The influence of processing methods on the dielectric properties of BaTi1-xGdxO3-x/2 - Based materials

In this study, the intermediate rare-earth oxide Gd₂O₃ (Gd) was substituted in different amounts (x = 0.2–2 mol%) for the formulation of BaTi_1-xGd_xO_3-x/2 (BTG_x) dielectric materials. The effect of B-site substitution was confirmed by the additional Raman active A_1g octahedral peak at ~835cm^-1 strengthened at x ≥ 0.4 mol%. Additionally, properties of 0.9BTG_0.007-0.1BA dielectric ceramics were analysed based on the influence of various processing methods as a function of sintering temperature. The focal samples were labelled Method-A (direct-mix) and Method-B (indirect-mix). As the sintering temperature (1075–1200 °C) increased, the 1 kHz response of the ε–T curves of Method-A samples transformed from a single peak to broad-narrow double peaks of high dielectric loss tangent (tan δ). Nonetheless, samples of Method-B possessed a clearly defined transmission electron microscopy (TEM) core-shell structure, flattened double-peak ε-T curves, optimised dielectric properties (ε = ~1563–1851 and tan δ < 1.5% at room temperature), and a wide-ranging temperature behaviour that meets the X8R dielectric standards (ΔC/C_25°C < ±15%). The maximum dielectric breakdown strength of Method-B samples reached ~131 kVcm, while the energy storage density was ~0.726 J/cm³ at a maximum efficiency of ~80% at 1100 °C. Thus, exhibiting good potentials for balancing temperature stability with energy storage applications.     

A CMOS monolithic amplifier for cardiac EIT applications

Analog Integrated Circuits and Signal Processing - This paper presents a low-noise amplifier (LNA) with superior linearity for ultra-wideband (UWB) purposes. Linearity is a significant parameter...     

Living Materials Herald a New Era in Soft Robotics

Living beings have an unsurpassed range of ways to manipulate objects and interact with them. They can make autonomous decisions and can heal themselves. So far, a conventional robot cannot mimic this complexity even remotely. Classical robots are often used to help with lifting and gripping and thus to alleviate the effects of menial tasks. Sensors can render robots responsive, and artificial intelligence aims at enabling autonomous responses. Inanimate soft robots are a step in this direction, but it will only be in combination with living systems that full complexity will be achievable. The field of biohybrid soft robotics provides entirely new concepts to address current challenges, for example the ability to self‐heal, enable a soft touch, or to show situational versatility. Therefore, “living materials” are at the heart of this review. Similarly to biological taxonomy, there is a recent effort for taxonomy of biohybrid soft robotics. Here, an expansion is proposed to take into account not only function and origin of biohybrid soft robotic components, but also the materials. This materials taxonomy key demonstrates visually that materials science will drive the development of the field of soft biohybrid robotics.     

Effects of firm and IT characteristics on the value of e-commerce initiatives: An inductive theoretical framework

We explore the theoretical foundations on how firm and IT characteristics explain the market value variations in e-commerce initiatives by examining the announcements of 946 e-commerce initiatives in the public media. Our approach combines the Event study methodology and Decision tree induction to examine the main and interaction effects of IT and firm characteristics on Cumulative Abnormal Returns (CAR). In particular, we generate complex interaction models that can guide e-commerce investment decisions so managers can know, for example, which combination of IT and firm characteristics are more likely to be viewed positively by investors. The selected study variables as well as explanation of the proposed framework are informed by innovation, resource-based view, transaction cost economics and complementarity theories. We have inductively developed a set of propositions that can be deductively tested to assess the validity of our proposed theoretical framework. Hence our study provides an initial roadmap for theory development on e-commerce and CAR.     

Subsurface Radioactive Contaminant Transport Modeling Using Particle and Kalman Filter Schemes

Contamination of groundwater by radioactive contaminants can be harmful to the environment. Various prediction models have been adopted to simulate the state of contaminants in the subsurface. Conventional numerical models are simplified by approximation and the model parameters are assumed to be constant, thereby introducing error to the prediction results. Particle and Kalman filters are used in this research to simulate the radioactive contaminant cobalt-57 transport in a subsurface environment by using a two-dimensional advection-dispersion model. A radioactive contaminant concentration was predicted spatially and temporally within boundary conditions. The errors in the prediction results were assessed by using the root-mean-square-error (RMSE) equation. The results show that the Kalman filter performs better than the particle filter when the prediction model is linear. Furthermore, the results from filters are closer to the true value in comparison with the numerical solution, and the filters are capable of reducing the RMSE of the numerical solution by approximately 80%.     

EFFECTS OF COAL SURFACE CHARGE ON THE ADSORPTION AND GASIFICATION ACTIVITIES OF CALCIUM AND POTASSIUM

ABSTRACT

The electrokinetic properties of lignite, subbituminous and bituminous coal particles and their demineralized derivatives have been measured as a function of pH. Compared to the raw coals, demineTalization generally increased the net negative surface charge densities on the coals. Calcium or potassium adsorption (by ion-exchange) onto the demineralized coals was strongly dependent on pH and the surface charge properties of the coals. Metals uptake was very low (0,1-0.4% wt.) in strongly acidic media (～pHl) but increased dramatically as the net negative charge on the coals was increased by increasing the pHs of the coal suspensions to pH 6 or 10. The reactivities of the calcium- ot potassium-loaded chars in carbon dioxide at 800^°C were similarly dependent upon the pHs at which the catalysts were ion-exchanged onto the coals. For the calcium-containing chars, the reactivities increased in the order: pH 6 > “pH 10 > pH 1 while the variation of the gasification rates with potassium loading pH was: pH 6 ～ pH 10 ? pH I. These findings are attributed to differences in the extent of electrostatic interaction between the calcium or potassium ions and the charged coal surface during catalyst loading from solution.     

An energy-efficient spike encoding circuit for speech edge detection

In speech processing applications, the instantaneous bandwidth of speech can be used to adaptively control the performance of an audio sensor’s analog front end. Extracting the instantaneous bandwidth of speech depends on the detection of speech edges in the time–frequency plane. In this paper, we propose a spike encoding circuit for real-time and low-power speech edge detection. The circuit can directly encode the signal’s envelope information—an important feature to identify the speech edge—by temporal spike density without additional envelope extraction. Furthermore, the spike encoding circuit automatically adapts its resolution to the amplitude of the input signal, which improves the encoding resolution for small signal without increasing the power consumption. We use the nonlinear dynamical approach to design this circuit and analyze its stability. We also develop a linearized model for this circuit to provide the design intuition and to explain its adaptive resolution. Fabricated in 0.5-μm CMOS process, the spike encoding circuit consumes 0.3-μW power and the experimental results are presented.     

A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition

The adoption of high-accuracy speech recognition algorithms without an effective evaluation of their impact on the target computational resource is impractical for mobile and embedded systems. In this paper, techniques are adopted to minimise the required computational resource for an effective mobile-based speech recognition system. A Dynamic Multi-Layer Perceptron speech recognition technique, capable of running in real time on a state-of-the-art mobile device, has been introduced. Even though a conventional hidden Markov model when applied to the same dataset slightly outperformed our approach, its processing time is much higher. The Dynamic Multi-layer Perceptron presented here has an accuracy level of 96.94% and runs significantly faster than similar techniques.