Deciding whether or not eradication of an invasive species has been successful is one of the main dilemmas facing managers of eradication programmes. When the species is no longer being detected, a decision must be made about when to stop the eradication programme and declare success. In practice, this decision is usually based on ad hoc rules, which may be inefficient. Since surveillance undertaken to confirm species absence is imperfect, any declaration of eradication success must consider the risk and the consequences of being wrong. If surveillance is insufficient, then eradication may be falsely declared (a Type I error), whereas continuation of surveillance when eradication has already occurred wastes resources (a Type II error). We review the various methods that have been developed for quantifying these errors and incorporating them into the decision-making process. We conclude with an overview of future developments likely to improve the practice of determining invasive species eradication success.

The internet has expanded opportunities for wildlife traders to conduct business globally, but monitoring this activity is often challenging. As monitoring techniques frequently employ language-based searches for particular words, it is important to understand the language traders use, particularly the nomenclature used to refer to traded species. We recorded advert data from four UK websites, focusing on reptiles, which constitute a large proportion of the international pet trade. We analysed whether the language types used by advertisers to refer to individual reptiles were associated with certain trader characteristics. We found English common names were the most frequent name type used to refer to species, regardless of the geographical location of the seller and the CITES Appendix listing of the species. However, scientific names were recorded in twice as many adverts for threatened and Near Threatened species as in adverts for non-threatened species, and only scientific names were used for three families: Anguidae, Pseudaspididae and Sphaerodactylidae. These findings could inform the creation of tailored keyword detection tools, which must account for the numerous language types in use within online wildlife trade communities. Future studies should examine the nuances of language used by other online wildlife trader communities in other contexts, such as different online platforms or different languages. The ultimate aim of these language detection tools will be to track in detail trends in the online reptile trade, offering a better understanding of potentially unsustainable trade patterns and helping authorities to enforce laws against illegal online reptile trade.

Anxiety is common in long-term care (LTC), but it is unclear which anxiety detection tools are accurate when compared to a reference standard for residents of LTC. Four databases and grey literature sources were searched using the search concepts “anxiety” and “LTC”. Included studies evaluated the diagnostic accuracy of an anxiety detection tool compared to a reference standard in LTC residents. Diagnostic accuracy measures were extracted. Four articles out of 4,620 met the inclusion criteria. Despite limited evidence and poorly reported study procedures and characteristics, the Geriatric Anxiety Inventory (sensitivity: 90.0%, specificity: 86.2%) and the Hospital Anxiety and Depression Scale-Anxiety (sensitivity: 90.0%, specificity: 80.6%) had the best performance when detecting generalized anxiety disorder. We identified four anxiety detection tools appropriate for use in LTC; a critical first step to diagnosing and managing anxiety in residents of LTC. Non-generalized anxiety disorders and tool feasibility must be further evaluated.

Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.

In 1889, The British Medical Journal published a piece titled, “Detective Medicine,” which describes feats of medical detection performed by physicians attending malingering prisoners. Though simulating illness had a long history, the medicalization of malingering at the fin de siècle led to a proliferation of such case histories and cheerful records of pathological feigners thwarted.

For torpedo electronic components tested by functional verification, there are characteristics of a few samples and few failures. During service life, it is difficult to analyse and predict the changes in reliability. At present, management's observation of quality is mainly base on failure data, and it is difficult to make predictions about the moments without failure in service life. In this paper, according to the failure data, we consider such factors as performance degradation and detection and use the model of instantaneous failure rate to evaluate the reliability of the detection moments periodically, and predict the reliability of stages through the results of detection moments. The method proposed in this paper, on the one hand, considers the service experience, and on the other combines the detection data, to make the final evaluation result more credible. In addition, this paper predicts the changing trend of reliability between adjacent detection moments, which can provide a useful reference for quality management work.

Somatic symptoms and anxiety symptoms are often disregarded in the detection of depression in primary care. The present investigation examined to what extent somatic and anxiety symptoms recorded with the Composite International Diagnostic Interview—Primary Health Care Version (CIDI—PHC) can improve the detection of depression as compared to the General Health Questionnaire—12-item version alone. Data from the World Health Organization study on Psychological Problems in General Health Care were used. The study sample consisted of primary care attenders from 15 centres from all over the world who underwent a psychiatric examination with the CIDI—PHC. Medically unexplained somatic symptoms (back pain, feelings of heaviness/lightness in parts of the body, periods of bodily weakness, seizures/convulsions, permanent tiredness, exhaustion after a minimum of effort) and—to a smaller extent—diverse anxiety symptoms (e.g. feelings of anxiousness/nervousness, feelings of tension, difficulties relaxing) significantly contributed to the detection of depression in a logistic regression analysis. The results confirm the observation that in primary care somatic symptoms play an important role in the manifestation of depressive disorders. The items investigated herein could prove beneficial for future depression screening instruments to improve the detection of depressive disorders in primary care.

One of the ways of human parasitic infection is the accidental ingestion of vegetables contaminated with parasites, which represents a major human health hazard. This non-exhaustive review aims to evaluate studies carried out on five types of vegetables (lettuce, parsley, coriander, carrot and radish) since 2000, particularly the methods used for recovery, concentration, detection and identification of protozoan parasites such as Toxoplasma gondii, Giardia duodenalis and Cryptosporidium spp., and the results of each work. Various studies have determined the presence of pathogenic parasites in fresh vegetables with different rates; this variation in rate depends particularly on the detection method used which is related to each parasite and each vegetable type. The variation in parasitic prevalence in food could be due to different factors such as the geographical location, the size of analysed samples and the methods used for parasite detection.