Advancing nonlinear optics: discovery and characterization of new non-centrosymmetric phenazine-based halides.

Organic-inorganic metal halides (OIMHs) have drawn considerable attention due to their remarkable optoelectronic properties and substantial promise for nonlinear optical applications. In this research, phenazine has been selected as the organic cation because of its π-conjugated feature. Three compounds, (C12H9N2)PbCl3, (C12H9N2)SbCl4, and (C12H9N2)2InBr4·Br, were synthesized. Initial space group assignments were centrosymmetric for (C12H9N2)PbCl3 and (C12H9N2)SbCl4. However, under 1550 nm laser excitation, (C12H9N2)PbCl3 and (C12H9N2)SbCl4 exhibited second harmonic generation intensities ∼1.7 times greater than that of the benchmark KH2PO4. Structural reevaluation ultimately confirmed non-centrosymmetric P1 and P21 space groups for (C12H9N2)PbCl3 and (C12H9N2)SbCl4, respectively. Upon excitation at 335 nm and 470 nm, (C12H9N2)PbCl3, (C12H9N2)SbCl4, and (C12H9N2)2InBr4·Br emit fluorescence at room temperature. (C12H9N2)2InBr4·Br exhibits reversible phase transitions, showing potential for phase change energy storage. Our research underscores the critical role of comprehensive experimental validation in determining the precise crystallographic space groups and reveals the extensive potential of OIMHs as versatile candidates for advanced optoelectronic applications.

High External Quantum Efficiency and Dual-Band Emission of (C7H18N)3Sb2Cl9 for Sensitivity Temperature Sensing.

Organic-inorganic hybrid halides have gained attention for their ease of processing and remarkable optoelectronic properties. However, the relationship between the structure and optical properties requires further exploration. In this study, the butyltrimethylammonium cation (C7H18N+) was chosen, and seven compounds were synthesized: (C7H18N)3Sb2X9 (X = Cl, Br), (C7H18N)3Bi2X9 (X = Cl, Br, I), and (C7H18N)(C2H8N)MBr5 (M = Sb, Bi). Crystals with a single organic cation exhibit a zero-dimensional structure, while the introduction of dimethylamine ions increases the crystal dimensionality from zero-dimensional (C7H18N)3Sb2Br9 to one-dimensional (C7H18N)(C2H8N)SbBr5. Under 372 nm excitation, (C7H18N)3Sb2Cl9 showed broad orange-red single-band emission with a high photoluminescence quantum yield of 88.4% and an external quantum efficiency of up to 56.9%. A white light-emitting diode based on (C7H18N)3Sb2Cl9 achieved a high color rendering index of 96.3. Moreover, dual-band emission was observed in (C7H18N)3Sb2Cl9 under 308 nm excitation, which exhibits an absolute temperature sensitivity of 1.96 × 10-3 K-1 (320 K), and a flexible film was prepared by incorporating polydimethylsiloxane. This shows the promise of hybrid metal halides as photoluminescent materials and their possibilities for temperature sensing.

Donor selection for fecal bacterial transplantation and its combined effects with inulin on early growth and ileal development in chicks.

AIMS: To select the best donor and investigate its combined effects with inulin on growth performance, and ileal health of chicks. METHODS AND RESULTS: The chicks (Hy-line Brown) were treated with fecal microbiota suspension from different breeder hens to select the best donor. Treatment with fecal microbiota transplantation (FMT) alone or in combination with inulin resulted in improvement in gut microbiome in chicks. The organ indexes were improved on day 7, especially the bursa of fabricius index (P < 0.05). On day 14, immune performance, ileal morphology, and barrier were improved, and simultaneously, the concentration of short-chain fatty acids was also increased. In addition, for the expression of ileal barrier-related genes, Anaerofustis and Clostridium were positively correlated with them (P < 0.05), Blautia, Prevotella, Veillonella, and Weissella showed a negative correlation (P < 0.05), and RFN20 showed a positive correlation with gut morphology (P < 0.05). CONCLUSION: Combination of homologous FMT and inulin promoted early growth and intestinal health of chicks.

