Temperature dependence of electrical properties in In/Cu2ZnSnTe4/Si/Ag diodes

WOS: 000458625200001Cu2ZnSnTe4 (CZTTe) thin films with In metal contact were deposited by thermal evaporation on monocrystalline n-type Si wafers with Ag ohmic contact to investigate the device characteristics of an In/CZTTe/Si/Ag diode. The variation in electrical characteristics of the diode was analysed by carrying out current-voltage (I-V) measurements in the temperature range of 220-360 K. The forward bias I-V behaviour was modelled according to the thermionic emission (TE) theory to obtain main diode parameters. In addition, the experimental data were detailed by taking into account the presence of an interfacial layer and possible dominant current transport mechanisms were studied under analysis of ideality factor, n. Strong effects of temperature were observed on zero-bias barrier height (Phi(B0)) and n values due to barrier height inhomogeneity at the interface. The anomaly observed in the analysis of TE was modelled by Gaussian distribution (GD) of barrier heights with 0.844 eV mean barrier height and 0.132 V standard deviation. According to the Tung's theoretical approach, a linear correlation between Phi(B0) and n cannot be satisfied, and thus the modified Richardson plot was used to determine Richardson constant (A*). As a result, A* was calculated approximately as 120.6 A cm(-2) K-2 very close to the theoretical value for n-Si. In addition, the effects of series resistance (R-s) by estimating from Cheng's function and density of surface states (N-ss) by taking the bias dependence of effective barrier height, were discussed

Evaluation of Radiomics to Predict the Accuracy of Markerless Motion Tracking of Lung Tumors: A Preliminary Study

Template-based matching algorithms are currently being considered for markerless motion tracking of lung tumors. These algorithms use tumor templates derived from the planning CT scan, and track the motion of the tumor on single energy fluoroscopic images obtained at the time of treatment. In cases where bone may obstruct the view of the tumor, dual energy fluoroscopy may be used to enhance soft tissue contrast. The goal of this study is to predict which tumors will have a high degree of accuracy for markerless motion tracking based on radiomic features obtained from the planning CT scan, using peak-to-sidelobe ratio (PSR) as a surrogate of tracking accuracy. In this study, CT imaging data of 8 lung cancer patients were obtained and analyzed through the open source IBEX program to generate 2,287 radiomic features. Agglomerative hierarchical clustering was used to narrow down these features into 145 clusters comprised of the highest correlation to PSR. The features among the clusters with the least inter-correlation were then chosen to limit redundancy in the data. The results of this study demonstrated a number of radiomic features that are positively correlated to PSR. The features with the highest degree of correlation included complexity, orientation and range. This approach may be used to determine patients for whom markerless motion tracking would be beneficial

The power of diversity over large solution spaces

We consider a team of agents with limited problem-solving ability facing a disjunctive task over a large solution space. We provide sufficient conditions for the following four statements. First, two heads are better than one: a team of two agents will solve the problem even if neither agent alone would be able to. Second, teaming up does not guarantee success: if the agents are not sufficiently creative, even a team of arbitrary size may fail to solve the problem. Third, "defend it numerous": when the agent's problem-solving ability is adversely affected by the complexity of the solution space, the solution of the problem requires only a mild increase in the size of the team. Fourth, groupthink impairs the power of diversity: if agents' abilities are positively correlated, a larger team is necessary to solve the problem

Receptor-Specific Mechanisms Regulate Phosphorylation of AKT at Ser473: Role of RICTOR in β1 Integrin-Mediated Cell Survival

A tight control over AKT/PKB activation is essential for cells, and they realise this in part by regulating the phosphorylation of Ser473 in the “hydrophobic motif” of the AKT carboxy-terminal region. The RICTOR-mTOR complex (TORC2) is a major kinase for AKT Ser473 phosphorylation after stimulation by several growth factors, in a reaction proposed to require p21-activated kinase (PAK) as a scaffold. However, other kinases may catalyse this reaction in stimuli-specific manners. Here we characterised the requirement of RICTOR, ILK, and PAK for AKT Ser473 phosphorylation downstream of selected family members of integrins, G protein-coupled receptors, and tyrosine-kinase receptors and analysed the importance of this phosphorylation site for adhesion-mediated survival. siRNA-mediated knockdown in HeLa and MCF7 cells showed that RICTOR-mTOR was required for phosphorylation of AKT Ser473, and for efficient phosphorylation of the downstream AKT targets FOXO1 Thr24 and BAD Ser136, in response to β1 integrin-stimulation. ILK and PAK1/2 were dispensable for these reactions. RICTOR knockdown increased the number of apoptotic MCF7 cells on β1 integrin ligands up to 2-fold after 24 h in serum-free conditions. β1 integrin-stimulation induced phosphorylation of both AKT1 and AKT2 but markedly preferred AKT2. RICTOR-mTOR was required also for LPA-induced AKT Ser473 phosphorylation in MCF7 cells, but, interestingly, not in HeLa cells. PAK was needed for the AKT Ser473 phosphorylation in response to LPA and PDGF, but not to EGF. These results demonstrate that different receptors utilise different enzyme complexes to phosphorylate AKT at Ser473, and that AKT Ser473 phosphorylation significantly contributes to β1 integrin-mediated anchorage-dependent survival of cells

Convolutional neural networks predict the onset of paroxysmal atrial fibrillation: Theory and applications

In this study, we aimed to detect paroxysmal atrial fibrillation episodes before they occur so that patients can take precautions before putting their and others' lives in potentially life-threatening danger. We used the atrial fibrillation prediction database, open data from PhysioNet, and assembled our process based on convolutional neural networks. Conventional heart rate variability features are calculated from time-domain measures, frequency-domain measures using power spectral density estimations, time-frequency-domain measures using wavelet transform, and nonlinear Poincaré plot measures. In addition, we also applied an alternative heart rate normalization, which gave promising results only in a few studies, before calculating these heart rate variability features. We used these features directly and their normalized versions using min-max normalization and z-score normalization methods. Thus, heart rate variability features extracted from six different combinations of these normalizations, in addition to no normalization cases, were applied to the convolutional neural network classifier. We tuned the classifiers' hyperparameters using 90% of feature sets and tested the classifiers' performances using 10% of feature sets. The proposed approach resulted in 87.76% accuracy, 91.30% precision, 80.04% recall, and 87.50% f1-score in heart rate variability with z-score feature normalization. When the heart rate normalization was also utilized, the suggested method gave 100% accuracy, 100% precision, 100% recall, and 100% f1-score in heart rate variability with z-score feature normalization. The proposed method with heart rate normalization and z-score normalization methods resulted in better classification performance than similar studies in the literature. By comparing the existing studies, we conclude that our approach provides a much better tool to determine a near-future paroxysmal atrial fibrillation episode. However, although the achieved benchmarks are impressive, we note that the approach needs to be supported by other studies and on other datasets before clinical trials. © 2021 Author(s).Javna Agencija za Raziskovalno Dejavnost RS, ARRS: J1-2457, P1-0403M. Perc was supported by the Slovenian Research Agency (Grant Nos. P1-0403 and J1-2457).2-s2.0-8511905931