ISAR Imaging of Missing Data Based on the Iterative Adaptive Approach

Through the coherent accumulation of target echoes, inverse synthetic aperture radar （ISAR） imaging achieves high azimuth resolution. However, because of the instability of the radar system, the echoes of the 1SAR will be randomly lost. The conventional FFT processing methods can cause image blur and high sidelobes or other issues. A novel algorithm for ISAR missing-data imaging based on the Iterative Adaptive Approach （IAA） is proposed. The algorithm enjoys global convergence properties and does not need to set the parameters in advance. The missing-data ISAR imaging results for simulated and measured data illustrate the effectiveness of the algorithm.

Liver transplantation for intermediate hepatocellular carcinoma: An adaptive approach

Hepatocellular carcinoma is becoming an increasing indication for liver transplantation, but selection and allocation of patients are challenging because of organ shortages. Conventional Milan criteria are the reference for the selection of patients worldwide, but many expanded criteria, like University of California San Francisco criteria and up-to-7 criteria, have demonstrated that survival and recurrence results are lower than those for restricted indications. Correct staging is crucial and should include surrogate markers of biological aggressiveness(α-fetoprotein, response to loco-regional treatments). Successful down-staging can select between patients with tumor burden initially beyond transplantation criteria those with a more favorable biology, provided a 3-mo stability in meeting the transplantation criteria. Allocation rules are constantly adjusted to minimize the imbalance between the priorities of candidates with and without hepatocellular carcinoma, and take into account local donor rate and waitlist dynamics. Recently, Mazzaferro et al proposed a benefit-oriented "adaptive approach", in which the selection and allocation of patients are based on their response to non-transplantation treatments: low priority for transplantation in case of complete response, high priority in case of partial response or recurrence, and no listing in case of progression beyond transplantation criteria.

Extreme Wave Simulation with Iterative Adaptive Approach in Numerical Wave Flume

Extreme wave is highly nonlinear and may occur due to diverse reasons unexpectedly.The simulated results of extreme wave based on wave focusing,which were generated using high order spectrum method,are presented.The influences of the steepness,frequency bandwidth as well as frequency spectrum on focusing position shift were examined,showing that they can affect the wave focusing significantly.Hence,controlled accurate generation of extreme wave at a predefined position in wave flume is a difficult but important task.In this paper,an iterative adaptive approach is applied using linear dispersion theory to optimize the control signal of the wavemaker.The performance of the proposed approach is numerically investigated for a wide variety of scenarios.The results demonstrate that this approach can reproduce accurate wave focusing effectively.

Extreme Adaptation to Extreme Environments in Hot Dry, Hot Sub-humid and Hot Humid Climates in Mexico

The paper discusses the results of a field study carried out in four cities in Mexico： Hermosillo, Mexicali, Merida and Colima, during the warmest seasons of 2006-2007. The survey is according to the adaptive approach of thermal comfort. The cities＇ climates are hot dry, hot sub-humid and hot humid. The respondents were inhabitants of low cost housings without air conditioning. The research was performed during warm seasons and according to ISO 10551. The measurements were processed by the common method of linear regression and also by alternative methods, useful for asymmetric climates. Individuals declared comfort at very high temperatures, either high or low humidity, therefore, the resulting neutral temperatures are higher than 30 ℃, except in Colima （28.8 ℃）. The upper limits of comfort ranges achieved temperatures up to 35 ℃. The results suggest how great is the capacity of humans to adapt to conditions as extreme as those measured in the study.

