Multiple Evaluation Methods Research Articles

A computer-based evaluation system: Its design, implementation and results obtained on a general chemistry course

Using multiple evaluation methods and systems give a comprehensive assessment. A computer-based multiple-choice assessment system was designed, implemented, posted online, and used to assess students as part of their final evaluation marks for a discipline. The online system of evaluation was intended to be used multiple times for evaluating the assimilation degree of a specific course at the end of the course. The data recorded for the period 2017–2023 with about 1400 distinct users were used to analyze the performance of the evaluation system. The system worked fine and a slight modification of it served well on remote evaluation during COVID-19 period. However, the upturn of mobile phone applications requires the creation of a system adapted to the new virtual reality.

Using Evaluation to Boost the Moral Development of Junior High School Students: Exploration of the Moral Evaluation Mechanism Based on the English Subject

Due to the transitional, closed, social, and turbulent characteristics of their psychological development, the evaluation of the moral development of junior high school students should focus on the dynamic evaluation of their daily performance and growth. This exploration of the moral evaluation mechanism for junior high school students precisely starts from this criterion, based on the characteristics of the English subject, takes “all-round education” as the cornerstone of the evaluation, uses the three dimensions of core competencies as the basis for evaluation, and adopts multiple evaluation and progressive implementation methods as the guide to conduct a diverse and objective assessment of the morality of junior high school students. With the expectation of achieving the ultimate goal of developing students’ individual specialties and promoting the comprehensive development.

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification

Objective.Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification networks. Therefore, we propose a novel data augmentation method and deep learning classification model to enhance the decoding performance of MI-EEG further.Approach.The raw EEG signals were transformed into the time-frequency maps as the input to the model by continuous wavelet transform. An improved Wasserstein generative adversarial network with gradient penalty data augmentation method was proposed, effectively expanding the dataset used for model training. Additionally, a concise and efficient deep learning model was designed to improve decoding performance further.Main results.It has been demonstrated through validation by multiple data evaluation methods that the proposed generative network can generate more realistic data. Experimental results on the BCI Competition IV 2a and 2b datasets and the actual collected dataset show that classification accuracies are 83.4%, 89.1% and 73.3%, and Kappa values are 0.779, 0.782 and 0.644, respectively. The results indicate that the proposed model outperforms state-of-the-art methods.Significance.Experimental results demonstrate that this method effectively enhances MI-EEG data, mitigates overfitting in classification networks, improves MI classification accuracy, and holds positive implications for MI tasks.

Research Review of Green Building Rating System under the Background of Carbon Peak and Carbon Neutrality

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of Carbon Peak and Carbon Neutrality, the green building rating system has become a research hotspot in the field of green building. This paper systematically summarizes the research progress of the GBRS in weight setting, indicator setting, and the evaluation process, and creatively proposes the following three directions for future research: (1) Weight determination methods based on machine learning or deep learning models, and reasonable weight allocation by mixing multiple evaluation methods. (2) Setting dynamic evaluation indicators, strengthening interdisciplinary research and regional consideration, and introducing a life cycle assessment to solve the problem of setting indicators in the existing evaluation system. (3) Combine building information modeling with GBRS to realize the automation and intelligence of evaluation and improve the comprehensiveness and accuracy of evaluation.

Construction and operation of college labor education curriculum evaluation system based on CIPP model

CIPP curriculum evaluation model is a new evaluation model of the whole course. Follow the scientific principles to construct and run the CIPP model based college labor education curriculum evaluation system, in order to mobilize multiple subjects to participate in the evaluation, use multiple evaluation methods, according to the evaluation feedback continuous improvement, help to promote the effective implementation of college labor education curriculum, labor education curriculum to achieve the purpose of education.

EVALUATION OF TRAINEES’ REACTION TO TRAINING PROGRAMS

In the contemporary workplace, where diversity in learning styles and preferences is paramount, understanding and enhancing the effectiveness of training programs has become critical for organizational success. This study investigates trainee reactions to employee training programs, emphasizing the dimensions of Perceived Training Efficiency (PTE), Perceived Trainer Performance (PTP), and Perceived Usefulness of Training (PUT). The research employs a quantitative approach, surveys 381 employees from eight private commercial banks in Bangladesh, and uses a structured questionnaire with a Likert scale. The study utilizes descriptive and inferential statistics. Findings reveal positive perceptions in terms of training efficiency and trainer performance. However, the perceived usefulness of training shows room for improvement. The statistical significance of the results confirms their applicability to a broader population. The study's implications suggest a need for personalized training approaches that align with diverse learning needs and career aspirations. Continuous feedback mechanisms are advocated to refine training programs in real time, emphasizing the importance of adaptive strategies in organizational training initiatives. This study acknowledges its limitations and calls for multiple evaluation methods and comparative analysis for the assessment of training programs.

