Traditional Evaluation Research Articles

Research on the Evaluation Model of Students' Foreign Language Learning Situation based on Oriented Online Teaching Collaboration Platform

Internet + Education makes online teaching gradually penetrate the education industry, and makes the industry enter a great revolution based on information technology. The traditional student learning evaluation system cannot satisfy the actual demand of current learning evaluation. This paper constructs an evaluation model for the foreign language learning of online students. Firstly, the DBSCAN algorithm with distance optimization is used to conduct cluster analysis on the description indicators of student behavior, and the student groups with different behavior characteristics are obtained. Then the ANOVA F-test was used to extract the features of different student groups. Finally, a novel N-Adaboost algorithm based on multiple classifiers is proposed and a model is constructed to evaluate students’ foreign language learning. The experimental results show that the accuracy of the evaluation model is 74.02% in the pass and fail groups and 73.74% in the excellent and non-excellent groups. Students’ listening, speaking, and reading abilities are in a state of upward development overall through the online teaching collaboration platform, but their writing ability is obviously declining. There is a great improvement in foreign language vocabulary. This study provides a new perspective of thinking for the improvement of the quality of school teaching management, the analysis of students’ behavior, and the evaluation of learning situations, and provides a new solution for the problem of students’ learning situations in modern information teaching.

Deep learning-based prediction of in-hospital mortality for sepsis

As a serious blood infection disease, sepsis is characterized by a high mortality risk and many complications. Accurate assessment of mortality risk of patients with sepsis can help physicians in Intensive Care Unit make optimal clinical decisions, which in turn can effectively save patients’ lives. However, most of the current clinical models used for assessing mortality risk in sepsis patients are based on conventional indicators. Unfortunately, some of the conventional indicators have been shown to be inapplicable in the accurate clinical diagnosis nowadays. Meanwhile, traditional evaluation models only focus on a small amount of personal data, causing misdiagnosis of sepsis patients. We refine the core indicators for mortality risk assessment of sepsis from massive clinical electronic medical records with machine learning, and propose a new mortality risk assessment model, DGFSD, for sepsis patients based on deep learning. The DGFSD model can not only learn individual clinical information about unassessed patients, but also obtain information about the structure of the similarity graph between diagnosed patients and patients to be assessed. Numerous experiments have shown that the accuracy of the DGFSD model is superior to baseline methods, and can significantly improve the efficiency of clinical auxiliary diagnosis.

Blockchain-driven framework for financing credit in small and medium-sized real estate enterprises

PurposeThis study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.Design/methodology/approachThe design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.FindingsThe results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.Originality/valueThis study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

Assessing Radiological Response to Immunotherapy in Lung Cancer: An Evolving Arena.

In the past decade, immune checkpoint inhibitors (ICIs) have entered the treatment landscape of non-small-cell lung cancer, signalling a paradigm shift within the field characterized by significant survival benefits for patients with advanced and metastatic disease, and especially those with non-targetable genetic oncogenic driver mutations. However, the shift towards immune-based treatments has created new challenges in oncology. Atypical immunotherapy response patterns, including pseudo-progression and hyperprogressive disease, as well as immune-related adverse events have generated the need for new methods to predict patient response to treatment. Hence, new versions of the traditional Response Evaluation Criteria for Solid Tumors (RECIST) have emerged to help characterise with better accuracy radiological findings concerning patient response classification to immunotherapy. This review discusses response evaluation criteria relevant to unique radiological findings observed in patients treated with immunotherapy for non-small-cell lung cancer.

Analysis of the coupling degree between regional logistics efficiency and economic development coordination.

This paper aims to study the relationship between regional logistics efficiency and economic development in 31 provinces of China and analyze their coupling coordination. To comprehensively evaluate the coordination between logistics and the economy, we introduced external indicators, such as carbon emissions, based on traditional evaluation indicators. We constructed an evaluation index system to coordinate regional logistics efficiency and economic development. The research approach used in this paper is the cross-DEA method, and data from 2010 to 2019 were selected for empirical calculation. The research findings indicate that Eastern and Northern regions of China show higher logistics efficiency, while Northwestern and Southwestern regions exhibit lower logistics efficiency. Coastal areas have relatively higher economic development levels compared to inland areas. Regarding the coupling coordination between logistics efficiency and economic development, different regions show temporal fluctuations and spatial disparities. Some regions demonstrate higher coordination between logistics efficiency and economic development, while others show lower coordination. Additionally, as the economy experiences rapid growth, logistics efficiency also improves, but the level of coordination varies among different provinces.

