Quantitative Performance Evaluation Research Articles

Benefits of hybridising the activist approach and teaching games for understanding to empower girls in physical education

ABSTRACT The aim of this work was to design and implement an effective hybridisation of two pedagogical models that would serve to overcome gender-related barriers to participation in games, based on a co-educational intervention and paying a special attention on the boys’ influence for the girls’ participation. First, we describe the process of creating the hybrid pedagogical model combining the activist approach (Oliver & Kirk [2015]. Girls, gender and physical education: An activist approach. Routledge.) and teaching games for understanding (Bunker & Thorpe [1982]. A model for the teaching of games in secondary schools. Bulletin of Physical Education, 18, 5–8). We then present its 11-week implementation in a physical education class of 20 pupils in the 5th grade of primary school. Finally, we analysed the achievement of the objectives established, whereby the qualitative information gathered from interviews, focus groups, and the participants’ observation diaries, is complemented with the quantitative evaluation of game performance. The whole group worked together to modify our actions, allowing the girls to participate more actively, what reflected a significant improvement of the girls’ involvement in the game.

Factors Influencing on Competitiveness of China’s Provincial Level Light and Engineering Industry

Establishing a competitiveness evaluation index system of lighting industry, quantitative evaluation of sustainable development performances of the industry, and scientific recognition of shortages against the industrial development have important strategic significance to guide local governments to adjust the industrial development strategies and realize the multi-dimensional goals of sound and sustainable development. In view of connotations of competitiveness of light and engineering industry, a competitiveness evaluation index system of this type industry is established in this study based on the overall goal of the new high-quality development layout. The dimensions of this system cover industrial input capacity, industrial output capacity, industrial technological innovation capability, and policy environmental capacity. Based on data in 2022, the provincial competitiveness level of light and engineering industry in China was evaluated by TOPSIS method and entropy method. Regional heterogeneity, constraints, and spatial characteristics of competitiveness of lighting industry in China were disclosed. Results demonstrate that: – Generally speaking, the sustainable development level of lighting industry in China is increasing gradually; there are relatively high competitiveness level of lighting industry in Guangdong, Zhejiang, and Jiangxi; – The industrial input capacities and the industrial output capacities have significant contributions to competitiveness of lighting industry in China, while industrial technological innovation capability and policy environmental capacity have less contributions; – Competitiveness levels of lighting industry are different in different provinces, showing a pattern of high level in Southeast China and low level in Northwest China, besides constraints against competitiveness of lighting industry also vary significantly among provinces, especially in improving output efficiency; – The competitiveness of lighting industry is weak in key development field, restricted development field, potential growth field and appropriate development field. Thus, improving product quality and safety level, promoting resource saving and strengthening production control ability are common directions for provinces to improve competitiveness of lighting industry. Therefore, this study believes that competitiveness level of lighting industry in China under the new high-quality development layout is required to combine with shortages of regional development, focus on establishment of the lighting industrial system, and solve problems like technological innovation and backward production facilities firstly. Moreover, improving product quality and optimizing resource utilization are breakthroughs to realize sustainable development of the national light and engineering industry.

Understanding street-level urban vibrancy via spatial-temporal Wi-Fi data analytics: Case LivingLine Shanghai

Urban vibrancy is a topic of great concern in the field of urban design and planning. However, the definition and measurement of urban vibrancy have not been consistently and clearly followed. With the development of technologies such as big data and machine learning, urban planners have adopted new methods that enable better quantitative evaluation of urban performance. This research attempts to quantify the impact on the urban vibrancy of the urban interventions introduced by the LivingLine project in a residential neighborhood renovation made in Siping Street, Shanghai. We use Wi-Fi probes to process collected mobile phone data and segment people into different categories according to commuting patterns analysis. We use a pre-trained random forest model to determine the specific locations of each person. Subsequently, we analyze the behavior patterns of people from stay points detection and trajectory analysis. Through statistical models, we apply multi-linear regression and find that urban intervention (well-curated and defined lab events deployed in the street) and people’s behavior are positively correlated, which helps us to prove the impact of urban intervention on street dynamics. The research proposes a novel, evidence-based, low-cost methodology for studying granular behavior patterns on a street level without compromising users’ data privacy.

