Performance Evaluation Method Research Articles

Performance of a modified built-middle photovoltaic-thermocatalytic Trombe wall system by fins

The photovoltaic-thermocatalytic Trombe wall is attracting attention due to the ability to generate electricity, heat and purify indoor air, while it has the disadvantages of low electrical and degradation performance. Also the overall performance evaluation method of multi-functional Trombe walls still needs to be further improved. To fill these gaps, a modified built-middle photovoltaic-thermocatalytic Trombe wall by vertical fins was proposed in this study. Based on the first and second laws of thermodynamics, the effects of fins spacing and fins height under different solar radiation intensities on the performance were investigated by numerical simulation method. In addition, the performance of this modified system was compared with other relevant systems. The results reveal that the existence of an optimal fins spacing or fins height can maximize the thermal efficiency. The narrower fins spacing and higher fins can enhance electrical efficiency and degradation rate. Without sacrificing or even improving thermal performance, the presence of fins with suitable structural parameters leads to higher electrical and purification performance. Compared with the system with no fins, when the solar radiation intensity is 400 W/m2 and 800 W/m2 respectively, the thermal efficiency, electrical efficiency, degradation rate, and overall exergy efficiency gain relative enhancements of 1.81 % and 2.71 %, 2.47 % and 5.18 %, 2.27 times and 2.19 times, and 3.53 % and 6.67 %; and the overall exergy efficiency reaches 12.26 % and 12.20 % at the fins spacing of 31.25 mm and fins height of 30 mm. The introduction of exergy concept provides an effective way for overall performance evaluation of multi-functional Trombe walls with different grades of energies (products). The obtained thermal exergy and electrical exergy dominate the overall exergy efficiency, and the diffusion exergy accounts for a negligible proportion due to the relatively low concentration of pollutant. In comparison with other relevant systems, the modified system shows the best comprehensive utilization of solar energy, and has great application potential. This study provides a new direction for the performance improvement and assessment of multi-functional Trombe wall systems.

Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.

Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs. A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria. The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics. NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets. This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.

Comprehensive performance evaluation method of working fluids for high temperature heat pump based on multi-objective optimization

The working fluid has garnered significant attention as a critical factor influencing the efficiency of high-temperature heat pumps. A novel method based on multi-objective optimization has been introduced for assessing the performance of heat pump working fluids. The method enhances the clarity of the objective and flexibility in addressing various operating conditions, which helps to improve the efficiency of working fluid screening, especially for the complex mixture system. This study utilizes eight pure refrigerants as the initial fluid set and evaluates 560 binary mixtures with a 5% composition variation. The NSGA-II algorithm is combined with the theoretical heat pump cycle model to obtain Pareto optimal solutions regarding the mixtures, with the coefficient of performance and volumetric heating capacity as the objective functions. Then the evaluation of the optimal solutions is conducted by considering their flammability, toxicity, environmental performance, and temperature glide properties. Subsequently, a comprehensive performance index is derived. The evaluation reveals the dominant solution set for each pure refrigerant. The findings suggest that the blend R142b/R1233zd(E) with a mass fraction of R142b between 0.15 and 0.25 demonstrates a comprehensive performance index of 0.322–0.350, indicating superior performance during the basic cycle. Furthermore, various cycle configurations have been investigated. In the vapor injection configuration, R600a/R601(0.6/0.4) shows good performance in all aspects except flammability. The mixture R245fa/R601(0.2/0.8) exhibits superior overall performance in the internal heat exchanger configuration. It displays excellent temperature glide matching characteristics and good environmental performance. Nevertheless, it shows suboptimal performance in terms of toxicity and flammability. This approach replaces the previous qualitative comparison-based decision making methods, offering high efficiency and practicality.