[Pharmaceutical changes and countermeasures for transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on human use experience].

Traditional Chinese medicine(TCM) preparations in medical institutions, as a unique and important form of preparations in China, have a long history of human use and serve as a bridge between clinical experience prescriptions and new Chinese medicine preparations. The state encourages medical institutions to transform their preparations into new traditional Chinese medicines, emphasizing their role as "incubators". Since the proposal of the traditional Chinese medicine registration and evaluation evidence system with the integration of TCM theory, human use experience(HUE), and clinical experience, the idea of transforming preparations used in medical institutions into new drugs based on HUE has been increasingly valued by drug research and development organizations. In the transformation process, pharmaceutical changes should be concerned from multiple aspects. This paper discusses the pharmaceutical changes and countermeasures based on the transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on HUE from the aspects of excipients, dosage forms, production technology, production scale, packaging materials and containers, production sites, and registration standards. It is emphasized that scientific decisions should be made according to the characteristics and clinical needs of drugs to ensure the stability of drug quality. The impacts of pharmaceutical changes on drug quality should be objectively assessed based on appropriate evaluation indexes and detection methods. The layout should be carried out in advance, and the key pharmaceutical information of the preparations should be kept stable, so as to underpin the transformation of traditional Chinese medicine preparations in medical institutions into new drugs based on HUE.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Democratizing Knowledge Creation Through Human-AI Collaboration in Academic Peer Review

In the rapidly evolving landscape of academic research, artificial intelligence (AI) is poised to revolutionize traditional academic peer review processes and knowledge evaluation systems. We believe that the growing collaboration between humans and AI will disrupt how academics assess scholarly manuscripts and disseminate published works in a way that facilitates the closing of gaps among diverse scholars as well as competing scholarly traditions. Such human-AI collaboration is not a distant reality but is unfolding before us, in part, through the development, application, and actual use of AI, including language learning models (LLMs). This opinion piece focuses on the academic peer review process. It offers preliminary ideas on how human-AI collaboration will likely change the peer review process, highlights the benefits, identifies possible bottlenecks, and underscores the potential for democratizing academic culture worldwide.

Efficiency of pharmaceutical toxicity prediction in computational toxicology.

The adverse effects and toxicity of chemical substances pose substantial challenges in drug discovery and environmental science. Their management, most especially in the early development stage, is crucial in preventing costly failures in clinical trials. Predictive methodologies, such as computational toxicology, offer an effective means of managing risks, particularly for new compounds with insufficient post-marketing surveillance and those lacking information on adverse effects. Computational approaches have become increasingly important in environmental science, in which the sheer number and diversity of chemicals present similar challenges to toxicity control. Traditional animal-based evaluation methods are resource intensive, time consuming, and ethically problematic, making them unsuitable for use in assessing the vast compound range. It is an urgent task for the academic community to minimize the risks associated with drug discovery and environmental exposure. This study focuses on systems used to predict toxicity from chemical structure information and outlines the prediction accuracy and systems developed in Japan.

Automated Writing Evaluation: Users’ Perception and Expectations

Automated Writing Evaluation (AWE) is a great tool for writing evaluation and correction due to its ability to analyze writing. Recent debates in writing have focused on the distinctions between the traditional method of teaching writing and the implementation of AWE in the teaching of writing and choosing the better of the two. The objective of this study is to explore the learners’ perception of AWE among university students as well as their expectation of AWE implementation. The design of the study is descriptive in nature, employing the quantitative method with 72 university students participating in the experiment. This group underwent an intervention of 10 weeks being exposed to the AWE use. In this study, a questionnaire was developed as a tool for data collection. The implementation of AWE was viewed favourably by respondents. It was discovered that respondents consider AWE to be successful in terms of improving vocabulary use, language use, spelling, and punctuation. Respondents however, consider AWE to be less helpful in improving the substance and organisation of students’ ESL writing. The findings indicate that using technology in this digital learning environment cannot sufficiently support the process of ESL writing assessment and still necessitates traditional evaluation methods such as educators explaining and helping their students organize their writing and develop successful material in ESL writing.

Research on the Application Effect of Metalearning Method Based on Prototype Network in Corrective Measures for Member State Audit

IMO Member State audits aim to identify non-compliant behavior with the requirements of relevant instruments, enabling the implementation of corrective measures to enhance performance. However, the complexity and diversity of IMO instruments’ requirements result in low evaluation effectiveness and efficiency in current assessment methods of implementation of IMO instruments. To address this challenge, this study proposes a meta-learning model based on prototype networks, focusing on the corrective measures outlined in consolidated audit summary reports approved and issued by the IMO Secretariat. The suggested model conducts meta-learning using small samples, offering a swift and straightforward assessment method. It facilitates the fine classification of corrective measures, providing a way for the consistent and effective assessment of various countries’ current implementation practices. Empirical results of two strategies demonstrate improved classification accuracy. In comparison with traditional manual evaluation, the proposed method achieves accuracy value 71.61% and 65.78% in two strategies respectively. Furthermore, the model exhibits varying prediction accuracy across different articles and demonstrates robust generalization capabilities, highlighting its practicality.

Study on Contribution of Different Journal Evaluation Indicators to Impact Factor Based on Machine Learning

Sci-Tech journals have long served as platforms for academic communication and the collision of ideas, facilitating advanced inventions and major discoveries in science. The speed of development and future prospects of a field in the current era can often be reflected by the quality and quantity of cutting-edge papers published in Sci-Tech journals within that field. Currently, the impact factor of Sci-Tech journals is a widely recognized journal evaluation index that comprehensively reflects the quality and influence of the journals under evaluation. However, traditional journal evaluation methods based on statistical formulas, while relatively simple and fast, have certain limitations. They are not comprehensive enough and do not support the comparison between journals from different disciplines. In recent times, researchers have delved into using multiple suitable indicators for comprehensive journal evaluation, attempting to understand the role each indicator plays in the evaluation process, such as the rank sum ratio. Our paper presents a new dataset constructed from data from journals across various fields obtained from the China Wanfang Literature Platform. We endeavor to explore a series of novel journal evaluation methods based on machine learning, including deep learning models. With these 9 methods, we aim to determine the contribution of 17 journal evaluation indicators to the impact factor and identify important factors that can further enhance the quality and influence of Sci-Tech journals, which has great guiding significance for the future development of journals.

Development and Validation of a Bayesian Network-Based Model for Predicting Coronary Heart Disease Risk From Electronic Health Records.

Traditional risk evaluation models have been applied to guide public health and clinical practice in various studies. However, the application of existing methods to data sets with missing and censored data, as is often the case in electronic health records, requires additional considerations. We aimed to develop and validate a predictive model that exhibits high performance with data sets that contain missing and censored data. This is a retrospective cohort study of coronary heart disease at Weihai Municipal Hospital on unique patients aged 18 to 96 years between 2013 and 2021. A total of 169 692 participants formed our study population, of which 10 895 participants were diagnosed with coronary heart disease. Models were built for the risk of coronary heart disease based on demographic, laboratory, and medical history variables. All complete samples were assigned to the training set (n=110 325), whereas the remaining samples were assigned to the validation set (n=59 367). The area under the receiver operating characteristic curve value was 0.800 (95% CI, 0.794-0.805), and the C statistic was 0.796 (95% CI, 0.791-0.801) in the derivation cohort, and the corresponding values were 0.837 (95% CI, 0.821-0.853) and 0.838 (95% CI, 0.822-0.854) in the validation cohort. The calibration curve demonstrated its good calibration ability, and decision curve analysis showed its clinical usefulness. Our proposed risk prediction model has demonstrated significant effectiveness in handling the complexities of electronic health record data, which often involve extensive missing data and censoring. This approach may offer potential assistance in the use of electronic health records to enhance patient outcomes.