Open Circuit Fault Detection of T-Type Grid Connected Inverters Using Fast S Transform and Random Forest.

To detect open circuit faults of grid-connected T-type inverters, this paper proposed a real-time method based on fast S transform and random forest. The three-phase fault currents of the inverter were used as the inputs of the new method and no additional sensors were needed. Some fault current harmonics and direct current components were selected as the fault features. Then, fast S transform was used to extract the features of fault currents, and random forest was used to recognize the features and the fault type, as well as locate the faulted switches. The simulation and experiments showed that the new method could detect open-circuit faults with low computation complexity and the detection accuracy was 100%. The real-time and accurate open circuit fault detection method was proven effective for grid-connected T-type inverter monitoring.

GaborNet Visual Encoding: A Lightweight Region-Based Visual Encoding Model With Good Expressiveness and Biological Interpretability.

Computational visual encoding models play a key role in understanding the stimulus-response characteristics of neuronal populations in the brain visual cortex. However, building such models typically faces challenges in the effective construction of non-linear feature spaces to fit the neuronal responses. In this work, we propose the GaborNet visual encoding (GaborNet-VE) model, a novel end-to-end encoding model for the visual ventral stream. This model comprises a Gabor convolutional layer, two regular convolutional layers, and a fully connected layer. The key design principle for the GaborNet-VE model is to replace regular convolutional kernels in the first convolutional layer with Gabor kernels with learnable parameters. One GaborNet-VE model efficiently and simultaneously encodes all voxels in one region of interest of functional magnetic resonance imaging data. The experimental results show that the proposed model achieves state-of-the-art prediction performance for the primary visual cortex. Moreover, the visualizations demonstrate the regularity of the region of interest fitting to the visual features and the estimated receptive fields. These results suggest that the lightweight region-based GaborNet-VE model based on combining handcrafted and deep learning features exhibits good expressiveness and biological interpretability.

Study on Representation Invariances of CNNs and Human Visual Information Processing Based on Data Augmentation.

Representation invariance plays a significant role in the performance of deep convolutional neural networks (CNNs) and human visual information processing in various complicated image-based tasks. However, there has been abounding confusion concerning the representation invariance mechanisms of the two sophisticated systems. To investigate their relationship under common conditions, we proposed a representation invariance analysis approach based on data augmentation technology. Firstly, the original image library was expanded by data augmentation. The representation invariances of CNNs and the ventral visual stream were then studied by comparing the similarities of the corresponding layer features of CNNs and the prediction performance of visual encoding models based on functional magnetic resonance imaging (fMRI) before and after data augmentation. Our experimental results suggest that the architecture of CNNs, combinations of convolutional and fully-connected layers, developed representation invariance of CNNs. Remarkably, we found representation invariance belongs to all successive stages of the ventral visual stream. Hence, the internal correlation between CNNs and the human visual system in representation invariance was revealed. Our study promotes the advancement of invariant representation of computer vision and deeper comprehension of the representation invariance mechanism of human visual information processing.

Uncertainty Principle and Power Quality Sensing and Analysis in Smart Substation.

Different kinds of power quality can be sensed in a smart substation. Power quality sensing and analysis are basic functions of a smart substation for situation awareness. The uncertainty principle, which states that the time uncertainty and frequency uncertainty cannot be minimized simultaneously, is a bottleneck problem that undermines the faithfulness of sensing and confines the accuracy of analysis. This paper studies the influence of the uncertainty principle on the power quality monitoring issue in detail and solves the problem by ideal atomic decomposition (IAD). The new method employs a pair of time and frequency bases where the power quality waveform is sensed. Then, both time uncertainty and frequency uncertainty can be minimized simultaneously. The sensing process is realized by orthogonal matching pursuit (OMP). By simulated and field power quality tests with comparisons of developed methods, the new method can give faithful sensing and accurate analysis for various power qualities, and is validated as an effective power quality monitoring method in smart substations.