Excited-state absorption of meso-tetrasulfonatophenyl porphyrin: Effects of pH and micelles

The influence of the environment on the excited state transitions of meso-tetrakis(p-sulfonatophenyl) porphyrin (TPPS) is reported. TPPS was investigated in protonated and non-protonated forms, and in the presence of the cationic cetyltrimethylammonium bromide (CTAB) micelles. The singlet excited-state absorption spectra were measured by using the white-light continuum Z-scan technique and the triplet–triplet absorption spectra were acquired employing an association of laser flash photolysis and Z-scan techniques. Our results show that the perseveration of the molecular symmetry, upon excitation, depends on the state of multiplicity of the molecules, as well as on the environment and structural characteristics of the porphyrin. Additionally, it was observed that for excited molecules, the ring distortion caused by the protonation of porphyrin ring has great influence on the changes observed for the symmetry and vibronic structure. The results clearly show that the porphyrin investigated is a promising candidate for optical limiting applications for all investigated environments.     

Transformation of hardening to softening behaviors induced by Sb substitution in CuO-doped KNN-based piezoceramics

Pb-free piezoceramics of K_0.5Na_0.5Nb_1-xSb_xO₃ + 1 mol% CuO are synthesized via a solid-state reaction. Furthermore, the transformation of hardening to softening behaviors induced by Sb substitution is exhibited and the corresponding microscopic mechanism is proposed. The CuO-doped K_0.5Na_0.5NbO₃ ceramic without adding Sb exhibits extremely hardening characteristics (i.e., ultrahigh Q_m of ∼2426, low tanδ of 0.32%, and pinched ferroelectric hysteresis loop) due to the formation of defect combinations (${({\text{Cu}}_{\text{Nb}}^{\text{'''}}-{\text{V}}_{\text{o}}^{••})}^{\text{'}}$ and ${({\text{V}}_{\text{o}}^{••}-{\text{Cu}}_{\text{Nb}}^{\text{'''}}-{\text{V}}_{\text{o}}^{••})}^{•}$). Whereas, the addition of Sb dramatically reduces the levels of defect combinations, leading to obviously softening properties (d₃₃ > 210 pC N⁻¹, k_p > 40%, low Q_m, and normal single P-E hysteresis loop). Our results indicate that the decrease of defect combinations with Sb addition should be responsible for the hardening-softening transformation of piezoelectricity and ferroelectricity in CuO-doped K_0.5Na_0.5Nb_1-xSb_xO₃ piezoceramics.     

HELOP: Multi-target tracking based on heuristic empirical learning algorithm and occlusion processing

Multi-target tracking is one of the important fields in computer vision, which aims to solve the problem of matching and correlating targets between adjacent frames. In this paper, we propose a fine-grained track recoverable (FGTR) matching strategy and a heuristic empirical learning (HEL) algorithm. The FGTR matching strategy divides the detected targets into two different sets according to the distance between them, and adopts different matching strategies respectively, in order to reduce false matching, we evaluated the trust degree of the target’s appearance feature information and location feature information, adjusted the proportion of the two reasonably, and improved the accuracy of target matching. In order to solve the problem of trajectory drift caused by the cumulative increase of Kalman filter error during the occlusion process, the HEL algorithm predicts the position information of the target in the next few frames based on the effective information of other previous target trajectories and the motion characteristics of related targets. Make the predicted trajectory closer to the real trajectory. Our proposed method is tested on MOT16 and MOT17, and the experimental results verify the effectiveness of each module, which can effectively solve the occlusion problem and make the tracking more accurate and stable.     

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.     

Prediction of stable high-pressure structures of tantalum nitride TaN2

Structure searches based on a combination of first-principles calculations and a particle swarm optimization technique unravel two new stable high-pressure structures (C2/m and Cmce) for TaN₂. The structural features, mechanical properties, formation enthalpies, electronic structure, and phase diagram of TaN₂ are fully investigated. Being mechanically and dynamically stable, the two phases could be made metastable experimentally at ambient conditions.     

