Incorporation of sediment- and soil-specific aspects in the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)

In environmental risk assessment either for registration purposes or for retrospective assessments of monitoring data, the hazard assessment is predominantly based on effect data from ecotoxicity studies. Most regulatory frameworks require studies used for risk assessment to be evaluated for reliability and relevance. Historically, the Klimisch methodology was used in many regulatory procedures where reliability needed to be evaluated. More recently, the Criteria for Reporting and Evaluating Ecotoxicity Data (CRED) have been developed for aquatic ecotoxicity studies, providing more detailed guidance on the evaluation and reporting of not only the reliability but also the relevance of a scientific study. Here, we discuss the application of the CRED methodology for assessing sediment and soil ecotoxicity studies, addressing important sediment- and soil-specific criteria that should be included as part of the CRED evaluation system. We also provide detailed recommendations for the design and reporting of sediment and soil toxicity studies that can be used by scientists and researchers wishing to contribute ecotoxicological data for effect assessments carried out within regulatory frameworks. Integr Environ Assess Manag 2024;00:1-13. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Evaluation of the nutrition literacy assessment questionnaire for college students and identification of the influencing factors of their nutrition literacy

BACKGROUND

Nutrition health has become a major public health issue in both high and middle-income countries. Nutrition literacy is an important indicator to evaluate the effect of public health intervention and one of the important concepts in health promotion. Thus, this study aimed to verify the reliability and validity of a nutrition literacy assessment questionnaire (NLAQ) and investigate the associated factors of nutrition literacy among college students.

METHODS

We conducted a cross-sectional online survey of college students from April to November 2022 in Wuhan (N = 774). We employed the Cronbach's alpha coefficient, exploratory and confirmatory factor analysis to evaluate the reliability and validity. We used latent profile analysis to classify the nutrition literacy. We conducted Chi-square test and binary logistic regression to identify the influencing factors.

RESULTS

The Cronbach's alpha coefficient of the NLAQ and its dimension was ranging from 0.837 to 0.909. The common factors were consistent with the original dimensions. All indicators met the requirements (χ2/df = 6.16 < 8, GFI = 0.929, NFI = 0.939, CFI = 0.948, RMSEA = 0.082 < 0.1). College students' disciplines (χ2 = 7.769, P = 0.021), mothers' education level (χ2 = 26.599, P < 0.001), and fathers' occupation type (χ2 = 11.218, P = 0.024) had impacts on nutrition literacy.

CONCLUSION

The NLAQ has good reliability and validity, and could be used as a measurement tool to evaluate college students' nutrition literacy. Schools and families should take targeted measures to improve the college students' nutrition literacy.

Research on three-state reliability evaluation method of high reliability system based on multi-source prior information

A high reliability system has the characteristics of complexity, modularization, high cost and small sample size. Throughout the entire lifecycle of system development, storage and use, the high reliability requirements and the risk analysis form a direct contradiction with the testing expenses. In order to ensure the system, module or component maintains good reliability status and effectively reduces the cost of sampling tests, it is necessary to make full use of multi-source prior information to evaluate its reliability. Therefore, in order to evaluate the reliability of highly reliable equipment under the condition of a small sample size correctly, the equipment reliability evaluation model should be built based on multi-source prior information and form scientific computing methods to meet the needs of condition evaluation and fund assurance of high reliability system. In engineering practice, high reliability system or module gradually develops from normal state to failure state, generally going through three working states of "safety-potential failure-functional failure". Firstly, the historical test data under the three states can be used for the data source for the reliability evaluation of the system at the current stage, which supplements the deficiency of the field data; secondly, due to the lack of accurate judgment on the working state of a high reliability system or modules and analysis of the health status, the unnecessary maintenance may aggravate the evolution speed from potential failure to functional failure; thirdly, when high reliability system or module operates under overload or harsh conditions, the potential failure will be worsened to a certain extent. Aiming at the difficulty of multi-state system reliability evaluation, a reliability evaluation method based on non-information prior distribution is proposed by fusing multi-source prior information, which provides ideas and methods for reliability evaluation and optimization analysis of high reliability system or module. The results show that the three-state reliability evaluation method proposed in this article is consistent with the actual engineering situation, providing a scientific theoretical basis for preventive maintenance of high reliability system. At the same time, the research method not only helps evaluate the reliability state of a high reliability system accurately, but also achieves the goal of effectively reducing test costs with good economic benefits and engineering application value.

