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Lead‐free tin halide perovskites, particularly Cs2SnBr6, are gaining significant attention for their potential in optoelectronic applications. In this study, we investigate the material's electrical and vibrational properties under varying illumination conditions, providing novel insights into the impact of light on its conduction mechanisms. Cs2SnBr6 was synthesized via liquid‐phase and solid‐state reactions, and we report comprehensive analyses of its impedance, AC conductivity, and dielectric properties over a wide frequency range. Notably, our results reveal that light‐induced photogenerated charge carriers enhance conductivity, which is well explained by the overlapping large‐polaron tunneling (OLPT) model, while in darkness, the material follows the correlated barrier hopping (CBH) model. Moreover, Raman spectroscopy highlights structural changes, including a slight shortening of Sn–Br bond distances under illumination, which influences the vibrational frequencies and intensities. These findings emphasize the crucial role of illumination in tuning the electrical and dielectric responses of Cs2SnBr6, thus demonstrating its considerable promise for future optoelectronic applications, such as solar cells and photodetectors. [ABSTRACT FROM AUTHOR]

Plants offer a bountiful source of natural pest control solutions through their essential oils. This research introduces and analyzes an eco-friendly natural essential oil for red flour beetle control. Therefore, the current study was included to show the chemical profile and the insecticidal efficacy of resin essential oil (REO) and its fractions (F1–3), resulting from chromatographic separation, from the plant Schinus molle against Tribolium castaneum adults. The trunk bark resin essential oil and its fractions' composition were analyzed by GC-MS. Overall, 33 constituents with 98.3% of the total EO composition were identified. REO and F1–3 displayed impressive repellent properties at a concentration of 0.12 µL/cm2. After 120 min of exposure, repellency ranged from 73.3% to a remarkable 96.7%. They also exhibited noteworthy fumigant properties, with median lethal doses of LD50 = 120.6–160.8 μL/L. The fractions F1 and F3 showed the most notable topical toxicity at a concentration of 10%, with LD50 values of 8.6% and 5.6%, respectively. Fractions F3 and F2 demonstrated the most effective inhibition of acetylcholinesterase (AChE) activity, providing insight into their insecticidal mechanisms. The in silico molecular docking and DFT studies corroborate the results of in vitro tests performed to identify new insecticide products derived from natural sources. [ABSTRACT FROM AUTHOR]

Simple Summary: Laryngeal cancer poses a major global health burden, with late-stage diagnoses contributing to decreased survival rates. Recently, deep learning and deep convolutional neural network models have exhibited significant attention in the diagnosis of various diseases like skin cancer and diabetic retinopathy. Therefore, this study focuses on the design and development of a deep learning-based laryngeal cancer detection and classification model. The proposed model exploited a hyperparameter-tuned EfficientNetB0 model with a multi-head bidirectional gated recurrent unit for classification. In addition, the Dwarf Mongoose Optimization algorithm is applied for the hyperparameter tuning process. The experimental results stated that the proposed model is found to be an accurate and reliable approach for automated detection of laryngeal cancer. Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures. [ABSTRACT FROM AUTHOR]

Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.