Nonlinear Interest Rate Reaction Functions for the UK

We empirically analyze Taylor-type equations for short-term interest rates in the United Kingdom using quarterly data from 1970Q1 to 2006Q2. Starting from strong evidence against a simple linear Taylor rule, we model nonlinearities using logistic smooth transition regression (LSTR) models. The LSTR models with time-varying parameters consistently track actual interest rate movements better than a linear model with constant parameters. Our preferred LSTR model uses lagged interest rates as a transition variable and suggests that in times of recessions the Bank of England puts more weight on the output gap and less so on inflation. A reverse pattern is observed in non-recession periods. Parameters of the model change less frequently after 1992, when an inflation target range was announced. We conclude that for the analysis of historical monetary policy, the LSTR approach is a viable alternative to linear reaction functions.interest rate reaction functions, smooth transition regression model, monetary policy

Explainable Artificial Intelligence: Approaching it From the Lowest Level

The increasing complexity of artificial intelligence models has given rise to extensive work toward understanding the inner workings of neural networks. Much of that work, however, has focused on manipulating input data feeding the network to assess their affects on network output or pruning model components after the often-extensive time-consuming training. It is postulated in this study that understanding of neural network can benefit from model structure simplification. In turn, it is shown that model simplification can benefit from investigating network node, the most fundamental unit of neural networks, evolving trends during training. Whereas studies on simplification of model structure have mostly required repeated model training at prohibitive time costs, assessing evolving trends in node weights toward model stabilization may circumvent that limitation. Node Positional and magnitude stabilities were the central construct to investigate neuronal patterns in time for this study and to determine node influence in model predictive ability. Positional stability was defined as the number of epochs wherein nodes held their location compared to those from the stable model, defined in this study as a model with accuracy \u3e0.90. Node magnitude stability was defined as the number of epochs where node weights retained their magnitude within a tolerance value when compared to the stable model. To test evolving trends, a manipulated, a contrived, two life science data sets were used. Data sets were run convolutional (CNN) and deep neural network (DNN) models. Experiments were conducted to test neural network training for patterns as a predicate for investigating node evolving trends. It was postulated that highly stable nodes were most influential in determining model prediction, measured by accuracy. Furthermore, this study suggested that influential node addition to model during training followed a biological growth curve. Findings indicated that neural network weight assignment, weight spatial structure, and progression through time were not random, strongly by model choice and choice of data set. Moreover, progress toward stability differed by model, where CNNs added influential nodes more evenly during training. The CNN model runs generally followed a biological growht curve covering an entire life, whereas for DNN model runs, the growth curve shape was more characteristic of an organism during its early life or a population unconstrained by resources, where growth tends to be exponential. The stability approach of this study showed superior time efficiencies when compared to competing methods. The contributions of this work may assist in making AI models more transparent and easier to understand to all stakeholders, adding to the benefits of AI technologies by minimizing and dispelling the fears associated with adoption of black-box automation approaches in science and industry

Development and Characterization of Mg-SiC Nanocomposite Powders Synthesized by Mechanical Milling

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Magnesium powder in micron scale and various volume fractions of SiC particles with an average diameter of 50 nm were co-milled by a high energy planetary ball mill for up to 25 h to produce Mg-SiC nanocomposite powders. The milled Mg-SiC nanocomposite powders were characterized by scanning electron microscopy (SEM) and laser particle size analysis (PSA) to study morphological evolutions. Furthermore, XRD, TEM, EDAX and SEM analyses were performed to investigate the microstructure of the magnesium matrix and distribution of SiC-reinforcement. It was shown that with addition of and increase in SiC nanoparticle content, finer particles with narrower size distribution are obtained after mechanical milling. The morphology of these particles also became more equiaxed at shorter milling times. The microstructural observation revealed that the milling process ensured uniform distribution of SiC nanoparticles in the magnesium matrix even with a high volume fraction, up to 10 vol%

Enterprise Zones in the Courts: Legal Challenges to State Economic Redevelopment Legislation

The declining state of our nation\u27s cities has been, and continues to be, a frequent source of news and fodder for political debate. Unemployment, urban blight, crime, and economic dislocation are just a few of the inner-city\u27s afflictions which occupy the American mind. A multitude of theories have been advanced in order to explain the persistence of urban deterioration, accompanied by an array of governmental attempts to reverse, or at least stem, the trend of inner-city decay

Feeding Patterns of Two Commercially Important Fish Species \u3ci\u3eScomberoides commersonnianus\u3c/i\u3e and \u3ci\u3eS. tol\u3c/i\u3e In the Northern Arabian Sea Coast of Pakistan