Evaluation of Site Suitability for Photovoltaic Power Plants in the Beijing–Tianjin–Hebei Region of China Using a Combined Weighting Method

As the construction of photovoltaic power plants continues to expand, investors have placed great importance on the suitability assessment of site selection. In this study, we have developed a multi-level evaluation system and proposed an AHP–XGBoost–GIS comprehensive evaluation model for assessing site suitability in the Beijing–Tianjin–Hebei region. The findings revealed that approximately 48,800 km2 (21.59% of the total area) constituted a suitable area in this region, surpassing previous research results. Generally suitable areas accounted for the largest proportion at 35.89%. Suitable areas in Beijing and Tianjin were relatively limited, with most of them concentrated in Baoding City, Zhangjiakou City, and Chengde City on the northwest side of the “Yanshan-Taihang Mountains”, where there are ample wastelands with gentle slopes conducive to constructing photovoltaic power plants southeast of the “Yanshan-Taihang Mountains”, and solar radiation conditions were better. However, ecological and economic factors restricted suitable areas resulting in smaller coverage including cities such as Handan, Xingtai, Qinhuangdao, and Tangshan, among others. This study successfully overcame limitations associated with traditional subjective evaluation methods by providing decision support for regional energy allocation planning and spatial planning efforts, as well as environmental protection endeavors.

Enhancing Faculty Development Through Compiled Verbal Feedback on Clinical Teaching From Trainees.

Feedback from fellows-in-training (FITs) is important for faculty development and to enrich clinical teaching. We sought to evaluate the effectiveness of traditional online evaluations and a novel compiled verbal feedback mechanism. An annual feedback system was implemented in our rheumatology division in which FITs provided verbal feedback on all faculty to a facilitator who compiled, deidentified, and shared the feedback with individual faculty members. FITs also completed standard online annual evaluations of faculty. FITs and faculty completed surveys assessing the perceived effectiveness and confidentiality of each feedback mechanism. Thirteen of 15 eligible faculty and all 4 eligible FITs completed both surveys. Responses by FITs and faculty regarding the quality of online evaluations were generally unfavorable or neutral. Faculty responses regarding compiled verbal feedback were more favorable in all questions and significantly more favorable with respect to the feedback's ability to explain strengths (54% favorable for online evaluations vs 100% for compiled verbal feedback), the feedback's specificity (0% vs 54%), and the feedback's actionable nature (15% vs 62%). All FITs' responses regarding quality of compiled verbal feedback were favorable. FITs had concerns regarding confidentiality with both online evaluations (0% favorable) and compiled verbal feedback (25% favorable), though FITs had less concern for future faculty interactions with compiled verbal feedback (100% favorable) than with online evaluations (0% favorable). Compiled verbal feedback by FITs produced more actionable and effective feedback for faculty, with less concerns regarding future faculty interactions compared with traditional online evaluations. Further study of this method across different programs and institutions is warranted.

Risk Evaluation of Elevators Based on Fuzzy Theory and Machine Learning Algorithms

Elevators have become an integral part of modern buildings, and technological advances have enabled the monitoring of their operational status through sensor technology. In response to the development of the elevator industry and the need for practical elevator operation risk evaluation, this paper proposes an elevator risk evaluation method based on fuzzy theory and machine learning methods. The method begins by establishing an elevator operation risk evaluation index system. The traditional fuzzy comprehensive evaluation method is then employed to evaluate the risk levels of the 50 elevators studied. The collected index data and labels (fuzzy comprehensive evaluation results) are used as inputs to train the support vector machine (SVM) model. To optimize the SVM model, the maximum information coefficient method, enhanced by the correlation-based feature selection (MIC-CFS) method, is employed to select features for the index input to the SVM model. The improved gray wolf algorithm (IGWO) method optimizes the SVM. Finally, the model’s performance is verified using new index data. The experimental results demonstrate that introducing machine learning methods for elevator risk evaluation saves time and effort while providing good accuracy compared to the traditional expert evaluation method. The optimization of the SVM model by IGWO and feature selection by the MIC-CFS method results in a more concise SVM model that converges faster during training, exhibits better stability, and achieves higher accuracy.

