Quantitative Performance Evaluation Research Articles

Experimental study on the quantitative evaluation of the thermal stability performance and heat insulation characteristics of fire‐fighting foam

AbstractFoam extinguishing agents are crucial for the suppression of flammable liquid fires. Their thermal stability performance and heat insulation characteristics are critical indicators to evaluate the efficiency of the fire‐fighting foam. There have been some studies focused on exploring the behavior of fire‐fighting foams exposed to radiant heating. However, the decay mechanisms and heat transfer behaviors of the foam at the micro‐scale are still unclear and require further clarification. Therefore, in this study, the volume reduction coefficient, falling time of foam column height, and the temperature profiles of the foam layer under the thermal radiation environment of different conditions are discussed. The results indicate that the high temperature generated by the radiative heat flux will accelerate the collapse rate of the foam layer. The stability of the foam structure will be seriously damaged. There is a relationship between heat radiation intensity and foam attenuation coefficient. The empirical model for reflecting the fire‐fighting foam collapse process under the fire environment with high heat radiation flux is modified. Moreover, the average collapse rate and temperature difference gradient are used to characterize the thermal stability performance and heat insulation characteristics of the foam. Analysis of the micro‐scale foam structure parameters from the foam scans has revealed that the thermal stability performance and heat insulation characteristics of the foam are stronger when the surface tension of the foam is within the range of 17.4–20.4 mN/m.

Bicycle Data-Driven Application Framework: A Dutch Case Study on Machine Learning-Based Bicycle Delay Estimation at Signalized Intersections Using Nationwide Sparse GPS Data.

Data-driven approaches are helpful for quantitative justification and performance evaluation. The Netherlands has made notable strides in establishing a national protocol for bicycle traffic counting and collecting GPS cycling data through initiatives such as the Talking Bikes program. This article addresses the need for a generic framework to harness cycling data and extract relevant insights. Specifically, it focuses on the application of estimating average bicycle delays at signalized intersections, as this is an essential variable in assessing the performance of the transportation system. This study evaluates machine learning (ML)-based approaches using GPS cycling data. The dataset provides comprehensive yet incomplete information regarding one million bicycle rides annually across The Netherlands. These ML models, including random forest, k-nearest neighbor, support vector regression, extreme gradient boosting, and neural networks, are developed to estimate bicycle delays. The study demonstrates the feasibility of estimating bicycle delays using sparse GPS cycling data combined with publicly accessible information, such as weather information and intersection complexity, leveraging the burden of understanding local traffic conditions. It emphasizes the potential of data-driven approaches to inform traffic management, bicycle policy, and infrastructure development.

Assessment of Postural Control and Proprioception Using the Delos Postural Proprioceptive System

Background. Proper postural control and proprioception are essential for maintaining balance, preventing falls, and performing daily activities. Traditional methods of assessing postural control and proprioceptive function often lack objectivity and precision. The DPPS offers a promising solution by providing a quantitative and comprehensive evaluation of postural stability and proprioceptive performance.
 Methods. The DPPS utilises advanced sensor technology and computerised analysis to measure various parameters related to postural control, including stability indexes, weight distribution, sway patterns, and proprioceptive responses. It incorporates both static and dynamic tests, allowing for a comprehensive assessment of postural control in different conditions.
 Results. Numerous studies have demonstrated the effectiveness of the DPPS in evaluating postural control and proprioception in various populations, such as athletes, older adults, individuals with balance disorders, and patients undergoing rehabilitation. The DPPS has shown good reliability and validity, with its measurements correlating well with other established assessment tools. It has also proven to be sensitive in detecting changes in postural control and proprioceptive function over time.
 Conclusions. The DPPS represents a valuable tool for objective assessment and monitoring of postural control and proprioceptive function. Its ability to provide quantitative data in a reliable and sensitive manner makes it an attractive option for researchers, clinicians, and rehabilitation professionals. By enhancing our understanding of postural control and proprioception, the DPPS can contribute to the development of targeted interventions and personalised treatment strategies aimed at improving balance, preventing falls, and optimising functional performance.
 Keywords: Delos Postural Proprioceptive System, Postural control, Proprioception, Assessment, Balance.

Experimental investigation on comprehensive thermal-hydraulic performance of supercritical CO2 in a NACA 0020 airfoil fin printed circuit heat exchanger

Printed circuit heat exchanger (PCHE) is a promising compact heat exchanger for supercritical CO2 power conversion systems. Among the channel shapes of PCHE, an airfoil fin channel was considered for the high thermal-hydraulic performance of the PCHE. However, quantitative experimental evaluation of the thermal-hydraulic performance between the optimized airfoil fin and the wavy channel (commercially utilized channel shape) has not been performed. Therefore, in this study, to compare the performance according to channel shape, experiments on the optimized arrangement of airfoil fin PCHE (NACA 0020 airfoil fin with non-dimensional arrangement parameters, staggered number 1.0, horizontal number 2.0, and vertical number 1.8) and wavy PCHE (120° bending angle) were conducted. The experimental conditions of supercritical CO2 were temperatures ranging from 70°C (inlet) to 40.5°C (outlet) with pressures varying from 7.5 to 8.5 MPa. To reflect the real etched shape of airfoil fin channel, the real shape of airfoil fin was measured by using optical 3-Dimensional scanner. And hydraulic diameter was calculated based on measured information of real etched airfoil fin shape. The pressure drop of the airfoil fin PCHE was one-fifth of that of the wavy PCHE with the same total heat transfer rate per unit volume. New empirical correlations for the friction factor and Nusselt number of the airfoil fin PCHE were proposed based on the experimental data for the precooler design in the supercritical CO2 Brayton cycle. A friction factor correlation with maximum deviations of ±5.04 % and Nusselt number correlation with maximum deviations of ±14.0 % were proposed.

