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In this study, the optimum conditions for manufacturing particleboard-based waste cotton stalks were evaluated to achieve a good performance of mechanical properties. The response surface methodology (RSM) is used to calibrate the experiment results based on input variables consisting of the weight ratio of melamine formaldehyde to urea-formaldehyde (MU) resins, shelling ratio (SR), and the proportion of cotton particles to poplar particle (CP) in the core layer. An adaptive harmony search (AHS) algorithm is offered to search the optimum constructing conditions of mechanical properties for the composite particleboard using two optimization models. The optimum conditions are evaluated using maximum performance of mechanical properties. Besides, the optimum conditions are searched based on the material cost of the mechanical properties of composite particleboard that are utilized in its constraints. The results showed that the RSM can provide a perfect prediction for the mechanical properties of particleboard. The AHS is successfully applied to optimize the composite conditions. In the first optimization application, the optimal point is obtained for input variables in composite as 21.91% MU, 37.10% SR, and 13.54% CP. However, in the second condition, the optimum conditions are obtained for a good level as 18.32% MU, 51.71% SR, and 8.37% CP in the core layer.

As population aging is becoming more common worldwide, applying artificial intelligence into the diagnosis of Alzheimer's disease (AD) is critical to improve the diagnostic level in recent years. In early diagnosis of AD, the fusion of complementary information contained in multimodality data (e.g., magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF)) has obtained enormous achievement. Detecting Alzheimer's disease using multimodality data has two difficulties: (1) there exists noise information in multimodal data; (2) how to establish an effective mathematical model of the relationship between multimodal data? To this end, we proposed a method named LDF which is based on the combination of low-rank representation and discriminant correlation analysis (DCA) to fuse multimodal datasets. Specifically, the low-rank representation method is used to extract the latent features of the submodal data, so the noise information in the submodal data is removed. Then, discriminant correlation analysis is used to fuse the submodal data, so the complementary information can be fully utilized. The experimental results indicate the effectiveness of this method., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Zhigang Li et al.)

Risk assessment have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.

This paper reports the novel use of almond gum as a binder in manufacturing Rhizophora spp. particleboard. X-ray fluorescence spectroscopy was employed for analysis under photon energy range of 16.6-25.3 keV. Results showed that almond gum-bonded Rhizophora spp. particleboard can be used as tissue-equivalent phantom in diagnostic radiation. The calculated mass attenuation coefficients of the particleboards were consistent with the values of water calculated using XCOM program for the same photon energies, with p values of 0.056, 0.069, and 0.077 for samples A8, C0, and C8, respectively. However, no direct relationship was found between the percentage of adhesive and the mass attenuation coefficient. The results positively supported the use of almond gum as a binding agent in the fabrication of particleboards, which can be used as a phantom material in dosimetric and quality control applications.

We explore the application of terahertz spectroscopic techniques for the remote determination of the water content of paper. The aim is the development of a rapid diagnostic imaging tool applicable in paper fabrication processes. THz radiation offers a high sensitivity for water, a good spatial resolution, and insensitivity to scattering at the paper surface. The advent of THz cameras makes fast large-area image detectors feasible. In this paper, we show for the case of a 0.6-THz fixed-frequency system, that the water content of paper can be determined with high accuracy. We demonstrate a quantitative (calibrated) method for determining the moisture content in paper based on extinction and phase measurements in the lower THz range with a spatial resolution in the mm-range and scanning times below two minutes.

An analysis method for separating chromatographic overlapped peaks and purifying infrared spectra is put forward, based on the blind source separation technique and the multi-dimensional data of GC-FTIR, Using various information from hyphenated instruments, this method was used to separate completely a organic mixture, the xylene isomerism system, a problem unable to solve usually. The method can confirm the rationality of theory and algorithm and give integral explanations of the independent component analysis data. The reason for the error in quantitative analysis is discussed.