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Fulltext PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network

Nazri A, Mazlan N, Muharam F

PLoS One, 2018;13(12):e0208501.
PMID: 30571683 DOI: 10.1371/journal.pone.0208501

Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and monitoring of its abundance has been conducted manually by experts and is time-consuming, fatiguing and tedious. Automated detection of BPH has been proposed by many studies to overcome human fallibility. However, all studies regarding automated recognition of BPH are investigated based on intact specimen although most of the specimens are imperfect, with missing parts have distorted shapes. The automated recognition of an imperfect insect image is more difficult than recognition of the intact specimen. This study proposes an automated, deep-learning-based detection pipeline, PENYEK, to identify BPH pest in images taken from a readily available sticky pad, constructed by clipping plastic sheets onto steel plates and spraying with glue. This study explores the effectiveness of a convolutional neural network (CNN) architecture, VGG16, in classifying insects as BPH or benign based on grayscale images constructed from Euclidean Distance Maps (EDM). The pipeline identified imperfect images of BPH with an accuracy of 95% using deep-learning's hyperparameters: softmax, a mini-batch of 30 and an initial learning rate of 0.0001.
Fulltext The bidirectional temporal relationship between parenting stress and child maltreatment: A cross-lagged study based on intervention and cohort data

Han Q, Jocson R, Kunovski I, Raleva M, Juhari R, Okop K, et al.

J Affect Disord, 2024 Jun 01;354:302-308.
PMID: 38479502 DOI: 10.1016/j.jad.2024.03.063

BACKGROUND: Parenting stress has long been proposed as a major risk factor for child maltreatment. However, there is a lack of evidence from existing studies on the temporal sequence to establish a causal relationship. This study aims to examine bidirectional temporal relationships between parenting stress and child maltreatment.
METHODS: Longitudinal data from two different sources were analysed: a pre-post study of an online parenting programme conducted across six countries - the ePLH Evaluation Study, and a prospective cohort study in the United States - LONGSCAN. Cross-lagged panel model on parenting stress and child maltreatment was used in each dataset.
RESULTS: Based on repeatedly measured data of 484 caregivers in the ePLH study across five time points (every two weeks), we found that parenting stress at an earlier time point predicted later child maltreatment (IRR = 1.14, 95 % CI: 1.10,1.18). In addition, the occurrence of child maltreatment was associated with higher subsequent short-term parenting stress (IRR = 1.04, 95 % CI: 1.01,1.08) and thus could form a vicious circle. In the LONGSCAN analysis with 772 caregivers who were followed up from child age of 6 to child age of 16, we also found parenting stress at an earlier time point predicted later child maltreatment (β = 0.11, 95 % CI: 0.01,0.20), but did not observe an association between child maltreatment and subsequent long-term parenting stress.
LIMITATIONS: Potential information bias on the measurements.
CONCLUSIONS: This study provides evidence for a bidirectional temporal relationship between parenting stress and child maltreatment, which should be considered in parenting intervention programmes.

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