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Complex antagonistic interactions between abscisic acid (ABA) and brassinosteroid (BR) signalling pathways have been widely documented. However, whether or how ABA interacts synergistically with BR in plants remains to be elucidated. Here, we report that low, but not high, concentration of ABA increases lamina joint inclination of rice seedling, which requires functional BR biosynthesis and signalling. Transcriptome analyses confirm that about 60% of low-concentration ABA early response genes can be regulated by BR in the same directions. ABA activates BR signal in a fast, limited and short-term manner and the BR-biosynthesis regulatory gene, OsGSR1, plays a key role during this process, whose expression is induced slightly by ABA through transcriptional factor ABI3. Moreover, the early short-term BR signal activation is also important for ABA-mediated salt stress tolerance. Intriguingly, the process and effect of short-term BR signal activation were covered by high concentration of ABA, implying adaptive mechanisms existed in plants to cope with varying degrees of stress.

A series of Ba5(B2O5)2F2:xEu3+ (x = 0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18 and 0.20) phosphors were synthesized for the first time by the high-temperature solid-state reaction method. The polycrystalline powder samples of Ba5(B2O5)2F2 and Ba5(B2O5)2F2:0.14Eu3+ were determined by the X-ray powder diffraction measurement. The optical properties of Ba5(B2O5)2F2:xEu3+ were detected by photoluminescence excitation and emission spectra, CIE chromaticity coordinate, quantum efficiency and attenuation curve. Under near-UV excitation (λex = 394 nm), the phosphors exhibit dominant emission peak at 616 nm and generate bright red light, with the calculated R/O value is 2.65 in Ba5(B2O5)2F2:0.14Eu3+ phosphor. The optimal doping amount of Eu3+ in Ba5(B2O5)2F2:xEu3+ phosphors is x = 0.14 for the maximum emission. With a relatively high quantum efficiency of 40.83%, Ba5(B2O5)2F2:0.14Eu3+ red emitting luminescent material is probably a promising candidate for near-UV-based white LEDs application.

Crystal structure and electronic properties of SnO2 doped with non-metal elements (F, S, C, B, and N) were studied using first-principles calculations. The theoretical results show that doping of non-metal elements cannot change the structure of SnO2 but result in a slight expansion of the lattice volume. The most obvious finding from the analysis is that F-doped SnO2 has the lowest defect binding energy. The doping with B and S introduced additional defect energy levels within the forbidden bandgap, which improved the crystal conductivity. The Fermi level shifts up due to the doping with B, F, and S, while the Fermi level of SnO2 doped with C or N has crossed the impurity level. The Fermi level of F-doped SnO2 is inside the conduction band, and the doped crystal possesses metallicity. The optical properties of SnO2 crystals doped with non-metal elements were analyzed and calculated. The SnO2 crystal doped with F had the highest reflectivity in the infrared region, and the reflectance of the crystals doped with N, C, S, and B decreased sequentially. Based on this theoretical calculations, F-doped SnO2 is found to be the best photoelectric material for preparing low-emissivity coatings.

This papera 1 investigates deep reinforcement learning (DRL) based on a Recurrent Neural Network (RNN) for Dynamic Spectrum Access (DSA) under partial observations, referred to as a Deep Recurrent Q-Network (DRQN). Specifically, we consider a scenario with multiple independent channels and multiple heterogeneous Primary Users (PUs). Two key challenges in our problem formulation are that we assume our DRQN node does not have any prior knowledge of the other nodes’ behavior patterns and attempts to predict the future channel state based on previous observations. The goal of the DRQN is to learn a channel access strategy with a low collision rate but a high channel utilization rate. With proper definitions of the state, action and rewards, our extensive simulation results show that a DRQN-based approach can handle a variety of communication environments including dynamic environments. Further, our results show that the DRQN node is also able to cope with multi-rate and multi-agent scenarios. Importantly, we show the following benefits of using recurrent neural networks in DSA: (i) the ability to learn the optimal strategy in different environments under partial observations; (ii) robustness to imperfect observations and (iii) the ability to utilize multiple channels, and (iv) robustness in the presence of multiple agents. 1 A parton of this work was presented at MILCOM 2018 in [1] .

