Transaction Method of Warehouse Sharing Platform Based on Blockchain Technology

With the continuous development of big data and blockchain technology, there are more applications of warehousing sharing platform, and warehousing transaction method has become the research focus. The original barter method can not solve the problem of accurate warehousing transactions, and the calculation accuracy of warehousing transactions is poor. Therefore, this paper proposes a warehouse transaction model based on blockchain technology, and comprehensively analyzes the form and accuracy of warehouse transactions. Firstly, the warehouse trading platform is used to count the transaction data and transaction methods, and the transaction forms and results are judged according to the warehouse characteristics, and irrelevant transaction information is abandoned. Then, according to the change rate of transaction data and transaction mode, the results are calculated, and compared with the actual transaction situation, and the parameters and indicators of transaction calculation are adjusted. MATLAB simulation test analysis shows that blockchain calculation method can improve the accuracy of warehousing transactions, and the accuracy rate reaches 95.3%. According to different transaction contents, the platform and form are judged, and the transaction time is calculated. It is found that the blockchain calculation method can meet the needs of warehousing transactions

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data

Text document pre-processing using the Bayes formula for classification based on the vector space model

This work utilizes the Bayes formula to vectorize a document according to a probability distribution based on keywords reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics (categories). Using this probability distribution as the vectors to represent the document, the text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM) and Self-Organizing Map (SOM) can then be used to classify the documents on a multi-dimensional level, thus improving on the results obtained using only the highest probability to classify the document, such as that achieved by implementing the naïve Bayes classifier by itself. The effects of an inadvertent dimensionality reduction can be overcome using these algorithms. We compare the performance of these classifiers for high dimensional data

Polychotomiser for case-based reasoning beyond the traditional Bayesian classification approach

This work implements an enhanced Bayesian classifier with better performance as compared to the ordinary naïve Bayes classifier when used with domains and datasets of varying characteristics. Text classification is an active and on-going research field of Artificial Intelligence (AI). Text classification is defined as the task of learning methods for categorising collections of electronic text documents into their annotated classes, based on its contents. An increasing number of statistical approaches have been developed for text classification, including k-nearest neighbor classification, naïve Bayes classification, decision tree, rules induction, and the algorithm implementing the structural risk minimisation theory called the support vector machine. Among the approaches used in these applications, naïve Bayes classifiers have been widely used because of its simplicity. However this generative method has been reported to be less accurate than the discriminative methods such as SVM. Some researches have proven that the naïve Bayes classifier performs surprisingly well in many other domains with certain specialised characteristics. The main aim of this work is to quantify the weakness of traditional naïve Bayes classification and introduce an enhance Bayesian classification approach with additional innovative techniques to perform better than the traditional naïve Bayes classifier. Our research goal is to develop an enhanced Bayesian probabilistic classifier by introducing different tournament structures ranking algorithms along with a high relevance keywords extraction facility and an accurately calculated weighting factors facility. These were done to improve the performance of the classification tasks for specific datasets with different characteristics. Other researches have used general datasets, such as Reuters-21578 and 20_newsgroups to validate the performance of their classifiers. Our approach is easily adapted to datasets with different characteristics in terms of the degree of similarity between classes, multi-categorised documents, and different dataset organisations. As previously mentioned we introduce several techniques such as tournament structures ranking algorithms, higher relevance keyword extraction, and automatically computed document dependent (ACDD) weighting factors. Each technique has unique response while been implemented in datasets with different characteristics but has shown to give outstanding performance in most cases. We have successfully optimised our techniques for individual datasets with different characteristics based on our experimental results

A new class of Shariah-compliant portfolio optimization model: diversification analysis

