Qualitative Performance Evaluation Research Articles

Quantitative and qualitative performance evaluation of commercial metal artifact reduction methods: Dosimetric effects on the treatment planning

The presence of metal implants within CT imaging causes severe attenuation of the X-ray beam. Due to the incomplete information recorded by CT detectors, artifacts in the form of streaks and dark bands would appear in the resulting CT images. The metal-induced artifacts would firstly affect the quantitative accuracy of CT imaging, and consequently, the radiation treatment planning and dose estimation in radiation therapy. To address this issue, CT scanner vendors have implemented metal artifact reduction (MAR) algorithms to avoid such artifacts and enhance the overall quality of CT images. The orthopedic-MAR (OMAR) and normalized MAR (NMAR) algorithms are the most well-known metal artifact reduction (MAR) algorithms, used worldwide. These algorithms have been implemented on Philips and Siemens scanners, respectively. In this study, we set out to quantitatively and qualitatively evaluate the effectiveness of these two MAR algorithms and their impact on accurate radiation treatment planning and CT-based dosimetry. The quantitative metrics measured on the simulated metal artifact dataset demonstrated superior performance of the OMAR technique over the NMAR one in metal artifact reduction. The analysis of radiation treatment planning using the OMAR and NMAR techniques in the corrected CT images showed that the OMAR technique reduced the toxicity of healthy tissues by 10% compared to the uncorrected CT images.

Artificial Versus Human Intelligence in the Diagnostic Approach of Ophthalmic Case Scenarios: A Qualitative Evaluation of Performance and Consistency.

To evaluate the efficiency of three artificial intelligence (AI) chatbots (ChatGPT-3.5 (OpenAI, San Francisco, California, United States), Bing Copilot (Microsoft Corporation, Redmond, Washington, United States), Google Gemini (Google LLC, Mountain View, California, United States)) in assisting the ophthalmologist in the diagnostic approach and management of challenging ophthalmic cases and compare their performance with that of a practicing human ophthalmic specialist. The secondary aim was to assess the short- and medium-term consistency of ChatGPT's responses. Eleven ophthalmic case scenarios of variable complexity were presented to the AI chatbots and to an ophthalmic specialist in a stepwise fashion. Advice regarding the initial differential diagnosis, the final diagnosis, further investigation, and management was asked for. One month later, the same process was repeated twice on the same day for ChatGPT only. The individual diagnostic performance of all three AI chatbots was inferior to that of the ophthalmic specialist; however, they provided useful complementary input in the diagnostic algorithm. This was especially true for ChatGPT and Bing Copilot. ChatGPT exhibited reasonable short- and medium-term consistency, with the mean Jaccard similarity coefficient of responses varying between 0.58 and 0.76. AI chatbots may act as useful assisting tools in the diagnosis and management of challenging ophthalmic cases; however, their responses should be scrutinized for potential inaccuracies, and by no means can they replace consultation with an ophthalmic specialist.

Contrast Enhancement Method Using Region-Based Dynamic Clipping Technique for LWIR-Based Thermal Camera of Night Vision Systems.

In the autonomous driving industry, there is a growing trend to employ long-wave infrared (LWIR)-based uncooled thermal-imaging cameras, capable of robustly collecting data even in extreme environments. Consequently, both industry and academia are actively researching contrast-enhancement techniques to improve the quality of LWIR-based thermal-imaging cameras. However, most research results only showcase experimental outcomes using mass-produced products that already incorporate contrast-enhancement techniques. Put differently, there is a lack of experimental data on contrast enhancement post-non-uniformity (NUC) and temperature compensation (TC) processes, which generate the images seen in the final products. To bridge this gap, we propose a histogram equalization (HE)-based contrast enhancement method that incorporates a region-based clipping technique. Furthermore, we present experimental results on the images obtained after applying NUC and TC processes. We simultaneously conducted visual and qualitative performance evaluations on images acquired after NUC and TC processes. In the visual evaluation, it was confirmed that the proposed method improves image clarity and contrast ratio compared to conventional HE-based methods, even in challenging driving scenarios such as tunnels. In the qualitative evaluation, the proposed method demonstrated upper-middle-class rankings in both image quality and processing speed metrics. Therefore, our proposed method proves to be effective for the essential contrast enhancement process in LWIR-based uncooled thermal-imaging cameras intended for autonomous driving platforms.