DNA methylation and long non-coding RNAs (lncRNAs) regulate plant growth and development, but their relationship and effect on responses to the auxin phytohormone indole-3-acetic acid (IAA) remain largely unknown, particularly in woody plants such as poplar (Populus tomentosa). Following treatment of 1-year-old clonal plants with 100 µM IAA, key poplar lncRNA genes showed changes in methylation, but whole-genome methylation levels showed no significant change. Moreover, 100 µM IAA inhibited growth of the 1-year-old poplar clones, possibly through the suppression of photosynthesis. This inhibition had a long-term effect, persisting at 1 month after removal of the exogenous IAA. Transcriptome analysis identified two candidate lncRNA genes that show changes in expression following IAA treatment, TCONS_00003480 and TCONS_00004832. TCONS_00003480 contains the same microRNA target sites of ptc-miR6464 as the 4-coumarate: CoA ligase 2 transcript, which encodes a lignin biosynthesis enzyme. And TCONS_00004832 shares the same target sites of ptc-miR6437a with the Photosystem II reaction center protein D and Cytochrome C Oxidase 17 transcripts, which are related to photosynthesis. The two lncRNAs as the mimics to corresponding target genes of miRNAs to prevent them from degrading. Examination of lncRNA gene expression and methylation revealed a negative relationship (r = − 0.29, P

Titanium dioxide (TiO2)/fluorine-doped tin dioxide (FTO) bilayer films were deposited on glass substrates by spray pyrolysis. The effects of hydrogen plasma exposure on structure, morphology and optical properties of the films were studied. The distribution of elements in the double layer film was investigated by glow discharge spectrometer. The results indicate that the TiO2 film provides effective protection of the underlying FTO film from the bombardment with hydrogen ions and radicals. Without the TiO2 protective layer, SnO2 will be reduced to sub-oxidized SnO and metallic Sn by hydrogen ions and radicals with increasing the exposure time. In addition, the TiO2/FTO film presents a nano-flower shaped appearance due to the small particles (TiO2) agglomerated on the surface of large particles (SnO2), the novel textured morphology is preferred for improving light scattering and haze of the film. A high haze value of 36.7% was obtained when the approximate thickness of the prepared TiO2 layer is 213 nm. This work may provide an alternative for improving performance of a-Si based solar cells.

A vector sensor is a type of sensor array with six collocated antennas that measures all electromagnetic field components of incident waves. The vector sensor has been shown to be advantageous in estimating both the angle of arrival and polarization of the incident sources. While direction-of-arrival (DOA) estimation with machine learning for linear arrays has been well studied, there has not been a similar solution for the vector sensor. In this paper, we propose a neural network that is capable of determining the number of the sources and estimating the DOA of each source based on the covariance matrix extracted from the received data. Additionally, a solution is proposed to associate the estimated DOAs to their sources, and a customized training loss function to address the field-of-view limit problem. Simulation results show that neural networks can achieve a reasonably accurate DOA estimation measured by the RMSE for up to five sources.

With the increasing power of machine learning-based reasoning, the use of meta information (e.g., digital signal modulation parameters, channel conditions, etc.) to predict the performance of various signal processing techniques has become feasible. One such problem of practical interest is choosing a proper interference mitigation method based on the meta information of the received signal. Since heuristic table-based methods suffer from limited prediction capability for unseen cases, we propose a recommendation system based on the use of Random Forests (RF). Specifically, RF are used to predict the Bit-Error-Rate (BER) of all mitigation approaches so as to determine the approach with the best performance. In this work we show that RF can predict BER with high accuracy, and the Importance Factor (IF) demonstrates the input attributes which matter most. Furthermore, the IF can help to design a data augmentation of partial features. These BER prediction results can also benefit other functions such as adaptive modulation, channel sensing, beam selection, etc.

