Bi-Fi: an embedded sensor/system architecture for REMOTE biological monitoring.

Wireless-enabled processor modules intended for communicating low-frequency phenomena (i.e., temperature, humidity, and ambient light) have been enabled to acquire and transmit multiple biological signals in real time, which has been achieved by using computationally efficient data acquisition, filtering, and compression algorithms, and interfacing the modules with biological interface hardware. The sensor modules can acquire and transmit raw biological signals at a rate of 32 kb/s, which is near the hardware limit of the modules. Furthermore, onboard signal processing enables one channel, sampled at a rate of 4000 samples/s at 12-bit resolution, to be compressed via adaptive differential-pulse-code modulation (ADPCM) and transmitted in real time. In addition, the sensors can be configured to filter and transmit individual time-referenced "spike" waveforms, or to transmit the spike height and width for alleviating network traffic and increasing battery life. The system is capable of acquiring eight channels of analog signals as well as data via an asynchronous serial connection. A back-end server archives the biological data received via networked gateway sensors, and hosts them to a client application that enables users to browse recorded data. The system also acquires, filters, and transmits oxygen saturation and pulse rate via a commercial-off-the-shelf interface board. The system architecture can be configured for performing real-time nonobtrusive biological monitoring of humans or rodents. This paper demonstrates that low-power, computational, and bandwidth-constrained wireless-enabled platforms can indeed be leveraged for wireless biosignal monitoring.     

双相不锈钢中的相变:金相和磁性质研究

用金相和不同的无损磁试验对商业低合金双相不锈钢及超级双相不锈钢因热处理和冷加工产生的显微结构过程进行了研究。在双相不锈钢(DSS)和超级双相不锈钢(SDSS)中,亚稳态的铁素体由于热处理可以分解为σ相和二次奥氏体相。这些显微结构的改变显著影响着钢材的机械性能、耐腐蚀性能和磁特性。磁性测量使得破坏性实验中获取的谐频和机械性能存在定量关系。在低合金双相不锈钢中,当低含量的Ni被N和Mn取代后,可能导致奥氏体相的不稳定性,奥氏体相容易转变为亚稳态的马氏体相,而SDSS奥氏体会更加稳定。通过室温冷轧(10%～85%压下率)的方法进行固溶退火材料的塑性变形研究,2101材料仅考察了轧制条件,而冷轧2507样本还进行了热处理。冷轧样品用光学显微镜(OM)、扫描电子显微镜(SEM)、X-射线衍射(XRD)和磁试验进行了研究。还对双相不锈钢样品进行了三种不同的磁试验,以确定其磁性能。结果表明冷轧对晶粒的大小和形状有一定影响,磁测试结果还显示2101样品中存在马氏体。     

“台风”战斗机FGR4

作为本书的彩蛋,本章我们为您带来向不列颠之战75周年致敬的第29飞行中队"台风"战斗机。这是IstvanMichalko用威望(REVELL)1/48模型完成的杰作,机身涂装为战役中唯一获得维多利亚十字勋章的尼科尔森中尉座机。     

A Detailed Experimental Study of the LEO Satellite to Indoor Channel Characteristics

General investigations and results from ratherextensive measurement campaigns are presented tocharacterize the propagation channel of SatellitePersonal Communication Systems; in the measurements the satellite was simulated by a helicopter andmeasurements at S band and L band were made. A newconceptual partitioning of the full link is introduced;the measrement equipment and the environment aredescribed. The major part of the paper deals withpresentation of the measurement results and theiranalyses. Measured and analyzed are average lossincluding satellite elevation and azimuth positiondependence, first-order statistics, Doppler characteristics, andwide-band characteristics. Finally, some rather generalconclusions are drawn.     

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.     

Materials Fabrication from Native and Recombinant Thermoplastic Squid Proteins

Natural elastomers made from protein extracts have received significant interest as eco‐friendly functional materials due to their unique mechanical and optical properties emanating from secondary structures. The next generation sequencing approach is used to identify protein sequences in a squid ring teeth complex extracted from Loligo vulgaris and the use of recombinant expression is demonstrated in the fabrication of a new generation of thermoplastic materials. Native and recombinant thermoplastic squid proteins exhibit reversible solid to melt phase transition, enabling them to be thermally shaped into 3D geometries such as fibers, colloids, and thin films. Direct extraction or recombinant expression of protein based thermoplastics opens up new avenues for materials fabrication and synthesis, which will eventually be competitive with the high‐end synthetic oil based plastics.     

Independence divergence-generated binary trees of amino acids

The discovery of the relationship between amino acids is importantin terms of the replacement ability, as used in protein engineeringhomology studies, and gaining a better understanding of theroles which various properties of the residues play in the creationof a unique, stable, 3-D protein structure. Amino acid sequencesof proteins edited by evolution are anything but random. Themeasure of nonrandomness, i.e. the level of editing, can becharacterized by an independence divergence value. This parameteris used to generate binary tree relationships between aminoacids. The relationships of residues presented in this paperare based on protein building features and not on the physico-chemicalcharacteristics of amino acids. This approach is not biasedby the tautology present in all sequence similarity-based relationshipstudies. The roles which various physico-chemical characteristicsplay in the determination of the relationships between aminoacids are also discussed.     

Tests on the photomultiplier Burle C83061E for underground physics applications

In this article we describe the main results of the test activity performed on the large cathode area phototube Burle C83061E. This tube, originally developed for a large-scale Cherenkov detector, has been considered as a potential candidate for the use in the proposed low energy solar neutrino experiment Borexino. Thus the measurements carried out have been particularly focused to evaluate the parameters of this device more critically affecting the overall physics performances of the detector.     

Assessment of the risk imposed by the presence of corroded shallow voids on the initialization and long-term reliability of hard disk drives

Microsystem Technologies - This paper investigates how corrosion growth that originates from shallow film voids on thin film sputtered magnetic media can impact the final HDD system performance....     

Synaptic noise as a means of implementing weight-perturbation learning

Weight-perturbation (WP) algorithms for supervised and/or reinforcement learning offer improved biological plausibility over backpropagation because of their reduced circuitry requirements for realization in neural hardware. This paper explores the hypothesis that biological synaptic noise might serve as the substrate by which weight perturbation is implemented. We explore the basic synaptic noise hypothesis (BSNH), which embodies the weakest assumptions about the underlying neural circuitry required to implement WP algorithms. This paper identifies relevant biological constraints consistent with the BSNH, taxonomizes existing WP algorithms with regard to consistency with those constraints, and proposes a new WP algorithm that is fully consistent with the constraints. By comparing the learning effectiveness of these algorithms via simulation studies, it is found that all of the algorithms can support traditional neural network learning tasks and have similar generalization characteristics, although the results suggest a trade-off between learning efficiency and biological accuracy.