126 research outputs found
Correction: Metallacyclopentadienes: synthesis, structure and reactivity
No full textCorrection for ‘Metallacyclopentadienes: synthesis, structure and reactivity’ by Wangyang Ma et al., Chem. Soc. Rev., 2017, 46, 1160–1192.</p
Glaucoma Visual Field Quantification with An Eye Tracker
No full textGlaucoma affects over 100 million people worldwide, and its prevalence continues to rise. This disease often progresses unnoticed in its early stages, with many patients only seeking medical attention when the condition has advanced significantly. Regular screening is crucial for early detection, with visual field testing established as one of the gold standards. However, the high costs associated with professional perimetry equipment, typically over $100,000, alongside the need for frequent hospital visits, make routine screening inaccessible for many. Recent advancements have explored the use of eye trackers for visual field testing in a research context, though these devices remain prohibitively expensive, often exceeding tens of thousands of dollars. This paper uses the low-cost eye tracker to build the diagnosing environment, obtains the subject's eye movement data through the eye tracker, and imitates the experimental process of visual field detection in traditional methods to get the visual field range. Later, according to the visual field range and the medical symptoms of glaucoma, it can be used to preliminarily diagnose whether there is glaucoma. In this experiment, only the blind spot area is tested as a demo. After a small scale test, this system can successfully test the size of the blind spot area of the visual field. In addition, according to the eye movement characteristics of glaucoma patients during the searching process, we provide a set of material image resources that require a large amount of search and establish a tool for subsequent visual analysis.</p
Estimation of Source and Receiver Positions, Room Geometry and Reflection Coefficients From a Single Room Impulse Response
No full textWe propose an algorithm to estimate source and receiver positions, room
geometry and reflection coefficients from a single room impulse response
simultaneously. It is based on a symmetry analysis of the room impulse
response. The proposed method utilizes the times of arrivals of the direct
path, first order reflections and second order reflections. The proposed method
is robust to erroneous pulses and non-specular reflections. It can be applied
to any room with parallel walls as long as the required arrival times of
reflections are available. In contrast to the state-of-art method, we do not
restrict the location of source and receiver
Dynamical Behaviors of a Translating Liquid Crystal Elastomer Fiber in a Linear Temperature Field
No full textLiquid crystal elastomer (LCE) fiber with a fixed end in an inhomogeneous temperature field is capable of self-oscillating because of coupling between heat transfer and deformation, and the dynamics of a translating LCE fiber in an inhomogeneous temperature field are worth investigating to widen its applications. In this paper, we propose a theoretic constitutive model and the asymptotic relationship of a LCE fiber translating in a linear temperature field and investigate the dynamical behaviors of a corresponding fiber-mass system. In the three cases of the frame at rest, uniform, and accelerating translation, the fiber-mass system can still self-oscillate, which is determined by the combination of the heat-transfer characteristic time, the temperature gradient, and the thermal expansion coefficient. The self-oscillation is maintained by the energy input from the ambient linear temperature field to compensate for damping dissipation. Meanwhile, the amplitude and frequency of the self-oscillation are not affected by the translating frame for the three cases. Compared with the cases of the frame at rest, the translating frame can change the equilibrium position of the self-oscillation. The results are expected to provide some useful recommendations for the design and motion control in the fields of micro-robots, energy harvesters, and clinical surgical scenarios
Modeling and analytics of multi-factor disease evolutionary process by fusing petri nets and machine learning methods
No full textRecent years, informatization methods have been gradually applied to medical treatment, in which machine learning and evolutionary computation play an important role. However, the effective methods for the study of multi-factor disease evolutionary process are still largely open. There are some issues in the field of disease analysis, such as the lack of visual multi-factor disease evolution model and effective analysis methods. For a universal method of data analysis and medical diagnosis, the machine learning algorithms should be combined with the formal modeling methods to fully realize the complementary advantages, make model has the advantages of visualization and efficient data analysis. This work proposes a novel research idea for the modeling analysis of current multi-factor diseases and reveal its feasibility, so as to explore potential pharmaceutical targets and enable doctors and patients to better understand the evolution process of multi-factor diseases. It is worth mentioning that, in order to verify the feasibility of the proposed idea, we applied it to the analysis of the role of monoamine hormones in depression. The model incorporates the machine learning algorithms, and it finally outputs the pathogenic probability under different hormone levels, reflecting the importance of different factors on depression. The application case proved that we provide a clear process model and a novel research method for multi-factor disease evolutionary process analysis
An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting
No full textEnergy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure
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