Port landing of Scomberoides commersonnianus and S. tol were obtained between July 2013 and June 2015 for stomach content analysis. Analysis of prey composition was done using permutational analysis of variance (permanova), with species, life stage (juvenile and adults), gender, and weather (rainy and dry season) as factors. Patterns of empty stomachs were investigated to estimate feeding intensity. Feeding intensity was estimated with logistic regression, using the same independent variables as above. Prey importance was also investigated. Prey importance was assessed using a Wilcox Rank Correlation analysis on the Index of Relative Importance (IRI) by species and life-stage. Permanova analysis showed that fish was the most important dietary item for juveniles and adults. Adults secondarily preferred crustaceans. Fish was predominant for S. commersonnianus and crustaceans, especially of the genus Acetes sp., was equally important for S. tol. Acetes sp. was more important during the dry season for both, S. commersonnianus and S. tol. Adults of both species showed a higher feeding activity. The IRI showed fish, followed by crustaceans, to be the most important food item for S. commersonnianus and S. tol. This study is to offer baseline data toward implementing a fishery in Pakistan for current and future generations

Following Dr. King

The authors developed a coating concept based on amorphous Si/C/N coatings, with promising high temperature properties. Due to a graded coating design, the adhesion, the coating hardness as well as the coefficient of friction were optimized. The coatings showed excellent high temperature stability against chemical decomposition, structural changes and surface oxidation up to 1 350 °C. To allow the deposition of coatings with a certain chemical composition, a numerical relationship between the composition of the coatings and the deposition parameters was established

Cyclic deformation behavior of Mg–SiC nanocomposites on the macroscale and nanoscale

Metal‐ceramic nanocomposites are promising candidates for applications necessitating light weight and excellent fatigue resistance. We produced Mg–SiC nanocomposites from mechanically milled powders, yielding a homogeneous nanocrystalline structure and excellent quasistatic strength values. Little is known, however, about the fatigue behavior of such composites. Here, we used load increase tests on the macroscale to yield estimation values of the fatigue endurance limit. Fatigue strength increased significantly for the materials processed by the powder metallurgical route. We further investigated the cyclic deformation behavior under stress‐controlled conditions on the macroscale and nanoscale. Cyclic nanoindentation showed that indentation depth and cyclic plastic deformation decreased with increasing reinforcement content, hinting to a higher cyclic strength and corroborating the results from the macroscopic load increase tests. Our results therefore show that cyclic nanoindentation reliably determines the plastic deformation behavior of Mg nanocomposites offering the possibility of fast material analysis.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659Peer Reviewe

Facile Preparative Access to Bioactive Silicon Oxycarbides with Tunable Porosity

In the present work, Ca-containing silicon oxycarbides (SiCaOC) with varying Ca content have been synthesized via sol-gel processing and thermal treatment in inert gas atmosphere (pyrolysis). It has been shown that the as-prepared SiCaOC materials with low Ca loadings (Ca/Si molar ratios = 0.05 or 0.12) were X-ray amorphous; their glassy network contains Q(3) sites, indicating the presence of Ca2+ at non-bridging-oxygen sites. SiCaOC with high Ca content (i.e., Ca/Si molar ratio = 0.50) exhibits the presence of crystalline calcium silicate (mainly pseudowollastonite). Furthermore, it has been shown that the incorporation of Ca into the SiOC glassy network has a significant effect on its porosity and specific surface area. Thus, the as-prepared Ca-free SiOC material is shown to be non-porous and having a specific surface area (SSA) of 22.5 m(2)/g; whereas SiCaOC with Ca/Si molar ratio of 0.05 exhibits mesoporosity and a SSA value of 123.4 m(2)/g. The further increase of Ca content leads to a decrease of the SSA and the generation of macroporosity in SiCaOC; thus, SiCaOC with Ca/Si molar ratio of 0.12 is macroporous and exhibits a SSA value of 39.5 m(2)/g. Bioactivity assessment in simulated body fluid (SBF) confirms the hydroxyapatite formation on all SiCaOC samples after seven days soaking, unlike the relatively inert ternary silicon oxycarbide reference. In particular, SiCaOC with a Ca/Si molar ratio of 0.05 shows an increased apatite forming ability compared to that of SiCaOC with Ca/Si molar ratio of 0.12; this difference is considered to be a direct consequence of the significantly higher SSA of the sample with the Ca/Si ratio of 0.05. The present work indicates two effects of Ca incorporation into the silicon oxycarbide glassy network on its bioactivity: Firstly, Ca2+ is shown to contribute to the slight depolymerization of the network, which clearly triggers the hydroxyapatite formation (compare the bioactive behavior of SiOC to that of SiCaOC with Ca/Si molar ratio 0.12 upon SBF exposure); secondly, the Ca2+ incorporation seems to strongly affect the porosity and SSA in the prepared SiCaOC materials. There is an optimum of Ca loading into the silicon oxycarbide glassy network (at a Ca/Si molar ration of 0.05), which provides mesoporosity and reaches maximum SSA, both highly beneficial for the bioactive behavior of the materials. An increase of the Ca loading leads, in addition to the crystallization of calcium silicates, to a coarsening of the pores (i.e., macroporosity) and a significant decrease of the SSA, both negatively affecting the bioactivity