Fuzzy image fusion based on modified Self-Generating Neural Network

A new fusion algorithm for multi-sensor images based on Self-Generating Neural Network (SGNN) and fuzzy logic is proposed in this paper. This study is an extension of the work described in Qin and Bao (2005). First, the order and frequency modifications for the current McKusick and Langley (M–L) optimization are proposed; next, by combining optimization and pruning together, the Pruning-And-One-Optimization-Composite (PAOOC) processing method is raised; and finally, a modified fuzzy fusion scheme using improved SGNN is put forward. Experimental results demonstrate that the posed fuzzy fusion scheme outperforms region-based fusion using wavelet multi-resolution (MR) segmentation, and region-based fusion using tree-structure wavelet MR segmentation, both in visual effect and objective evaluation criteria. In the meantime, simulations also show the effectiveness of our modifications for the current optimization and pruning methods, visually and objectively.     

Global output-feedback stabilization for a class of uncertain time-varying nonlinear systems

This paper investigates the global output-feedback stabilization for a class of uncertain time-varying nonlinear systems. The remarkable structure of the systems is the presence of uncertain control coefficients and unmeasured states dependent growth whose rate is inherently time-varying and of unknown polynomial-of-output, and consequently the systems have heavy nonlinearities, serious uncertainties/unknowns and serious time-variations. This forces us to explore a time-varying plus adaptive methodology to realize the task of output-feedback stabilization, rather than a purely adaptive one. Detailedly, based on a time-varying observer and transformation, an output-feedback controller is designed by skillfully combining adaptive technique, time-varying technique and well-known backstepping method. It is shown that, with the appropriate choice of the design parameters/functions, all the signals of the closed-loop system are bounded, and furthermore, the original system states globally converge to zero. It is worth mentioning that, the heavy nonlinearities are compensated by an updating law, while the serious unknowns and time-variations are compensated by a time-varying function. The designed controller is still valid when the system has an additive input disturbance which, essentially different from those studied previously, may not be periodic or bounded by any known constant.     

Efficient cathode contacts through Ag-doping in multifunctional strong nucleophilic electron transport layer for high performance inverted OLEDs

This paper presents an efficient and stable green inverted organic light emitting diode (IOLED) using multifunctional and strong nucleophilic quality electron transport material (1,3-bis(2-phenyl-1,10-phenanthrolin-4-yl)benzene (m-bPPhenB)) with silver (Ag) as an n-dopant. By the energy level alignment study using in-situ ultraviolet photoelectron spectroscopy measurement, negligible electron injection barrier between indium tin oxide (ITO) and Ag-doped m-bPPhenB (Φ_e ≈ 0.03 eV) is observed and the electrons can be easily tunneled from ITO into Ag-doped m-bPPhenB layer. Also, Ag dopant forms coordination bonds with phenanthroline based unit, which improves electron injection from ITO. Fabricated IOLED devices using an Ag-doped m-bPPhenB have an extremely low driving voltage of 3.6 V and external quantum efficiency of 29.0%. Such good performances of IOLED are attributed to negligible electron injection barrier at the interface between ITO and Ag-doped m-bPPhenB. The Ag-doped IOLED device also shows a good air stability owing to the stable Ag n-dopant. The doping of Ag into special electron transport layer in the IOLED structure could be applicable to various displays and lighting applications.     

Free carrier absorption in self-activated PbWO4 and Ce-doped Y3(Al0.25Ga0.75)3O12 and Gd3Al2Ga3O12 garnet scintillators

Nonequilibrium carrier dynamics in the scintillators prospective for fast timing in high energy physics and medical imaging applications was studied. The time-resolved free carrier absorption investigation was carried out to study the dynamics of nonequilibrium carriers in wide-band-gap scintillation materials: self-activated led tungstate (PbWO₄, PWO) ant two garnet crystals, GAGG:Ce and YAGG:Ce. It was shown that free electrons appear in the conduction band of PWO and YAGG:Ce crystals within a sub-picosecond time scale, while the free holes in GAGG:Ce appear due to delocalization from Gd³⁺ ground states to the valence band within a few picoseconds after short-pulse excitation. The influence of Gd ions on the nonequilibrium carrier dynamics is discussed on the base of comparison the results of the free carrier absorption in GAGG:Ce containing gadolinium and in YAGG without Gd in the host lattice.