A special type of ordinal scale comprising a number of intervals of known numeric ranges can be used when estimating severity of a plant disease. The interval ranges are most often based on the percent area with symptoms [e.g. the Horsfall-Barratt (H-B) scale]. Studies in plant pathology and plant breeding often use this type of ordinal scale. The disease severity is estimated by a rater as a value on the scale and has been used to determine a disease severity index (DSI) on a percentage basis, where DSI (%) = [sum (class frequency × score of rating class)]/[(total number of plants) × (maximal disease index)] × 100. However, very few studies have investigated the effects of different scales on accuracy of the DSI. Therefore, the objectives of this study were to investigate the process of calculating a DSI on a percentage basis from ordinal scale data, and to use simulation approaches to explore the effect of using different methods for calculation of the interval range and the nature of the ordinal scales used on the DSI estimates (%). We found that the DSI is particularly prone to overestimation when using the above formula if the midpoint values of the rating class are not considered. Moreover, the results of the simulation studies show that, if rater estimates are unbiased, compared with other methods tested in this study, the most accurate method for estimation of a DSI is to use the midpoint of the severity range for each class with an amended 10% ordinal scale (an ordinal scale based on a 10% linear scale emphasising severities ≤50% disease, with additional grades at low severities). As for biased conditions, the accuracy for calculating DSI estimates (%) will depend mainly on the degree and direction of the rater bias relative to the actual mean value.

A disease severity index (DSI) is a single number for summarising a large amount of information on disease severity. The DSI has most often been used with data based on a special type of ordinal scale comprising a series of consecutive ranges of defined numeric intervals, generally based on the percent area of symptoms presenting on the specimen(s). Plant pathologists and other professionals use such ordinal scale data in conjunction with a DSI (%) for treatment comparisons. The objective of this work is to explore the effects on both of different scales (i.e. those having equal or unequal classes, or different widths of intervals) and of the selection of values for scale intervals (i.e. the ordinal grade for the category or the midpoint value of the interval) on the null hypothesis test for the treatment comparison. A two‐stage simulation approach was employed to approximate the real mechanisms governing the disease‐severity sampling design. Subsequently, a meta‐analysis was performed to compare the effects of two treatments, which demonstrated that using quantitative ordinal rating grades or the midpoint conversion for the ranges of disease severity yielded very comparable results with respect to the power of hypothesis testing. However, the principal factor determining the power of the hypothesis test is the nature of the intervals, not the selection of values for ordinal scale intervals (i.e. not the mid‐point or ordinal grade). Although using the percent scale is always preferable, the results of this study provide a framework for developing improved research methods where the use of ordinal scales in conjunction with a DSI is either preferred or a necessity for comparing disease severities.

Light pollution is a new source of environmental pollution after exhaust gas, wastewater, waste residue, and noise. Research shows that light pollution is threatening people's health. In order to accurately measure the level of light pollution risk in a region, this paper established a light pollution risk index level model, and used this model to detect the level of light pollution risk in different types of places. Specifically, by determining the main factors that affect the risk of light pollution, and searching for corresponding data indicators from different countries to calculate the weight, this paper uses a combination of entropy weight and coefficient of variation to obtain the index weight, and establishes a light pollution risk index level model based on the weight. Finally, the model is applied to four specific types of locations to determine their risk level of light pollution.

Background: Numerous studies have suggested that Baduanjin, a traditional Chinese exercise, can alleviate fatigue symptoms in patients with various illnesses. The aim of this review was to evaluate the efficacy of Baduanjin in reducing fatigue symptoms. Methods: A comprehensive literature search was conducted using several databases, including PubMed, Web of Science, Embase, Medline, China Biology Medicine disc, China National Knowledge Infrastructure, and Wanfang, from inception to June 2023. Relevant studies reporting on the effects of Baduanjin on fatigue symptoms were included. A random-effects meta-analysis model with standardized mean differences was used to estimate the treatment effects. Moderator analyses were conducted using continuous variables and meta-regression. This review was registered in the International Prospective Register of Systematic Reviews (identifier CRD42023411532). Grading of recommendations, assessment, development and evaluations framework was used to assess the certainty of evidence. Results: Ten randomized controlled trials with patients diagnosed with 9 different diseases were included in the meta-analysis. The Baduanjin intervention groups showed significant improvements in total fatigue intensity (standard mean difference = −0.49, 95% confidence interval = −0.69 to −0.30, P = .000; I2 = 56%, P = .009). The statistically significant differences in the subgroup analyses, including intervention durations, age of participants, fatigue types, and practice location, remained unchanged. Meta-regression showed that practice place might have significant effect on the results. The certainty of the evidence was moderate for participants 55-year younger or in hospital training. However, fatigue, different groups, participants 55-year or older, training at home, and different fatigue types had lower evidence certainty. Conclusion: Baduanjin can effectively alleviate fatigue symptoms with relatively flexible requirements. However, studies investigating the same disease types and including non-Chinese populations are scarce. Therefore, further studies with long-term interventions, larger sample sizes, and well-designed methodologies are warranted.