Our previous studies introduced a mid-grained intermediate-level channel measurement – spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the 60-GHz IEEE 802.11ad/ay standards – for the fingerprinting-based indoor localization. In this paper, we take one step further to use the mid-grained channel measurement for human monitoring applications including human pose and seat occupancy classifications. The effectiveness of the mid-grained channel measurement is validated by an in-house experimental dataset that includes 5 separate data collection sessions using classical classification methods and modern deep neural networks. Our preliminary result shows that mmWave beam SNRs are capable of delivering high classification accuracy above 90%.

The crystal structure, electronic properties of F, S, C, B and N doped SnO2 were studied with the First-Principle Method. The theoretical results show that doping of non-metal elements did not change the structure of SnO2 but result in slight lattice volume expansion. The dope of the non-metal elements of B, F, and S cause the Fermi level to shift upThe most obvious finding to emerge from the analysis is that F-doped SnO2 has the lowest defect binding energy, stable crystal structure, and the easiest doping. The B, F, and S element doped SnO2 can modulate the fermi level. The doping of the B and S elements introduced additional defect energy levels to appear within the forbidden band-gap，which improved the crystal conductivity. Analysis of the energy band structure of SnO2 crystals doped with C and N elements shows that the Fermi level has crossed the impurity level. The Fermi level of F doped SnO2 is inside the conduction band, and the doped crystal has metallicity. The optical properties of SnO2 crystals doped with non-metallic elements were analyzed and calculated. The SnO2 crystal doped with F element had the highest reflectivity in the infrared region, and the reflectance of the crystals doped with N, C, S, and B elements decreased sequentially. Based on this theoretical calculations, F doped SnO2 is found to be the best photoelectric material for for preparing low-e thin film.

This paper addresses the multi-source direction-of-arrival (DOA) estimation problem involving a single electromagnetic vector sensor. The Uni-Vector-Sensor ESPRIT algorithm was originally devised for the harmonic signal model to estimate the DOA and polarization of up to five uncorrelated sources. Here we show that this algorithm may be applied to the case where the sources are digitally modulated signals. The performance with this signal model is comparable to the case of harmonic sources. However, this requires the time lag between data sets in the Uni-Vector-Sensor ESPRIT algorithm to be small relative to the symbol periods of the digital signals. This is necessary for high correlation of the signals between the data sets. However, the delay must still be large enough to maintain distinct phase offsets between the sources. The performance of the algorithm for digitally modulated sources and its limitations were verified through simulations and compared to other algorithms.

This paper aims at solving two major drawbacks of fingerprint-based localization methods: 1) existing fingerprint-based methods mainly rely on the received signal strength indicator (RSSI) as the input feature, which renders centimeter-level localization impossible; 2) existing deep learning methods that rely on channel state information (CSI) as a feature do not consider the user trajectory and/or the signal-to-noise-ratio (SNR) information. To address these issues, this paper introduces a recurrent neural network (RNN) for centimeter-level indoor localization. The proposed RNN takes into consideration the user trajectory, as well as, the SNR information. We show that when the training data set is small, our proposed network beats the state-of-the-art neural networks. Moreover, for the first time, we present an extensive comparison between neural-network-based and decision-tree-based localization methods. The simulation results show that neural networks have higher estimation accuracy than tree-based methods.