DNN based reliability evaluation for telemedicine data

Telemedicine data are measured directly by untrained patients, which may cause problems in data reliability. Many deep learning-based studies have been conducted to improve the quality of measurement data. However, they could not provide an accurate basis for judgment. Therefore, this study proposed a deep neural network filter-based reliability evaluation system that could present an accurate basis for judgment and verified its reliability by evaluating photoplethysmography signal and change in data quality according to judgment criteria through clinical trials. In the results, the deviation of 3% or more when the oxygen saturation was judged as normal according to each criterion was 0.3% and 0.82% for criteria 1 and 2, respectively, which was very low compared to the abnormal judgment (3.86%). The deviation of diastolic blood pressure (≥ 10 mmHg) according to criterion 3 was reduced by about 4% in the normal judgment compared to the abnormal. In addition, when multiple judgment conditions were satisfied, abnormal data were better discriminated than when only one criterion was satisfied. Therefore, the basis for judging abnormal data can be presented with the system proposed in this study, and the quality of telemedicine data can be improved according to the judgment result.

Reliability assessment for systems suffering competing degradation and random shocks under fuzzy environment

Reliability assessment of multi-component systems under competing degradation and random shocks has been intensively investigated in recent years. In most cases, the parameters associated with competing degradation and random shocks are represented by crisp values. However, due to insufficient data and vague judgments from experts, it may produce epistemic uncertainty with those parameters and they are befitting to be described as fuzzy numbers. In this article, the internal degradation is treated as a continuous monotonically increasing random process with respect to operating time, whereas the amount of cumulative damage produced by each external random shock is modeled by a geometric process. As components in a system suffer the same environmental condition, an external random shock will produce different amounts of cumulative damage to each component simultaneously. Each component fails when either the internal degradation or cumulative damage from the random shocks, whichever comes first, exceeds its corresponding random thresholds. Moreover, the parameters associated with the internal degradation and the random shocks are represented by triangular fuzzy numbers. The fuzzy reliability functions of components and the entire system are evaluated by a set of optimization models. A multi-component system, together with some comparative results, is presented to illustrate the implementation of the proposed method.

Criteria for Reporting and Evaluating ecotoxicity Data (CRED): comparison and perception of the Klimisch and CRED methods for evaluating reliability and relevance of ecotoxicity studies

BACKGROUND

The regulatory evaluation of ecotoxicity studies for environmental risk and/or hazard assessment of chemicals is often performed using the method established by Klimisch and colleagues in 1997. The method was, at that time, an important step toward improved evaluation of study reliability, but lately it has been criticized for lack of detail and guidance, and for not ensuring sufficient consistency among risk assessors.

RESULTS

A new evaluation method was thus developed: Criteria for Reporting and Evaluating ecotoxicity Data (CRED). The CRED evaluation method aims at strengthening consistency and transparency of hazard and risk assessment of chemicals by providing criteria and guidance for reliability and relevance evaluation of aquatic ecotoxicity studies. A two-phased ring test was conducted to compare and characterize the differences between the CRED and Klimisch evaluation methods. A total of 75 risk assessors from 12 countries participated. Results show that the CRED evaluation method provides a more detailed and transparent evaluation of reliability and relevance than the Klimisch method. Ring test participants perceived it to be less dependent on expert judgement, more accurate and consistent, and practical regarding the use of criteria and time needed for performing an evaluation.

CONCLUSIONS

We conclude that the CRED evaluation method is a suitable replacement for the Klimisch method, and that its use may contribute to an improved harmonization of hazard and risk assessments of chemicals across different regulatory frameworks.

Real-time reliability evaluation methodology based on dynamic Bayesian networks: A case study of a subsea pipe ram BOP system

A novel real-time reliability evaluation methodology is proposed by combining root cause diagnosis phase based on Bayesian networks (BNs) and reliability evaluation phase based on dynamic BNs (DBNs). The root cause diagnosis phase exactly locates the root cause of a complex mechatronic system failure in real time to increase diagnostic coverage and is performed through backward analysis of BNs. The reliability evaluation phase calculates the real-time reliability of the entire system by forward inference of DBNs. The application of the proposed methodology is demonstrated using a case of a subsea pipe ram blowout preventer system. The value and the variation trend of real-time system reliability when the faults of components occur are studied; the importance degree sequence of components at different times is also determined using mutual information and belief variance.