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Accurately localizing and identifying vertebrae from CT images is crucial for various clinical applications. However, most existing efforts are performed on 3D with cropping patch operation, suffering from the large computation costs and limited global information. In this paper, we propose a multi-view vertebra localization and identification from CT images, converting the 3D problem into a 2D localization and identification task on different views. Without the limitation of the 3D cropped patch, our method can learn the multi-view global information naturally. Moreover, to better capture the anatomical structure information from different view perspectives, a multi-view contrastive learning strategy is developed to pre-train the backbone. Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae. Evaluation results demonstrate that, with only two 2D networks, our method can localize and identify vertebrae in CT images accurately, and outperforms the state-of-the-art methods consistently. Our code is available at https://github.com/ShanghaiTech-IMPACT/Multi-View-Vertebra-Localization-and-Identification-from-CT-Images., Comment: MICCAI 2023

Two-dimensional arrays of magnetically coupled nanomagnets provide a mesoscopic platform for exploring collective phenomena as well as realizing a broad range of spintronic devices. In particular, the magnetic coupling plays a critical role in determining the nature of the cooperative behaviour and providing new functionalities in nanomagnet-based devices. Here, we create coupled Ising-like nanomagnets in which the coupling between adjacent nanomagnetic regions can be reversibly converted between parallel and antiparallel through solid-state ionic gating. This is achieved with the voltage-control of magnetic anisotropies in a nanosized region where the symmetric exchange interaction favours parallel alignment and the antisymmetric exchange interaction, namely the Dzyaloshinskii-Moriya interaction, favours antiparallel alignment. Applying this concept to a two-dimensional lattice, we demonstrate a voltage-controlled phase transition in artificial spin ices. Furthermore, we achieve an addressable control of the individual couplings and realize an electrically programmable Ising network, which opens up new avenues to design nanomagnet-based logic devices and neuromorphic computers

In the field of spintronics, the potential of applications exploiting magnetic materials are explored such as computation and data storage. Particularly important for such applications are the different interactions such as the Ruderman–Kittel–Kasuya–Yosida interaction and Dzyaloshinskii–Moriya interaction. Until now, these interactions have been mainly exploited in vertical stacks but little attention has been given to mechanisms that couple laterally patterned regions. In this doctoral work, it is shown how magnetic patterning techniques are a powerful tool to demonstrate new functionalities in lateral spintronic devices, with the focus on the development of two novel coupling mechanisms, namely lateral chiral coupling and lateral exchange coupling. With the detailed characterization of these couplings, new tools in the spintronics device design kit are provided for future applications. The first coupling mechanism addressed is lateral chiral coupling. Here, the lateral chiral coupling strength in a ferromagnetic Co-based system was characterized by measuring the asymmetric domain wall motion. The chiral coupling mechanism is extended to a CoB-based system, which is interesting because of the low domain wall pinning. With this dramatic reduction of the domain wall pinning, a spontaneous domain wall motion is observed in absence of any applied magnetic field. It is shown that the effective chiral coupling strength can be tuned by modifying the device geometry. In particular, by decreasing the domain wall track width, the effective chiral coupling strength increases. An analytical model combined with micromagnetic simulations provided the quantitative basis for the experiments. The simulated stopping fields had a good match to the experimental results. Furthermore, the existence of chiral coupling in a ferrimagnetic CoBGd based system was demonstrated with the realization of a Boolean NOT gate function. The second coupling mechanism that was developed is lateral exchange coupling. This was realized by modifying the compensation temperature in a ferrimagnetic GdCo-based system via both oxidation and He+ irradiation methods. The lateral exchange coupling strength was determined from asymmetric domain wall propagation experiments at different temperatures, with a maximum in the exchange bias of 2.5 T. Furthermore, the evolution of the electrical current heating induced exchange bias in a Hall bar at different temperatures is explored. Kerr microscopy of the asymmetric domain wall motion in microscopic tracks provided a better understanding of domain wall dynamics at different applied magnetic fields and the interfacial origin of the lateral exchange coupling at room temperature. Other patterns such as a single square divided into two regions and checkerboard patterns with their dimensions ranging from a few hundreds of nm to a few µm were imaged via magnetic force microscopy and nitrogen-vacancy microscopy for further quantitative understanding of the domain wall profiles by measuring the stray field across the domain wall regions. In addition, spin orbit torques were combined with lateral exchange coupling to realize reliable switching of exchange bias in laterally coupled devices. In order to manipulate the above couplings after device fabrication, an electrolyte GdOₓ was included, so that the coupling could be adjusted by applying a gate voltage. For lateral chiral coupling, successful turning on and off of the coupling was successfully demonstrated in ferromagnetic Co-based nanostructures by changing the anisotropy from out-of-plane to in-plane and vice versa. For lateral exchange coupling, preliminary results of the manipulation of the compensation temperature are presented using electrolyte-based gating, with the potential for further manipulation of lateral exchange coupling with different patterns such as domain wall tracks and checkerboard. In summary, lateral chiral and exchange couplings in ferromagnetic and ferrimagnetic based systems have been developed and characterized. Furthermore, a voltage control design has been implemented to the devices to realize manipulation of couplings after device fabrication. These open the possibilities for new spintronic designs such as reprogrammable domain wall based Boolean logic devices.