Assessing the efficacy of gutiferone E in photodynamic therapy for oral candidiasis

The rise in antifungal resistance and side effects of conventional treatments drive the search for innovative therapies like Photodynamic Therapy (PDT). This study explored the efficacy of PDT mediated by gutiferone, an isolated compound from red propolis, for candidiasis treatment. Multiple evaluation methods were employed, including determining the minimum inhibitory concentration (MIC) via broth microdilution, quantifying biomass using crystal violet detachment, and cell counting through total plate count. PDT mediated by gutiferone was also assessed in five groups of mice, followed by histopathological examination and agar plating of lingual tissue samples. Among the seven Candida species tested, gutiferone displayed efficacy against C. albicans, C. glabrata, and C. tropicalis, with MIC values of 1000 μg/mL. In C. tropicalis biofilms, exposure to gutiferone led to a reduction of 1.61 Log10 CFU/mL. PDT mediated by gutiferone achieved an average reduction of 3.68 Log10 CFU/mL in C. tropicalis biofilm cells, underscoring its potent fungicidal activity. Histopathological analysis revealed fungal structures, such as pseudohyphae and hyphae, in infected groups (G2) and irradiated mice. In contrast, groups treated with gutiferone or subjected to gutiferone-assisted PDT (G5) exhibited only few blastoconidia. Furthermore, CFU/mL assessments in lingual tissue post-treatment demonstrated a significantly lower count (0.30 Log10 CFU/mL) in the G5 group compared to G2 (2.43 Log10 CFU/mL). These findings highlight the potential of PDT mediated by gutiferone as a promising alternative for managing denture stomatitis. Future research and clinical investigations offer the promise of validating its clinical applicability and improving outcomes in the treatment of oral candidiasis.

Multi-harmonic sources identification and evaluation method based on cloud-edge-end collaboration

With the increase of power electronic devices such as distributed renewable energies, energy storage, and flexible loads, the disturbance sources are rapidly raised and distributed in multiple voltage levels in the distributed network. The harmonics they produce are random and uncertain, and the massive amounts of data propose high demands on server computing capability, which restrict harmonic sources evaluation and significantly influence the judgments of the power quality in the distribution network. In order to improve the power quality and the harmonic source evaluation function of the distribution network, in this paper, a framework for multi-harmonic source identification and evaluation based on cloud-edge-end collaboration is proposed. Firstly, based on the analysis of the functional requirements harmonic sources evaluation in multiple voltage levels, a novel framework of cloud-edge-end collaboration is proposed. Secondly, the cloud-edge-end collaborative harmonic sources evaluation method which executes different service strategies according to voltage levels and grid operation based on the multi-level interaction of edge computing is proposed. Further, the working principle of the proposed novel framework with multiple evaluation methods including the dominant harmonic source identification, concentrated multiple harmonic sources evaluation (CMHSE) and hierarchical harmonic sources evaluation (HHSE) is described in detail in order to show the completed work processes of the proposed framework. Finally, the performance of the proposed method is verified based on the real data measured from different realistic urban power grids in China. The proposed method can effectively evaluate harmonic contributions in distribution systems and in turn provide a basis for harmonic mitigation measures.

Study on the Flow Behavior of 5052 Aluminum Alloy over a Wide Strain-Rate Range with a Constitutive Model Based on the Arrhenius Model Extension

The formability at room temperature and low speed limits the application of aluminum alloy, while high strain rates positively improve the formability of materials. The constitutive behaviors of materials under high strain rates or impact loadings are significantly different from those under quasi-static conditions, while few constitutive models consider the effect of the mobile dislocation and forest dislocation evolution on the dynamic strain aging (DSA) over a wide strain-rate range. The 5052 aluminum alloy, of which the primary source of strain-hardening is dislocation–dislocation interaction, is widely used in manufacturing automotive covering parts and is considered one of the most promising alloys. Therefore, this study conducts uniaxial tensile tests on AA5052-O under conditions of temperatures ranging from 293 K to 473 K and strain rates ranging from 0.001 s−1 to 3000 s−1, and compares the stress–strain relationships of AA5052-O under different conditions to illustrate the constitutive relationship affected by the dislocation evolution over a wide strain-rate range. The Arrhenius model based on the thermal activation mechanism is modified and extended by considering the effects of dynamic strain aging (DSA), drag stress, and the evolution of mobile dislocation and forest dislocation. Thus, a new physics-based constitutive model for AA5052-O is proposed, which can well reflect the change in strain-rate sensitivity with the strain rate increasing. The mobile dislocation density and total dislocation density are predicted with a modified Kubin–Estrin (KE) model, and the influences of variable mobile dislocation on DSA and dislocation drag are discussed as well. In order to verify the reliability of the new constitutive model, the dislocation densities of the specimens before and after deformation are obtained with TEM and XRD, which are in good agreement with the predicted values. This study also compares the newly proposed model with classic constitutive models using multiple statistical evaluation methods, which shows that the new physics-based constitutive model has not only more clear physical meanings for its parameters but also has a higher prediction accuracy.

