Ion-beam radiography (iRad) could potentially improve the quality control of ion-beam therapy. The main advantage of iRad is the possibility to directly measure the integrated stopping power. Until now there is no clinical implementation of iRad. Topics of ongoing research include developing dedicated detection systems to achieve the desired spatial resolution (SR) and investigating different ion types as imaging radiation. This work focuses on the theoretical and experimental comparison of proton (pRad) and helium-beam radiography (αRad). The experimental comparison was performed with an in-house developed detection system consisting of silicon pixel detectors. This system enables the measurement of energy deposition of single ions, their tracking, and the identification of the ion type, which is important for αRad due to secondary fragments. A 161 mm-thick PMMA phantom with an air gap of 1 mm placed at different depths was imaged with a 168 MeV u proton/helium-ion beam at the Heidelberg ion-beam therapy center. The image quality in terms of SR and contrast-to-noise ratio (CNR) was evaluated. After validating MC simulations against experiments, pRad and αRad were compared to carbon-beam radiography (cRad) in simulations. The theoretical prediction that the CNR of pRad and αRad is equal at similar imaging doses was experimentally confirmed. The measured SR of αRad was 55% better compared to pRad. The simulated cRads showed the expected improvement in SR and the decreased CNR at the same dose compared to the αRads, however only at dose levels exceeding typical doses of diagnostic x-ray projections. For clinically applicable dose levels, the cRads suffered from an insufficient number of carbon ions per pixel (220 μm × 220 μm). In conclusion, it was theoretically and experimentally shown that αRad provides a better SR than pRad without any disadvantages concerning the CNR. Using carbon ions instead of helium ions leads to a better SR at the cost of higher doses.
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Nat Protoc
January 2025
Donders Institute for Brain, Behaviour, and Cognition, Nijmegen, The Netherlands.
Templates for the acquisition of large datasets such as the Human Connectome Project guide the neuroimaging community to reproducible data acquisition and scientific rigor. By contrast, small animal neuroimaging often relies on laboratory-specific protocols, which limit cross-study comparisons. The establishment of broadly validated protocols may facilitate the acquisition of large datasets, which are essential for uncovering potentially small effects often seen in functional MRI (fMRI) studies.
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January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
Off-grid water pumping systems (OGWPS) have become an increasingly popular area of research in the search for sustainable energy solutions. This paper presents a finite element method (FEM)-based design and analysis of Brushless-DC (BLDC) and Switched Reluctance Motors (SRM) designed for low-power water pumping applications. Utilizing adaptive finite element analysis (FEA), both motors were designed with identical ratings and design parameters to ensure a fair comparison.
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January 2025
School of Computer Science and Technology, Donghua University, Shanghai, 201620, China.
Extracting high-order abstract patterns from complex high-dimensional data forms the foundation of human cognitive abilities. Abstract visual reasoning involves identifying abstract patterns embedded within composite images, considered a core competency of machine intelligence. Traditional neuro-symbolic methods often infer unknown objects through data fitting, without fully exploring the abstract patterns within composite images and the sequential sensitivity of visual sequences.
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January 2025
Department of Industrial Engineering/Graduate School of Data Science/Research Center for Electrical and Information Science, Seoul National University of Science and Technology, Seoul, South Korea.
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets.
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January 2025
Engineering Research Center of Grain Storage and Security of Ministry of Education, Henan Provincial Engineering Technology Research Center on Grain Post Harvest, School of Food and Strategic Reserves, Henan University of Technology, Lianhua Road 100, Zhengzhou High-Tech Development Zone, Zhengzhou, 450001, Henan, China. Electronic address:
Aflatoxin B1 (AFB1) has strong carcinogenicity, mutagenicity, and teratogenicity even at low concentrations, presenting a major risk to food safety and human health, hence, it is crucial to develop a sensitive detection technique for AFB1. Consequently, cadmium telluride (CdTe) quantum dots conjugated with AFB1 aptamers serve as fluorescent signal probes, whereas FeO@UiO-66-NH nanocomplexes are employed as magnetic carriers and fluorescence quenchers. FeO@UiO-66-NH reduces background signal interference, thereby enhancing detection sensitivity and Förster Resonance Energy Transfer (FRET) efficiency.
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