Comparative effect of conventional and adaptive management approaches on watershed health

Watershed management initiatives are designed to conserve essential soil, water, and vegetation resources while ensuring production and agricultural sustainability. As a result, assessing watershed management projects is a must for future executive planning and natural resource management. However, there has yet to be a quantitative performance evaluation of alternative watershed management techniques on the watershed's overall health. The current study used the pressure-state response (PSR) conceptual framework to assess watershed health in the current scenario and identify the related functional criteria and indicators at the sub-watershed level. Field surveys and reviews of available information were used to identify and characterize current watershed concerns and challenges. The PSR framework was used to assess the impact of conventional watershed management measures and a problem-oriented adaptive management strategy on watershed health in the Mikhsaz Watershed in northern Iran. The influence of changes in the contribution of the study criteria to the health and corresponding scenarios on modifying the watershed health was evaluated. A health index score of 0.68 revealed a healthy class in the current circumstances. The health index was somewhat enhanced after adding the typical or conventional measurements in the research area (i.e., 0.69). Adaptive management techniques in response to the identified issues improved the watershed's health index by 7.2% (i.e., 0.74) in a more efficient, technically acceptable, and ecologically benign manner. As a result, the adaptive management measure increased economic productivity by 61% compared to the conventional measures. The study's findings call for a global revival in watershed management policy, focusing on developing nations.

Quantitative evaluation of collector flotation performance I: The creation of a flotation index based on mineral recovery

Collectors are crucial for successful flotation, and their performance evaluation is one of the most important steps in their development process. It is urgent to create new evaluation indices/methods because the existing indices/methods are not ideal in terms of standardization and universality. Collector flotation performance is conventionally evaluated using two parameters, i.e., collecting ability and selectivity. Thus, a two-dimensional evaluation space exists. Through the creation and normalization of a flotation index (FI), based solely upon mineral recovery, this evaluation space may be reduced to one-dimension, in the range of 0 ∼ 1 and verified mathematically. In the construction of FI, the relative recovery (Rr; collector-induced recovery) of the target mineral replaces the absolute recovery (R; used for the existing indices/methods), quantifying actual collecting ability and excluding the adverse effects of variable minerals’ natural or inherent floatability (Rn). Moreover, selectivity is quantified by the recovery gap (ΔR) between the target and gangue minerals. Compared with separation efficiency (SE; a conventional evaluation index), FI fills a gap in collector performance evaluation for single minerals by a convenient process. Additionally, FI can qualitatively/semi-quantitatively evaluate collector flotation performance for mineral mixtures and industrial ores based on the evaluation results for single minerals, reducing laboratory costs. It should be noted that if FI is directly used to calculate collector flotation performance for mineral mixtures and industrial ores, grade analysis is indispensable. The normalized and dimensionless FI is more universal, providing a standardized comparison of collector flotation performance for different flotation systems, different minerals/ores or the same mineral/ore from different mining areas. As the scoring function of flotation performance, FI is the basis for the subsequent construction of a standardized method to comprehensively and quantitatively predict collector flotation performance in combination with quantitative structure–activity relationship (QSAR), machine learning (ML) and Quantum chemistry (QC).

A FRAMEWORK FOR COOPERATIVE DYNAMIC TASK ASSESSMENT FOR COTSBOT AUVs

ABSTRACT-This project presents a proposed framework aimed at collaborative and dynamic allocation of tasks for a specific group of self- governing underwater vehicles known as autonomous underwater vehicles (AUVs). These AUVs are utilized to manage the proliferation of Crown-Of-Thorns starfish (COTS) on the Great Barrier Reef situated in Australia. The concept of constrained task allocation is employed to reframe the supervision and regulation of COTS into a challenge where a central task involves eradicating clusters of COTS using the injection mechanism integrated into the COTSbot AUVs. The operational setting, encompassing underwater topography, COTS clusters, and shorelines, is mapped probabilistically. The COTSbot AUVs are then collaboratively deployed to target as many COTS clusters as possible within a designated mission timeframe, utilizing an inventive heuristic technique termed Heuristic Fleet Cooperation (HFC). To substantiate the efficiency and dependability of the proposed collaborative task allocation algorithm for COTS eradication in the Great Barrier Reef, comprehensive simulations and quantitative performance evaluations are conducted.