Carbon footprint and embodied nutrition evaluation of 388 recipes

Food consumption, which delivers fundamental energy and essential nutrients to human beings, is crucial for achieving a series of sustainable goals. Alongside rising population growth and living standards, there has been a significant increase in food cultivation demands, supply chain complexities, and waste management. Therefore, to protect human health and the environment, promoting sustainable food systems and the uptake of sustainable dietary habits are vital. Yet, information on the environmental and health impact of dietary choices remains inconsistent across multiple evaluation methods, which fail to deliver essential ideas to consumers. In this study, we formulate an integrated approach using Environmentally Extended Input-Output analysis, covering the food supply chain from production to the distribution phase, complemented with a hybrid Life Cycle Assessment for cooking and disposal processes, to quantify the carbon footprint of specific recipes. Our dataset also includes the distinct nutritional values of each recipe. This dataset not only informs the food industry and recipe platforms, enabling more sustainable choices, but also helps individuals balance nutritional value with environmental impact, leading to more informed and sustainable dietary decisions.

A Review of Empirical Studies of Cultural Ecosystem Services in National Parks: Current Status and Future Research

Cultural ecosystem services (CES) provided by national parks (NP) have been increasingly recognized and appreciated by the public and researchers. However, they are often under-represented in ecosystem services evaluations due to their intangible nature. As a result, their application in supporting NP conservation and management remains limited. To map the knowledge generated by CES within NP and to support NP practices, this study conducted a review of 199 empirical studies to identify the geographic distribution of research, specific NP habitats/ecosystems that supply CES, frequently addressed CES subcategories, CES evaluation methods, and challenges and prospects for future studies. The results revealed the following: a disparity exists in the global distribution of studies, and the majority of research is conducted in Europe and the USA, with limited knowledge about CES in developing countries. Studies on CES derived from specific NP habitats/ecosystems are limited, and not all the services have received equal attention. Multiple evaluation methods have been employed to assess CES, primarily relying on non-monetary approaches. Among these, participatory mapping-related methods and social-media-based methods are widely favored by researchers. Based on those findings, this study makes the following recommendations: (1) further research is needed in order to investigate a wider range of NP habitats/ecosystems worldwide, especially in developing countries; (2) comprehensive evaluation methods should be employed, considering all services, especially those less addressed; (3) more diverse methods for evaluating CES should be developed, with a particular emphasis on combining various methods to enhance evaluation accuracy; (4) the use of diverse techniques, such as machine learning for social-media-based methods, is encouraged to support data collection and processing to improve the efficacy of evaluation; (5) further studies on the relationships between CES and NP features can assist stakeholders in more effectively addressing CES by enabling the management and adjustment of these features; (6) future studies should integrate CES evaluation into an ecosystem services framework to support application in NP conservation and sustainable management.

Insights into momentous aroma dominating the characteristic flavor of jasmine tea.

Jasmine tea is loved by most people who drink flower tea owing to its unique aroma, and it is known as the top of flower teas. In our study, the quantitative evaluation of the quality of jasmine tea and detection of aroma components were carried out. First, the flavor quality of 92 kinds of jasmine tea was evaluated using multiple sub-factor quality evaluation methods. According to the evaluation results, jasmine tea was divided into three types: "fresh and lovely" (FL), "heavy and thick" (HT), and "fresh and heavy" (FH). Gas chromatography-mass spectrometry (GC-MS) was used to detect the aroma components of the three types of jasmine tea samples. α-Farnesene, cis-3-hexenyl benzoate, acid phenylmethyl ester, linalool, methyl anthranilate, and indole were the main substances that constituted the basic aroma quality characteristics of jasmine tea. Compared to the FL type, the HT and FH types were weaker in the diversification of the characteristic aroma and accumulation of green, herb, sweet, and roast aroma substances. Green and herb aromas play crucial roles in the fresh and persistent qualities of the three types of jasmine tea, which are the key quality factors research focus of jasmine tea.