Quantitative evaluation of collector flotation performance II: The creation of a collector property index based on molecular structure

The evaluation and prediction of flotation performance is a key step in the development of high-performance collectors for the efficient flotation separation of low-grade complex ores. In our previous work, we have created the flotation index (FI) for a comprehensive and standardized evaluation of collector flotation performance. However, as an experiment-based index, FI does not possess the quantitative predictive power that is fundamental to rational flotation reagent design. In this work, the collector properties were obtained by quantum chemistry (QC) calculation. By ranking the importance of the QC properties, machine learning (ML) aids the selection of key properties representing different aspects of a collector (polar, non-polar and overall polarity). The key properties were incorporated into a theory-based collector property index (CPI). Based on a quantitative structure–activity relationship (QSAR), the bridge between theory and experiment were established by searching for the relationship CPI and FI, enabling convenient evaluation and prediction of collector flotation performance. The accuracy of our QSAR model was verified using the galena-pyrite separation system. Our QSAR model demonstrates transferability in that it can predict collectors with diverse skeletons or types, pushing the limit of most conventional QSAR models, which only apply to collectors with similar skeletons or types. This work provides an alternative pathway for the rational design and performance prediction of flotation surfactants.

EPCO-15. DETECTING HISTOLOGIC & CLINICAL GLIOBLASTOMA PATTERNS OF PROGNOSTIC RELEVANCE

Abstract BACKGROUND Glioblastoma is the most common adult primary brain tumor. Limited improvement in patient prospects over the last 20 years reflects our limited disease understanding. Robust prognostic patient stratification from whole slide images (WSI) & clinical data using interpretable computational methods could improve disease understanding and patient management. MATERIAL AND METHODS The TCGA-GBM and TCGA-LGG data collections were reclassified according to the 2021 WHO classification criteria, identifying 188 glioblastoma (IDH-wt, Gr.4) cases and stratifying them to short- (< 9 months) and long-survivors ( > 13 months). H&E-stained WSI were comprehensively curated to remove artifactual content. An interpretability mechanism based on weakly-supervised attention-based multiple-instance-learning was employed. The prognostic relevance of associated clinical data was also evaluated both independently and integratively, using XGBoost and ‘SHapely Additive exPlanations’. Late fusion amalgamates the advantage of WSI patterns with clinical features by averaging the output probabilities of two distinct models at the decision level. Quantitative performance evaluation was based on a 10-fold monte carlo cross-validation, with training (80%), validation (10%), and test (10%) sets. RESULTS Our imaging, clinical, and integrative multi-modal models revealed AUCTest of 0.68/0.72/0.75 respectively. The imaging analysis revealed correctly predicted long-survivors associated with histologically malignant areas, including necrosis, hypercellularity, atypia, infiltration, and proliferation. Correctly classified short survivors highlighted influence by aggressive infiltrative features, including leptomeningeal involvement. The clinical analysis revealed age, mutation count, and TERT promoter status as the top three features contributing to stratification, in the XGBoost model. CONCLUSION Our findings support data-driven histology & data interpretation of algorithmic decisions contribute to further understanding of glioblastoma by identifying morphology patterns & clinical patterns of prognostic relevance. Integration of multiple modalities can be a powerful approach for improving the performance of AI models.

Wind tunnel testing of a Formula Student vehicle for checking CFD simulation trends

The aerodynamic performance analysis of Formula Student racecars has been mostly done by teams with CFD tools, for time and cost savings, that often lack proper validation. To address this, the FST Lisboa team performed a detailed wind tunnel (WT) test campaign, using a one-third scale model, under different configurations, including variable ride heights, bullhorn appendix, and rear wing flap settings, also replicated in CFD. The simulations used RANS with the SST k-omega turbulence model, with a 13.7 million polyhedral mesh for the test chamber region domain. Both experimental and numerical errors were estimated from the instrumentation and mesh convergence analysis, respectively. Comparisons were made between WT and CFD both in terms of local flow, using tufts for flow visualization, and global flow, using lift, drag, and pitching moment coefficients. Overall, the numerical streamlines agreed very well with the orientations of the tufts in experiments, but some discrepancies were found in regions of cross-flow and high-frequency unsteadiness, mainly caused by limitations of the visualization technique. The gamma transition model in CFD was abandoned as it could not replicate the WT observations. In terms of aerodynamic coefficients, a strong correlation was found between WT and CFD. The parametric studies revealed that the simulations captured the experimental sensitivity to each car setting parameter studied but the uncertainties did not enable a full quantitative evaluation of the aerodynamic performance. The drag reduction system significantly impacted the aerodynamic balance of the racecar, while the current bullhorn design proved to be ineffective. The ride height increase led to higher downforce, mostly due to the higher pitch angle of the vehicle, with negligible variation of the aerodynamic balance. This work validated the team CFD studies, building confidence in that trends estimated in numerical parametric studies are likely to be translated to the real prototype performance.

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.