Estimation of lithium-ion battery health state using MHATTCN network with multi-health indicators inputs.

Accurately predicting the state of health (SOH) of lithium-ion batteries is fundamental in estimating their remaining lifespan. Various parameters such as voltage, current, and temperature significantly influence the battery's SOH. However, existing data-driven methods necessitate substantial data from the target domain for training, which hampers the assessment of lithium-ion battery health at the initial stage. To address these challenges, this paper introduces the multi-head attention-time convolution network (MHAT-TCN), amalgamating multi-head attention learning with random block dropout techniques. Additionally, it employs grey relational analysis (GRA) to select health indicators (HIs) highly correlated with battery capacity, thereby enhancing the accuracy of the model training. Employing leave-one-out crossvalidation (LOOCV), the MHAT-TCN network is pre-trained using data from batteries of the same model to facilitate comprehensive prediction of the target battery throughout its operational period. Results demonstrate that the MHAT-TCN network trained on HIs outperforms other models, enabling precise predictions across the entire operational period.

Folic acid protects against isoniazid-induced liver injury via the m6A RNA methylation of cytochrome P450 2E1 in mice.

Background: Cytochrome P450 2E1 (CYP2E1) converts isoniazid (INH) to toxic metabolites and is critical in INH-induced liver injury. The aim is to investigate the effect of folic acid (FA) on CYP2E1 and INH-induced liver injury. Methods: Male Balb/c mice were used. The mice in the control group only received an AIN-93M diet. The AIN-93M diet was supplemented with 0.66 g INH/kg diet for the mice in the INH and FA groups. The mice in the FA group were treated with additional 0.01 g FA/kg diet. The one-carbon cycle metabolites, the expressions of CYP2E1 and the DNA and RNA methylation levels were detected to reveal the potential mechanism. Results: FA treatment significantly reduced the alanine aminotransferase level and alleviated the liver necrosis. The mRNA and protein expressions of CYP2E1 were significantly lower in the FA group than those in the INH group. The N6-methyladenosine RNA methylation level of Cyp2e1 significantly increased in the FA group compared with the INH group, while the DNA methylation levels of Cyp2e1 were similar between groups. Additionally, the liver S-adenosyl methionine (SAM)/S-adenosyl homocysteine (SAH) was elevated in the FA group and tended to be positively correlated with the RNA methylation level of Cyp2e1. Conclusion: FA alleviated INH-induced liver injury which was potentially attributed to its inhibitory effect on CYP2E1 expressions through enhancing liver SAM/SAH and RNA methylation.

A novel and efficient electrochemiluminescence sensing strategy for the determination of trimethylamine oxide in seafood.

A novel and efficient electrochemiluminescence (ECL) sensing strategy and a solid-state ECL sensor was proposed to detect trimethylamine oxide (TMAO), which is widely presented in marine species and has important physiological functions. TMAO was reduced by Fe(II)-EDTA complex to trimethylamine, acting as coreactant, to amplify the ECL response of the Ru (bpy)32+ system. To improve the detection sensitivity and efficiency, a robust solid-state ECL probe was prepared and a flow injection ECL detection system was established with a specially designed flow ECL unit, under the excitation of stepping pulse potentials. Under optimized experimental conditions, the developed ECL sensor worked well for TMAO detection in a wide linear range of 10.00 µM to 1.00 mM with a limit of detection of 3.41 µM. It was successfully applied to determine TMAO in various species of seafood samples. This work provides a promising strategy for TMAO detection.

Resonant fiber-optic thermometry with high resolution and wide range.

We report a high-resolution and wide-range thermometer using a fiber Bragg grating Fabry-Perot cavity (FBG-FP) combined with beat frequency interrogation. Two distributed feedback (DFB) lasers are locked to the FBG-FP sensing head and a hydrogen cyanide H13C14N (HCN) gas cell, respectively, both using the Pound-Drever-Hall (PDH) technique. The light beams from two lasers are brought together to interfere on a photodetector producing a beat frequency signal which provides a measure of the temperature change. Our sensor exhibits a dynamic range of ∼109 °C, a high resolution of 2×10-4 °C with an averaging time of 1 s. By introducing the reference frequency, the sensor has demonstrated good long-term stability. This sensor provides a useful tool for those fields where resolving slight temperature changes is crucial, such as deep ocean temperature measurement.

[Relationship Between the Vegetation Community and Soil Nutrient and Enzyme Activity During the Restoration of Abandoned Land in the Loess Hilly Region].

The trends of and relationships among the plant community, soil nutrients, and four soil enzymes were investigated after being abandoned for 10, 15, 20, 30, and 45 years to reveal the soil properties during the restoration in the Loess Hilly Region. The results indicate the following ranking of dominant plant community species:Artemisia scoparia→Lespedeza dahurica+Artemisia sacrorum→Artemisia giraldii+irons Artemisia annua→Bothriochloa flaccidum+Artemisia selengensis. The ranking reflects an increase along the chronosequence of abandoned land. Moreover, the ratio of the total species of Compositae, Poaceae, and Leguminosae decreases from 66.67% to 50% and then increases up to 75%. The SOC, TN, TP, AN, AP, and four enzyme activity types (ALP, CAT, UE, and SC) increase but respond differently to restoration, while the stoichiometric ratio fluctuates. In contrast to the number of plant families, genus, species, and plant diversity, Compositae, Poaceae, and Leguminosae have major effects on the soil nutrient and enzyme activity, which explains the total variation of 72.8%, 69.1%, and 66.0%, respectively. The effects of these three families on the soil enzymes are greater than that on soil nutrients. Poaceae and Leguminosae have a positive effect on the nutrient and enzyme activity, while the family Compositae has a negative effect. Overall, the changes of dominant species of grassland communities during restoration significantly affect the soil enzyme and thereby are responsible for the soil nutrient dynamics.