Identifying private gardens in the U.K. as key sites of environmental engagement, we look at how a longer-term online citizen science programme facilitated the development of new and personal attachments of nature. These were visible through new or renewed interest in wildlife-friendly gardening practices and attitudinal shifts in a large proportion of its participants. Qualitative and quantitative data, collected via interviews, focus groups, surveys and logging of user behaviours, revealed that cultivating a fascination with species identification was key to both ‘helping nature’ and wider learning, with the programme creating a space where scientific and non-scientific knowledge could co-exist and reinforce one another.

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Plagiarism detection has been widely discussed in recent years. Various approaches have been proposed such as the text-similarity calculation, structural-approaches, and the fingerprint. In fingerprint-approaches, small parts of document are taken to be matched with other documents. In this paper, fingerprint and Winnowing algorithm is proposed. Those algorithms are used for detecting plagiarism of scientific articles in Bahasa Indonesia. Plagiarism classification is determined from those two documents by a Dice Coefficient at a certain threshold value. The results showed that the best performance of fingerprint algorithm was 92.8% while Winnowing algorithm's best performance was 91.8%. Level-of-relevance to the topic analysis result showed that Winnowing algorithm has got stronger term-correlation of 37.1% compared to the 33.6% fingerprint algorithm.

This paper presents the realisation of self-erecting inverted pendulum controls via two switched control approaches, a rule based fuzzy control for swing up inverted pendulum rod to pose upright position from downright position and an optimal state feedback control for stabilization as pendulum on upright position close to its equilibrium vertical line. The aim of this study is to solve two important problems on self-erecting inverted pendulum; swing up and stability in its upright balance position. Simulation and experimental results showed that control methods enabled the inverted pendulum swinging up and reaching its stable attitude in upright position even though small impulse and pulse disturbances were given. INDEX TERMS Fuzzy swing up, optimal state feedback stabilization, self erecting inverted pendulum.