Evaluation of Kato-Katz and multiplex quantitative polymerase chain reaction performance for clinical helminth infections in Thailand using a latent class analysis.

Using an appropriate diagnostic tool is essential to soil-transmitted helminth control and elimination efforts. Kato-Katz (KK) is the most commonly used diagnostic, but recently other tools, such as real-time quantitative polymerase chain reaction (multiplex qPCR), are starting to be employed more. Here, we evaluated the performance of these two diagnostic tools for five helminth species in Thailand. In the absence of a gold standard, diagnostic performance can be evaluated using latent class analysis. Our results suggest that in moderate to high prevalence settings above 2% multiplex qPCR could be more sensitive than KK, this was particularly apparent for Opisthorchis viverrini in the northeastern provinces. However, for low prevalence, both diagnostics suffered from low sensitivity. Specificity of both diagnostics was estimated to be high (above 70%) across all settings. For some specific helminth infection such as O. viverrini, multiplex qPCR is still a preferable choice of diagnostic test. KK performed equally well in detecting Ascaris lumbricoides and Taenia solium when the prevalence is moderate to high (above 2%). Neither test performed well when the prevalence of infection is low (below 2%), and certainly in the case for hookworm and Trichuris trichiura. Combination of two or more diagnostic tests can improve the performance although the cost would be high. Development of new methods for helminth surveillance at the pre-elimination phase is therefore very important. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Quantitative evaluation of thermal conductivity of earth materials with different particle size distributions

The thermal conductivity of earth materials is closely related to density and particle size distribution. However, existing studies on the thermal conductivity of these materials have neglected the influence of particle size distribution, leading to inconsistent results and hindering the quantitative evaluation of the thermal performance of earth materials. To promote the utilization of earthen building materials in energy-efficient design, this study establishes a quantitative assessment model for the thermal conductivity of earth materials, grounded in their microscopic agglomeration morphology and pore structure properties, through experimental analyses and theoretical derivation. The average prediction error of the model is about 1.77%, which improves the precision of characterizing the thermal conductivity of earth materials. Utilizing this model, the impact of clay, sand, and gravel content on the thermal conductivity of earth materials was investigated quantitatively. The findings reveal that the thermal conductivity of earth materials attains its minimum value when the sand content is approximately 37.3%, and diminishes progressively with tthe increase of the clay content. The above findings offer a theoretical foundation for optimizing the thermal performance and fabrication process of earth materials, thereby fostering the integration and application of these materials in sustainable building systems.

FCEVAL: An effective and quantitative platform for evaluating fuzzer combinations fairly and easily

Multiple base fuzzers collaborate as a fuzzer combination. Fuzzer combinations have been proven to perform more robustly and efficiently when fuzzing complicated real-world programs. The efficiency of finding bugs with limited computational resources would greatly benefit from the fuzzer combinations chosen by an effective and quantitative performance evaluation. However, evaluating fuzzer combinations remains challenging due to the lack of infrastructure for collaborative fuzzing, high enough collaboration efficiency of base fuzzers, unified benchmarks, comprehensive metrics, and unified analysis methods of coverage and bugs. This prevents us from selecting efficient fuzzer combinations and thus impairs vulnerability mining on real-world targets. In this paper, we design and implement FCEVAL, the first open-source platform for evaluating fuzzer combinations. In detail, we propose a new test case-sharing policy for increasing fuzzing potential so that we can provide a more efficient running environment for fuzzer combinations and thus improve evaluation effectiveness. Also, we select a unified set of diverse benchmarks and comprehensive metrics while adopting unified independent methods of real-time coverage statistics and multiple-sanitizers-based bug analysis for evaluation fairness and quantification. In addition, we design tools and guidelines covering the whole evaluation process for usability. With the above methodologies, we first construct an infrastructure special for collaborative fuzzing as the base of FCEVAL. After comparing two test case-sharing policies on the infrastructure and choosing the promising one as a substantial part of FCEVAL, we leverage FCEVAL to evaluate fuzzer combinations for more than 40,000 CPU hours and come up with five important conclusions, including (a) an efficient test case-sharing policy improving fuzzing potential and thus evaluation effectiveness, (b) comprehensive metrics being essential, (c) 24-hour duration and 20 repetitions for evaluation being substantial, (d) independent analysis methods of code coverage and bugs deserving of extensive adoption, and (e) FCEVAL being able to evaluate fuzzer combinations effectively, fairly, comprehensively, and easily. Meanwhile, we suggest how to improve collaborative fuzzing. In addition, source codes and test data are publicly available.