The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the interference type added to a signal-of-interest, also known as interference classification. In this paper, we propose an interference classification method using a deep neural network. We generate five distinct types of interfering signals then use both the power-spectral density (PSD) and the cyclic spectrum of the received signal as input features to the network. The computer experiments reveal that using the received signal PSD outperforms using its cyclic spectrum in terms of accuracy. In addition, the same experiments show that the feed-forward networks yield better accuracy than classic methods. The proposed classifier aids the subsequent stage in the receiver chain with choosing the appropriate mitigation algorithm and also can coexist with modulation-classification methods to further improve the classifier accuracy., Comment: paper get rejected, need more editing

F and Nb co-doped tin oxide films were deposited from monobutyltin trichloride (C 4 H 9 SnCl 3 ) on glass substrates at 470 °C by spray pyrolysis. The evolution of the film structure and morphology was investigated by X-ray diffraction and scanning electron microscopy, respectively. All the films were a single phase with polycrystalline, exhibiting a tetragonal cassiterite structure with (200) orientation. The introduction of F and Nb contributed to the growth of the films towards (200) crystal orientation and the formation of pyramidal shape particles with (101) twin planes. Resistivity, carrier concentration, and Hall mobility values of 3.62 × 10 −4 Ω cm, 7.463 × 10 20 cm −3 , and 27.8 cm 2 V −1 s −1 , respectively, were achieved in the films for a dopant concentration of 20 at.% F and 1 at.% Nb in the precursor solution. Also, the transmittance of all the films in the visible range reached about 80% and the infrared reflectivity was oscillated between 92 and 96%.

Aluminium-doped zinc oxide (AZO)/fluorine-doped tin dioxide (FTO) double-layered transparent conducting films were manufactured on quartz glass substrates by spray pyrolysis. Like top films, AZO ha...

The TiO 2 nanotube (TONT) based heterojunction photocatalyst was developed via the coupling of reduced graphene oxide (rGO) and SnO 2 :F film (FTO). Based on the characterization of Raman analysis, XRD, SEM, TEM, XPS and ESR, the crystal phase, morphology, heterojunction interfacial interaction and the photoinduced electron chemical environment of the samples are studied. In the photodegradation of methylene blue (MB) solution under UV irradiation, the rGO-TONT/FTO heterojunction photocatalyst exhibits the improved photocatalytic reaction rate, 3 times greater than that of pure TONT. The enhanced photocatalytic mechanism was discussed by PL. The effectively separate charge in heterojunction structure of rGO-TONT/FTO is responsible for the enhanced photocatalytic activity. Wherein, the abundant oxygen vacancies at TiO 2 surface and the chemically bonded interface in rGO-TONT heterojunction also contributes to the interfacial electron transfer. Besides, the introduction of rGO enhanced its optical absorption capacity.

This paper investigates deep reinforcement learning (DRL) based on Recurrent Neural Networks for Dynamic Spectrum Access (DSA), referred to as a Deep Recurrent Q-Networks (DRQN). The approach uses sensing and cache occupancy as observations. Specifically, we consider a scenario with multiple independent channels and multiple different Primary Users (PU). Three key challenges in our problem formulation are (1) no prior knowledge; (2) prediction based on partial observations, and (3) multi-rate transmission capability. The goal of the DRQN is to learn an optimal channel access strategy to achieve a global objective

In this paper we address radar-communication coexistence by modeling the radar environment as a Markov Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to successfully avoid interfering with the communication system when necessary. We also show that the DQN method outperforms previous methods in terms of memory and handling new situations.

A new fluoroborate compound Al7-xLi1+xB4O16+xF1-x (x = 1/3) has been successfully prepared via high temperature solution method from the Li2O-H3BO3-LiF system which was melted in Al2O3 crucible. The crystal structure of Al7-xLi1+xB4O16+xF1-x (x = 1/3) was determined from single crystal X-ray diffraction data, and it crystallized in tetragonal system, space group I4/m with unit cell of dimensions a = b = 10.5082(11) A, c = 5.6062(12) A, Volume = 619.05(16) A3 and Z = 1. The crystal structure consists of interconnecting AlO6 octahedra, (Li/Al)O4(F/O) polyhedra and BO3 groups. The UV–Vis–NIR absorption spectrum shows that the ultraviolet absorption edge for the Al7-xLi1+xB4O16+xF1-x (x = 1/3) crystal is about 245 nm. Functional groups in Al7-xLi1+xB4O16+xF1-x (x = 1/3) were identified by Infrared spectrum. The electronic structure for Al7-xLi1+xB4O16+xF1-x (x = 1/3) crystal have been calculated by density functional theory (DFT) method.

