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A method based on Long Short-Term Memory (LSTM) networks is proposed to forecast hourly energy consumption. Using an office building in Shanghai as a case study, hourly data on occupancy, weather, and energy consumption were collected. Daily energy consumption was analyzed using single-link clustering, and days were classified into three types. The key input variables significantly influencing energy consumption, solar radiation, occupancy, and outdoor dry bulb temperature are identified by the Pearson correlation coefficient. By comparing five algorithms, it was found that the LSTM model performed the best. After considering the occupancy, the hourly MAPE was reduced from 11% to 9%. Accuracy improvements for each day type were noted as 1% for weekdays, 4% for Saturday, and 7% for Sunday. Further analysis indicated that the model started to predict the time (1:00) and commute time (7:00 and 17:00) with large errors. The model was optimized by varying the time step. For the times 1:00, 7:00, and 17:00, the best optimization of the model was achieved when the time step values were set to 6 h, 24 h, and 18 h with an MAPE of 3%, 6%, and 5%, respectively. As the model time step increased (≤2 weeks), the accuracy of the model decreased to 6%. [ABSTRACT FROM AUTHOR]

Industrial wastewater containing heavy metal ions presents serious economic risk to the environment. In this study, a novel compound of aminated cellulose with jeffamine EDR148 was prepared to improve cellulose's adsorptive behavior towards metal ions. This study undertook a straightforward and efficient cellulose modification through homogeneous chlorination in N,N′-butylmethylimidazolium chloride to produce 6-deoxychlorocellulose (Cell-Cl), followed by a reaction with jeffamine EDR148 and ultimately resulting in the formation of aminated cellulose (Cell-Jef148). Structural and chemical characteristics of Cell-Cl and Cell-Jef148 were determined using different techniques. Various adsorption conditions were applied to evaluate the optimal adsorption conditions for the removal of Cu(II), Ni(II), and Pb(II) ions. Cell-Jef48 revealed a greater affinity and higher adsorption efficiency of 480.3, 420.5, and 463.2 mg/g for Cu(II), Ni(II), and Pb(II) ions, respectively. Different kinetics and adsorption isothermal models were studied to investigate the adsorption mechanism and interactions between Cell-Jef148 and metal ions. The results fitted the Langmuir and pseudo-second-order models. Corresponding to the Langmuir model, Cell-Jef148's maximum adsorption capacities were 952.38, 609.76, and 769.23 mg/g for Cu(II), Ni(II), and Pb(II) ions, respectively, with a high correlation coefficient, R2, in the range of 0.99575–0.99855. The research results of this study support Cell-Jef148's adsorption of heavy metal ions, and the regeneration of adsorbent highlights the potential applications of cellulose-based materials in wastewater treatment. [ABSTRACT FROM AUTHOR]

In the current research, a chitosan/broccoli extract/ZnO nanoparticle (CH/BE/ZnO) bionanocomposite was created. The physicochemical properties of CH/BE/ZnO bionanocomposite were investigated using a variety of methods, including field emission scanning electron microscopy (FESEM), elemental analysis (CHN-O), X-ray diffraction (XRD), Fourier transform infrared spectrum (FTIR), Brunauer-Emmett-Teller (BET), and transmission electron microscopy (TEM). The CH/BE/ZnO bionanocomposite's biological activity was assessed by examining its cytotoxicity capabilities against a bone cancer cell line (MG63). The total pore volume and specific surface area of CH/BE/ZnO are 0.134 cm 3 /g and 16.99 m 2 /g, respectively. The IC50 results for CH/BE/ZnO bionanocomposite in bone cancer investigations using the MTT test against the MG63 cell line was 115 μg/mL. The results indicate that the CH/BE/ZnO bionanocomposite is an effective chemotherapeutic agent against human osteosarcoma. The CH/BE/ZnO bionanocomposite showed high performance and structure, which means innovating nanomaterial agents for biological applications in the future., Competing Interests: Declarations. Ethical Approval: Not applicable. Consent to Participate: Not applicable. Consent for Publication: Not applicable. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)