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows.

Research on the Construction of the Performance Evaluation Index System of the Engineering Agency Construction in the Army Barracks

<p>Based on the understanding of engineering construction management and military agency construction mode, this paper proposes performance evaluation indicators based on the principle of balanced scorecard, uses Delphi method and statistical analysis method to screen indicators, and finally establishes the evaluation indicator system. The construction of this system not only provides a reliable statistical tool for the quantitative evaluation of performance, but also has important practical significance for promoting the healthy development of the military agency construction model.</p>

3D Shape Generation via Variational Autoencoder with Signed Distance Function Relativistic Average Generative Adversarial Network

3D shape generation is widely applied in various industries to create, visualize, and analyse complex data, designs, and simulations. Typically, 3D shape generation uses a large dataset of 3D shapes as the input. This paper proposes a variational autoencoder with a signed distance function relativistic average generative adversarial network, referred to as 3D-VAE-SDFRaGAN, for 3D shape generation from 2D input images. Both the generative adversarial network (GAN) and variational autoencoder (VAE) algorithms are typical algorithms used to generate realistic 3D shapes. However, it is very challenging to train a stable 3D shape generation model using VAE-GAN. This paper proposes an efficient approach to stabilize the training process of VAE-GAN to generate high-quality 3D shapes. A 3D mesh-based shape is first generated using a 3D signed distance function representation by feeding a single 2D image into a 3D-VAE-SDFRaGAN network. The signed distance function is used to maintain inside–outside information in the implicit surface representation. In addition, a relativistic average discriminator loss function is employed as the training loss function. The polygon mesh surfaces are then produced via the marching cubes algorithm. The proposed 3D-VAE-SDFRaGAN is evaluated with the ShapeNet dataset. The experimental results indicate a notable enhancement in the qualitative performance, as evidenced by the visual comparison of the generated samples, as well as the quantitative performance evaluation using the chamfer distance metric. The proposed approach achieves an average chamfer distance score of 0.578, demonstrating superior performance compared to existing state-of-the-art models.

Data-Driven Quantitative Performance Evaluation of Construction Supervisors

The performances of construction supervisors are essential for the monitoring, control, and coordination of the construction process of a project in order to adhere to a predefined schedule, cost, quality and other factors. However, it is challenging to evaluate their performance due to limitations such as data deficiency, human error, etc. Thus, this paper proposes an approach to data-driven quantitative performance evaluation of construction supervisors by integrating an analytic hierarchy process (AHP) and activity tracking. The proposed approach contains three parts, namely, index extraction, weighting, data-driven index calculation, and then validation by case study. Firstly, performance indexes were developed based on a literature review as well as surveys and function analysis of the information system for construction supervision (CSI system). Then, the weights of and relationships among of the indexes are determined by AHP. After that, with daily workflow and inspection activities tracked in the CSI system, a method and a software module for automatic calculation of indexes were developed. Lastly, the proposed approach was validated by a real-world case. The result showed that the proposed approach can quantify the performance of a construction supervisor systematically and automatically, which shed lights on how to evaluate the performance of a worker based on the tracking of daily activities. The data-driven process enhanced our strong interpretation of member actions and evaluation indexes, and can boost the performance of every member in an organization.

Simulating analogue film damage to analyse and improve artefact restoration on high‐resolution scans

