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Thermal interface materials (TIMs), which consist of polymers and thermally conductive fillers, are crucial for improving heat dissipation. This study examines the impact of surface functionalization of Si₃N₄ thermal conductive fillers on the performance of TIMs. Si₃N₄ fillers are modified with silane coupling agents of varying alkyl chain lengths, producing fillers with contact angles ranging from 25° to 151.2°, thereby ensuring enhanced interfacial compatibility with various polymers. The modified fillers are incorporated into three common polymers—silica gel (SG), epoxy resin (EP), and polyurethane (PU)—to fabricate TIMs. When the contact angle of Si₃N₄ fillers is 73.3°, they demonstrate excellent interfacial compatibility with EP, leading to a 54.37% increase in thermal conductivity and a 162.75% enhancement in elongation at break for the TIM. At a contact angle of 132.7°, the TIMs prepared with SG exhibit an 86.36% increase in thermal conductivity and a 23.88% increase in elongation at break. Given that the original Si₃N₄ already possesses adequate interfacial compatibility with PU, no further modification is required. These findings offer valuable insights for future research aimed at optimizing Si₃N₄ fillers and TIMs to achieve enhanced thermal and mechanical properties. [ABSTRACT FROM AUTHOR]

Pathological myopia (PM) is a severe ocular disease leading to blindness. As a traditional noninvasive diagnostic method, fundus color photography (FCP) is widely used in detecting PM due to its high fidelity and precision. However, manual examination of fundus photographs for PM is time-consuming and prone to high error rates. Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions. In this paper, we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions. The system subdivided different stages of PM into eight subcategories, aiming to enhance the precision and efficiency of the diagnostic process. It achieved an average accuracy rate of 98.86% in detection of PM, with an area under the curve (AUC) of 98.96%. For the eight subcategories of PM, the detection accuracy reached 99.63%, with an AUC of 99.98%. Compared with other widely used multi-class models such as VGG16, Vision Transformer (VIT), EfficientNet, this system demonstrates higher accuracy and AUC. This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools, providing an efficient solution for large-scale PM screening. [ABSTRACT FROM AUTHOR]

Echinococcus granulosus causes echinococcosis, an important zoonotic disease worldwide and a major public health issue. Vaccination is an economical and practical approach for controlling E. granulosus. We have previously revealed that a recombinant protein P29 (rEg.P29) is a good vaccine candidate against E. granulosus. However, T cell immunogenic epitopes have not been identified. In the present study, we use rEg.P29-immunized mice as models to screen immunogenic epitopes for the construction of a novel multi-epitope vaccine. We search for immunodominant epitopes from an overlapping peptide library to screen the peptides of rEg.P29. Our results confirm that rEg.P29 immunization in mice elicits the activation of T cells and induces cellular immune responses. Further analyses show that a T cell epitope within amino acids 86–100 of rEg.P29 elicits significant antigen-specific IFN-γ production in CD4+ and CD8+ T cells and promotes specific T-cell activation and proliferation. Collectively, these results provide a reference for the construction of a novel vaccine against broad E. granulosus genotypes based on epitopes of rEg.P29.

Attributed to its advantages of super mechanical flexibility, very low-temperature processing, and compatibility with low cost and high throughput manufacturing, organic thin-film transistor (OTFT) technology is able to bring electrical, mechanical, and industrial benefits to a wide range of new applications by activating nonflat surfaces with flexible displays, sensors, and other electronic functions. Despite both strong application demand and these significant technological advances, there is still a gap to be filled for OTFT technology to be widely commercially adopted. This paper provides a comprehensive review of the current status of OTFT technologies ranging from material, device, process, and integration, to design and system applications, and clarifies the real challenges behind to be addressed.

Aiming to address the timing skew mismatch in the time‐interleaved analog‐to‐digital converter (TIADC) system, this paper presents a timing skew mismatch calibration method based on a back propagation (BP) neural network optimized by an adaptive genetic algorithm (AGA). In this paper, a trained BP neural network is used to detect the timing skew mismatch in the TIADC system, and the variable delay line is used to calibrate it. In this paper, AGA is used to optimize the BP neural network, accelerating its training speed and improving the detection accuracy of timing skew mismatch in the system. The proposed approach boasts superior detection speed and accuracy compared to other methods. In this paper, an 18‐bit 1GS/S 4‐channel TIADC system is simulated and the timing skew mismatch in the system is corrected. Simulation results show that the proposed calibration method has fast detection speed, high detection accuracy, and calibration accuracy. After completing the timing skew mismatch correction, the performance of the TIADC system is dramatically improved. The effective number of bits (ENOB) of the system increases by 9.5 bits, and the spurious‐free dynamic range (SFDR) increases by 59.9 dB. [ABSTRACT FROM AUTHOR]