The composition and microbial quality of pre-packed dates purchased in Greater Glasgow

Composition and microbiological contamination was measured in samples of pre-packed dates purchased from a number of stores and retail outlets in Greater Glasgow. The sugar content of dried pitted dates was c . 74%, about 10% more than reported on those packs where nutritional information was given. Potentially pathogenic bacteria like Escherichia coli, Staphylococcus aureus and Bacillus cereus were identified in some of the samples together with lactic acid bacteria, yeasts and moulds, Aspergillus flavus and A. parasiticus . Fresh dates were the most heavily contaminated of all samples, probably owing to the high moisture content.     

Neural Machine Translation by Fusing Key Information of Text

When the Transformer proposed by Google in 2017, it was first used for machine translation tasks and achieved the state of the art at that time. Although the current neural machine translation model can generate high quality translation results, there are still mistranslations and omissions in the translation of key information of long sentences. On the other hand, the most important part in traditional translation tasks is the translation of key information. In the translation results, as long as the key information is translated accurately and completely, even if other parts of the results are translated incorrect, the final translation results’ quality can still be guaranteed. In order to solve the problem of mistranslation and missed translation effectively, and improve the accuracy and completeness of long sentence translation in machine translation, this paper proposes a key information fused neural machine translation model based on Transformer. The model proposed in this paper extracts the keywords of the source language text separately as the input of the encoder. After the same encoding as the source language text, it is fused with the output of the source language text encoded by the encoder, then the key information is processed and input into the decoder. With incorporating keyword information from the source language sentence, the model’s performance in the task of translating long sentences is very reliable. In order to verify the effectiveness of the method of fusion of key information proposed in this paper, a series of experiments were carried out on the verification set. The experimental results show that the Bilingual Evaluation Understudy (BLEU) score of the model proposed in this paper on the Workshop on Machine Translation (WMT) 2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test dataset. The experimental results show the advantages of the model proposed in this paper.     

Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework

We explore the theoretical foundations on how firm and IT characteristics explain the market value variations in e-commerce initiatives by examining the announcements of 946 e-commerce initiatives in the public media. Our approach combines the Event study methodology and Decision tree induction to examine the main and interaction effects of IT and firm characteristics on Cumulative Abnormal Returns (CAR). In particular, we generate complex interaction models that can guide e-commerce investment decisions so managers can know, for example, which combination of IT and firm characteristics are more likely to be viewed positively by investors. The selected study variables as well as explanation of the proposed framework are informed by innovation, resource-based view, transaction cost economics and complementarity theories. We have inductively developed a set of propositions that can be deductively tested to assess the validity of our proposed theoretical framework. Hence our study provides an initial roadmap for theory development on e-commerce and CAR.     

A CMOS monolithic amplifier for cardiac EIT applications

Analog Integrated Circuits and Signal Processing - This paper presents a low-noise amplifier (LNA) with superior linearity for ultra-wideband (UWB) purposes. Linearity is a significant parameter...     

An energy-efficient spike encoding circuit for speech edge detection

In speech processing applications, the instantaneous bandwidth of speech can be used to adaptively control the performance of an audio sensor’s analog front end. Extracting the instantaneous bandwidth of speech depends on the detection of speech edges in the time–frequency plane. In this paper, we propose a spike encoding circuit for real-time and low-power speech edge detection. The circuit can directly encode the signal’s envelope information—an important feature to identify the speech edge—by temporal spike density without additional envelope extraction. Furthermore, the spike encoding circuit automatically adapts its resolution to the amplitude of the input signal, which improves the encoding resolution for small signal without increasing the power consumption. We use the nonlinear dynamical approach to design this circuit and analyze its stability. We also develop a linearized model for this circuit to provide the design intuition and to explain its adaptive resolution. Fabricated in 0.5-μm CMOS process, the spike encoding circuit consumes 0.3-μW power and the experimental results are presented.