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Previous studies suggest that individuals with low self-esteem tend to undervalue themselves, a tendency that may reflect a metacognitive monitoring bias, which in turn can be shaped by feedback valence. However, research specifically exploring how self-esteem and feedback valence affect metacognitive monitoring remains scarce. Through three experiments, the study revealed that individuals tend to exhibit an underestimation bias in perceptual metacognitive monitoring. However, compared to individuals with high self-esteem, those with low self-esteem exhibited more pronounced local and global monitoring biases in the absence of feedback. Furthermore, feedback valence influenced both local and global metacognitive monitoring in low self-esteem individuals, whereas its effects in high self-esteem individuals were limited to global monitoring. Additionally, feedback valence reduced global monitoring disparities between self-esteem groups, while only positive feedback narrowed the gap in local monitoring. These findings suggest that the influence of feedback valence on metacognitive monitoring biases varies depending on both self-esteem level and the level of monitoring. These results provide valuable insights into the interplay among self-esteem, metacognitive monitoring and feedback, with implications for the development of targeted educational strategies, particularly for individuals with low self-esteem.

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg−1·rpm−1. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis.

Purpose This paper aims to provide an overview of South African perspectives on preventing, monitoring and combating hate victimisation, towards informing international understandings. Design/methodology/approach Using a general review approach, this paper provides a historical examination of measures proposed by the South African Government and civil society since 1994, to prevent, monitor and combat hate crime, hate speech and intentional unfair discrimination. Findings Regardless of a constitutional commitment to social inclusion, diversity and minority rights, significant progress remains lacking after almost three decades of related advocacy, lobbying and limited government intervention. Findings of the South African Hate Crimes Working Group (HCWG) longitudinal Monitoring Project emphasise the need for decisive legal responses to hate victimisation. Social implications A Bill, recognising hate crime and hate speech as distinct criminal offences, has been in development for almost 15 years and will soon serve before Parliament. Enactment of this legislation will be ground-breaking in Africa. Originality/value This paper contributes to the field of hate studies by providing an overview of the journey towards current conceptual understandings of hate in (South) Africa. It sets the stage for evaluating the potential of the redesigned HCWG monitoring tool, which holds promise for early identification and intervention in hate hotspots and targeted sectors. This instrument can establish trends not only in South Africa but also across the African continent.

Camera traps have become an important tool in wildlife monitoring. However, an issue in interpreting their data in statistical analyses of population densities, demography or behaviour is that the probability of detecting the target animals and their behaviours may vary depending on environmental and methodological factors. A specific problem is the type of flash used in the camera trap, as animals may react differently to different flash types and change their avoidance or habituation behaviour accordingly over time. Here, we provide the first systematic test of the impact of flash type in studies of red deer (Cervus elaphus) and roe deer (Capreolus capreolus), based on an analysis of behavioural responses to white, standard infrared and black flashes during 900 camera trap deployments in the Bavarian Forest National Park and the Northern Black Forest. The results revealed that both deer species were more likely to react to standard infrared than to black flash, but trigger delays prevented comparisons to white flash. Red deer reacted more frequently to camera traps than did roe deer, and responses were more common in the Northern Black Forest than in the Bavarian Forest National Park, where hunting is severely restricted. Contrary to our expectations, camera trapping rates of free‐ranging deer did not significantly decline over time for any flash type or species. Despite the lack of evidence for avoidance behaviour, we recommend the use of black flash for behavioural studies of deer populations to minimize the risk of introducing a source of disturbance whereas infrared and white flash are equally suitable for determinations of demographic parameters. We analysed the behavioural responses of red deer and roe deer to white, standard infrared and black flashes during 900 camera trap deployments in two different study areas. Both species responded more frequently to standard infrared flash than to black flash, but roe deer showed considerably fewer reactions than red deer. Irrespective of flash type and species, we found no evidence for camera trap avoidance.

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

We present new data and analyses revealing fundamental flaws in a critique of two recent meta-analyses of local-scale temporal biodiversity change. First, the conclusion that short-term time series lead to biased estimates of long-term change was based on two errors in the simulations used to support it. Second, the conclusion of negative relationships between temporal biodiversity change and study duration was entirely dependent on unrealistic model assumptions, the use of a subset of data, and inclusion of one outlier data point in one study. Third, the finding of a decline in local biodiversity, after eliminating post-disturbance studies, is not robust to alternative analyses on the original data set, and is absent in a larger, updated data set. Finally, the undebatable point, noted in both original papers, that studies in the ecological literature are geographically biased, was used to cast doubt on the conclusion that, outside of areas converted to croplands or asphalt, the distribution of biodiversity trends is centered approximately on zero. Future studies may modify conclusions, but at present, alternative conclusions based on the geographic-bias argument rely on speculation. In sum, the critique raises points of uncertainty typical of all ecological studies, but does not provide an evidence-based alternative interpretation.

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set ( p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF’s internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in using RF to develop predictive models with large environmental data sets.

•High quality community-based vaccination coverage surveys are resource-intensive.•Other monitoring methods provide useful data for programme managers.•Health facility-based assessments evaluate multiple aspects of service provision.•Purposeful community samples give local health workers programmatic insights.•To be useful, monitoring should lead to action to improve performance. Vaccination coverage is a widely used indicator of programme performance, measured by registries, routine administrative reports or household surveys. Because the population denominator and the reported number of vaccinations used in administrative estimates are often inaccurate, survey data are often considered to be more reliable. Many countries obtain survey data on vaccination coverage every 3–5years from large-scale multi-purpose survey programs. Additional surveys may be needed to evaluate coverage in Supplemental Immunization Activities such as measles or polio campaigns, or after major changes have occurred in the vaccination programme or its context. When a coverage survey is undertaken, rigorous statistical principles and field protocols should be followed to avoid selection bias and information bias. This requires substantial time, expertise and resources hence the role of vaccination coverage surveys in programme monitoring needs to be carefully defined. At times, programmatic monitoring may be more appropriate and provides data to guide program improvement. Practical field methods such as health facility-based assessments can evaluate multiple aspects of service provision, costs, coverage (among clinic attendees) and data quality. Similarly, purposeful sampling or censuses of specific populations can help local health workers evaluate their own performance and understand community attitudes, without trying to claim that the results are representative of the entire population. Administrative reports enable programme managers to do real-time monitoring, investigate potential problems and take timely remedial action, thus improvement of administrative estimates is of high priority. Most importantly, investment in collecting data needs to be complemented by investment in acting on results to improve performance.

Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear. To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness. PubMed, Embase, Cochrane Library and CINAHL were searched in December 2017. Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest. Performed separately by two authors using a preformed extraction sheet. Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated. This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.