This paper investigates the use of deep reinforcement learning (DRL) to solve the dynamic spectrum access problem. Specifically, we examine the scenario where multiple discrete channels are shared by different types of nodes which lack the ability to communicate (with other node types) and do not have a priori knowledge of the other nodes' behaviors. Each node's objective is to maximize its own long-term expected number of successful transmissions. The problem is formulated as a Markov Decision Process (MDP) with unknown system dynamics. In order to overcome the challenge of an unknown environment combined with a prohibitively large transition matrix, we apply two specific DRL approaches: The Deep Q Network (DQN) and The Double Deep Q Network (DDQN). Additionally, we also introduce techniques to improve DQNs including an eligibility trace, prior experience and the “guess process”. We first study the proposed DQN approach in a simple environment. The simulations show that both DQN and DDQN can effectively learn different nodes' communication patterns and achieve near optimal performance without prior knowledge. Then, we examine these techniques in a more complex environment and conclude that although different implementation variations obtain different performance, our proposed DQ N nodes can still learn to avoid collisions and achieve near optimal performance even in the more complex scenario.

This paper investigates the use of deep reinforcement learning (DRL) to solve a fundamental problem in dynamic spectrum access. Specifically, we develop a technique to allow a secondary radio node to share the spectrum with multiple existing (primary) radio nodes in the presence of partial observations and no a priori knowledge of the primary nodes' behaviors. There are multiple discrete channels shared by the radio nodes, and the secondary node cannot communicate with the primary nodes, but instead must base its spectrum access decisions on partial observations of the spectrum at each time step. The secondary radio node's objective is to maximize its own long-term expected number of successful transmissions (i.e., probability of full spectrum utilization) while minimizing collisions using limited feedback. The problem is formulated as a Partially-Observable Markov Decision Process (POMDP) with unknown system dynamics. In order to overcome the challenge of an unknown environment combined with partial observations, we apply a specific DRL approach: the Deep Recurrent Q Network (DRQN). We examine two partial observation patterns along with full observations in order to gain insight into the impact partial observations. Further, we compare previously proposed deep Q-networks (DQN) with the DRQN approach and demonstrate that the proposed DRQN approach can overcome the limitations of DQN-based solutions. We examine four specific scenarios which involve different types of primary nodes and show that the proposed DRQN technique can learn to avoid collisions and achieve near optimal performance.

A new electrochemical discharge machining (EDM) method that combines slotted metal wheel and mist-jetting electrolyte was proposed based on existing problems in the traditional electrolytic EDM compound machining of hard and brittle insulating materials. The physical process of this new method can be divided into four stages, namely the gas film generation, discharge channel formation, material removal, and deionization. We analyzed the inter-polar voltage and current characteristic during machining and found the suitable machining voltage and range of rotation speed for the slotted metal wheel to achieve stable electrochemical discharge machining. The voltage should be more than 220 V, and the rotation speed of the slotted metal wheel should range from 1200 to 1800 rpm during machining. By analyzing the waveform of the voltage and current during mist-jetting electrochemical machining, we found that the machining status included direct gas spark discharge and electrochemical gas spark discharge and that the electrochemical gas spark discharge was primary. Finally, we realized the stable machining of Al2O3 ceramic by using the proposed method. The efficiency of material removal was 53.5 mg/min, and the surface roughness of the machining surface was Ra 5.4 μm.

Delay time has been a key factor to consider in many real-time wireless applications, especially in industrial applications that the new TSCH mode proposed in IEEE 802.15.4e targets. Existing methods focus on minimizing the use of communication resources, yet they are unable to exploit the availability of channel multiplicity for achieving shorter average delay time in TSCH networks. In this paper, we propose a new method to construct cost-balanced routing trees that can utilize multiple interfaces at the coordinator. Our simulation results show that the proposed method is able to achieve low-delay data collection in TSCH networks.

This paper presents a novel method of thickness measurement for microelectromechanical system (MEMS) structures using micro-Raman spectroscopy. When heating by a constant power laser, the local temperature rise of a microscale structure depends on the thickness and thermal characteristics of the structure. The thickness information can then be evaluated by the temperature induced Raman shift. Theoretical analysis and simulation of this method are performed. The small spot size of the laser in micro-Raman spectroscopy enables thickness measurement with a high spatial resolution. This measurement method is confirmed by measuring the thickness of a MEMS single-crystalline silicon (c-silicon) membrane. The measurement result also consists of that of scanning electron microscopy (SEM) for the same sample. It has the advantages of being a non-contact and nondestructive process, no preparation, and spatial mapping aspects. The proposed method is also feasible for materials with a temperature sensitive Raman signal.

Understanding microscale stress characterization of integrated circuits and micro-electro-mechanical systems (MEMS) structures is essential for the successful design and operation of the devices. In this paper, we introduce the use of Raman spectroscopy to measure the stress in single-crystal silicon. The constant coefficient between stress and Raman shift was measured, which is -462 in our experiment case. The effect of laser heating of the sample was studied and discussed. Normally, low laser power should be used on the stress measurement.

Summary For accurately locating microseismic events, it is crucial to have an accurate velocity model. A common way to determine the velocity model is to use the grid search method to find the optimal model to fit the arrival times from perforation shots. However, the accuracy of the velocity model depends on the model search range and the search interval as well as the number of layers in the case of the one-dimensional model. It will be extremely timeconsuming if there are many layers and the search interval is small for a reasonable search range. In our study, a fast and robust global optimization method, known as the differential evolution (DE), is used to speed up the search process. The objective function for finding the optimal velocity model is related to the minimization of station-pair travel time residuals, which has the advantage of removing the effect of uncertain origin times of perforation shots. Considering that the method evolves from the Monte Carlo method, the randomness it brings to the inversion results cannot be neglected. Thus the final velocity model is given as the weighted mean of the 50 models with weighting parameters inverse proportionally to the corresponding objective function values. We tested this method on a synthetic dataset and showed its effectiveness.

An MS-MS method has been developed to detect N,N′-diphenyl-N,N′-dimethylurea (methyl centralite, MC) in gunshot residues. Residues were recovered with cotton wool swabs soaked with methanol. MC solution was squeezed from the swabs using a medical syringe. After dilution, the extracted sample was injected into a tandem mass spectrometer (MS-MS) instrument directly. The conditions of MS were optimized in multiple reaction monitoring (MRM) mode, using ions of m/z 241 as precursor ions and ions of m/z 134 as product ions. High selectivity was obtained and interference was eliminated for the application of the MRM method. The proposed method is quite sensitive and the detection limit is 60 pg of MC per injection. The operations are very simple and rapid and the whole analytical procedure needs no more than five minutes if the instrument has been tuned to optimum conditions. The reliability of the method was confirmed by a test in which persons who had fired a pistol and persons who had not were discriminated successfully. In the test, twenty actual samples were analyzed. Ten of them were extracted from the hands of ten persons who had fired a pistol ten minutes earlier and the other ten were from the hands of persons who had never fired a pistol. There was a marked difference between the results of the shooters and that of the non-shooters and they could be easily and exactly distinguished. The effects of time and washing were also investigated. The results indicated that even if eight hours had passed after one person discharged a pistol for one round and he had washed his hand three times with water during this time, MC on his hand could still be detected reliably.