Assessment of agrobusiness structures’ performance and pespectives for strengthening market positions

Agribusiness, as a component of the agricultural sector, is defined as a system of economic relations encompassing the production, processing, and marketing of agricultural products, which influences the socio-economic development of the country. Its importance lies not only in providing food for the population but also in creating jobs, forming export potential, and stabilizing the agrarian sector. Ensuring a high level of competitiveness of agribusiness is a strategic task for ensuring sustainable economic development and improving the living standards of the population. In the modern conditions of globalization and rapid changes in market trends, evaluating the performance of agribusiness structures becomes critical for the success of enterprises. This is an important stage of strategic planning, which allows identifying key factors influencing competitiveness, understanding risks and opportunities, and developing effective development strategies. Evaluating the performance of agribusiness structures depends on the use of various methods, including analysis of financial indicators, determining market competitiveness, assessing innovation potential, and analyzing the efficiency of management processes. The article emphasizes the importance of evaluating agribusiness activity in the context of its competitiveness and sustainable development. In particular, strategic success factors, methods of performance evaluation, and the role of management and innovation in strengthening agribusiness competitiveness are considered.

Development and implementation of real-time staff performance monitoring and evaluation system based on Global Positioning System (GPS)

This work presents the development and implementation of a Real-Time Staff Performance Monitoring and Evaluation System (RT-SPMES) using Global Positioning System (GPS) technology. In line with the dynamism of the workplace, due to rapid changes in technology, existing staff performance appraisal systems have fallen short of expectations due to their manual nature. Hence, the need arises for a more robust approach to monitoring and evaluating employee work performance. To achieve this, this work carried out an exhaustive review of existing performance evaluation methods within the academic and a conceptual architecture was developed. A real-time monitoring and evaluation system was developed, using PHP programming language, Hyper Text Markup Language and Cascading Styles Sheet. The system is hosted on Azure Cloud infrastructure. Through sufficient testing of the developed system developed in this work, it was asserted effectively monitors employee performance in real-time, paving the way for an objective appraisal process devoid of bias and manipulation.

Optimization of Piano Performance Teaching Mode Using Network Big Data Analysis Technology

To effectively avoid subjective bias in manual evaluation. This article proposes a MIDI piano teaching performance evaluation method based on bidirectional LSTM. This method utilizes a three-layer bidirectional LSTM neural network mechanism to make it easier for the model to capture useful information. In addition, the Spark clustering training model is constructed using the deeplearning4j deep learning framework, and the model parameters are adjusted through the UI dependency relationships provided by deeplearning4j to improve work efficiency. The experimental results verified the superiority of the bidirectional LSTM model. The methods provided in this article can improve students' independent practical abilities and reduce the pressure on teachers during the teaching process. These measures can promote the development of music education, improve students' music literacy and learning skills, and make positive contributions to the music education industry.

Performance evaluation and testing methods of clinched joint

Performance evaluation and testing methods of clinched joint

A high-precision experimental measurement system and method for the parahydrogen concentration and the Ortho-Para hydrogen catalyst's catalytic performance

An experimental system that combines the measurement of parahydrogen concentration with the assessment of ortho-para hydrogen catalyst's catalytic performance was created for this investigation. The concentration of parahydrogen was detected by a thermal conductivity detector integrated into the gas chromatograph. The catalyst sample and the generator of standard gas were integrated into one experimental setup for easier operation. Meanwhile, a new method for catalytic performance evaluation of ortho-para hydrogen catalyst was developed based on this system. This method can decrease test time and yield more valuable test results than the conventional way. Throughout the experiments, the system's dependability was increased by optimizing the utilization of the carrier gas. The deviation analysis and repeatability verification of the relevant test data showed that the relative standard deviation was less than 0.35% and the mean value of the variation coefficient was less than 0.20%, which demonstrated the method had high accuracy, stability, and reliability. Further studies have shown that the catalysts should be densely packed inside the converter and kept out of the air to maximize their catalytic activity. These pertinent findings regarding the application of ortho-parahydrogen catalysts in this study can guide the design of the ortho-parahydrogen converter in actual hydrogen liquefiers. Moreover, the method and the procedure proposed in this study may contribute to the optimization of the ortho-parahydrogen catalyst production process as well as the calibration of the parahydrogen concentration in liquid hydrogen products.

