized incremental damage and the efficacy of numerical simulation for stability diagnosis and targeted remediation of intricate landslides. Advancements in constitutive relations development are vital for further innovation in geohazard evaluation and infrastructure safety assurance.

Abstract This paper presents a cement-content controlled method for quality assessment and quality control of the deep soil mixing (DSM) columns in slope reinforcement. The ethylene diamine tetraacetic acid (EDTA) titration method was modified and used for the cement content measurement of core samples, and the effects of curing conditions and curing period on the titration results were investigated. 35 DSM columns with different construction parameters were installed in the test section, and cement content and unconfined compression tests of field core samples were conducted. The relationship between the unconfined compressive strength (UCS) and cement content of DSM columns was formulated, and the quality of DSM columns with different construction parameters was assessed. The test results suggested that the failure strength of the field cores was approximately 15–55% lower than that of laboratory samples with the same cement content. In single columns, the coefficient of variation (CV) of cement content had a negative correlation with the average failure strength and a positive correlation with the coefficient of variation of failure strength. Bidirectional mixing method, lower penetration and withdrawal velocity, more mixing blades and larger number of mixings could improve the uniformity of the DSM columns.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman’s to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.

Daugman’s design of IrisCode continues to be fascinating in the research world with its practicality, efficiency, and outstanding performance. The limits of Daugman’s recognition system, however, remain a topic of active discussion. Multiple approaches to scale performance have been explored in the past. Despite them, the problem of finding the maximal population of IrisCode remains open. Because of this, we appeal to Rate-Distortion theory (limits of error-correction codes) to establish bounds on the maximum possible population of iris classes that IrisCode can support under the constraint of a minimum Hamming Distance (HD) between any two codewords. This approach considers the distribution of iris data within and across iris classes and the quality of iris data. We first present the Hamming, Plotkin, and Elias-Bassalygo upper bounds and the Gilbert-Varshamov lower bound on the population of IrisCode. The bounds relate the number of iris classes that the IrisCode algorithm can sustain and the quality of iris data expressed in terms of HD. Then, we analyze our results and draw conclusions regarding the relationship of IrisCode population size and the level of quality that enrolled data must have to ensure a particular population coverage. By applying the theory presented here, researchers can better understand what maximum population is achievable based on the quality of their iris dataset.

Crop traits such as aboveground biomass (AGB), total leaf area (TLA), leaf chlorophyll content (LCC), and thousand kernel weight (TWK) are important indices in maize breeding. How to extract multiple crop traits at the same time is helpful to improve the efficiency of breeding. Compared with digital and multispectral images, the advantages of high spatial and spectral resolution of hyperspectral images derived from unmanned aerial vehicle (UAV) are expected to accurately estimate the similar traits among breeding materials. This study is aimed at exploring the feasibility of estimating AGB, TLA, SPAD value, and TWK using UAV hyperspectral images and at determining the optimal models for facilitating the process of selecting advanced varieties. The successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were used to screen sensitive bands for the maize traits. Partial least squares (PLS) and random forest (RF) algorithms were used to estimate the maize traits. The results can be summarized as follows: The sensitive bands for various traits were mainly concentrated in the near-red and red-edge regions. The sensitive bands screened by CARS were more abundant than those screened by SPA. For AGB, TLA, and SPAD value, the optimal combination was the CARS-PLS method. Regarding the TWK, the optimal combination was the CARS-RF method. Compared with the model built by RF, the model built by PLS was more stable. This study provides guiding significance and practical value for main trait estimation of maize inbred lines by UAV hyperspectral images at the plot level.

Municipal sludge is a sedimentation waste produced during the wastewater process in sewage treatment plants. Among recent studies, pilot and field tests showed that chemical conditioning combined with vacuum preloading can effectively treat municipal sludge. To further understand the drainage and consolidation characteristics of the conditioning sludge during vacuum preloading, a large deformation nonlinear numerical simulation model based on the equal strain condition was developed to simulate and analyze the pilot and field tests, whereas the simulation results were not satisfactory. The results of the numerical analysis of the pilot test showed that the predicted consolidation degree was greater than that measured by the field tests, which is attributed to the relatively low permeability layer formed during the preloading process of the prefabricated vertical drain. To better reflect the consolidation process of the conditioned sludge, a simplified analysis method considering the low permeability layer around the prefabricated vertical drain was proposed. The initial permeability coefficient of the low permeability layer is determined via numerical simulations using finite difference method. The predicted settlement curve was in good agreement with the measured results, which indicated that the numerical simulation based on the equal strain condition considering the relatively low permeability layer can better analyze the consolidation process of ferric chloride-conditioning sludge with vacuum preloading. [ABSTRACT FROM AUTHOR]

Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, the West Virginia University (WVU) data, and the WVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildes's algorithm and Masek's algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms. [ABSTRACT FROM AUTHOR]

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is one of the most reliable biometrics in terms of recognition and identification performance. However, the performance of these systems is affected by poor-quality imaging. In this paper, we extend iris quality assessment research by analyzing the effect of various quality factors such as defocus blur, off-angle, occlusion/specular reflection, lighting, and iris resolution on the performance of a traditional iris recognition system. We further design a fully automated iris image quality evaluation block that estimates defocus blur, motion blur, offangle, occlusion, lighting, specular reflection, and pixel counts. First, each factor is estimated individually, and then, the second step fuses the estimated factors by using a Dempster-Shafer theory approach to evidential reasoning. The designed block is evaluated on three data sets: Institute of Automation, Chinese Academy of Sciences (CASIA) 3.0 interval subset, West Virginia University (WVU) non-ideal iris, and Iris Challenge Evaluation (ICE) 1.0 dataset made available by National Institute for Standards and Technology (NIST). Considerable improvement in recognition performance is demonstrated when removing poor-quality images selected by our quality metric. The upper bound on computational complexity required to evaluate the quality of a single image is O(n² log n). [ABSTRACT FROM AUTHOR]

The popularity of iris biometric has grown considerably over the past two to three years. It has resulted in the development of a large number of new iris encoding and processing algorithms. Since there are no publicly available large-scale and even medium-size data bases, neither of the newly designed algorithms has undergone extensive testing. The designers claim exclusively high recognition performance when the algorithms are tested on a small amount of data. In a large-scale setting, systems are yet to be tested. Since the issues of security and privacy slow down the speed of collecting and publishing iris data, an optional solution to the problem of algorithm testing is to synthetically generate a large scale data base of iris images. In this work, we describe a model-based method to generate iris images and evaluate the performance of synthetic irises by using a traditional Gabor filter-based iris recognition system. A comprehensive comparison of synthetic and real data is performed at three levels of processing: 1) image level, 2) texture level, and 3) decision level. A sensitivity analysis is performed to conclude on the importance of various parameters involved in generating iris images [ABSTRACT FROM PUBLISHER]