AbstractDigital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and dissemination of photographs of historical and cultural importance. While state‐of‐the‐art deep learning models have shown impressive results in general image inpainting and denoising, film artefact removal is an understudied problem. It has particularly challenging requirements, due to the complex nature of analogue damage, the high resolution of film scans, and potential ambiguities in the restoration. There are no publicly available high‐quality datasets of real‐world analogue film damage for training and evaluation, making quantitative studies impossible. We address the lack of ground‐truth data for evaluation by collecting a dataset of 4K damaged analogue film scans paired with manually‐restored versions produced by a human expert, allowing quantitative evaluation of restoration performance. We have made the dataset available at https://doi.org/10.6084/m9.figshare.21803304. We construct a larger synthetic dataset of damaged images with paired clean versions using a statistical model of artefact shape and occurrence learnt from real, heavily‐damaged images. We carefully validate the realism of the simulated damage via a human perceptual study, showing that even expert users find our synthetic damage indistinguishable from real. In addition, we demonstrate that training with our synthetically damaged dataset leads to improved artefact segmentation performance when compared to previously proposed synthetic analogue damage overlays. The synthetically damaged dataset can be found at https://doi.org/10.6084/m9.figshare.21815844, and the annotated authentic artefacts along with the resulting statistical damage model at https://github.com/daniela997/FilmDamageSimulator. Finally, we use these datasets to train and analyse the performance of eight state‐of‐the‐art image restoration methods on high‐resolution scans. We compare both methods which directly perform the restoration task on scans with artefacts, and methods which require a damage mask to be provided for the inpainting of artefacts. We modify the methods to process the inputs in a patch‐wise fashion to operate on original high resolution film scans.

Ship Hull Inspection Using Autonomous an Underwater Vehicle with a Stereo Camera

Regular visual inspection of the underwater surface of the ship’s hull to check its integrity and the degree of biofouling is necessary to ensure the safety and efficient operation of the ship. The article proposes a method for the automated solution of this problem using an autonomous uninhabited underwater vehicle (AUV) equipped with a stereo camera controlled by a tilt actuator. The movement of the AUV is carried out along equidistant lines along the ship’s hull with simultaneous video filming of the hull surface. The AUV trajectory is calculated using the visual navigation method (visual odometry). Estimated data on the localization of the AUV relative to the ship’s hull are used in the method of controlling the movement of the vehicle to ensure the stable movement of the vehicle at a given distance from the inspected surface. Direct calculation of AUV localization data is done using the proposed original algorithm, which is implemented in the form of a software tool "stereo rangefinder". The operation of the "stereo rangefinder" is based on the matching of features in images using the SURF detector, followed by the construction of a 3D point cloud. The technique for performing automatic inspection of the ship as a whole is described. Fixed stereo images are also used to build a 3D model of the surface being examined. The applied method of constructing a global spatial model of the surface is based on the union of 3D point clouds obtained for local views. The construction of a 3D point cloud for a particular view is based on a matching of 2D point features on images of a stereo pair (SURF detector / correlation calculation), followed by the use of the ray triangulation method to obtain the spatial coordinates of the points. The presence of a 3D model makes it possible to conduct a detailed visual analysis of the state of the ship’s hull. Simulation modeling of the functioning of the developed tools on virtual scenes was carried out. Quantitative and qualitative performance evaluations obtained as a result of testing showed the acceptability of the proposed method for automatic inspection of the underwater part of the ship’s hull.

Progress Analysis of Business Environment Research based on Citespace

Good business environment is a necessary condition to stimulate market vitality and promote high-quality economic development, but also a significant symbol of the modernization of national governance. In recent years, the research on business environment has attracted the wide attention of scholars all over the world. Considering the important role of China in world economic development, this paper adopts text analysis and visualization graph analysis method, and takes the journal literatures published in Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) from 2012 to 2022 as samples. The research progress of the business environment is visually analyzed from the aspects of the number of publications, authors and cooperative relations, publishing institutions and the evolution of research topics. The results show that the number of articles published in both Chinese and English literature presents an increasing trend. Chinese literature focuses on national policy support and government governance, while English literature focuses on the correlation analysis between business environment and enterprises. From the perspective of the evolution path of relevant research, the research on business environment in China is not long. At present, except for a few quantitative performance evaluation analysis, most qualitative research on development countermeasures is conducted. Foreign research on business environment appeared earlier, with a wide range of research topics, and has achieved more mature results.