This study proposes a novel Shariah-compliant portfolio optimization model tested on the daily historical return of 154 Shariah-compliant securities reported by the Shariah Advisory Council of Securities Commission Malaysia from 2011 to 2020. The mathematical model employs an annual rebalancing strategy subject to a Conditional Value-at-Risk (CVaR) constraint while considering practical and Islamic trading concerns, including transaction costs, holding limits, and zakat payment. To validate the model, the optimal portfolios are compared against an Islamic benchmark index, a market index, and portfolios generated by the mean-variance model, as well as a forecast accuracy test by the Mean Absolute Percentage Error and Mean Absolute Arctangent Percentage Error. Furthermore, this study examines the inter-stock relationship within the generated portfolios using correlation and Granger causality tests to identify the diversification performance. Results show an outperformance of the model in offering portfolios with higher risk-adjusted returns under a comparably short computational time and an indication of generally well-diversified portfolios by the weak correlations between securities. The study further noted that the model is adept at risk management in addition to higher forecast accuracy during financial crises by showing remarkably fewer causal relationships during bear markets in 2011, 2014, and 2020. The findings of an inversed relationship between portfolio risk and the number of causalities between securities offer new insights into the effect of dynamic relationships between securities on portfolio diversification. In conclusion, the proposed model carries higher moral and social values than the conventional models while portraying high potential in enhancing the efficiency of asset allocation, contributing to economic diversification and the scarce literature on Islamic portfolio optimization modelling. The study also supports the substantially increasing demand for Shariah-compliant strategies following globalization and the changing demographic of the real financial world with growing priorities of social and sustainability values

Effects of contexts in urban residential areas on the pleasantness and appropriateness of natural sounds

Before introducing natural sounds to potentially improve the soundscape quality, it is important to understand how key contextual factors (i.e. expected activities and audio-visual congruency) affect the soundscape in a given location. In this study, the perception of eight natural sounds (i.e. 4 birdsongs, 4 water sounds) at five urban recreational areas under the constant influence of road traffic was explored subjectively under three laboratory settings: visual-only, audio-only, and audio-visual. Firstly, expected socio-recreational activities of each location were determined in the visual-only setting. Subsequently, participants assessed the pleasantness and appropriateness of the soundscape at each site, for each of the eight natural sounds augmented to the same road traffic noise, in both audio-only and audio-visual settings. Interestingly, it was found that the expected activities in each location did not significantly affect natural sound perception, whereas audio-visual congruency of the locations significantly affected the pleasantness and appropriateness of the natural sounds. Particularly, the pleasantness and appropriateness decreased for water sounds when water features were not visually present. In contrast, perception with birdsongs was unaffected by their visibility likely due to the presence of vegetation. Hence, audio-visual coherence is central to the perception of natural sounds in outdoor spaces

A mixed-reality approach to soundscape assessment of outdoor urban environments augmented with natural sounds

To investigate the effect of augmenting natural sounds in noisy environments, an in-situ experiment was conducted using a mixed-reality head-mounted display (MR HMD). Two outdoor locations close to an expressway were selected for the experiment. A natural sound (birdsong or stream) along with a hologram (sparrow/fountain or loudspeaker) was projected through the MR HMD. Participants were asked to adjust the natural sound levels to their preferred level under ambient traffic noise conditions at each location. Participants also assessed the perceived loudness of traffic (PLN) and overall soundscape quality (OSQ) in conditions with and without the augmented natural sounds. The results showed that both natural sounds significantly reduced the PLN and enhanced the OSQ. No significant differences in subjective responses were found between the loudspeaker and visual representations of the natural sound source as holograms. Analysis on the preferred signal-to-noise ratio (SNR), i.e. ratio of natural sound to traffic levels, indicated a strong negative correlation between the preferred SNRs and ambient traffic noise levels. Overall, the preferred SNR of the birdsong was significantly higher than that of the water sound. Among the acoustic parameters tested, the A-weighted traffic noise level was the strongest predictor for the preferred SNR of both the birdsong and water sound. However, the correlation for the water sound was relatively higher than the birdsong. This was due to the larger variance in the subjective evaluation for the birdsong

Detection of Pre-invasive Endobronchial Tumors with D-light/Autofluorescence System