Establishing a standard Raman spectral database for minerals and related inorganic crystals is a very important basis for further increasing the applications of Raman spectroscopy in the geosciences. However, the Raman spectral pattern of a crystal is a function not only of its composition and structure but also of the scattering geometry during the measurement. Therefore, the standard Raman spectrum of a crystal must be measured under well-defined standard conditions. It would be of great interest to establish a standard measuring configuration with which the characteristic Raman spectra of all mineral crystals could be obtained. Factor-group analysis of the main types of minerals indicates that almost all of them possess a Raman-active totally symmetric (TS) vibrational mode. Therefore, we propose a scheme to measure standard Raman spectra under a scattering geometry set up on the basis of these TS modes. Using this scheme, we were able to establish a database for standard Raman spectra of minerals and related inorganic crystals.

A novel method for determination of diphenylamine (DPA) and its nitrated derivatives, which are considered as characteristic components in smokeless powder and gunshot residues, is described. A tandem mass spectrometric method is established and mass spectrometer parameters optimized for each compound to obtain higher sensitivity. Under optimum conditions, quantitative analysis was carried out. The linear ranges are 5.0-200.0, 2.0-200.0 and 5.0-250.0 ng ml-1 and the detection limits are 1.0, 0.5 and 2.5 ng ml-1 for diphenylamine (DPA), N-NO-diphenylamine (N-NO-DPA) and 4-NO2-diphenylamine (4-NO2-DPA), respectively. Intra-assay and inter-assay precision and accuracy of analysis of these three samples were investigated. Based on the regression lines obtained above, smokeless samples were analyzed. It was found that there are 0.952% DPA, 0.384% N-NO-DPA and 0.128% 4-NO2-DPA in smokeless powder. Recovery tests showed that using cotton swabs, 80.3 +/- 4.9% DPA, 79.6 +/- 3.1% N-NO-DPA and 83.1 +/- 5.4% 4-NO2-DPA could be recovered from human hands.

A novel method, by using micro-Raman spectroscopy, is developed to measure the thickness of microelectromechanical system structures with high spatial resolution. When a microscale structure is heated by a laser, the temperature rise of the structure depends on the structure thickness and material properties. Therefore, the structure thickness can be measured using Raman shift, which is a function of temperature. Micro-Raman spectrometer is capable of measuring the thickness distribution of microscale structures with micron spatial resolution. This technique is evaluated by characterizing the thickness distribution of a single-crystal silicon (c-Si) membrane. The measured thickness distributions are verified by scanning electron microscope measurement.

This paper describes a novel method to detect gunshot residues (GSRs). The components contained in the double-based smokeless powder were separated by using a HPLC system with UV detection at 223 nm. The result indicated that there are mainly two kinds of organic ingredients in the double-base smokeless powder used. These two organics do not include nitrocellulose because it has no evidential value in the HPLC-UV trace of the smokeless powder sample. By matching retention times of these two compounds with that of the five standards occurring in smokeless powder, the compounds are tentatively identified as methyl centralite (MC) and nitroglycerin (NG). These two compounds were finally identified by using MS spectrum. This MS-MS method was developed to detect MC since MC is less commonly found in the environment than NG. In Chinese ammunition, MC is widely used as a stabilizer instead of EC. Sixteen samples were used to evaluate the reliability of the method. The mean (±SD) MC level in the eight samples is 520.1 ± 271.9 pg/µL and that in the blanks is 31.0 ± 20.8 pg/µL. The result (P = 0.0007) shows that there is a significant difference between the two serial data. Sampling hands over a 24 h period showed that GSR could be detected even after 8 h with a 75% decrease in the level of MC. The result of the washing test shows that it is still possible to apply the proposed method to detect GSRs on a shooter's hands even if their hands have been washed three times. Another test shows that if the shooters wear gloves while firing, MC on the gloves can be easily detected using the proposed method.