Optimal Sensing and Transmission in Energy Harvesting Sensor Networks

Sensor networks equipped with energy harvesting (EH) devices have attracted great attentions recently. Compared with conventional sensor networks powered by batteries, the energy harvesting abilities of the sensor nodes make sustainable and environment-friendly sensor networks possible. However, the random, scarce and non-uniform energy supply features also necessitate a completely different approach to energy management. A typical EH wireless sensor node consists of an EH module that converts ambient energy to electrical energy, which is stored in a rechargeable battery, and will be used to power the sensing and transmission operations of the sensor. Therefore, both sensing and transmission are subject to the stochastic energy constraint imposed by the EH process. In this dissertation, we investigate optimal sensing and transmission policies for EH sensor networks under such constraints. For EH sensing, our objective is to understand how the temporal and spatial variabilities of the EH processes would affect the sensing performance of the network, and how sensor nodes should coordinate their data collection procedures with each other to cope with the random and non-uniform energy supply and provide reliable sensing performance with analytically provable guarantees. Specifically, we investigate optimal sensing policies for a single sensor node with infinite and finite battery sizes in Chapter 2, status updating/transmission strategy of an EH Source in Chapter 3, and a collaborative sensing policy for a multi-node EH sensor network in Chapter 4. For EH communication, our objective is to evaluate the impacts of stochastic variability of the EH process and practical battery usage constraint on the EH systems, and develop optimal transmission policies by taking such impacts into consideration. Specifically, we consider throughput optimization in an EH system under battery usage constraint in Chapter 5

SIMULATION OF MICROBUBBLE RESISTANCE REDUCTION ON A SUBOFF MODEL

This paper presents a mixture-model based computational fluid dynamics (CFD) simulation on the two-phase microbubble flow over the hull of a SUBOFF model, aimed at assessing the roles of air-injection-to-freestream velocity ratio and air volume fraction in microbubble resistance reduction. The numerical framework consists of the Reynolds-average Navier-Stokes (RANS) equations and the standard turbulence model with standard wall function treatment, which is validated, without microbubbles, by existing experimental data of the same SUBOFF model. The effect of velocity ratio is then investigated by comparing different types of the resistance reduction at various water speeds, and the effect of air volume fraction on the friction resistance reduction is also studied with various air injection velocities. This study confirms that both the velocity ratio and air volume fraction play important roles in the microbubble resistance reduction phenomenon

An information-flow-based model with dissipation, saturation and direction for active pathway inference

<p>Abstract</p> <p>Background</p> <p>Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.</p> <p>Results</p> <p>In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.</p> <p>Conclusions</p> <p>Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.</p

Microwave-Assisted Oxidation of Electrospun Turbostratic Carbon Nanofibers for Tailoring Energy Storage Capabilities

We report the systematic structural manipulation of turbostratic electrospun carbon nanofibers (ECNFs) using a microwave-assisted oxidation process, which is extremely rapid and highly controllable and affords controlled variation of the capacitive energy storage capabilities of ECNFs. We find a nonmonotonic relationship between the oxidation degree of ECNFs and their electrocapacitive performance and present a detailed study on the electronic and crystalline structures of ECNFs to elucidate the origin of this nonmonotonic relation. The ECNFs with an optimized oxidation level show ultrahigh capacitances at high operation rates, exceptional cycling performance, and an excellent energy–power combination. We have identified three key factors required for optimal energy storage performance for turbostratic carbon systems: (i) an abundance of surface oxides, (ii) microstructural integrity, and (iii) an appropriate interlayer spacing

Differential expression of six genes and correlation with fatness traits in a unique broiler population