An evaluation method for nuclear radiation detection performance of glass scintillator

An evaluation method for nuclear radiation detection performance of glass scintillator

Pyr-GC-Orbitrap-MS method for the target/untargeted analysis of microplastics in air

Pyrolysis-gas chromatography coupled to Orbitrap-mass spectrometry is a novel technique that allows the low level and precise determination of microplastics in environmental samples. In this paper, we develop and assess the target and untargeted performance of Pyr-GC-Orbitrap-MS. The method was optimized for 10 plastic polymers: polymethyl methacrylate, nylon-6,6, polypropylene, nitrile butadiene rubber, polyvinyl chloride, polyethylene terephthalate, acrylonitrile butadiene styrene, polyethylene, polycarbonate, and polystyrene. Standards were home-made using a diamond driller to attain mean sizes within the range of 45–382 µm. A step-by-step optimization of the analytical procedure was carried out. First, accurate mass measurement of each polymer at 60,000 resolution was studied to select the 3 most intense and selective quantification and confirmation ions. Second, internal standard quantification was optimized, and good linearity, repeatability, and reproducibility were obtained. Blank contribution and instrumental detection limit were evaluated for each polymer. Finally, the combined and expanded uncertainty of the Pyr-GC-Orbitrap-MS method was calculated to determine the sources of variation, considering that home-made standards were used. To evaluate method performance, targeted and non-targeted analysis of indoor air samples collected from gyms and department stores were carried out. The Pyr-GC-Orbitrap-MS methodology herein described can be applied for the quantitative assessment of MPs and other substances in different matrices.

Comprehensive Performance Evaluation of an Unmanned Excavator Based on Signal Stability Extraction

The comprehensive performance of unmanned excavators is crucial for the development and optimization of the field of construction machinery. To effectively improve the unmanned excavator to meet the needs of the market, it is imperative to quantify the evaluation method of the comprehensive performance of unmanned excavators. In this study, an evaluation method combining a fuzzy analytic hierarchy process and multivariate image area analysis method is proposed. Firstly, based on the feature extraction of the signal stability of the unmanned excavators, fifteen evaluation indexes were proposed. Then, the case study is used to obtain the scores corresponding to these indexes. The fuzzy analytic hierarchy process is applied to determine the relative weight of the selected evaluation criteria, in which the uncertain and imprecise judgments of decision makers are converted into fuzzy numbers. At the same time, the braking performance of the three types of unmanned excavators was comprehensively evaluated and ranked using the multivariate image area analysis method as an empirical example. Finally, a weight analysis is performed to check the robustness of the ranking results. The results show that the proposed method is effective and feasible. It provides a reference for the performance improvement and efficiency optimization of unmanned excavators.

Performance Evaluation and Improvement of Deep Echo State Network Models in English Writing Assistance and Grammar Error Correctionn

INTRODUCTION: The research on the performance evaluation model of English writing tutoring and grammar error correction is very necessary, which is not only conducive to the rational allocation of teachers' writing tutoring resources, but also more conducive to the timely and effective correction of students' grammatical errors.OBJCTIVES: Aiming at the problems of non-specific quantification, low precision, and low real-time performance evaluation methods for English writing grammar error correction in current methods.METHODS: This paper proposes a grammar error correction performance evaluation method based on deep echo state network with gold rush optimisation algorithm. Firstly, by analysing the process of English writing assistance and grammatical error correction, we extract the evaluation features of grammatical error correction type and construct the performance evaluation system; then, we improve the deep confidence network through the gold rush optimization algorithm and construct the grammatical error correction performance evaluation model; finally, we analyse it through simulation experiments.RESULTS: The results show that the proposed method improves the evaluation accuracy, robustness. The absolute value of the relative error of the evaluation value of the syntactic error correction performance of the method is controlled within the range of 0.02.CONCLUSION: The problems of non-specific quantification, low precision and low real-time performance of the application of English writing grammar error correction performance assessment methods are solved.