Cone Beam Computed Tomography (CBCT) is the most widely used imaging method in dentistry. As hundreds of X-ray projections are needed to reconstruct a high-quality CBCT image (i.e., the attenuation field) in traditional algorithms, sparse-view CBCT reconstruction has become a main focus to reduce radiation dose. Several attempts have been made to solve it while still suffering from insufficient data or poor generalization ability for novel patients. This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field. The key insight is when building the volumetric feature, we comply with the multi-view CBCT reconstruction nature and emphasize the view consistency property by geometry-aware spatial feature querying and adaptive feature fusing. Moreover, the prior knowledge information learned from data population guarantees our generalization ability when dealing with sparse view input. Comprehensive evaluations have demonstrated the superiority in terms of reconstruction quality, and the downstream application further validates the feasibility of our method in real-world clinics.

Background The tumor microenvironment is associated with prognosis in advanced non‐small cell lung carcinoma (NSCLC). The aim of this study was to explore the relationship between blood T cell diversity and survival of patients treated with pemetrexed‐based chemotherapy for nonsquamous NSCLC. Methods This prospective clinical study enrolled 26 patients with advanced NSCLC treated with 4–6 cycles of first‐line pemetrexed combined with platinum‐based therapy. The complementarity‐determining region 3 (CDR3) located in the T cell receptor beta chain (TCR β chain) was captured and deeply sequenced using next‐generation sequencing (NGS) technology, and the correlation between TCR changes and efficacy after chemotherapy was analyzed. Results Patients with an inferior quarter diversity index showed a significantly shorter progression‐free survival (PFS) than the others (median, 5.0 months vs. 8.1 months, P = 0.014). After two cycles of chemotherapy, the TCR diversity was significantly higher than the baseline (P = 0.034). Just as with the baseline, patients with an inferior quarter diversity index at the endpoint of cycle 2 showed a shorter progression‐free survival (PFS) than the others (median, 5.0 months vs. 8.4 months, P = 0.024). Conclusions In advanced NSCLC patients treated with first‐line pemetrexed combined with platinum, the low level of blood TCR diversity at baseline with an endpoint of two cycles of chemotherapy was correlated with a poor prognosis., The aim of this study was to explore the relationship between blood T cell diversity and the survival of patients treated with pemetrexed‐based chemotherapy for nonsquamous non‐small cell lung cancer (NSCLC). In advanced NSCLC patients treated with first‐line pemetrexed combined with platinum, the low level of blood TCR diversity at baseline with an endpoint of two cycles of chemotherapy was correlated with a poor prognosis. The level of diversity after two cycles was the only independent prognostic factor among the multifactor analysis.

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths. Optical aperture synthesis is an important high-resolution imaging technology used in astronomy. Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations, and the technical difficulty increases rapidly as the wavelength decreases. The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations, but suffers from a narrow spectral bandwidth which results in a lack of detection sensitivity. In this study, we propose optical synthetic aperture imaging based on spatial intensity interferometry. This not only realizes diffraction-limited optical aperture synthesis in a single shot, but also enables imaging with a wide spectral bandwidth. And this method is insensitive to the optical path difference between the sub-apertures. Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 $nm$ spectral width of visible light, whose maximum optical path difference between the sub-apertures reach $69.36\lambda$. This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

Additional file 1. Supplementary information.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioning as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline. Specifically, we establish a unified mathematical representation of all variational autoencoder, autoencoder, and generative adversarial network models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative use of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing., Comment: 74 pages

When light travels through scattering media, speckles (spatially random distribution of fluctuated intensities) are formed due to the interference of light travelling along different optical paths, preventing the perception of structure, absolute location and dimension of a target within or on the other side of the medium. Currently, the prevailing techniques such as wavefront shaping, optical phase conjugation, scattering matrix measurement, and speckle autocorrelation imaging can only picture the target structure in the absence of prior information. Here we show that a scattering medium can be conceptualized as an assembly of randomly packed pinhole cameras, and the corresponding speckle pattern is a superposition of randomly shifted pinhole images. This provides a new perspective to bridge target, scattering medium, and speckle pattern, allowing one to localize and profile a target quantitatively from speckle patterns perceived from the other side of the scattering medium, which is impossible with all existing methods. The method also allows us to interpret some phenomena of diffusive light that are otherwise challenging to understand. For example, why the morphological appearance of speckle patterns changes with the target, why information is difficult to be extracted from thick scattering media, and what determines the capability of seeing through scattering media. In summary, the concept, whilst in its infancy, opens a new door to unveiling scattering media and information extraction from scattering media in real time.

By encoding the high-dimensional light-field imaging information into a detectable two-dimensional speckle plane, ghost imaging camera via sparsity constraints (GISC camera) can directly catch the high-dimensional light-field imaging information with only one snapshot. This makes it worth to revisit the spatial resolution limit of this optical imaging system. In this paper we show both theoretically and experimentally that, while the resolution in high-dimensional light-field space is still limited by diffraction, the statistical spatial resolution of GISC camera can be greatly improved comparing to classical Rayleigh's criterion by utilizing the discernibility in high-dimensional light-field space. The interaction between imaging resolution, degrees of freedom of light field, and degrees of freedom of objects in high-dimensional light-field space is also demonstrated., 7pages, 6 figures

Increasing the application of machine learning algorithms in engine development has the potential to reduce the number of experimental runs and the computation cost of computational fluid dynamics simulations. The objective of this study is to assess if such a statistical modelling approach can predict engine efficiency and emissions at any given condition for an already calibrated spark ignition (SI) engine. Engine responses at various engine speeds and load are recorded and used for correlative modelling. The artificial neural network (ANN) algorithm is utilized in this study, with engine speed and load as the model inputs, and fuel consumption and emission as the model outputs. The comparisons between experimentally measured data and model predictions indicate that the well-trained network is capable of forecasting engine efficiency, unburned hydrocarbons, carbon monoxide, and nitrogen oxide emissions with close-to-zero root mean squared error performance metric. In addition, the relatively small errors do not affect the relations between model inputs and outputs, as evidenced by the close-to-unity coefficient of determination. Overall, all these results indicate ANN model is appropriate for the application investigated in this study. Moreover, this study also suggests that the “black-box” modelling approach has the potential to effectively predict engine-related variables. And the predicted engine map can be used as a reference to accelerate the motor development in the hybrid vehicles. Also, the ANN model forecast the fuel consumption and emissions under transient operating conditions, while the literature is scarce to date on the investigation of the prediction of engine responses for transient conditions.

Cylindrical Lithium-Ion Batteries have been widely used as power source for electric and hybrid vehicles because of their compact size and high power density. The battery pack is commonly consisted by hundreds of cylindrical Lithium-Ion battery cells in several strings. Because the distance among battery cells is only a few millimeters, the thermal status of battery would directly influent the current efficiency and battery life. In order to maintain proper function of the battery pack, the heat dissipation around battery cells should be deeply investigated and well controlled. This question is undeniably important and which has gained increasing attentions. Researchers have developed some models of the transient temperature distribution in Lithium-Ion battery during the discharge cycle and the thermal management on various kinds of battery packs has been studied. However, because of the compacted and complicated structure inside battery pack, the full thermal status and detail distributions are difficult to be revealed in the same time. In this work, three-dimensional simulation methods have been used to solve the above questions on the combination of several cylindrical Lithium-Ion battery cells. Existing heat generation models in Lithium-Ion battery is defined as the thermal boundary conditions. The flow and convection on the spacing has been studied. The transient thermal interactions and convections among adjacent battery cells have been investigated to explore the influences by spacing and transient heat release rules. The achieved results can be used as critical reference for designing the structures of battery pack and planning the cooling strategies.

Different cooling structures for cylinder head and block and the corresponding thermal status, thermal dissipation and frictional power dissipation were investigated in this study. In a conventional engine cooling configuration, the coolant flow travels from the block to the head, leading to the coolant temperature of the head being higher than that of block. Furthermore, the over-cooling problem in the block will occur because the cooling system is designed for the cooling requirements of the cylinder head without considering the cooling load of the block. This paper developed a 3D single cylinder model to analyse the influence of coolant flow direction and split structure cooling for the head and block. The analysis indicated that the top-bottom flow cooling structure can effectively reduce the cylinder head thermal load and slightly increase the temperature of the cylinder liner. The combined split and top-bottom cooling structure is proven as the optimal solution with the advantages of lower thermal and frictional power dissipation compared with that of the conventional cooling structure.

The applications of present nanoscopy techniques for live cell imaging are limited by the long sampling time and low emitter density. Here we developed a new single frame wide-field nanoscopy based on ghost imaging via sparsity constraints (GISC Nanoscopy), in which a spatial random phase modulator is applied in a wide-field microscopy to achieve random measurement for fluorescence signals. This new method can effectively utilize the sparsity of fluorescence emitters to dramatically enhance the imaging resolution to 80 nm by compressive sensing (CS) reconstruction for one raw image. The ultra-high emitter density of 143 {\mu}m-2 has been achieved while the precision of single-molecule localization below 25 nm has been maintained. Thereby working with high-density of photo-switchable fluorophores GISC nanoscopy can reduce orders of magnitude sampling frames compared with previous single-molecule localization based super-resolution imaging methods.

Failure mode (FM) and bearing capacity of reinforced concrete (RC) columns are key concerns in structural design and/or performance assessment procedures. The failure types, i.e., flexure, shear, or mix of the above two, will greatly affect the capacity and ductility of the structure. Meanwhile, the design methodologies for structures of different failure types will be totally different. Therefore, developing efficient and reliable methods to identify the FM and predict the corresponding capacity is of special importance for structural design/assessment management. In this paper, an intelligent approach is presented for FM classification and bearing capacity prediction of RC columns based on the ensemble machine learning techniques. The most typical ensemble learning method, adaptive boosting (AdaBoost) algorithm, is adopted for both classification and regression (prediction) problems. Totally 254 cyclic loading tests of RC columns are collected. The geometric dimensions, reinforcing details, material properties are set as the input variables, while the failure types (for classification problem) and peak capacity forces (for regression problem) are set as the output variables. The results indicate that the model generated by the AdaBoost learning algorithm has a very high accuracy for both FM classification (accuracy = 0.96) and capacity prediction (R2 = 0.98). Different learning algorithms are also compared and the results show that ensemble learning (especially AdaBoost) has better performance than single learning. In addition, the bearing capacity predicted by the AdaBoost is also compared to that by the empirical formulas provided by the design codes, which shows an obvious superior of the proposed method. In summary, the machine learning technique, especially the ensemble learning, can provide an alternate to the conventional mechanics-driven models in structural design in this big data time.

In this paper, an intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete. This approach employs the adaptive boosting algorithm to construct a strong learner by integrating several weak learners, which can find the mapping between the input data and output data. The weak learner whose predicting error is small will have a larger weight in the entire system, thus the overall accuracy of the strong learner will be enhanced. A total of 1030 sets of concrete compressive strength tests is collected to train and test the learners, in which the concrete mixture components (e.g., coarse/fine aggregates, cement, water, additive, etc.) and the curing time are set as the input data while the compressive strength value is set as the output data. The proposed approach is validated through a 10-fold cross validation method, and reaches an average accuracy of over 95% in sense of determination coefficient. In addition, a new dataset of 103 samples for concrete compressive strength is also adopted to demonstrate the generalization power of the proposed mode. The proposed approach is also compared to some other individual machine learning techniques that are already applied in this field, e.g., artificial neural network (ANN) and support vector machine (SVM), and shows superior advantages over these methods. Finally, the influence of some key factors in the adaptive boosting approach is also investigated, e.g., the amount of training data, the choice of weak learner, and the influence of the sensitivity and number of the input parameters. It is shown that using 80% of the total data for training can obtain acceptable prediction results and decision tree is the best choice for the weak learner in the boosting framework. Also, the importances of different input variables are obtained based on the sensitivity analysis results.

As one of the most important components of the vehicle thermal management system, multi-fans cooling module can potentially be used to optimal the system performance in order to save the energy consumption and reduce the vehicle fuel consumption. In this study, a test rig has been designed, constructed and used to test the heat transfer performance of the multi-fans cooling module using four different fan shroud structures. Results indicate the separated plates, which have limited or worse effect on the performance of the system, are not recommended to be used in the multi-fans cooling module. The optimal shroud structure for multi-fans cooling module has been identified and experimentally tested. By adding shutters at the ventilation part of the fans (Shroud C) can significantly improve the overall performance of the module (Shroud A) by 13.25 % to 69.08 % under various operation conditions.

Asako and Kanoh (1997) 等によって，日本銀行が公表する政策目標が実際にマクロ経済の安定化に役立ったことが計量分析によっても裏付けられているが，こうした評価が，ゼロ金利政策や量的緩和政策といった非伝統的金融政策下でも当てはまるのかを，1990年代以降のデータで検証する．モデル分析からは，新しいデータでは累積国債残高の役割が大きく変わった推計結果が得られ，その背景を検証する．また，金融政策決定会合での委員の投票行動の分析も行い，金融政策の目標設定と政策決定過程の相互関連を考察する．, We attempt to identify significant objectives of monetary policy for the period from 1990 to 2013 and compare the results to those obtained by Asako and Kanoh (1997) for an earlier sample period. With the introduction of nontraditional monetary policy management such as zero-interest-rate policy and quantitative-easing policy, we find and examine some important shifts in the role of government bonds modeled by Asako and Kanoh as one of five stabilizing objectives of the monetary policy. We also look into the making of the monetary policy by focusing on the votes by the Bank of Japan Policy Board members., （小特集：マクロ経済変動と安定化政策 = Macroeconomic Fluctuations and the Stabilization Policy）

CFD/CHT simulations have been widely used in simulating cross flow compact heat exchangers, but the calculating accuracy is commonly limited. The multi-scale thermal analysis approach was adopted to study an intercooler, one of the representative examples of cross flow heat exchangers in automotive cooling systems. With the application of mesh refinement and datum interpolation technique, the inside flow and heat transfer mechanism were analyzed, the pressure and temperature data were also calculated. The model was validated with experimental data based on wind tunnel tests, and the results show that the multi-scale coupled calculation is in good agreement with experimental values, especially in the heat transfer simulations. The research of the inside flow and heat transfer mechanism is capable of providing a basis for optimization of compact heat exchangers.

本論文では，日米中3国の株式市場をめぐって，通常の線形共和分関係及び非線形共和分関係の有無といった長期的な観点から，国際間の株価の連動性について検証を試みる．2つの国の株価が，短期的にある安定的な関係から非線形的に乖離する動きがあったとしても，それが長期的には安定的な関係に戻るならば，それは非線形の共和分関係にあるとする．具体的には，株価比率にバブルの発生・膨脹・崩壊のプロセスが内包されるとして，それを統計的なモデルとして定式化し，関係するパラメータをBayesian流に推計する．実証結果としては，日米，日中，米中の2国間株価指数同士には，通常の線形共和分関係は認められないが，確かにバブル型非線形性が認められ，したがって，長期の連動性が検証されたと解釈される．, This paper proposes and estimates a statistical model of nonlinear cointegration with applications to the stock markets of Japan, United States, and China. We define nonlinear cointegration as a long-run stable relationship between two time series variables even in the presence of temporary nonlinear divergence from this long-run relationship. More concretely, extending the bubble model of Asako and Liu (2013) to stock price ratio variables, both upward and downward divergent bubble processes are estimated at a time. We conclude that, although no pair of stock price series among the three is linearly cointegrated, they are considered to be cointegrated nonlinearly.

The concept of Fuzzy Atmosfield (FA) is proposed to represent a communication atmosphere in society consisting of multiple individuals such as humans and/or robots, where the FA is characterized by 3D fuzzy cubic space with “Friendly-Hostile” (FH), “Lively-Calm” (LC), and “Casual-Formal” (CF) axes, and each state of the FA is visualized using shape-colorlength graphics. It is targeted to be a tool for identifying an atmosphere using quantitative analysis and graphical representation. By a humans-robots interaction experiment in which the FA is used to represent the real-time atmosphere created by four humans and five eye robots in a home party scenario, it shows that Pearson’s correlation coefficient values of 0.92, 0.86, and 0.72 for the FH, LC, and CF axes, respectively, indicate the correspondence between the proposed FA and results of questionnaires, and that subjective estimation of graphical representation of the FA achieves 84% accuracy for shape, 76% for color, and 58% for length. The FA is being extended to the representation of complex atmosphere generated by humans, robots, and background music, and part of results is also shown.

This paper presents an Emotion-Driven Attention (EDA) model for the service robot based on cognitive structure of human, which integrates the emotion mechanism with cognitive system to manage attention allocation, and enable the robot react to emotional social cues appropriately. For a service robot, the typical tasks including interactive manipulation and object handover. During the interaction with human, the human's facial expression can be recognized by the robot in the real-time, which is considered as a social cue to trigger a corresponding emotion of the robot by Self-Organizing Map (SOM). Then, the robot's emotion plays as a reinforcement signal to regulate the attention parameters, which may result in grasping or avoiding the identified object according to the human intention. We estimate the proposal in both simulated situations and interactive scenarios, the dynamic variations of attention intensity and behaviors based on different emotional states indicate the effectiveness of the proposed model.

The purpose of this paper is to investigate whether demand and supply have been equilibrated in the Japanese bank loan market over the period especially after the bubble era, and if not, what the main reasons are for this state of disequilibrium. For this purpose, we have improved and extended the analysis of Asako and Uchino (1987) by taking into account notable changes in the Japanese economy. We conclude that situation of the Japanese loan market has basically been same as in the old days of Asako and Uchino (1987), although the prevailing state of disequilibrium has shifted from excess demand to excess supply. The market loan rate lacks a mechanism for rapid adjustment towards the market equilibrium rate and it is instead guided by some policy-related interest rate that does not clear the market.

We confirm that there are changes in the features of the foreign exchange market since the Euro introduction through empirical experiments on five major exchange rate series in the world. We verify the existence of asymmetry in volatility process of Japanese Yen (JPY)/United States Dollar (USD), Australian Dollar (AUD)/USD and New Zealand Dollar (NZD)/USD while JPY appreciates and AUD and NZD depreciate. We also ascertain that volatility for those five major exchange rate series has become larger and the correlation between exchange rate series has become stronger since the Euro introduction.

We examine the existence of liquidity effects by using the equilibrium interest rates estimated from the disequilibrium analysis instead of the observed interest rate. The use of equilibrium interest rates allows us to take the growth rate in the money supply as being exogenous and circumvent the identification problem away. Following the traditional AR model and the method proposed by Hamilton (1997), we obtain evidences showing the negative relationship between the equilibrium interest rate and the growth rate in money supply.

In the present work, a green-emitting Cu(I) complex [Cu(BT-Et)(POP)]BF4 was synthesized and fully characterized, where BT-Et=4-(1-ethyl-1H-benzo[d]imidazol-2-yl)thiazole, POP=bis(2-(diphenylphosphanyl)phenyl) ether, respectively. An ethyl group was connected onto the diamine ligand to breach π-π attraction within solid [Cu(BT-Et)(POP)]BF4, favoring O2 molecule attack and sensitivity improvement. Its molecular identity was confirmed by single crystal analysis and theoretical calculation. [Cu(BT-Et)(POP)]BF4 emitted long-lived green emission peaking at 521nm upon photoexcitation which was vulnerable towards O2 molecule, making itself a potential oxygen sensing material. [Cu(BT-Et)(POP)]BF4 was then doped into a silica supporting matrix MCM-41. The resulting composite samples showed sensing behavior towards O2 molecule, with short response time of 10s and sensitivity of 5.56.

A multimodal gesture recognition method for mascot robot system is proposed based on Choquet integral by fusing camera and 3D accelerometer data. The optimization of two fuzzy measures in the training phase for two recognition units, i.e., camera-based and accelerometer-based units, obtains enough recognition rate of 92.7% for 8 types of gestures by improving the recognition rate approximate 20% compared with that of each unit. The proposed method targets casual communication from humans to robots by integrating nonverbal gesture messages and verbal messages.

Because of the essential role played by energy in economic development, particularly in view of the two major global energy crises and recent high oil prices, whether or not a region or the whole world can successfully satisfy its energy demand has been an issue of great importance. This study uses stochastic models to forecast regional energy demand in the situation of insufficient statistical data. Autoregressive Integrated Moving Average (ARIMA) model needs less data than other models and can represent economic time series well. So we use it in this study and apply it to the case of Taiwan. The study concludes that there will be an average annual growth of 3.1% for Taiwani¯s total energy demand during 2008-2012, and we suggests more cross-strait energy cooperation.