AbstractPrevious results from genome wide association studies (GWASs) in chickens divergently selected for abdominal fat content of Northeast Agricultural University (NEAUHLF) showed that many single nucleotide polymorphism (SNP) variants were associated with abdominal fat content. Of them, six top significant SNPs at the genome level were located within SRD5A3, SGCZ, DLC1, GBE1, GALNT9 and DNAJB6 genes. Here, expression levels of these six candidate genes were investigated in abdominal fat and liver tissue between fat and lean broilers from the 14th generation population of NEAUHLF. The results showed that expression levels of SRD5A3, SGCZ and DNAJB6 in the abdominal fat and SRD5A3, DLC1, GALNT9, DNAJB6 and GBE1 in the liver tissue differed significantly between the fat and lean birds, and were correlated with abdominal fat traits. The findings will provide important references for further function investigation of the six candidate genes involved in abdominal fat deposition in chickens

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network- enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity

Pseudonocardians A–C, New Diazaanthraquinone Derivatives from a Deap-Sea Actinomycete Pseudonocardia sp. SCSIO 01299

Pseudonocardians A–C (2–4), three new diazaanthraquinone derivatives, along with a previously synthesized compound deoxynyboquinone (1), were produced by the strain SCSIO 01299, a marine actinomycete member of the genus Pseudonocardia, isolated from deep-sea sediment of the South China Sea. The structures of compounds 1–4 were determined by mass spectrometry and NMR experiments (1H, 13C, HSQC, and HMBC). The structure of compound 1, which was obtained for the first time from a natural source, was confirmed by X-ray analysis. Compounds 1–3 exhibited potent cytotoxic activities against three tumor cell lines of SF-268, MCF-7 and NCI-H460 with IC50 values between 0.01 and 0.21 μm, and also showed antibacterial activities on Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212 and Bacillus thuringensis SCSIO BT01, with MIC values of 1–4 μg mL−1

ORF8-Related Genetic Evidence for Chinese Horseshoe Bats as the Source of Human Severe Acute Respiratory Syndrome Coronavirus

Several lineage B betacoronaviruses termed severe acute respiratory syndrome (SARS)–like CoVs (SL-CoVs) were identified from Rhinolophus bats in China. These viruses are characterized by a set of unique accessory open reading frames (ORFs) that are located between the M and N genes. Among unique accessory ORFs, ORF8 is most hypervariable. In this study, the ORF8s of all SL-CoVs were classified into 3 types, and, for the first time, it was found that very few SL-CoVs from Rhinolophus sinicus have ORF8s that are identical to that of human SARS-CoV. This finding provides new genetic evidence for Chinese horseshoe bats as the source of human SARS-CoV

Mechanical Properties and Damage Evolution of Concrete Materials Considering Sulfate Attack

In order to study the durability of concrete materials subjected to sulfate attack, in a sulfate attack environment, a series of concrete tests considering different fly ash contents and erosion times were conducted. The mechanical properties and the micro-structure of concrete under sulfate attack were studied based on the following: uniaxial compressive strength test, split tensile test, ultrasonic impulse method, scanning electron microscopy (SEM) and X-ray diffraction (XRD). The mechanical properties were compressive strength, splitting tensile strength, and relative dynamic elastic modulus, respectively. Additionally, according to the damage mechanical theory, experimental results and micro-structure analysis, the damage evolution process of concrete under a sulfate attack environment were studied in detail. Finally, according to the sulfate attack time and fly ash content, a damage model of the sulfate attack of the binary surface was established. The specific results are as follows: under the action of sulfate attack, the change law of the rate of mass change, relative dynamic modulus of elasticity, corrosion resistance coefficient of compressive strength, and the corrosion resistance coefficient of the splitting tensile strength of concrete all increase first and then decrease. Under the same erosion time, concrete mixed with 10% fly ash content has the best sulfate resistance. Through data regression, the damage evolution equation of the sulfate attack was developed and there is an exponential function relationship among the different damage variables. The binary curved surface regression effect of the concrete damage and the erosion time and the amount of fly ash is significant, which can predict deterioration of concrete damage under sulfate attack. During the erosion time, the combined expansion of ettringite and gypsum caused micro cracks. With an increase of corrosion time, micro cracks developed and their numbers increased