High-resolution vessel wall imaging for quantitatively and qualitatively evaluating in-stent stenosis of intracranial aneurysms.

It is critical to accurately and noninvasively evaluate the stented parent artery of intracranial aneurysms (IAs) with endovascular treatment. To investigate high-resolution vessel wall imaging (HR-VWI) for quantitative and qualitative evaluation of in-stent stenosis (ISS) in IAs treated with stent placement (SP). Fifty-five patients (58 aneurysms) underwent HR-VWI, contrast-enhanced (CE)-HR-VWI, CE-MR angiography (MRA), time-of-flight (TOF)-MRA, and digital subtraction angiography (DSA) six months after SP, and the reliability of quantitative stent lumen measurements was evaluated by intraclass correlation coefficient (ICC) analysis. Agreement and correlation of quantitative evaluation were estimated by comparing the four MR imaging modalities with DSA. The diagnostic performance for >0%, ≥25%, and ≥50% of ISS degrees and overall diagnostic accuracy for the ISS degrees of the four MR imaging modalities were calculated to qualitative evaluation. The reliability of CE-HR-VWI and HR-VWI for ISS quantitative measurements was excellent (ICC 0.955-0.989). The agreement and correlation of CE-HR-VWI, HR-VWI versus DSA for ISS quantitative measurements were better than those of CE-MRA and TOF-MRA (p < 0.05). The diagnostic performance for distinguishing the degree of ISS >0%, ≥25%, and ≥50% by CE-HR-VWI and HR-VWI was superior to CE-MRA and TOF-MRA, and their overall diagnostic accuracy was 96.55 and 94.83%, respectively. HR-VWI and CE-HR-VWI were not statistically significant in the quantitative and qualitative evaluation of ISS performance (p > 0.05). HR-VWI and CE-HR-VWI have similar performance and value in the quantitative and qualitative evaluation of ISS, and HR-VWI without contrast media could be used as an ideal long-term follow-up approach after SP treatment for IAs.

Multi-working condition performance assessment based on knowledge extraction of optimal operating states for continuous annealing processes

Performance assessment is a key to strip quality improvement and energy consumption reduction of Continuous Annealing Processes (CAP). However, existing methods focus on performing the assessment under a single working condition, and the assessment accuracy must be improved. This study proposes a new multi-working-condition performance assessment method based on the knowledge extraction of the optimal operating states for CAP. First, a mechanism–data fusion-based assessment index construction method is proposed for the key parameter selection. Second, a knowledge extraction strategy for the optimal operating states under multiple working conditions is proposed to construct a benchmark library. Third, a knowledge-enhanced assessment model is built to achieve qualitative performance evaluation and quantitative non-optimal traceability. The experiment based on the process data shows the effectiveness of assessing the operating performance, providing decision guidance for strip quality improvement and energy consumption reduction.

Generalizable stereo depth estimation with masked image modelling.

Generalizable and accurate stereo depth estimation is vital for 3D reconstruction, especially in surgery. Supervised learning methods obtain best performance however, limited ground truth data for surgical scenes limits generalizability. Self-supervised methods don't need ground truth, but suffer from scale ambiguity and incorrect disparity prediction due to inconsistency of photometric loss. This work proposes a two-phase training procedure that is generalizable and retains the high performance of supervised methods. It entails: (1) performing self-supervised representation learning of left and right views via masked image modelling (MIM) to learn generalizable semantic stereo features (2) utilizing the MIM pre-trained model to learn robust depth representation via supervised learning for disparity estimation on synthetic data only. To improve stereo representations learnt via MIM, perceptual loss terms are introduced, which improve the model's stereo representations learnt by explicitly encouraging the learning of higher scene-level features. Qualitative and quantitative performance evaluation on surgical and natural scenes shows that the approach achieves sub-millimetre accuracy and lowest errors respectively, setting a new state-of-the-art. Despite not training on surgical nor natural scene data for disparityestimation.

