ENERGY CONSERVATION FOR WIRELESS AD HOC ROUTING

Self-configuring wireless ad hoc networks have attracted considerable attention in the last few years due to their valuable civil and military applications. One aspect of such networks that has been studied insufficiently is the energy efficiency. Energy efficiency is crucial to prolong the network lifetime and thus make the network more survivable.Nodes in wireless ad hoc networks are most likely to be driven by battery and hence operate on an extremely frugal energy budget. Conventional ad hoc routing protocols are focused on handling the mobility instead of energy efficiency. Energy efficient routing strategies proposed in literature either do not take advantage of sleep modes to conserve energy more efficiently, or incur much overhead in terms of control message and computing complexity to schedule sleep modes and thus are not scalable.In this dissertation, a novel strategy is proposed to manage the sleep of the nodes in the network so that energy can be conserved and network connectivity can be kept. The novelty of the strategy is its extreme simplicity. The idea is derived from the results of the percolation theory, typically called gossiping. Gossiping is a convenient and effective approach and has been successfully applied to several areas of the networking. In the proposed work, we will developa sleep management protocol from gossiping for both static and mobile wireless ad hoc networks. Then the protocol will be extended to the asynchronous network, where nodes manage their own states independently. Analysis and simulations will be conducted to show thecorrectness, effectiveness and efficiency of the proposed work. The comparison between analytical and simulation results will justify them for each other. We will investigate the most important performance aspects concerning the proposed strategy, including the effect ofparameter tuning and the impacts of routing protocols. Furthermore, multiple extensions will be developed to improve the performance and make the proposed strategy apply to different network scenarios

Rational Expectation and Education Rewarding: The Case of Chinese Off-Farm Wage Employment

This study establishes a life-cycle model that a representative agent chooses optimal time of education to maximize his/her life earning, which implies that there may exist nonlinear relation between education and earning. Using the data of Chinese off-farm wage employment, we find that the duration of schooling years will increase by 1.7 years with 1 percent increase in rate of return to education. The empirical results also indicate that controversies about return to education might arise from model misspecification without consideration of nonlinearity and sample selection.return to schooling, life-cycle model, rational expectation, China, Labor and Human Capital, I20, J43, Q01,

Improvements on "Multi-Party Quantum Summation without a Third Party based on dd-Dimensional Bell States"

In 2021, Wu et al. presented a multi-party quantum summation scheme exploiting the entanglement properties of d-dimensional Bell states (Wu et al. in Quantum Inf Process 20:200, 2021). In particular, the authors proposed a three-party quantum summation protocol and then extended their work to a multi-party case. It is claimed that their protocol is secure against outside and participants' attacks. However, this work points out that Wu's protocol has a loophole, i.e., two or more dishonest participants who meet a specific location relationship can conspire to obtain the private inputs of some honest participants without being detected. Accordingly, improvements are proposed to address these issues

Adaptive Meshing Based on the Multi-level Partition of Unity and Dynamic Particle Systems for Medical Image Datasets

Surface meshes extracted from sparse medical images contain surface artifacts, there will produce serious distortion and generate numerous narrow triangle meshes. In order to eliminate the impact of the above factors, this paper presents a novel method for generating smooth and adaptive meshes from medical image datasets. Firstly, extracting the stack of contours by means of image segmentation and translating the contours into point clouds. The improved Multi-Level Partition of Unity (MPU) implicit functions are used to fit the point clouds for creating the implicit surface. Then, sampling implicit surface through dynamic particle systems based on Gaussian curvature, dense particles sampling in the high curvature region, sparse particles sampling in the low curvature region. Finally, generating triangle meshes based on particle distribution by using the Delaunay triangulation algorithm. Experimental results show that the proposed method can generate high-quality triangle meshes with distributed adaptively and have a nice gradation of triangle mesh density on the surface curvature

Evaluating essential features of proppant transport at engineering scales combining field measurements with machine learning algorithms

The behaviours of the particle settlement, stratified flow and inception of settled particles are essential features that determine the proppant transport in low-viscosity fracturing fluids. Although great efforts have been made to characterize these features, limited research work is performed at field scales. To test the laboratory outcomes, we propose a machine-learning-based workflow to evaluate the essential features using the measurements obtained from shale gas fracturing wells. Over 430,000 groups of fracturing data (1 s time interval) are collected and pre-processed to extract the particle settlement, stratified flow and inception features during fracturing operations. The GRU and SVM algorithms, trained by these features, are applied to predict fracturing pressure. Error analysis (the root mean squared error, RMSE) is carried out to compare the contributions of different features to the pressure prediction, based on which the features and the corresponding calculations are evaluated. Our result shows that the stratified-flow feature (fracture-level) possesses better interpretations for the proppant transport, in which the Bi-power model helps to produce the best predictions. The settlement and inception features (particle-level) perform better in cases where the pressure fluctuates significantly. The features characterize the state of proppant transport, based on which the development of subsurface fracture is also analyzed. Moreover, our analyses of the remaining errors in the pressure-ascending cases suggest that (1) an introduction of the alternate-injection process, and (2) the improved calculation of proppant transport in highly-filled fractures will be beneficial to both experimental observations and field applications

Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery

The Shigella bacteria cause bacillary dysentery, which remains a significant threat to public health. The genus status and species classification appear no longer valid, as compelling evidence indicates that Shigella, as well as enteroinvasive Escherichia coli, are derived from multiple origins of E.coli and form a single pathovar. Nevertheless, Shigella dysenteriae serotype 1 causes deadly epidemics but Shigella boydii is restricted to the Indian subcontinent, while Shigella flexneri and Shigella sonnei are prevalent in developing and developed countries respectively. To begin to explain these distinctive epidemiological and pathological features at the genome level, we have carried out comparative genomics on four representative strains. Each of the Shigella genomes includes a virulence plasmid that encodes conserved primary virulence determinants. The Shigella chromosomes share most of their genes with that of E.coli K12 strain MG1655, but each has over 200 pseudogenes, 300∼700 copies of insertion sequence (IS) elements, and numerous deletions, insertions, translocations and inversions. There is extensive diversity of putative virulence genes, mostly acquired via bacteriophage-mediated lateral gene transfer. Hence, via convergent evolution involving gain and loss of functions, through bacteriophage-mediated gene acquisition, IS-mediated DNA rearrangements and formation of pseudogenes, the Shigella spp. became highly specific human pathogens with variable epidemiological and pathological features

Altered Brain Function in Treatment-Resistant and Non-treatment-resistant Depression Patients: A Resting-State Functional Magnetic Resonance Imaging Study

ObjectiveIn this study, we used amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) to observe differences in local brain functional activity and its characteristics in patients with treatment-resistant depression (TRD) and non-treatment-resistant depression (nTRD), and to explore the correlation between areas of abnormal brain functional activity and clinical symptoms.MethodThirty-seven patients with TRD, 36 patients with nTRD, and 35 healthy controls (HCs) were included in resting-state fMRI scans. ALFF and ReHo were used for image analysis and further correlation between abnormal brain regions and clinical symptoms were analyzed.ResultsANOVA revealed that the significantly different brain regions of ALFF and ReHo among the three groups were mainly concentrated in the frontal and temporal lobes. Compared with the nTRD group, the TRD group had decreased ALFF in the left/right inferior frontal triangular gyrus, left middle temporal gyrus, left cuneus and bilateral posterior lobes of the cerebellum, and increased ALFF in the left middle frontal gyrus and right superior temporal gyrus, and the TRD group had decreased ReHo in the left/right inferior frontal triangular gyrus, left middle temporal gyrus, and increased ReHo in the right superior frontal gyrus. Compared with the HC group, the TRD group had decreased ALFF/ReHo in both the right inferior frontal triangular gyrus and the left middle temporal gyrus. Pearson correlation analysis showed that both ALFF and ReHo values in these abnormal brain regions were positively correlated with HAMD-17 scores (P < 0.05).ConclusionAlthough the clinical symptoms were similar in the TRD and nTRD groups, abnormal neurological functional activity were present in some of the same brain regions. Compared with the nTRD group, ALFF and ReHo showed a wider range of brain area alterations and more complex neuropathological mechanisms in the TRD group, especially in the inferior frontal triangular gyrus of the frontal lobe and the middle temporal gyrus of the temporal lobe

High-Throughput Functional MicroRNAs Profiling by Recombinant AAV-Based MicroRNA Sensor Arrays

BACKGROUND: microRNAs (miRNAs) are small and non-coding RNAs which play critical roles in physiological and pathological processes. A number of methods have been established to detect and quantify miRNA expression. However, method for high-throughput miRNA function detection is still lacking. PRINCIPAL FINDINGS: We describe an adeno-associated virus (AAV) vector-based microRNA (miRNA) sensor (Asensor) array for high-throughput functional miRNA profiling. Each Asensor contains a Gaussia luciferase (Gluc) and a firefly luciferase (Fluc) expression cassette to sense functional miRNA and to serve as an internal control respectively. Using this array, we acquired functional profiles of 115 miRNAs for 12 cell lines and found "functional miRNA signatures" for several specific cell lines. The activities of specific miRNAs including the let-7 family, miR-17-92 cluster, miR-221, and miR-222 in HEK 293 cells were compared with their expression levels determined by quantitative reverse transcriptase polymerase chain reaction (QRT-PCR). We also demonstrate two other practical applications of the array, including a comparison of the miRNA activity between HEK293 and HEK293T cells and the ability to monitor miRNA activity changes in K562 cells treated with 12-O-tetradecanoylphorbol-13-acetate (TPA). CONCLUSIONS/SIGNIFICANCE: Our approach has potential applications in the identification of cell types, the characterization of biological and pathological processes, and the evaluation of responses to interventions