Designing and implementing an OMMID midwifery professional competence mixed test: A multimethod, multiphasic study

The assessment of professional competence is essential to ensure the achievement of competence standards. The goal of this study is to design and implement a professional competence test model to make the test unified, comprehensive, and fair. This research was a multimethod, multiphasic study. The first qualitative phase of the nominal group technique was conducted to design the test model with specific guidelines. The second phase was a mixed-method parallel field trial conducted on 161 senior midwifery bachelor students in universities of Iran. The test was conducted following the traditional method in the control group and OMMID model in the intervention group. This model consists of three parts: the MCQs (multiple choice questions), OSCE (objective structured clinical examination), and clinically-oriented tests, which included Mini-CEX (mini-clinical evaluation exercise) and DOPS (direct observation of procedural skills). Data were collected using questionnaires and interviews, and the results were assessed qualitatively and quantitatively. The OMMID model and the associated guideline were designed. Qualitative data analysis resulted in six main themes, including organizing, structure, tension due to change, fairness, unification, and outcome. The OMMID model did not increase students' anxiety and stress and did not decrease their satisfaction. The merits of this model include having a centralized guideline, using multiple evaluation methods, comprehensive evaluation of necessary skills, promotion of fairness, and increased student satisfaction.

Effect of Fe on the oxidation, sulfidation and carburization behaviors of alloys in CO2 containing SO2

The effect of Fe on the oxidation, sulfidation and carburization behaviors of alloys in CO2 containing SO2 at 1000 ℃ was investigated using multiple evaluation methods, including first-principles, molecular dynamics simulation, high-temperature corrosion experiment and thermodynamic analysis. The adsorption capacity of both gas molecules on FeCr alloy surface was stronger than that on NiCr alloy surface. The molar content of O, C and S atoms in Fe-25Ni-22Cr alloy was higher than that in Ni-25Fe-22Cr alloy. The increase of Fe content promoted the reactions between alloy elements and O, C, S atoms, which caused serious corrosion of Fe-Ni-Cr alloys.

Evaluation of Online Learners’ Learning Performance Based on Fuzzy Borda Method

Since the traditional evaluation system of learning focuses only on the summative evaluation of learners’ test scores, ignoring the process evaluation of learners in learning process, the evaluation system should not only consider the content of the online learning process but also that of face-to-face classroom teaching process to evaluate learners’ learning performance in multiple dimensions and levels. This approach evaluates online learning more reasonably, operably and feasibly. To overcome limitation of using more than one method in the evaluation of learning performance, the learning performance of online learners was first evaluated using the principal component analysis method, entropy method, comprehensive indicator method, and TOPSIS method. Based on these four evaluation results, a combination evaluation model of online learners’ learning performance was constructed. The Fuzzy Borda method was used to evaluate nine administrative classes of accounting majors in three colleges and universities in Hainan. The results show that simply using a single evaluation method to evaluate the learning performance of online learners has defects, and the evaluation results are too biased. By using the Fuzzy Borda method, multiple evaluation methods can be combined, allowing a single evaluation method to realize complementary advantages and obtain more comprehensive and credible evaluation results. Using the Fuzzy Borda method to systematically evaluate the learning performance of online learners improves the scientificity of evaluation results and provides a new idea and method for the evaluation of online learning performance. The results of this study have significant reference value for using scientific evaluation methods and objective data to evaluate online learning performance, rank online learners scientifically, and summarize teaching experience to improve online learning performance.

My Baby, My Move+: feasibility of a community prenatal wellbeing intervention

BackgroundExcessive gestational weight gain (EGWG), insufficient prenatal physical activity and sleep, and poor psychological wellbeing independently increase risks for adverse maternal and infant outcomes. A novel approach to mitigate these risks is utilizing peer support in a community-based prenatal intervention. This study assessed the feasibility (acceptability, demand, implementation, and practicality) of a remotely delivered prenatal physical activity intervention called My Baby, My Move + (MBMM +) that aims to increase prenatal physical activity, enhance mood and sleep hygiene, and reduce EGWG.MethodsParticipants were recruited through community organizations, local clinics, and social media platforms in the Fall of 2020 and Spring of 2021. Eligible pregnant women were randomized to either the MBMM + intervention or the control group. Each group met over Zoom for 16 sessions (twice weekly for 60 min over 8 weeks) to learn either behavioral change and wellbeing knowledge and skills (MBMM +) or knowledge and skills related to parenting (control group). Multiple methods of evaluation to better understand the feasibility of the intervention were conducted.ResultsA total of 49 women (25 MBMM + intervention, 24 control) completed both pre- and post-survey assessments and were included in the analyses. A subsample of 19 (39%) intervention participants completed a combination of semi-structured interviews/surveys to assess acceptability, demand, implementation, and practicality. Participants expressed positive feedback regarding acceptability (satisfaction and intent to continue use) and were extremely likely or likely to recommend the program to a friend (demand). Implementation metrics were assessed by observation and feedback forms completed by peer leaders and demonstrated high-quality control. Findings suggest that the intervention was practical due to remote sessions and cost-effectiveness.ConclusionThe MBMM + intervention was deemed to be a feasible intervention with high acceptability, demand, implementation, and practicality. These findings can be used to inform the scalability of the intervention and implementation of a larger efficacy trial.Trial registration19–1366, initial date is on January 23, 2020.