Comparative study on collection performance of two back-end methods of double-row hydraulic sluicing structure in deep-sea mining

This study aims to investigate two different back-end action methods, induced flow type and suction type for deepsea mining. The nodule collection characteristics of both methods are compared based on the internal flow field, particle movement and comprehensive performance using an inside-outside numerical method that is validated by experimental results. The differences between the two models from these three perspectives strengthen the understanding of the harvesting mechanism. An energy analysis and quantitative evaluation method for hydraulic harvesting are proposed to assess the combined efficiency of the two methods. The results indicate that the energy utilisation rate is 34.68% for the induced flow type and 29.89% for the suction type under the same collection rate and flow rate. Furthermore, the overall efficiency of the induced flow type is 15.89% higher than that of the suction type. Regarding the construction of the flow field, the suction flow under the suction type plays the leading role, and the double jet plays a secondary role, whereas the opposite is true for the induced flow type. The suction type and the induced flow type have their own advantages in terms of their effect on particles in different regional stages. Finally, a qualitative evaluation of the comprehensive performance of the two acquisition methods is performed.

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.

Overview of Stator Slot-Opening Permanent Magnet Machines

In the past few decades, the stator permanent magnet (PM) machines have attracted much research attention because of their reliable rotor structure, high power density, wide speed range, and strong fault-tolerant ability. Among the recently proposed stator PM machine topologies, the stator slot-opening PM (SS-PM) machines are becoming increasingly popular because of their higher stator space utilization and robust rotor structure. According to the SS-PM magnetization direction, the SS-PM machines can be classified into three categories, namely, the tangentially magnetized stator slot-opening PM (TMSS-PM) machines, the radially magnetized stator slot-opening PM (RMSS-PM) machines, and the compound magnetized stator slot-opening PM (CMSS-PM) machines. The associated working principle and electromagnetic performance of different SS-PM machines can be diverse. Therefore, this article gives an overview of SS-PM machines in terms of machine design, working principle, and machine performance. A qualitative performance evaluation and comparative study of SS-PM machines are presented, and the future challenges and potential opportunities in the SS-PM machine design are discussed.

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.

Monocular Depth Estimation of Old Photos via Collaboration of Monocular and Stereo Networks

Old photos that were captured about a century ago have archaeological and historical significance. Many of the old photos have been successfully digitized, but most of them suffer from severe and complicated distortion. Thus, prior studies have focused on image restoration tasks such as denoising, inpainting, and colorization. In this paper, we pay attention to the depth estimation of old photos, enabling a more enjoyable appreciation of them and helping better understand past human life, activities, and environments. Because most old photos are available as single-view images, monocular depth estimation techniques can be considered a solution. However, most high-performance techniques are based on supervised learning, which requires ground-truth depth maps. Because this kind of supervised learning is not feasible for old photos, in this paper, we present a learning framework that finetunes a pretrained monocular depth estimation network for each old photo. Specifically, the pretrained monocular depth estimation network predicts stereo depth maps for stereo image rendering. Then, the pretrained stereo network predicts depth estimates from the rendered stereo image pair. By extracting reliable depth estimates and using them for supervision of the monocular network, the monocular network can be gradually learned to produce a high-quality depth map of the given old photo. From the qualitative and quantitative performance evaluations on old photos, we demonstrate the effectiveness of the proposed method.

Evaluating the Qualitative Performance of Algerian Universities after the Implementation of Reform and Change: From the Student's View

This paper aims to evaluate the performance of Algerian universities after the reform and change during the period from 2004 to 2022. To achieve this purpose, its performance was measured and compared using quality indicators. Data on these indicators were collected using a questionnaire, the first axis is made up of (7) questions concerning the basic personal data of the sample members and the second axis, consisting of (21) indicators which reflects the dimension of academic and administrative operations, scientific research, and community service before and after the implementation of reform and change, and were targeting a sample of (248) students studied classical system and LMD system. Due to the analysis and interpretation of the results of qualitative performance evaluation, it is noted that the overall performances of Algerian universities have improved long after reform and change were implemented. However, there were no statistically significant differences in qualitative performance levels between one university and another

Evaluating Global Container Shipping Companies: A Novel Approach to Investigating Both Qualitative and Quantitative Criteria for Sustainable Development

