Enhanced proton transport properties of Nafion via functionalized halloysite nanotubes

The introduction of naturally occurring fibrillary structured halloysite clays (HNTs) into the Nafion matrix resulted in an enhanced proton conductivity and a reduced activation energy for proton transport. To assess the effect of the various states of the HNTs as a membrane additive, the σ_H⁺ of the composite membranes (Nafion/HNTs, Nafion/HNTs-NH₂, Nafion/HNTs-SO₃H) was measured at 30 °C and 80 °C and at different levels of relative humidity (%RH). An overall improvement of the σ_H⁺ of the Nafion was observed in the presence of the nanotubular additives (HNTs or HNTs-NH₂) which can be attributed to the high specific surface area accommodating hydrophilic functional groups and therefore contributing to the water retention/management in the composite membrane. However, the Nafion/HNT-SO₃H composites are distinguished in terms of enhanced performance in σ_H⁺ which persist in the whole range of % RH levels and noted that the σ_H⁺ values are almost 2 times higher than native Nafion in the low humidity region (30%–50%) and at 80 °C. In agreement with the σ_H⁺ enhancement, the activation energy is lower than that of native Nafion indicating that the proton transport is facilitated in the presence of -SO₃H modified HNTs, probably due to an improved connectivity and arrangement of ionic conducting domains.     

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.     

Vertical distributions and gamma dose rates of 40K, 232Th, 238U and 137Cs in the selected forest soils in Izmir, Turkey

This paper presents the results obtained from a radioecological study carried out in the selected forest sites of Izmir, Turkey. The levels of gamma-emitting radionuclides, 40K, 232Th, 238U and 137Cs, in soils were determined using gamma spectroscopy. The activity profile for 40K, 232Th and 238U exhibits a uniform distribution with respect to depth. The depth distribution of 137Cs activity was established by fitting the experimental points to an exponential or a Gaussian function. About 42-97% of the 137Cs deposition was found in the first 10 cm of soil even after 18 y from Chernobyl accident. The dose-rate and annual-effective dose received from external irradiation were quantified. It is indicated that 137Cs from the fallout plays a less role in external radiation exposures in forest sites of Izmir than in potential exposures from naturally occurring radionuclides accounted for by soil.     

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.     

Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways

Proteins with similar structures may have different functions. Here, using a non-redundant two-chain protein-protein interface dataset containing 103 clusters, we show that this paradigm extends to interfaces. Whereas usually similar interfaces are obtained from globally similar chains, this is not always the case. Remarkably, in some interface clusters, although the interfaces are similar, the overall structures and functions of the chains are different. Hence, our work suggests that different folds may combinatorially assemble to yield similar local interface motifs. The preference of different folds to associate in similar ways illustrates that the paradigm is universal, whether for single chains in folding or for protein-protein association in binding. We analyze and compare the two types of clusters. Type I, with similar interfaces, similar global structures and similar functions, is better packed, less planar, has larger total and non-polar buried surface areas, better complementarity and more backbone-backbone hydrogen bonds than Type II (similar interfaces, different global structures and different functions). The dataset clusters may provide rich data for protein-protein recognition, cellular networks and drug design. In particular, they should be useful in addressing the difficult question of what the favorable ways for proteins to interact are.     

The mediating effect of rewarding on the relationship between employee involvement and job satisfaction

Employee support provides enormous benefits to help sustain a competitive advantage, respond to changes more quickly than competitors, and position the organization ahead of others. Awareness of this fact triggers organizations to prepare new motivational programs and practices. Employee involvement and rewards are among the many ways to achieve employee job satisfaction and motivational needs. Employee involvement entails giving employees an opportunity to influence decisions and actions regarding their jobs. Furthermore, rewards have the potential to prompt employees to act in line with organizational goals. This study aims to examine the relationship between employee involvement and job satisfaction based on the mediation effect of rewarding. Four hundred employees from the financial sector responded to a questionnaire. The relationship between employee involvement and job satisfaction was tested using hierarchical linear regression analysis. Results revealed that rewarding does mediate the relationship between employee involvement and job satisfaction. Organizations that give priority to employee needs and motivational processes should take both employee involvement and rewarding into consideration.