CFRP Rehabilitation of Concrete Frame Joints with Inadequate Shear and Anchorage Details

The research presented in this study involves full-scale experimental evaluation of carbon fiber-reinforced polymer (CFRP) rehabilitation for existing beam-column joints designed for gravity load with common pre-1970s deficient reinforcement details when subjected to cyclic loading. Numerous studies have demonstrated effectiveness of externally bonded fiber-reinforced polymer (FRP) materials for retrofitting the deteriorating RC structures. Although these materials are widely used in bridges, their applications in buildings have been somewhat limited. In particular, the experimental investigations on external FRP retrofit of deficient beam-column joints have not thoroughly been investigated and they are mainly on scaled-down specimens. The failure of these subassemblies, which possess lack of shear reinforcement within the joint core and shortly embedded positive beam reinforcement, would possibly result in catastrophic collapse of reinforced concrete frame structure during an earthquake event. Recognizing the urgent need to upgrade these structural subassemblies, the current investigation uses CFRP retrofit techniques to enhance the performance of such deficient joints. Experimental variables studied entail the developed CFRP retrofit configurations, and magnitude of the applied column axial load. Comparative analysis of the lateral loads versus drift hysteresis loops, stiffness degradation, and total dissipated energy curves of three as-built and three corresponding CFRP-retrofitted RC joints revealed that significant improvement in the shear capacity of the upgraded joints occurred. More importantly, the slippage of short embedded beam positive reinforcement into the joint was substantially controlled due to the developed CFRP retrofit. The results demonstrate the effectiveness of CFRP retrofit configurations in enhancing the structural performance of actual size connections.     

An effective wavelet-based feature extraction method for classification of power quality disturbance signals

This paper presents a wavelet norm entropy-based effective feature extraction method for power quality (PQ) disturbance classification problem. The disturbance classification schema is performed with wavelet-neural network (WNN). It performs a feature extraction and a classification algorithm composed of a wavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The PQ signals used in this study are seven types. The performance of this classifier is evaluated by using total 2800 PQ disturbance signals which are generated the based model. The classification performance of different wavelet family for the proposed algorithm is tested. Sensitivity of WNN under different noise conditions which are different levels of noises with the signal to noise ratio is investigated. The rate of average correct classification is about 92.5% for the different PQ disturbance signals under noise conditions.     

Near ground propagation model for pine tree forest environment

A propagation model experimentally derived from both free space and near ground plane earth path loss models for pine tree forest with foliage depth lower than 400 m has been proposed. It is a piecewise model taking foliage depth of 200 m as a boundary. The model is compared with several well-known models, namely Weissberger, ITU-R and COST235. Different from other models, the proposed one takes into account trunk height gain k. Observed average error is about 6 dB for proposed model whereas it is about 32 dB, 16 dB and 20 dB for COST235, Weissberger and ITU-R models, respectively. Also, it is observed that COST235 estimates the highest path loss among the models and it has the highest absolute errors especially in a forest with low foliage density.     

A problem space algorithm for single machine weighted tardiness problems

We propose a problem space genetic algorithm to solve single machine total weighted tardiness scheduling problems. The proposed algorithm utilizes global and time-dependent local dominance rules to improve the neighborhood structure of the search space. They are also a powerful exploitation (intensifying) tool since the global optimum is one of the local optimum solutions. Furthermore, the problem space search method significantly enhances the exploration (diversification) capability of the genetic algorithm. In summary, we can improve both solution quality and robustness over the other local search algorithms reported in the literature.     

Efficient and Thermally Stable Wide Bandgap Perovskite Solar Cells by Dual-Source Vacuum Deposition

Wide bandgap perovskites are being widely studied in view of their potential applications in tandem devices and other semitransparent photovoltaics. Vacuum deposition of perovskite thin films is advantageous as it allows the fabrication of multilayer devices, fine control over thickness and purity, and it can be upscaled to meet production needs. However, the vacuum processing of multicomponent perovskites (typically used to achieve wide bandgaps) is not straightforward, because one needs to simultaneously control several thermal sources during the deposition. Here a simplified dual-source vacuum deposition method to obtain wide bandgap perovskite films is shown. The solar cells obtained with these materials have similar or even larger efficiency as those including multiple A-cations, but are much more thermally stable, up to 3500 h at 85 °C for a perovskite with a bandgap of 1.64 eV. With optimized thickness, record efficiency of >19% and semitransparent devices with stabilized power output in excess of 17% are achieved.     

On-chip template training system and image processing applications using iterative annealing on ACE16k chip

Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we have implemented the Iterative Annealing Optimization Method on the analog CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications.     

Robustness and stability measures for scheduling: single-machine environment

This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.     

Do tests of divergent thinking have an experiential bias?

Divergent thinking (DT) tests are widely used as an estimate of creativity. However, tests of DT may be biased by experience. Scores from these tests may depend on the amount and types of experiences of examinees. This investigation was designed to determine the degree to which personal and social experiences influence DT scores. Two different tasks were administered: Uses task and Problem Generation (PG). Fluency and originality scores were calculated for each. Analyses indicated that the impact of experience was similar in the PG and Uses tasks. Personal and social experience explained 44% and 30% of fluency scores for PG and Uses tasks, respectively, and 65% of originality scores for both PG and Uses. The differences between uncorrected scores (all ideas, including those reflecting experience) and corrected scores (where ideas tied to personal or social experiences were eliminated) were statistically significant, with the largest discrepancy in Uses fluency and lowest in Uses originality. Findings supported the claim that divergent thinking tests may depend heavily on experience. Alternatives for using DT tests without an experiential bias are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A mathematical approach for integrating the cell design and production planning decisions

A mixed integer programming model that selects cells that can handle demand variability is presented. Valid inequalities are added to the linear programming relaxation of the model. By adding these valid inequalities, the lower bound on the model's optimal objective function value is significantly improved. An efficient heuristic procedure that generates good solutions to this model is also presented.