The COVID-19 pandemic has implications for the container shipping industry and global supply chains. Measuring the efficiency of major international container shipping companies (CSCs) is an important issue that helps them make strategic decisions to improve performance, especially in the context that all businesses and governments are adapting to build back better the post-pandemic world. This paper develops a new integrated approach using both a qualitative assessment tool and a performance assessment tool as a systematic and flexible framework for evaluating the container shipping industry. This new methodology is implemented in two phases to consider both qualitative and quantitative criteria for assessing the performance of CSCs based on efficiency. In the first phase, qualitative performance evaluation is performed using spherical fuzzy analytical hierarchical process (AHP-SF) to find criteria weights and then the grey complex proportional assessment methodology (COPRAS-G) is used to find the ranking of CSCs. Qualitative variables are converted into a quantitative variable for use in the data envelopment analysis (DEA) model as an output called an output variable called expert-based qualitative performance (EQP). Then, DEA is performed to identify efficient and inefficient CSCs with the EQP variable and other quantitative parameters (i.e., capacity, lifting, expenses, revenue, and CO2 emissions). The efficiency of 14 major global CSCs is empirically evaluated, and the scores for CSCs’ efficiency in all dimensions are measured and examined. The results show that the average cargo efficiency of the CSCs is lower than their eco-efficiency performance, revealing the operational disruption caused by the pandemic. Moreover, by identifying efficient and inefficient CSCs, our findings provide practical implications for decision-makers in the maritime field and assist in modifying applicable policies and strategies to achieve sustainable performance.

Qualitative Evaluation of Wastewater Treatment Plant Performance by Neural Network Model Optimized by Genetic Algorithm

Qualitative Evaluation of Wastewater Treatment Plant Performance by Neural Network Model Optimized by Genetic Algorithm

Multi-level landmark-guided deep network for face super-resolution

Recent years deep learning-based methods incorporating facial prior knowledge for face super-resolution (FSR) are advancing and have gained impressive performance. However, some important priors such as facial landmarks are not fully exploited in existing methods, leading to noticeable artifacts in the resultant SR face images especially under large magnification. In this paper, we propose a novel multi-level landmark-guided deep network (MLGDN) for FSR. More specifically, to fully exploit the dependencies between low and high resolution images and to reduce network parameters as well as capture more reliable feature representation, we introduce a recursive back-projection network with a particular feedback mechanism for coarse-to-fine FSR. Furthermore, we incorporate an attention fusion module in the front of backbone network to strengthen face components and a feature modulation module to refine features in the middle of backbone network. By this way, the facial landmarks extracted from face images can be fully shared by the modules in different levels, which benefit to produce more faithful facial details. Both quantitative and qualitative performance evaluations on two benchmark databases demonstrate that the proposed MLGDN can achieve more impressive SR results than other state-of-the-art competitors. Code will be available at https://github.com/zhuangcheng31/MLG_Face.git/

Assessing the Best Supplier Selection Criteria in Supply Chain Management During the COVID-19 Pandemic.

In the last 10 years, organizations and researchers have recognized the importance of sustainable supply chain management (SSCM) because of the consumers, -profit and non-profit organizations, laws and regulations, and consumer social and corporate responsibilities. Supplier selection, environmental effects such as social cooperation, and other SSCM programmes, can all help to achieve the “triple bottom line (TBL)” of economic, environmental, and social advantages. Sustainable supplier selection (SSS) and firm performance are important factors in supply chain management (SCM). Organizations will traditionally consider a new framework when evaluating SSS performance to obtain all-encompassing criteria/sub-criteria of the sustainability index by encapsulating sustainability. This paper compiles 12 subcriteria for three sustainability pillars, namely economic, environmental, and social performance. Despite the fact that many articles on SSS and evaluation were published during COVID-19, there seems to be little research on sustainability issues to date. The goal of this study is to suggest a fuzzy multicriteria approach to SSCM planning. Additionally, using the TBL method, the problem of determining a current model for SSS in the supply chain was investigated. The linguistic value of the subjective preference of experts is represented by triangular fuzzy numbers. Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) is proposed to use standard weights to rank SSS for qualitative performance evaluation. COVID-19, on the other hand, has a detrimental impact on SSS and company results. The organization’s performance suffers as a result of the COVID-19 shutdown. The proposed method is demonstrated using an example.

A comparative study of electrical interfaces for tunable piezoelectric vibration energy harvesting

The present work deals with tunable electrical interfaces able to enhance both the harvested power and bandwidth of piezoelectric vibration energy harvesters. The aim of this paper is to propose a general, normalized, and unified performance evaluation (with respect to the harvested power and bandwidth) of the various electrical strategies that can tune the harvester’s frequency response. By mean of a thorough analysis, we demonstrate how such interfaces influence the electromechanical generator response through an electrically-induced damping and an electrically-induced stiffness. The choice of the strategy determines these two electrical quantities, and thus the achievable frequency response of the system. Thereafter, we introduce a collection of graphical and analytical tools to compare and analyze single- and multi-tuning electrical strategies, including a qualitative performance evaluation of existing strategies. Finally, we establish a unified comparison of single- and multiple-tuning strategies which is supported by the definition and evaluation of a new optimization criterion. This comparison reveals which strategy performs best depending on the electromechanical coupling of the piezoelectric harvester and on the losses in the electrical interface. Furthermore, it quantifies the power and bandwidth gain brought by single- and multi-tuning strategies. Such quantitative criterion provides guidance for the choice of a harvesting strategy in any specific applicative case.