Retarding effect of submicron carbides on short fatigue crack propagation: Mechanistic modeling and Experimental validation

High-strength steels generally contain a large number of spherical carbides with the size ranging from several hundred nanometers to several micrometers, which have an important impact on fatigue performance. Most studies believe that the carbides, similar to inclusions as fatigue crack initiators, should be refined as much as possible to improve fatigue performance. However, when the carbide size reaches submicron scale (several hundred nanometers to one micrometer), the traditional theory regarding the effect of carbides on fatigue performance may not be applicable. In this paper, it is found that smaller carbides are not always better, and an optimal submicron carbide size exists. By means of experiment and simulation, the mechanism as to how submicron carbides affect the fatigue performance of high-strength steels is systematically studied. It is found that a kind of microstructural transition occurs around carbides as a result of local stress concentration. This leads to the formation of Effective Strengthening Layers (ESLs), which force the short fatigue cracks to propagate along the ESL-matrix interface and decelerate crack propagation. The stress concentration required to generate ESL decreases with carbide size. Therefore, the competition between increasing the specific area of ESL and decreasing stress concentration with regard to decreasing carbide size yields an optimum carbide size. Based on this finding, a novel quantitative fatigue performance evaluation model with experimental validation is proposed for high-strength steels, providing a theoretical guidance for the microstructure design of submicron carbides for fatigue performance improvement.

Medical image data augmentation: techniques, comparisons and interpretations.

Designing deep learning based methods with medical images has always been an attractive area of research to assist clinicians in rapid examination and accurate diagnosis. Those methods need a large number of datasets including all variations in their training stages. On the other hand, medical images are always scarce due to several reasons, such as not enough patients for some diseases, patients do not want to allow their images to be used, lack of medical equipment or equipment, inability to obtain images that meet the desired criteria. This issue leads to bias in datasets, overfitting, and inaccurate results. Data augmentation is a common solution to overcome this issue and various augmentation techniques have been applied to different types of images in the literature. However, it is not clear which data augmentation technique provides more efficient results for which image type since different diseases are handled, different network architectures are used, and these architectures are trained and tested with different numbers of data sets in the literature. Therefore, in this work, the augmentation techniques used to improve performances of deep learning based diagnosis of the diseases in different organs (brain, lung, breast, and eye) from different imaging modalities (MR, CT, mammography, and fundoscopy) have been examined. Also, the most commonly used augmentation methods have been implemented, and their effectiveness in classifications with a deep network has been discussed based on quantitative performance evaluations. Experiments indicated that augmentation techniques should be chosen carefully according to image types.

Quantitative Evaluation of the Carrier Separation Performance of Heterojunction Photocatalysts: The Case of g-C3N4/SrTiO3

Heterojunction photocatalysts are of great interest in the energy and environmental fields, because of their potential to significantly increase the efficiency of harvesting solar energy. Advances in design have been hampered by the continued use of only qualitative analyses. Quantitative evaluation of the carrier separation performance is urgently needed for the design and application of heterojunction photocatalysts. Taking the g-C3N4/SrTiO3 heterojunction as an example, we address the conventional energy band and electronic structure issues by first-principles analysis. After interface coupling, the band edge alignment reverses from that of the respective isolated states of the heterojunction components, suggesting new ways of thinking about the catalytic mechanism of the heterojunction. More significantly, we show the carrier separation performance of heterojunction photocatalysts can be quantitatively predicted by the nonadiabatic molecular dynamics method, enabling more precisely directed research and promoting the quantified design and application of heterojunction photocatalysis, making a contribution of great scientific significance.

Accurate edge-preserving stereo matching by enhancing anisotropy

Stereo matching has attracted substantial research attention, as it plays an important role in many visual tasks such as autonomous driving. Despite the remarkable progress has been made by all kinds of stereo matching methods, they still suffer in some challenging scenes like disparity discontinuities. The root cause of blurred depth boundaries lies in information diffusion across edge boundaries. Aiming at this problem, this paper presents a novel edge-preserving stereo matching method by enforcing anisotropy, where information is allowed to propagate throughout the image except through large discontinuities. Specifically, we propose an intra-scale cost aggregation algorithm to smooth out noise in homogeneous regions while well preserving strong edges in the guided image. And weighted averaging scheme, where weights are calculated according to the variances of pixels in different overlapping support windows, is utilized for enhancing anisotropy to improve accuracy at disparity discontinuities. We carry out comprehensive experiments on Middlebury public datasets to demonstrate the accuracy and edge-preserving property of our method. Qualitative and quantitative performance evaluation on Middlebury data sets demonstrate the superior performance of our method for strong edge preservation with 20.77% decreases in wrong matching ratios in discontinuous regions.