Machine learning for lumbar and pelvis kinematics clustering

Clustering algorithms such as k-means and agglomerative hierarchical clustering (HCA) may provide a unique opportunity to analyze time-series kinematic data. Here we present an approach for determining number of clusters and which clustering algorithm to use on time-series lumbar and pelvis kinematic data. Cluster evaluation measures such as silhouette coefficient, elbow method, Dunn Index, and gap statistic were used to evaluate the quality of decision making. The result show that multiple clustering evaluation methods should be used to determine the ideal number of clusters and algorithm suitable for clustering time-series data for each dataset being analyzed.

Evaluating the usability of a co-designed power assisted exercise graphical user interface for people with stroke

BackgroundDigital advancement of power assisted exercise equipment will advance exercise prescription for people with stroke (PwS). This article reports on the remote usability evaluation of a co-designed graphical user interface (GUI) and denotes an example of how video-conference software can increase reach to participants in the testing of rehabilitation technologies. The aim of this study was to evaluate the usability of two sequential versions of the GUI.MethodsWe adopted a mixed methods approach. Ten professional user (PU) (2M/8F) and 10 expert user (EU) participants (2M/8F) were recruited. Data collection included a usability observation, a ‘think aloud’ walk through, task completion, task duration and user satisfaction as indicated by the Post Study System Usability Questionnaire (PSSUQ). Identification of usability issues informed the design of version 2 which included an additional submenu. Descriptive analysis was conducted upon usability issues and number of occurrences detected on both versions of the GUI. Inferential analysis enabled comparison of task duration and PSSUQ data between the PU and EU groups.ResultsAnalysis of the ‘think aloud’ walkthrough data enabled identification of 22 usability issues on version 1 from a total of 100 usability occurrences. Task completion for all tasks was 100%. Eight usability issues were directly addressed in the development of version 2. Two recurrent and 24 new usability issues were detected in version 2 with a total of 86 usability occurrences. Paired two tailed T-tests on task duration data indicated a significant decrease amongst the EU group for task 1.1 on version 2 (P = 0.03). The mean PSSUQ scores for version 1 was 1.44 (EU group) and 1.63 (PU group) compared with 1.40 (EU group) and 1.41 (PU group) for version 2.ConclusionsThe usability evaluation enabled identification of usability issues on version 1 of the GUI which were effectively addressed on the iteration of version 2. Testing of version 2 identified usability issues within the new submenu. Application of multiple usability evaluation methods was effective in identifying and addressing usability issues in the GUI to improve the experience of PAE for PwS. The use of video-conference software to conduct synchronous, remote usability testing is an effective alternative to face to face testing methods.

Comparison of optical myopia control interventions: effect on peripheral image quality and vision.

This study compares the effects on peripheral vision and image quality of four myopia control interventions: a) Perifocal spectacles/ArtOptica, b) Stellest spectacles/Essilor), c) MiyoSmart spectacles/Hoya and d) MiSight contact lenses/CooperVision. Five subjects participated with habitual or no correction as reference. Three techniques were used: 1) Hartmann-Shack sensors for wavefront errors, 2) double-pass imaging system for point-spread-functions (PSF), and 3) peripheral acuity evaluation. The results show that multiple evaluation methods are needed to fully quantify the optical effects of these myopia control interventions. Perifocal was found to make the relative peripheral refraction (RPR) more myopic in all subjects and to interact with the natural optical errors of the eye, hence showing larger variations in the effect on peripheral vision. MiSight had a smaller effect on RPR, but large effect on peripheral vision. Stellest and MiyoSmart also showed small effects on RPR but had broader double-pass PSFs for all participants, indicating reduced retinal contrast. Reduction in peripheral retinal contrast might thereby play a role in slowing myopia progression even when the peripheral refraction does not turn more myopic.

Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction (PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.