Autofluorescence bronchoscopy (AFB) is one of the newly developed diagnostic tools to detect the pre-cancerous lesions in the bronchial tissue. The utility of D-Light/AFB in the detection of pre-cancerous lesions was compared to the standard white light bronchoscopy (WLB). In 113 patients (male 106, female 7), who visited hospital for evaluation of lung cancer, WLB and AFB were done and 364 biopsy specimens were obtained from November 2001 to August 2002. The bronchoscopic findings on WLB and AFB were compared to the pathological findings. The pathologic diagnoses of the specimens were as follows: normal in 96; hyperplasia in 69; metaplasia in 32; mild dysplasia in 13, moderate dysplasia in 6, severe dysplasia in 4; carcinoma in situ in 6; invasive carcinoma in 57. The relative sensitivity of adjunctive AFB to WLB vs. WLB alone was 1.5 in moderate dysplasia or worse lesions, and 3.2 in intraepithelial neoplasia. The specificity of adjunctive AFB and WLB alone were 0.91 and 0.5, respectively. The adjunctive AFB to the standard WLB increased the detection rate of the localized pre-invasive lesions. However, there was high rate of false positive in AFB

The effect of localized surface plasmon resonance on the emission color change in organic light emitting diodes

Three primary colors, cyan, yellow, and green, are obtained from Ag nano-dot embedded organic light emitting diodes (OLEDs) by localized surface plasmon resonance (LSPR). By changing the thickness of the Ag film, the size and spacing of Ag nano-dots are controlled. The generated light from the emissive layer in the OLEDs interacts with the free electrons near the surface of the Ag nano-dots, which leads to LSPR absorption and scattering. The UV-visible absorption spectra of glass/ITO/Ag nano-dot samples show intense peaks from 430 nm to 520 nm with an increase of Ag nano-dot size. And also, the Rayleigh scattering spectra results show the plasmon resonance wavelength in the range of 470-550 nm. The effect of the LSPR of Ag nano-dots on the change of emission color in OLEDs is demonstrated using 2 dimensional finite-difference time-domain simulations. The intensity of the electro-magnetic field in the sample with 5 nm-thick Ag is low at the incident wavelength of 500 nm, but it increases with the incident wavelength. This provides evidence that the emission color change in OLEDs originates from LSPR at the Ag nano-dots. As a result, the emission peak wavelength of OLEDs shifted toward longer wavelengths, from cyan to yellow-green, with the increase of Ag nano-dot size.open11107Nsciescopu

A Paclitaxel-Eluting Stent for the Prevention of Coronary Restenosis

Background Intimal hyperplasia and resulting restenosis limit the efficacy of coronary stenting. We studied a coronary stent coated with the antiproliferative agent paclitaxel as a means of preventing restenosis. Methods We conducted a multicenter, randomized, controlled, triple-blind study to evaluate the ability of a paclitaxel-eluting stent to inhibit restenosis. At three centers, 177 patients with discrete coronary lesions (<15 mm in length, 2.25 to 3.5 mm in diameter) underwent implantation of paclitaxel-eluting stents (low dose, 1.3 µg per square millimeter, or high dose, 3.1 µg per square millimeter) or control stents. Antiplatelet therapies included aspirin with ticlopidine (120 patients), clopidogrel (18 patients), or cilostazol (37 patients). Clinical follow-up was performed at one month and four to six months, and angiographic follow-up at four to six months. Results Technical success was achieved in 99 percent of the patients (176 of 177). At follow-up, the high-dose group, as compared with the control group, had significantly better results for the degree of stenosis (mean [±SD], 14±21 percent vs. 39±27 percent; P<0.001), late loss of luminal diameter (0.29±0.72 mm vs. 1.04±0.83 mm, P<0.001), and restenosis of more than 50 percent (4 percent vs. 27 percent, P<0.001). Intravascular ultrasound analysis demonstrated a dose-dependent reduction in the volume of intimal hyperplasia (31, 18, and 13 mm3, in the high-dose, low-dose, and control groups, respectively). There was a higher rate of major cardiac events in patients receiving cilostazol than in those receiving ticlopidine or clopidogrel. Among patients receiving ticlopidine or clopidogrel, event-free survival was 98 percent and 100 percent in the high-dose and control groups, respectively, at one month, and 96 percent in both at four to six months. Conclusions Paclitaxel-eluting stents used with conventional antiplatelet therapy effectively inhibit restenosis and neointimal hyperplasia, with a safety profile similar to that of standard stents.published_or_final_versio