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Background: Aleutian mink disease, mink viral enteritis and canine distemper are known as the three most serious diseases that cause great economic loss in the mink industry. In clinical practice, aleutian mink disease virus (AMDV), mink enteritis virus (MEV) and canine distemper virus (CDV) are common mixed infections, and they have similar clinical clinical signs, such as diarrhoea. Therefore, a rapid and accurate differential diagnosis method for use on mink ranches is essential for the control of these three pathogens.

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The Rapid Online Assessment of Reading (ROAR) is a web-based lexical decision task that measures single-word reading abilities in children and adults without a proctor. Here we study whether item response theory (IRT) and computerized adaptive testing (CAT) can be used to create a more efficient online measure of word recognition. To construct an item bank, we first analyzed data taken from four groups of students (N = 1960) who differed in age, socioeconomic status, and language-based learning disabilities.

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Self-supported ultrathin PtRuMoCoNi high-entropy alloy nanowires (HEANWs) were synthesized by a one-pot co-reduction method, whose peroxidase (POD)-like activity and catalytic mechanism were elaborated in detail. As expected, the PtRuMoCoNi HEANWs showed excellent POD-like activity. It can quickly catalyze the oxidization of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to blue TMB through decomposition of HO to superoxide radicals.

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Belt conveyor idler fault detection algorithm based on improved YOLOv5.

Sci Rep

January 2025

School of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Ürümqi, 830017, China.

The rapid expansion of the coal mining industry has introduced significant safety risks, particularly within the harsh environments of open-pit coal mines. The safe and stable operation of belt conveyor idlers is crucial not only for ensuring efficient coal production but also for safeguarding the lives of coal mine workers. Therefore, this paper proposes a method based on deep learning for real-time detection of conveyor idler faults.

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Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains an NP-hard challenge, as existing swarm intelligence and evolutionary algorithms (SIEAs) often suffer from limited exploration capabilities or susceptibility to local optima, particularly in high-dimensional scenarios. To address these challenges, we propose GWOGA, a novel hybrid algorithm that combines Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA), aiming to achieve an effective balance between exploration and exploitation.

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