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

A New Fuzzy Adaptive Algorithm to Classify Imbalanced Data

Classification of imbalanced data is a well explored issue in the data mining and machine learning community where one class representation is overwhelmed by other classes.The Imbalanced distribution of data is a natural occurrence in real world datasets,so needed to be dealt with carefully to get important insights.In case of imbalance in data sets,traditional classifiers have to sacrifice their performances,therefore lead to misclassifications.This paper suggests a weighted nearest neighbor approach in a fuzzy manner to deal with this issue.We have adapted the‘existing algorithm modification solution’to learn from imbalanced datasets that classify data without manipulating the natural distribution of data unlike the other popular data balancing methods.The K nearest neighbor is a non-parametric classification method that is mostly used in machine learning problems.Fuzzy classification with the nearest neighbor clears the belonging of an instance to classes and optimal weights with improved nearest neighbor concept helping to correctly classify imbalanced data.The proposed hybrid approach takes care of imbalance nature of data and reduces the inaccuracies appear in applications of original and traditional classifiers.Results show that it performs well over the existing fuzzy nearest neighbor and weighted neighbor strategies for imbalanced learning.

Cascade adaptive control of uncertain unified chaotic systems

The chaos control of uncertain unified chaotic systems is considered. Cascade adaptive control approach with only one control input is presented to stabilize states of the uncertain unified chaotic system at the zero equilibrium point. Since an adaptive controller based on dynamic compensation mechanism is employed, the exact model of the unified chaotic system is not necessarily required. By choosing appropriate controller parameters, chaotic phenomenon can be suppressed and the response speed is tunable. Sufficient condition for the asymptotic stability of the approach is derived. Numerical simulation results confirm that the cascade adaptive control approach with only one control signal is valid in chaos control of uncertain unified chaotic systems.

Breed and adaptive response modulate bovine peripheral blood cells＇ transcriptome

Background： Adaptive response includes a variety of physiological modifications to face changes in external or internal conditions and adapt to a new situation. The acute phase proteins（APPs） are reactants synthesized against environmental stimuli like stress, infection, inflammation.Methods： To delineate the differences in molecular constituents of adaptive response to the environment we performed the whole-blood transcriptome analysis in Italian Holstein（IH） and Italian Simmental（IS） breeds. For this, 663 IH and IS cows from six commercial farms were clustered according to the blood level of APPs. Ten extreme individuals（five APP＋ and APP-variants） from each farm were selected for the RNA-seq using the Illumina sequencing technology. Differentially expressed（DE） genes were analyzed using dynamic impact approach（DIA）and DAVID annotation clustering. Milk production data were statistically elaborated to assess the association of APP＋ and APP-gene expression patterns with variations in milk parameters.Results： The overall de novo assembly of cDNA sequence data generated 13,665 genes expressed in bovine blood cells. Comparative genomic analysis revealed 1,152 DE genes in the comparison of all APP＋ vs. all APP-variants; 531 and 217 DE genes specific for IH and IS comparison respectively. In all comparisons overexpressed genes were more represented than underexpressed ones. DAVID analysis revealed 369 DE genes across breeds, 173 and 73 DE genes in IH and IS comparison respectively. Among the most impacted pathways for both breeds were vitamin B6 metabolism, folate biosynthesis, nitrogen metabolism and linoleic acid metabolism.Conclusions： Both DIA and DAVID approaches produced a high number of significantly impacted genes and pathways with a narrow connection to adaptive response in cows with high level of blood APPs. A similar variation in gene expression and impacted pathways between APP＋ and APP-variants was found between two studied breeds. Such similarity was also confirmed by annotation clustering of the DE genes. However, IH breed showed higher and more differentiated impacts compared to IS breed and such particular features in the IH adaptive response could be explained by its higher metabolic activity. Variations of milk production data were significantly associated with APP＋ and APP-gene expression patterns.

Hybrid approach used for extended image-based wavefront sensor-less adaptive optics

The stochastic paralld gradient descent （SPGD） algorithm is widely used in wavefront sensor-less adaptive optics （WSAO） systems. However, the convergence is relatively slow. Modal-based algorithms usually provide much faster convergence than SPGD; however, the limited actuator stroke of the deformable mirror （DM） often prohibits the sensing of higher-order modes or renders a closed-loop correction inapplicable. Based on a comparative analysis of SPGD and the DM-modal-based algorithm, a hybrid approach involving both algorithms is proposed for extended image-based WSAO, and is demonstrated in this experiment. The hybrid approach can achieve similar correction results to pure SPGD, but with a dramatically decreased iteration number.