Characterization of the thermoelastic martensitic transformation in a NiTi alloy driven by temperature variation and external stress

In order to test the concept of the physics of dissipation during first-order phase transitions in solids, we measured the internal friction (Q-1) and the relative shear modulus (μ) during a thermoelastic martensitic transformation in a NiTi alloy. We adopted two approaches: temperature variation and application of external stress. This investigation of internal friction was carried out with various vibration frequencies ω, temperature variation rates Ṫ, and strain variation rates ɛ̇. The index l (coupling factor between phase interface and oscillating stress) and index n (rate exponent for the effective phase transformation driving force) have been calculated from the experimental data for each case and the values of l and n are about the same in the two (doped) NiTi samples, irrespective of whether the phase transition is driven by a temperature variation or stress induced process. We compare the values of n and l for the NiTi samples with that of the other samples (VO2 ceramics and FeMn alloys), reinforcing the previous physical interpretations of these indices. We believe the indices n and l are indeed fingerprints of first-order phase transitions in solids.published_or_final_versio

Analysis of dissipation of a burst-type martensite transformation in a Fe-Mn alloy by internal friction measurements

Recently, we have proposed a theory to analyze the first-order phase transition (FOPT) in solids. In order to test the concept of the physics of dissipation during FOPT in solids, it is necessary to test the theory with different FOPT system. We study here a burst-type martensite transformation in a Fe-18.8% Mn alloy sample for this purpose. We investigate the characteristics of γ(fcc)⇌ɛ(hcp) transformation in this alloy and measure the dependence of internal friction (IF) during γ/ɛ transformation in varying rate of temperature Ṫ and vibration frequency ω. For free oscillations, the IF was defined to be Qδ-1=δ/π where δ is the logarithmic decrement. For general (forced) oscillations, IF is usually defined to be Qw-1=(1/2π)(ΔW/W), where ΔW is the dissipation over one cycle, while W is the maximum stored energy. During our analysis, the relation between Qδ-1 and Qw-1 is deduced. The parameter l (coupling factor between phase interface and oscillating stress) takes a small value (0.015–0.035) during PT, but takes a large value (0.86) during static state. The parameter n (exponent of rate for effective PT driving force) takes a large value 0.33 during heating and 0.47 during cooling. The physical meaning of n and l is discussed. The methodology introduced here appears to be an effective way of studying FOPT in solids. © 1996 The American Physical Society.published_or_final_versio

Induced defense and its cost in two bryophyte species

Premise: Current knowledge about defense strategies in plants under herbivore pressure is predominantly based on vascular plants. Bryophytes are rarely consumed by herbivores since they have ample secondary metabolites. However, it is unknown whether bryophytes have induced defenses against herbivory and whether there is a trade-off between growth and defense in bryophytes. Methods: In an experiment with two peatland bryophytes, Sphagnum magellanicum Brid. and S. fuscum (Schimp.) H. Klinggr., two kinds of herbivory, clipping with scissors and grazing by mealworms (Tenebrio molitor L.) were simulated. At the end of the experiment, we measured growth traits, carbon-based defense compounds (total phenolics and cellulose) and storage compounds (total nonstructural carbohydrates) of these two Sphagnum species. Results: Grazing but not clipping increased total phenolics and C:N ratio and reduced biomass production and height increment. A negative relationship between biomass production and total phenolics was found in S. magellanicum but not in S. fuscum, indicating a growth–defense trade-off that is species-specific. Grazing reduced the sugar starch content of S. magellanicum and the sugar of S. fuscum. Either clipping or grazing had no effect on chlorophyll fluorescence (including actual and maximum photochemical efficiency of photosystem II) except that a significant effect of clipping on actual photochemical efficiency in S. fuscum was observed. Conclusions: Our results suggest that Sphagnum can have induced defense against herbivory and that this defense can come at a cost of growth. These findings advance our knowledge about induced defense in bryophytes, the earliest land plants

Triple-Mode Cavity Bandpass Filter on Doublet with Controllable Transmission Zeros

© 2013 IEEE. On the basis of doublet and its properties, a class of multiple-mode narrow band bandpasss filter is designed and fabricated by simultaneously exploiting the three resonant modes in a single rectangular cavity: TE101, TE011, and TM110 modes. The input/output ports of the proposed filter are fed by coupling a microstrip line to a slot on the side wall of a rectangular cavity. Different modes are excited by changing the position and shape of the two slots at input and output of the rectangular cavity without any intra-cavity coupling. Besides three poles within the passband, a pair of transmission zeros (TZs) is achieved, which can be controlled independently by setting the positions of the two TZs at the lower and/or upper stopband. High stopband attenuation and high filtering selectivity are achieved by considerably allocating three transmission poles and two zeros. In order to verify the proposed theory, two filter prototypes are fabricated and measured

IoT Device Identification Using Deep Learning

The growing use of IoT devices in organizations has increased the number of attack vectors available to attackers due to the less secure nature of the devices. The widely adopted bring your own device (BYOD) policy which allows an employee to bring any IoT device into the workplace and attach it to an organization's network also increases the risk of attacks. In order to address this threat, organizations often implement security policies in which only the connection of white-listed IoT devices is permitted. To monitor adherence to such policies and protect their networks, organizations must be able to identify the IoT devices connected to their networks and, more specifically, to identify connected IoT devices that are not on the white-list (unknown devices). In this study, we applied deep learning on network traffic to automatically identify IoT devices connected to the network. In contrast to previous work, our approach does not require that complex feature engineering be applied on the network traffic, since we represent the communication behavior of IoT devices using small images built from the IoT devices network traffic payloads. In our experiments, we trained a multiclass classifier on a publicly available dataset, successfully identifying 10 different IoT devices and the traffic of smartphones and computers, with over 99% accuracy. We also trained multiclass classifiers to detect unauthorized IoT devices connected to the network, achieving over 99% overall average detection accuracy

Characterisation of thermo-elastic Martensitic transformation in NiTi and FeMn alloys driven by temperature variation and external stress

Abstract no. D41.109published_or_final_versio

深圳市1km高分辨率厘米级高精度大地水准面的确定

Author name used in this publication: 宁津生Author name used in this publication: 罗志才Author name used in this publication: 杨沾吉Author name used in this publication: 陈永奇Author name used in this publication: 张天纪Title in Traditional Chinese: 深圳市1km高分辨率厘米級高精度大地水準面的確定Journal title in Traditional Chinese: 測繪通報2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Temporal Model Adaptation for Person Re-Identification

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%

Characterizing Spatiotemporal Dynamics of Methane Emissions from Rice Paddies in Northeast China from 1990 to 2010

BACKGROUND: Rice paddies have been identified as major methane (CH(4)) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH₄ emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. METHODOLOGY/PRINCIPAL FINDINGS: Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH(4) emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH₄ fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH₄ emission in the cool paddies. CONCLUSIONS/SIGNIFICANCE: The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory