High-rate Decoupled Water Electrolysis System Integrated with α-MoO3 as a Redox Mediator with Fast Anhydrous Proton Kinetics

Hydrogen is a promising alternative to fossil fuels that can reduce greenhouse gas emissions. Decoupled water electrolysis system using a reversible proton storage redox mediator, where the oxygen evolution reaction and hydrogen evolution reaction are separated in time and space, is an effective approach to producing hydrogen gas with high purity, high flexibility, and low cost. To realize fast hydrogen production in such a system, a redox mediator capable of releasing protons rapidly is required. Herein, α-MoO₃, with an ultrafast proton transfer property that can be explained by a dense hydrogen bond network in the lattice oxygen arrays of HxMoO₃, is examined as a high-rate redox mediator for fast hydrogen production in acidic electrolytes. The α-MoO₃ redox mediator shows both a large capacity of 204 mAh g⁻¹ and fast hydrogen production at a current rate of 10 A cm⁻²(≈153 A g⁻¹), outperforming most of the previously reported solid-state redox mediators.     

Factors associated with serum magnesium and vascular stiffness in maintenance hemodialysis patients

Introduction : We evaluated the associated factors of serum magnesium in patients on maintenance hemodialysis (MHD). Furthermore, we evaluated the relationship between low serum magnesium and arteriosclerosis in these patients. Methods : In 129 patients on MHD, we evaluated the blood levels of magnesium, brachial‐ankle pulse wave velocity (ba‐PWV), ankle‐brachial index (ABI), and intima‐media thickness of the common carotid artery (IMT). Findings : In MHD patients, the serum level of magnesium was significantly correlated with age, calcium, TNF‐α, albumin, and ba‐PWV but not with ABI or IMT. In the multiple regression analysis, albumin (P = 0.0001, β = 0.31) and calcium (P = 0.029, β = 0.18) were selected as significant predictors of the magnesium level in MHD patients. Furthermore, the serum level of magnesium, as well as systolic blood pressure (P = 0.0001, β = 0.32) and age (P = 0.005, β = 0.25), were selected as significant (P = 0.012, β = ?0.22) predictors of ba‐PWV in MHD patients. Discussion : In MHD patients, the serum magnesium level was associated with the serum levels of calcium and albumin. Furthermore, a low serum magnesium level in MHD patients was associated with the index of vascular stiffness.     

Use of solid-electrolyte Galvanic cells to determine the activity of CaO in the CaO-ZrO2 system and the standard Gibbs free energies of formation of CaZrO3 from CaO and ZrO2

The activity of CaO in the CaO-ZrO₂ system has been measured at 1572 to 1877 K with a Galvanic cell composed of 4CaO ? P₂O₅ as the solid electrolyte. The activity ZrO₂ was calculated from the activity of CaO by integrating the Gibbs-Duhem relation. From the activities of CaO and ZrO₂, the standard Gibbs free energy of formation of CaO ? ZrO₂ was determined as follows: ΔG _f ⁰ /J mol^?1 = -25,200 (±150) - 17.58 (±0.085)T (1633 to 1873 K)     

Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

Parametric polynomial spectral factorization using the sum of roots and its application to a control design problem

This paper presents an algebraic approach to polynomial spectral factorization, an important mathematical tool in signal processing and control. The approach exploits an intriguing relationship between the theory of Gröbner bases and polynomial spectral factorization which can be observed through the sum of roots, and allows us to perform polynomial spectral factorization in the presence of real parameters. It is discussed that parametric polynomial spectral factorization enables us to express quantities such as the optimal cost in terms of parameters and the sum of roots. Furthermore an optimization method over parameters is suggested that makes use of the results from parametric polynomial spectral factorization and also employs two quantifier elimination techniques. This proposed approach is demonstrated in a numerical example of a particular control problem.     

Optimal linear combination of neural networks for improvingclassification performance

This paper presents a new method for linearly combining multiple neural network classifiers based on the statistical pattern recognition theory. In our approach, several neural networks are first selected based on which works best for each class in terms of minimizing classification errors. Then, they are linearly combined to form an ideal classifier that exploits the strengths of the individual classifiers. In this approach, the minimum classification error criterion is utilized to estimate the optimal linear weights. In this formulation, because the classification decision rule is incorporated into the cost function, a more suitable better combination of weights for the classification objective could be obtained. Experimental results using artificial and real data sets show that the proposed method can construct a better combined classifier that outperforms the best single classifier in terms of overall classification errors for test data     

Parametric embedding for class visualization

We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.     

Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals

This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT??= 670 ms reverberant environment.     

Wide-gap layered oxychalcogenide semiconductors: Materials, electronic structures and optoelectronic properties

Applying the concept of materials design for transparent conductive oxides to layered oxychalcogenides, several p-type and n-type layered oxychalcogenides were proposed as wide-gap semiconductors and their basic optical and electrical properties were examined. The layered oxychalcogenides are composed of ionic oxide layers and covalent chalcogenide layers, which bring wide-gap and conductive properties to these materials, respectively. The electronic structures of the materials were examined by normal/inverse photoemission spectroscopy and energy band calculations. The results of the examinations suggested that these materials possess unique features more than simple wide-gap semiconductors. Namely, the layered oxychalcogenides are considered to be extremely thin quantum wells composed of the oxide and chalcogenide layers or 2D chalcogenide crystals/molecules embedded in an oxide matrix. Observation of step-like absorption edges, large band gap energy and large exciton binding energy demonstrated these features originating from 2D density of states and quantum size effects in these layered materials.