To prevent the adulteration of agricultural resources and provide a solution to enhance the green coffee bean supply chain, authentication using the near-infrared spectroscopy (NIRS) technique was investigated. Partial least square with discrimination analysis (PLS-DA) models combined with various preprocessing methods were built from NIR spectra of 153 Vietnamese green coffee samples. The model combined with the standard normal variate and the first order of derivative yielded excellent performance in predicting coffee species with the error cross-validation of 0.0261. PLS-DA model of mean centre and first-order derivative spectra also yielded good performance in verifying geographical indication of green coffee with the error of 0.0656. By contrast, the predicting abilities of post-harvest methods were poor. The overall results showed a high potential of the NIRS in online authentication practices. 相似文献
The severe environmental pollution in many countries is caused by indiscriminate discharge of large quantities of food waste (FW), fat oil and grease (FOG) and sewage sludge (SS) to the environment. There are many possible treatment routes, but anaerobic digestion (AD) is now well accepted for treating several kinds of organic wastes. But AD of FW alone presents some operational challenges because of substrates and variability. Anaerobic co-digestion of two or more substrates is better than single substrate digestion. This can use a plant’s unused capacity, in line with the trend to renewable energy. Co-digestion technology, although well established in many European countries, is still in its infancy in Ireland. There are problems with different regulatory arrangements. They should be resolved. The paper reviews anaerobic co-digestion technology is reviewed, with special focus on possible application in Ireland. 相似文献
Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.
Rapid and sensitive point-of-care testing (POCT) is an extremely critical mission in practical applications, especially for rigorous military medicine, home health care, and in the third world. Here, we report a visual POCT method for adenosine triphosphate (ATP) detection based on Taylor rising in the corner of quadratic geometries between two rod surfaces. We discuss the principle of Taylor rising, demonstrating that it is significantly influenced by contact angle, surface tension, and density of the sample, which are controlled by ATP-dependent rolling circle amplification (RCA). In the presence of ATP, RCA reaction effectively suppresses Taylor-rising behavior, due to the increased contact angle, density, and decreased surface tension. Without addition of ATP, untriggered RCA reaction is favorable for Taylor rising, resulting in a significant height. With this proposed method, visual sensitive detection of ATP without the aid of other instruments is realized with only a 5 μL droplet, which has good selectivity and a low detection limit (17 nM). Importantly, this visual method provides a promising POCT tool for user-friendly molecular diagnostics. 相似文献
FeO-doped TiO2 nanoparticle photocatalysts were immobilized onto the surface of fibrous activated carbon (ACF) via a sol-gel process. As an adsorbent and photocatalyst, FeO-TiO2 on immobilized ACFs (FeO-TiO2/ACF) greatly improved the photocatalysis rate of hydrogen production as compared with pure TiO2 and ACF-TiO2 under UV irradiation and visible light. The addition of ACFs surface significantly reduced the photogenerated pairs of electrons-hole recombination, thereby promoting the photocatalysis action of doped photo-metal oxides of FeO-TiO2. Co-doping of FeO onto the lattice of the TiO2 approach can improve the absorption activity of visible light through photo-metal oxide of TiO2 and further enhance hydrogen production under visible light. The photocatalytic fabrics (FeO-TiO2/ACF) were effortlessly split out from the experimental solution for re-utilization and exhibited high stability even after five complete regeneration cycles. 相似文献
Recent technological advances have made it possible to support long lifetime and large volume streaming data transmissions
in sensor networks. A major challenge is to maximize the lifetime of battery-powered sensors to support such transmissions.
Battery, as the power provider of the sensors, therefore emerges as the key factor for achieving high performance in such
applications. Recent study in battery technology reveals that the behavior of battery discharging is more complex than we
used to think. Battery powered sensors might waste a huge amount of energy if we do not carefully schedule and budget their
discharging. In this paper we study the effect of battery behavior on routing for streaming data transmissions in wireless
sensor networks. We first give an on-line computable energy model to mathematically model battery discharge behavior. We show
that the model can capture and describe battery behavior accurately at low computational complexity and thus is suitable for
on-line battery capacity computation. Based on this battery model we then present a battery-aware routing (BAR) protocol to
schedule the routing in wireless sensor networks. The routing protocol is sensitive to the battery status of routing nodes
and avoids energy loss. We use the battery data from actual sensors to evaluate the performance of our protocol. The results
show that the battery-aware protocol proposed in this paper performs well and can save a significant amount of energy compared
to existing routing protocols for streaming data transmissions. Network lifetime is also prolonged with maximum data throughput.
As far as we know, this is the first work considering battery-awareness with an accurate analytical on-line computable battery
model in sensor network routing. We believe the battery model can be used to explore other energy efficient schemes for wireless
networks as well. 相似文献