RESUMEN
Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. "Electric-force-acoustic" multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers.
RESUMEN
Aqueous all-iron flow batteries (AIFBs) are attractive for large-scale and long-term energy storage due to their extremely low cost and safety features. To accelerate commercial application, a long cyclable and reversible iron anolyte is expected to address the critical barriers, namely iron dendrite growth and hydrogen evolution reaction (HER). Herein, we report a robust iron complex with triethanolamine (TEA) and 2-methylimidazole (MM) double ligands. By introducing two ligands into one iron center, the binding energy of the complex increases, making it more stable in the charge-discharge reactions. The Fe(TEA)MM complex achieves reversible and stable redox between Fe3+ and Fe2+ , without metallic iron growth and HER. AIFBs based on this anolyte perform a high energy efficiency of 80.5 % at 80â mA cm-2 and exhibit a record durability among reported AIFBs. The efficiency and capacity retain nearly 100 % after 1,400 cycles. The capital cost of this AIFB is $ 33.2â kWh-1 (e.g., 20â h duration), cheaper than Li-ion battery and vanadium flow battery. This double-ligand chelating strategy not only solves the current problems faced by AIFBs, but also provides an insight for further improving the cycling stability of other flow batteries.
RESUMEN
Obesity is a global health challenge with limited therapeutic solutions. Here, we demonstrate the engineering of an energy-dissipating hybrid tissue (EDHT) in the body for weight control. EDHT is constructed by implanting a synthetic gel matrix comprising immunomodulatory signals and functional cells into the recipient mouse. The immunomodulatory signals induce the host stromal cells to create an immunosuppressive niche that protects the functional cells, which are overexpressing the uncoupling protein 1 (UCP1), from immune rejection. Consequently, these endogenous and exogenous cells co-develop a hybrid tissue that sustainedly produces UCP1 to accelerate the host's energy expenditure. Systematic experiments in high-fat diet (HFD) and transgenic (ob/ob) mice show that EDHT efficiently reduces body weight and relieves obesity-associated pathological conditions. Importantly, an 18-month observation for safety assessment excludes cell leakage from EDHT and reports no adverse physiological responses. Overall, EDHT demonstrates convincing efficacy and safety in controlling body weight.
Asunto(s)
Dieta Alta en Grasa , Metabolismo Energético , Obesidad , Animales , Obesidad/metabolismo , Obesidad/terapia , Ratones , Proteína Desacopladora 1/metabolismo , Ingeniería de Tejidos/métodos , Masculino , Ratones Endogámicos C57BL , Ratones Transgénicos , Humanos , Peso Corporal , Ratones ObesosRESUMEN
Histone lysine crotonylation (Kcr) is a new acylation modification first discovered in 2011, which has important biological significance for gene expression, cell development, and disease treatment. In the past over ten years, numerous signs of progress have been made in the research on the biochemistry of Kcr modification, especially a series of Kcr modification-related "reader", "eraser", and "writer" enzyme systems are identified. The physiological function of crotonylation and its correlation with development, heredity, and spermatogenesis have been paid more and more attention. However, the development of disease is usually associated with abnormal Kcr modification. In this review, we summarized the identification of crotonylation modification, Kcr-related enzyme system, biological functions, and diseases caused by abnormal Kcr. This knowledge supplies a theoretical basis for further exploring the function of crotonylation in the future.
RESUMEN
The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are static, unable to associate inputs with past experiences, and run on digital computers with physically separated memory and processing. We propose a hardware-software co-design, a semantic memory-based dynamic neural network using a memristor. The network associates incoming data with the past experience stored as semantic vectors. The network and the semantic memory are physically implemented on noise-robust ternary memristor-based computing-in-memory (CIM) and content-addressable memory (CAM) circuits, respectively. We validate our co-designs, using a 40-nm memristor macro, on ResNet and PointNet++ for classifying images and three-dimensional points from the MNIST and ModelNet datasets, which achieves not only accuracy on par with software but also a 48.1 and 15.9% reduction in computational budget. Moreover, it delivers a 77.6 and 93.3% reduction in energy consumption.