ALBERT

Description: Nanoporous metals (NPMs) have proven to be all-round candidates in versatile and diverse applications. In this decade, interest has grown in the fabrication, characterization and applications of these intriguing materials. Most existing reviews focus on the experimental and theoretical works rather than the numerical simulation. Actually, with numerous experiments and theory analysis, studies based on computer simulation, which may model complex microstructure in more realistic ways, play a key role in understanding and predicting the behaviors of NPMs. In this review, we present a comprehensive overview of the computer simulations of NPMs, which are prepared through chemical dealloying. Firstly, we summarize the various simulation approaches to preparation, processing, and the basic physical and chemical properties of NPMs. In this part, the emphasis is attached to works involving dealloying, coarsening and mechanical properties. Then, we conclude with the latest progress as well as the future challenges in simulation studies. We believe that highlighting the importance of simulations will help to better understand the properties of novel materials and help with new scientific research on these materials.

Description: Apartments for university talent (AUT), are apartments provided to staff at non-market price, in order to attract outstanding scholars from around the world to work in universities and improve educational quality. This has been a critical issue in achieving social sustainability in China during rapid urbanization and industrialization. The agent construction mode has been adopted to build AUT because universities usually lack relevant management experience. The agent construction mode is a type of turnkey engineer construction based on the principal-agent model, project bidding mode, engineering contracting projects, and project supervision system. Risk factors are important considerations for both universities and agent construction companies. Although some studies have investigated the risk factors, only a few studies have identified the hierarchical structure of relevant risk factors. Therefore, the interrelationship and prioritization of the risk factors remain unknown, and this situation presents a barrier to better risk management. This paper investigates the interrelationship of risk factors with interpretative structural modeling (ISM). In addition, fuzzy MICMAC (matric d’impacts croises-multiplication appliqué a un classemen) analysis was conducted to prioritize the risk factors. The findings provide useful references for better risk management of building AUT through the agent construction mode. Although this study focuses on China, the analytical process can also be generalized to other research topics and other countries.

Description: An on-chip optical humidity sensor using Nano-anatase TiO2 coating is presented here. The coating material was prepared so that the result is in solution form, making the fabrication process quick and simple. Then, the solution was effortlessly spin-coated on an SU8 straight channel waveguide. Investigating the sensitivity and performance (response time) of the device revealed a great linearity in the wide range (35% to 98%) of relative humidity (RH). In addition, a variation of more than 14 dB in transmitted optical power was observed, with a response time of only ~0.7 s. The effect of coating concentration and UV treatment was examined on the performance and repeatability of the sensor. Interesting observations were found, and the attributed mechanisms were described. In addition, the proposed sensor was extensively compared with other state-of-the-art proposed counterparts from the literature and remarkable advantages were found. Since a high sensitivity of ~0.21 dB/%RH and high dynamic performances were demonstrated, this sensor is proposed for use in biomedical applications.

Description: Background: Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. Methods: This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with corresponding proportional reporting ratios (PRR) calculated. All the data was stored in an RDF file. We then applied network analysis approaches to the RDF data to illustrate a use case of the data for longititual studies. We further dicussed our vision on integrating the data with vaccine information from other sources using RDF linked approach to facilitate more comprehensive analyses. Results: The 1990–2013 data from VAERS has been extracted from the VAERS database. There are 83,148 unique vaccine-symptom pairs with 75 vaccine types and 5,865 different reported symptoms. The yearly and over PRR values for each reported vaccine-symptom pair were calculated. The network properties of networks consisting of significant vaccine-symptom associations (i.e., PRR larger than 1) were then investigated. The results indicated that vaccine-symptom association network is a dense network, with any given node connected to all other nodes through an average of approximately two other nodes and a maximum of five nodes.

Description: Background: Vaccines have been one of the most successful public health interventions to date. The use of vaccination, however, sometimes comes with possible adverse events. The U.S. FDA/CDC Vaccine Adverse Event Reporting System (VAERS) currently contains more than 200,000 reports for post-vaccination events that occur after the administration of vaccines licensed in the United States. Although the data from the VAERS has been applied to many public health and vaccine safety studies, each individual report does not necessarily indicate a casuality relationship between the vaccine and the reported symptoms. Further statistical analysis and summarization needs to be done before this data can be leveraged. Methods: This paper introduces our efforts on representing the vaccine-symptom correlations and their corresponding meta-information extracted from the VAERS database using Resource Description Framework (RDF). Numbers of occurrences of vaccine-symptom pairs reported to the VAERS were summarized with corresponding proportional reporting ratios (PRR) calculated. All the data was stored in an RDF file. We then applied network analysis approaches to the RDF data to illustrate a use case of the data for longititual studies. We further dicussed our vision on integrating the data with vaccine information from other sources using RDF linked approach to facilitate more comprehensive analyses. Results: The 1990?2013 data from VAERS has been extracted from the VAERS database. There are 83,148 unique vaccine-symptom pairs with 75 vaccine types and 5,865 different reported symptoms. The yearly and over PRR values for each reported vaccine-symptom pair were calculated. The network properties of networks consisting of significant vaccine-symptom associations (i.e., PRR larger than 1) were then investigated. The results indicated that vaccine-symptom association network is a dense network, with any given node connected to all other nodes through an average of approximately two other nodes and a maximum of five nodes.

