The current–voltage and capacitance–voltage characteristics of molecularly modified β-carotene/n-type Si junction structure with fluorescein sodium salt

β-Carotene–FSS organic semiconductor/n-type Si structure has been characterized by current–voltage and capacitance–voltage methods. A deviation in I–V characteristic of the diode is observed due to effect of series resistance and interfacial layer. Cheung's functions were used to calculate diode parameters. The ideality factor, series resistance and barrier height values of the diode are n = 1.77, R_s = 10.32 (10.39) kΩ and 0.78 eV. The obtained ideality factor suggests that Au/β-carotene–FSS/n-Si Schottky diode has a metal–SiO₂ oxide layer plus organic layer–semiconductor (MIOS) configuration. The capacitance–voltage characterizations of Au/β-carotene–FSS/n-Si diode at different temperatures were performed. The capacitance of the diode changes with temperature. The barrier height and ideality factor obtained from C–V curves are 0.67 eV and 1.68. The interface density properties of the diode are analyzed and the shape of the density distribution of the interface states is in the range of E_c −0.49 to −0.62 eV. It is evaluated that the FSS organic layer controls electrical charge transport properties of Au/β-carotene/n-Si diode by excluding effects of the β-carotene and SiO₂ residual oxides on the hybrid diode.     

Perceptually enhanced blind single-channel music source separation by Non-negative Matrix Factorization

We propose a new approach that improves perceptual quality of the separated sources in blind single-channel musical source separation. It uses the advantages of subspace learning based on Non-negative Matrix Factorization (NMF) in which the bases represent the notes. The cost function is formulated in the form of weighted β-divergence by adopting the PEAQ auditory model defined in ITU-R BS.1387 into the source separation. The proposed perceptually weighted factorization scheme is integrated into the Non-negative Matrix Factor 2-D Deconvolution (NMF2D) and Clustered Non-negative Matrix Factorization (CNMF) to overcome the source clustering problem encountered in under-determined source separation. It is shown that the introduced perceptually weighted NMF schemes, named as PW-NMF2D and PW-CNMF, efficiently learn the bases that enable us to apply a simple resynthesis of the musical sources based on the temporal model stored in the encoding matrix. Source separation performance has been reported on musical mixtures where 1–2 dB improvement is achieved in terms of SDR, SIR and SAR compared to the state-of-the-art methods. Performance has also been evaluated by perceptual measures resulting an improvement of 2–5 in OPS, TPS, IPS and APS values.     

Fuzzy FMEA application to improve purchasing process in a public hospital

Failure mode and effects analysis (FMEA) is one of the well-known techniques of quality management that is used for continuous improvements in product or process designs. While applying this technique, determining the risk priority numbers, which indicate the levels of risks associated with potential problems, is of prime importance for the success of application. These numbers are generally attained from past experience and engineering judgments, and this way of risk assessment sometimes leads to inaccuracies and inconsistencies during priority numbering. Fuzzy logic approach is preferable in order to remove these deficiencies in assigning the risk priority numbers. In this study, a fuzzy-based FMEA is to be applied first time to improve the purchasing process of a public hospital. Results indicate that the application of fuzzy FMEA method can solve the problems that have arisen from conventional FMEA, and can efficiently discover the potential failure modes and effects. It can also provide the stability of process assurance.     

Fair referee assignments for professional football leagues

Assignment of referees to football games is an important problem faced in professional football leagues. Despite its importance, the problem has received limited academic attention. This paper presents a model and analysis of the problem for fair referee assignments, and develops a constructive heuristic and a local search procedure for its solution. Results from an extensive computational study show that the methods are effective in solving the problem in a second of computation time and yielding an excellent solution quality.     

One-dimensional partitioning for heterogeneous systems: Theory and practice

We study the problem of one-dimensional partitioning of nonuniform workload arrays, with optimal load balancing for heterogeneous systems. We look at two cases: chain-on-chain partitioning, where the order of the processors is specified, and chain partitioning, where processor permutation is allowed. We present polynomial time algorithms to solve the chain-on-chain partitioning problem optimally, while we prove that the chain partitioning problem is NP-complete. Our empirical studies show that our proposed exact algorithms produce substantially better results than heuristics, while solution times remain comparable.     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

Fuzzy boundary layer tuning for sliding mode systems as applied to the control of a direct drive robot

Chattering in the control signal is a significant problem in sliding mode control (SMC). The boundary layer approach is one of the many modifications proposed in the literature to avoid the chattering. In this approach, instead of the discontinuous SMC, a continuous feedback control law is employed in a boundary layer around the sliding surface. The thickness of the boundary layer is an important design parameter. This paper proposes a fuzzy online tuning method to adjust the boundary layer thickness for the best system performance without chattering. The method features the measurement of the chattering in the control signal. The paper validates the performance of the algorithm by experiments on a direct drive robot with a range of different payloads.     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...