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Accurately estimating the state-of-charge (SOC) of lithium-ion batteries (LIBs) in electric vehicles is a challenging task due to the complex dynamics of the battery and the varying operating conditions. To address this, this paper proposes the establishment of an Industrial Internet-of-Things (IIoT)-based digital twin (DT) through the Microsoft Azure services, incorporating components for data collection, time synchronization, processing, modeling, and decision visualization. Within this framework, the readily available measurements in the LIB module, including voltage, current, and operating temperature, are utilized, providing advanced information about the LIBs' SOC and facilitating accurate determination of the electric vehicle (EV) range. This proposed data-driven SOC-estimation-based DT framework was developed with a supervised voting ensemble regression machine learning (ML) approach using the Azure ML service. To facilitate a more comprehensive understanding of historical driving cycles and ensure the SOC-estimation-based DT framework is accurate, this study used three application programming interfaces (APIs), namely Google Directions API, Google Elevation API, and OpenWeatherMap API, to collect the data and information necessary for analyzing and interpreting historical driving patterns, for the reference EV model, which closely emulates the dynamics of a real-world battery electric vehicle (BEV). Notably, the findings demonstrate that the proposed strategy achieves a normalized root mean square error (NRMSE) of 1.1446 and 0.02385 through simulation and experimental studies, respectively. The study's results offer valuable insights that can inform further research on developing estimation and predictive maintenance systems for industrial applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Paulownia trees are grown as a woody biofuel crop, and they yield large amounts of leafy biomass rich in nitrogen. However, there is limited information on the use of paulownia leaves as an animal feed resource. Hence, this study was conducted to assess the effects of paulownia leaf meal (0%, 15%, and 30% in diets) on the growth performance, nutrient digestibility, blood biochemistry, and intestinal microbiota of growing rabbits. The chemical analyses of paulownia leaves indicated that most of the nutrients are similar to those in alfalfa hay. The in vivo results showed that the use of up to 15% paulownia leaf meal instead of alfalfa hay in the diets of the rabbits did not have any negative effects on their performance, nutrient digestibility, and blood constituents. In addition, a notable reduction in both cecal and feed pathogenic bacteria was observed when paulownia leaf meal was included in the diets. This experiment was conducted to study the effects of paulownia leaf meal (PLM) as a nontraditional feed on the growth, carcasses, digestibility, blood chemistry, and intestinal microbiota of growing rabbits. Sixty rabbits (5-weeks old) were randomly allotted to three dietary treatments containing three amounts of PLM (0%, 15%, and 30%). The results showed that PLM has a higher content of ether extract, organic matter, methionine, tyrosine, histidine, manganese, and zinc than alfalfa hay. Body weight gain decreased when 30% PLM was provided. The best feed conversion ratio was recorded in the rabbits fed 15% PLM. A notable increase in high-density lipoprotein levels with a significant decrease in low-density lipoprotein was noted in the rabbits fed the PLM diets. Total fungi and Enterobacteriaceae and total bacterial count in the feed were significantly reduced because of PLM. In the cecum, coliforms, Enterobacteriaceae species, and total bacterial count declined in the rabbits fed the PLM diets. Conclusively, up to 15% PLM can be used in rabbit diets without any deleterious effects on the performance, nutrient digestibility, and blood constituents. In addition, dietary inclusion of PLM has the potential to reduce cecal pathogenic bacteria in rabbits. [ABSTRACT FROM AUTHOR]

The phytochemical investigation of the hydromethanolic extract of Carica papaya Linn. leaves (Caricaceae) resulted in the isolation and characterization of ten compounds, namely; carpaine (1), methyl gallate (2), loliolide (3), rutin (4), clitorin (5), kaempferol-3- O -neohesperidoside (6), isoquercetin (7), nicotiflorin (8) and isorhamnetin-3- O -β-d-glucopyranoside (9). The compounds 2, 3, 5-7 and 9 were isolated for the first time from the genus Carica . An in vitro breast cancer cytotoxicity study was evaluated with an MCF-7 cell line using the MTT assay. Methyl gallate and clitorin demonstrated the most potent cytotoxic activities with an IC 50 of 1.11 ± 0.06 and 2.47 ± 0.14 μM, respectively. Moreover, methyl gallate and nicotiflorin exhibited potential EGFR wt kinase inhibition activities with an IC 50 of 37.3 ± 1.9 and 41.08 ± 2.1 nM, respectively, compared with the positive control erlotinib (IC 50 = 35.94 ± 1.8 nM). On the other hand, clitorin and nicotiflorin displayed the strongest aromatase kinase inhibition activities with an IC 50 of 77.41 ± 4.53 and 92.84 ± 5.44 nM, respectively. Clitorin was comparable to the efficacy of the standard drug letrozole (IC 50 = 77.72 ± 4.55). Additionally, molecular docking simulations of the isolated compounds to EGFR and human placental aromatase cytochrome P450 (CYP19A1) were evaluated. Methyl gallate linked with the EGFR receptor through hydrogen bonding with a pose score of -4.5287 kcal mol -1 and RMSD value of 1.69 Å. Clitorin showed the strongest interaction with aromatase (CYP19A1) for the breast cancer receptor with a posing score of -14.2074 and RMSD value of 1.56 Å. Compounds (1-3) possessed a good bioavailability score with a 0.55 value., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)