Description: In the smart grid, large consumers can procure electricity energy from various power sources to meet their load demands. To maximize its profit, each large consumer needs to decide their energy procurement strategy under risks such as price fluctuations from the spot market and power quality issues. In this paper, an electric energy procurement decision-making model is studied for large consumers who can obtain their electric energy from the spot market, generation companies under bilateral contracts, the options market and self-production facilities in the smart grid. Considering the effect of unqualified electric energy, the profit model of large consumers is formulated. In order to measure the risks from the price fluctuations and power quality, the expected utility and entropy is employed. Consequently, the expected utility and entropy decision-making model is presented, which helps large consumers to minimize their expected profit of electricity procurement while properly limiting the volatility of this cost. Finally, a case study verifies the feasibility and effectiveness of the proposed model.

Description: A microfibre device integrating a microfibre knot resonator in a Sagnac loop reflector is proposed for refractive index and temperature sensing. The reflective configuration of this optical structure offers the advantages of simple fabrication and ease of sensing. To achieve a balance between responsiveness and robustness, the entire microfibre structure is embedded in low index Teflon, except for the 0.5–2 mm diameter microfibre knot resonator sensing region. The proposed sensor has exhibited a linear spectral response with temperature and refractive index. A small change in free spectral range is observed when the microfibre device experiences a large refractive index change in the surrounding medium. The change is found to be in agreement with calculated results based on dispersion relationships.

Description: Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged. Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E.Coli K-12. Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks.

Description: Background: Potassium (K+) is an important nutrient ion in plant cells and plays crucial roles in many plant physiological and developmental processes. In the natural environment, K+ deficiency is a common abiotic stress that inhibits plant growth and reduces crop productivity. Several microarray studies have been conducted on genome-wide gene expression profiles of rice during its responses to various stresses. However, little is known about the transcriptional changes in rice genes under low-K+ conditions. Results: We analyzed the transcriptomic profiles of rice roots in response to low-K+ stress. The roots of rice seedlings with or without low-K+ treatment were harvested after 6 h, and 3 and 5 d, and used for microarray analysis. The microarray data showed that many genes (2,896) were up-regulated or down-regulated more than 1.2-fold during low-K+ treatment. GO analysis indicated that the genes showing transcriptional changes were mainly in the following categories: metabolic process, membrane, cation binding, kinase activity, transport, and so on. We conducted a comparative analysis of transcriptomic changes between Arabidopsis and rice under low-K+ stress. Generally, the genes showing changes in transcription in rice and Arabidopsis in response to low-K+ stress displayed similar GO distribution patterns. However, there were more genes related to stress responses and development in Arabidopsis than in rice. Many auxin-related genes responded to K+ deficiency in rice, whereas jasmonic acid-related enzymes may play more important roles in K+ nutrient signaling in Arabidopsis. Conclusions: According to the microarray data, fewer rice genes showed transcriptional changes in response to K+ deficiency than to phosphorus (P) or nitrogen (N) deficiency. Thus, transcriptional regulation is probably more important in responses to low-P and -N stress than to low-K+ stress. However, many genes in some categories (protein kinase and ion transporter families) were markedly up-regulated, suggesting that they play important roles during K+ deficiency. Comparative analysis of transcriptomic changes between Arabidopsis and rice showed that monocots and dicots share many similar mechanisms in response to K+ deficiency, despite some differences. Further research is required to clarify the differences in transcriptional regulation between monocots and dicots.

Description: This paper deals with a fault-tolerant semi-12-phase permanent-magnet synchronous machine (PMSM) used for electric vehicles. High fault-tolerant and low toque ripple features are achieved by employing fractional slot concentrated windings (FSCWs) and open windings. Excessive magnetomotive force (MMF) harmonic components can lead to thermal demagnetization of rotor magnets as well as high core loss. An improved all-teeth-wound winding disposition that changes the winding factor of each harmonic is applied to suppress harmonics. A relatively large slot leakage inductance that limits the short-circuit current (SCC) induced in the short-circuited winding is proposed to deal with short-circuit fault. Fault-tolerant controls up to two phases open circuited are investigated in this paper based on keeping the same torque-producing MMF. The fault-tolerant control strategies corresponding to each faulty mode are studied and compared to ensure high performance operation.