Sustainability Performance of Green Building Rating Systems (GBRSs) in an Integration Model

In most countries, more than one green building rating system (GBRS) is available on the market. Because of their different approaches, it is difficult to confirm which system can respond to sustainability requirements better. A building considered to be “green” by one Green Building Council (GBC) may fail to satisfy the requirements set by another council. The aim of this study is to evaluate the sustainability performance of GBRSs and establish an integrated model of multi-certification for optimal sustainability. A direct content analysis method was utilized to evaluate GBRSs’ qualitative performance using the ISO sustainable building standards. In this study, seven purposively selected multi-certified buildings were evaluated, and an integrated model was proposed as a better mechanism for the multi-certification process. The proposed integrated model contributes to the achievement of sustainability indicators. The model was implemented on case study buildings, and the proposed combination shows better sustainability performance than existing practice. The developed model helps practitioners in their selection of GBRSs in the multi-certification process. In addition, the performance evaluation of GBRSs will support the future update of GBRSs. The qualitative performance evaluation was performed considering mandatory requirements and prerequisite and credit requirements of selected rating systems, making the work a stronger approach than previous studies.

3-D Printed Plug and Play Prototyping for Low-Cost Sub-THz Subsystems

Polymer-based additive manufacturing using 3-D printing for upper-millimeter-wave ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ca.</i> 100 to 300 GHz) frequency applications is now emerging. Building on our previous work, with metal-pipe rectangular waveguides and free-space quasi-optical components, this paper brings the two media together at G-band (140 to 220 GHz), by demonstrating a compact multi-channel front-end subsystem. Here, the proof-of-concept demonstrator integrates eight different types of 3-D printed components (30 individual components in total). In addition, the housing for two test platforms and the subsystem are all 3-D printed as single pieces, to support plug and play development; offering effortless component assembly and alignment. We introduce bespoke free-space TRM calibration and measurement schemes with our quasi-optical test platforms. Equal power splitting plays a critical role in our multi-channel application. Here, we introduce a broadband 3-D printed quasi-optical beamsplitter for upper-millimeter-wave applications. Our quantitative and/or qualitative performance evaluations for individual components and the complete integrated subsystem, demonstrate the potential for using consumer-level desktop 3-D printing technologies at such high frequencies. This work opens-up new opportunities for low-cost, rapid prototyping and small-batch production of complete millimeter-wave front-end subsystems.

Going Green Supply Chain Management During COVID-19, Assessing the Best Supplier Selection Criteria: A Triple Bottom Line (TBL) Approach

In the past ten years, sustainable supply chain management (SSCM) attach great importance due to consumers, for-profit and profitless organizations, laws and regulations to the social and corporate responsibilities of consumers, so it has been recognized by practitioners and scholars. Supplier selection, environmental effect like a lockdown, and social cooperation and other SSCM programs can play an important part in realizing the triple bottom line (TBL) of economic, environmental, social assistances. In supply chain management (SCM), the sustainable supplier selection (SSS) and firm performance plays an important role. Traditionally, when evaluating SSS performance, organizations will consider a new framework to obtain the overall criteria/sub-criteria of the sustainability index by encapsulating sustainability. In this paper 12 sub-criteria for 3 pillars of sustainability as economic, environment and social performance is collected. Although there are many articles on SSS and evaluation, so far, research on sustainability issues is very limited. This study endeavours to propose a fuzzy multi-criteria approach to discuss SSCM planning, and studies the issue of determining a current model for SSS in the supply chain during COVID-19 based on the TBL method. For express the linguistic value of the subjective preference of experts we use triangular fuzzy numbers. By using fuzzy numbers to find standard weights for qualitative performance evaluation, then fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed to find the ranking of SSS. However, COVID-19 has a negative role in SSS and in firm performance. The situation of lockdown due to COVID-19 has a negative effect on the performance of the organizations. An example is given of the proposed method.