Gas-in-place content and occurrence state of deep shale gas in the Luzhou area, Sichuan Basin, China

The exploration and development of deep shale gas has made significant breakthroughs, but due to the characteristics of deep shale gas, there is no recognized and effective method of evaluating gas-bearing. To effectively determine the gas-in-place (GIP) content and occurrence state of deep shale gas, a total of 909 shales were collected for the study utilizing pressure-holding and conventional coring technologies from Wufeng-Longmaxi Formations in the Luzhou area, Sichuan Basin, China. The GIP content of the above shales was evaluated using various methods, including the USBM, polynomial fitting, ACF, MCF, process analysis and carbon isotope fractionation (CIF) methods. The accuracy of the above methods was verified through comparison with the gas content measured by pressure-holding coring. The results demonstrate that (1) the degassing content obtained from conventional coring ranges 0.06–2.13 cm3/g, averaging 0.64 cm3/g; the measured gas content obtained from pressure-holding coring ranges 2.19–11.91 cm3/g, averaging 7.16 cm3/g. The gas content obtained is significantly higher because pressure-holding coring samples have excellent sealing and minimal lost time. (2) The GIP content is underestimated by the USBM method, while the polynomial fitting, ACF, and MCF methods overestimate it. The process analysis and CIF methods can evaluate the GIP content of deep shales more accurately. Based on the comparative results of the adsorbed gas ratio calculated using the process analysis and the CIF methods (33.49% and 24.53%, respectively), it is recommended that the CIF method be given priority when evaluating deep shale gas-bearing. (3) The GIP content of deep shales in the Luzhou area calculated by the CIF method ranges 2.52–11.99 cm3/g, averaging 7.19 cm3/g; the adsorbed gas ratio accounts for 3.41%–53.32%, averaging 24.53%; and the lost gas ratio accounts for 54.01%–97.45%, averaging 86.38%. Compared with medium and shallow shale gas reservoirs in other areas, deep shale gas reservoirs in the Luzhou area have gas-bearing characteristics of high GIP content, low adsorbed gas ratio and high lost gas ratio, which restricts the applicability of traditional evaluation methods.

DEA cross-efficiency models with prospect theory and distance entropy: An empirical study on high-tech industries

Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations.

Research on the Evaluation Method of Online Learning Satisfaction of College Students Based on Hierarchical Analysis Approach

Abstract This paper adopts the hierarchical analysis method as the weight calculation method for the evaluation of online learning satisfaction, calculates the weights between the evaluation indexes, and constructs the evaluation system of college students’ online learning satisfaction. Combining the teaching experience, the characteristics of online teaching and the items recorded in the web logs in the online teaching platform, the judgment matrix for online learning satisfaction evaluation is established, and the reasonableness of the matrix is judged by the consistency checking method. Evaluation standards are formulated based on the online learning evaluation information, and the research on online learning satisfaction evaluation is designed, while the online learning satisfaction evaluation of college students is analyzed by using the method of statistical analysis. The results show that the comprehensive learning evaluation scores derived from the use of hierarchical analysis are significantly more objective than the traditional evaluation of students’ learning effectiveness using only the end-of-term test scores, with a score difference of 15, and more than 85.24% of the students based on the hierarchical analysis method of college students’ online learning satisfaction evaluation method, which is effective in promoting the students’ regular participation in online learning. The correlation coefficients of online learning experience with clarity of teaching goals and expectations, teacher-student communication and exchange, learning support services, learning motivation, learning hardware equipment and operability, and teaching methods are 0.491, 0.524, 0.435, 0.596, 0.542, 0.391, respectively, which are all significant at 1% level, i.e., only by guaranteeing a higher level of learning effect can we improve students’ satisfaction with online learning.

Digital reading of undergraduate students in Vietnamese universities: a focus group study

PurposeThe development of digital technology and digital resources have influenced one’s reading habits. This paper aimed to study undergraduate students' reading in digital sphere at universities in Vietnam.Design/methodology/approachA focus group study with 31 undergraduate students of six groups at six public universities was conducted to get their perspectives on reading preference, strategies, effectiveness and psychological factors influencing reading ability.FindingsThe results confirmed that bachelor students' reading preference for document formats depended on their reading purposes. They used different reading strategies for digital and traditional reading, for instance, keyword searching, taking note, skimming, scanning, need-based reading, selective reading, comparison, evaluation and criticism. Students had faster reading speed for digital texts; however, they had better concentration and memorization in printed documents. When students have motivation, good attitudes and emotion, they could be motivated to read more.Originality/valueThese findings were useful in enhancing the understanding of digital reading competence and help stakeholders find out solutions to improve reading capacity of Vietnamese students in digital space.