Research on Condition Assessment Method of Transformer Based on Cloud Model

The traditional evaluation method of transformer has the problems of strong subjectivity of threshold value and fuzzy correlation of state quantity, which is difficult to accurately evaluate the operation quality of transformer. In order to solve these problems, this paper proposes a comprehensive evaluation method of transformer performance evaluation and operation quality based on cloud model. The evaluation index system of power transformer operation quality considering performance is constructed, including target layer, performance layer and index layer. The triangular fuzzy number is used to optimize the set of expert opinions, and the indexes are combined and weighted by fuzzy analytic hierarchy process and entropy weight method. After the data is degraded, the membership matrix of transformer operation index is constructed by using the cloud model, and the performance state weighting and membership degree are comprehensively considered to realize the performance evaluation of transformer and the comprehensive evaluation of operation quality based on performance. By using this method to predict 10 transformers in operation for a period of one month, it is possible to accurately identify abnormal situations on site, with a prediction accuracy of 96.28%. Compared with the traditional method of “predicting first and then diagnosing”, it has improved by 14.32%, overcoming the shortcomings of subjectivity, ambiguity, and ignoring the correlation of state variables in traditional evaluation methods. This method provides new ideas and methods for accurate evaluation of transformers.

Self-healing performance and rheological properties of styrene-butadiene-styrene block copolymer modified asphalt promoted by polyurea elastomers containing disulfide bonds

Self-healing asphalt is considered as one of the desirable methods to extend the service life of asphalt pavement. This research prepared a polyurea elastomer containing dynamic disulfide bonds (PUA)/styrene butadiene styrene block copolymer (SBS) composite modified asphalt. Moreover, a new asphalt self-healing performance evaluation method based on the damage process and results has been proposed. Subsequently, the self-healing ability of PUA, the distribution of modifier, the functional group and molecular weight of composite modified asphalt were characterized by fluorescence microscope, Fourier transform infrared spectroscopy (FTIR) and Gel permeation chromatography (GPC). The self-healing potential and limits of PUA/SBS composite modified asphalt was investigated through the proposed healing linear amplitude scanning (HLAS) test. Results show that the SBS content and fatigue load level are the main factors affecting the self-healing promotion effect of PUA. The critical point of healing fatigue strain (HFS) can indirectly characterize the threshold at which healing promoters exert their effects. Next, the addition of fillers will reduce the self-healing performance of PUA/SBS composite modified asphalt, but it still improves by at least 61.318% compared with SBS modified asphalt. Finally, the addition of self-healing elastomers (PUA) improves the permanent deformation resistance at high temperature and low-temperature crack resistance of SBS modified asphalt. The dynamic recombination of disulfide bonds can increase the fatigue cycle number of 4%SBS modified asphalt by 93.52% at a 5% load strain level.

Performance evaluation method of the service quality dimensions using Six Sigma metrics, the main components’ quality indicator and the geometric capacity indicator

Abstract This research aims to propose an evaluation and monitoring method with the Six Sigma performance metrics, the main component quality indicator, and the geometric capacity indicator to control service quality dimensions. The research was quantitative and evaluative. It was developed using primary historical information on the quality criteria of hotel service in twelve periods of 2019. It was possible to demonstrate that the geometric indicator was the most demanding capacity with a value of 0.91163, followed by the multivariate main components’ indicator with a value of 0.9559, establishing as a relevant finding the integrality of the three performance criteria to evaluate a service. Topics of service quality, Six Sigma metrics, multivariate main component and geometric capacity indicators were addressed as a theoretical foundation. The research provides a unique contribution in the form of an innovative and efficient continuous improvement method, which makes services more reliable and accurate. Univariate and multivariate statistics were intensively used to evaluate and improve the dimensions of a service from different perspectives. This method has not been considered from the same approach despite its great usefulness in quality control.

Measuring national economic resilience through industrial portfolios

Even though the importance of economic resilience has increased as economic shocks have become more frequent in the world, there is still a knowledge gap on how to measure it. In search for effective ways to measure national resilience, this article identifies and mathematically proves the existence of direct correlation between resilience and competitiveness through time-specific global correlation rate. This research proposes an economic performance evaluation method that measures the competitiveness of countries facing economic shocks and recovering from them. A quantified method for identification of global economic shocks through industrial portfolio is proposed as well. The holistic approach internalises most externalities and a nation’s resilience is pared down to its ability to compete in the international trade, linking the main determinants of resilience to the basics of human behaviour. The proposed methodology can be used effectively for national and global economic performance estimations. It also opens a range of new possibilities for economic resilience studies.

Advancements in Multilayer Coatings on Aluminium Alloy-Based Bipolar Plates for PEMFC Application

Fuel cells represent a revolutionary and sustainable approach to energy conversion, offering a clean alternative to traditional combustion-based power generation. These electrochemical devices convert the chemical energy of a fuel, typically hydrogen, directly into electricity. The fundamental working principle involves the electrochemical reaction between hydrogen and oxygen, facilitated by a catalyst, to produce electricity, water, and heat as byproducts in Proton Exchange Membrane (PEMFC). This strategic use of aluminum (Al) alloy for Bipolar plates (Bp) aligns with the industry's commitment to advancing materials and design methodologies, ultimately promoting the optimization of fuel cell technology in terms of performance, durability, and economic feasibility. This comprehensive review investigates recent progress in the development of multilayer coatings tailored for Al alloy-Bp in PEMFCs. Al alloys are a preferred substrate material due to their cost-effectiveness, lightweight nature, and high thermal conductivity. However, challenges such as corrosion, electrical conductivity, and interfacial contact resistance have spurred extensive research into innovative coating solutions. The review critically examines various coating materials, deposition techniques, and performance evaluation methods aimed at addressing the challenges associated with al alloy Bp. Special emphasis is placed on corrosion-resistant coatings, conductive layers, and interfacial modifiers. The assessment encompasses both established technologies and cutting-edge approaches, including nanocomposite coatings, self-healing materials, and advanced characterization methods. By synthesizing and analyzing existing literature, this review provides a comprehensive overview for researchers, engineers, and policymakers involved in advancing PEMFC technology. The findings underscore the crucial role of multilayer coatings in augmenting the performance and longevity of al alloy-based Bp, offering valuable insights to guide further research and development in the pursuit of efficient and durable fuel cell systems. In comparing the aluminum grades Al-356, Al-5052, Al-6061, and Al-7075, it is observed that Al 5052 exhibits lower corrosion density, providing greater efficiency compared to other aluminum grades. In the case of Al 356, the potentiostatic result yields a lower value than that of other aluminum grades. Consequently, these findings suggest a tendency for favorable corrosion resistance and electrodeposition in both Al 5052 and Al 356, making them noteworthy choices in applications requiring these particular material characteristics.

Sensitivity Analysis of Performance Tests for Six-Degree-of-Freedom Serial Industrial Robots

Abstract The international standard ISO 9283:1998 is popular for performance tests of industrial robots at present. It is desirable that the tests described in this standard should be sensitive to error sources of robot end positioning/orientation. In this paper, first, the kinematic and the joint stiffness model parameters are identified experimentally for two models of six-DOF (degree-of-freedom) serial industrial robots (i.e., the ABB IRB 1410 and UR5 robots). Then, the standard deviations of the derived model parameters are obtained as error inputs for the sensitivity analysis of the performance tests including the positioning/orientation accuracy/repeatability tests. By simulating the error sensitivity of the positioning/orientation accuracy/repeatability test methods for industrial robots, it is analyzed whether the tests described in the ISO 9283:1998 Standard are sensitive to the focused error sources, showing the limitations of the evaluation index of the ISO 9283:1998 Standard. The results show that for six-DOF serial industrial robots, the positioning accuracy test is the key to determining their motion performance. The orientation accuracy and repeatability tests are not necessary if the positioning accuracy and repeatability tests can be done for six-DOF serial industrial robots. Finally, the improvement suggestion of the performance test method is proposed. The research of this paper is beneficial for improving the performance evaluation methods of industrial robots. It can also help robot manufacturing enterprises analyze and improve the positioning/orientation accuracy/repeatability of their products.

A CASE STUDY: INTELLIGENT SHADING RETROFIT TO EXISTING HOME-OFFICE USING MULTI-OBJECTIVE OPTIMIZATION

ABSTRACT Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation. The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented.

Verification of the Applicability of Obstacle Recognition Distance as a Measure of Effectiveness of Road Lighting on Rainy and Foggy Roads

Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects’ obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.