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Deep development of hydropower is an important measure to optimize the structure of clean energy, meet the peak shaving and backup needs of the power system, and achieve the national dual carbon goals. This article conducts a systematic analysis of the development potential of developed and undeveloped hydraulic resources, including unplanned river sections, undeveloped planned power stations, unit renovation and capacity expansion, and the expansion of existing/ongoing power stations. We focused on studying the factors that affect the deep development of hydropower, including the reasonable operation mode of hydropower with different regulation performance in the power grid, the energy structure characteristics of the power system, and the comprehensive water use requirements. A calculation method for the reasonable installation and utilization hours and deep development resources of hydropower stations with different regulation performance after deep development has been proposed. According to calculations, Zhejiang Province can increase its scale by 3331MW, with great potential for deep development.

The rise of big data has deeply influenced various industries, especially the intelligent manufacturing of enterprises. However, traditional data analysis methods are difficult to adapt to the storage and analysis of sea volume data in intelligent production. To address this issue, a method relying on big data analysis and cluster analysis is proposed to design data analysis techniques for enterprise intelligent manufacturing. The proposed improved algorithm is subjected to performance testing. The accuracy of this algorithm is 97%, which exceeds the comparison algorithm. The error is 6% and the running time is 5 s, both of which are below the comparison algorithm. The effectiveness of the enterprise intelligent manufacturing data analysis technology is tested. The experimental group completes orders in 4.1 weeks, 5.2 weeks, 3 weeks, 3.4 weeks, and 4.9 weeks, respectively, shorter than the control group. The product qualification rates for the experimental group are 92%, 93%, 95%, 92%, and 92%, respectively, which exceed the control group. In summary, the proposed enterprise intelligent manufacturing data analysis technology relying on big data and cluster analysis can better utilize data resources and information technology, improving the production efficiency and competitiveness of enterprises. It is hope that this research result can provide useful guidance and reference for the application and development of intelligent manufacturing data analysis technology in enterprises.

Simple Summary: Cancer cells display elevated reactive oxygen species (ROS) and an altered redox status. For this study, taking advantage of these characteristics, we designed a multifunctional MRI-visualizable multi-drug delivery platform, AMB@PDAP-Fe (APPF), based on magnetotactic bacteria (AMB) for MRI-visualized tumor photo-thermotherapy (PTT) and enhanced chemodynamic therapy (CDT). Upon magnetic navigation, APPF accumulated in the tumor microenvironment. The PDAP-Fe in the composite was reduced by glutathione (GSH) in the tumor microenvironment and produced Fe2+, which catalyzed the generation of reactive oxygen species (ROS) through the Fenton reaction and induced tumor cell apoptosis. The magnetosomes in AMB displayed good dual-mode contrast capability in an MRI, which was later used to visualize the process of tumor treatment. Upon NIR light irradiation, the magnetosomes in magnetotactic bacteria immediately converted the light energy into heat energy and the tumor tissue was heated up, thus causing the death of tumor cells. It is noteworthy that the heat generated during the synergistic PTT process (to which PDAP partly contributed) accelerated the releasing of Fe2+ from PDAP-Fe, which enhanced the catalytic conversion of endogenous H2O2 into ·OH, and, thus, achieved synergistic photothermal-enhanced Fenton-reaction-mediated cancer therapy. This study demonstrates the feasibility of developing an MRI-visualizable multi-drug delivery platform from magnetic bacteria, which, compared with other manmade inorganic multi-drug delivery platforms, are lively, self-propelled, and magnetically navigated. This study also highlights the synergistic tumor-curing effect between magnetic bacteria and PDAP-Fe and the importance of understanding the underlying cooperative molecular mechanism between different drugs when scheduling combination therapy. Cancer cells display elevated reactive oxygen species (ROS) and altered redox status. Herein, based on these characteristics, we present a multi-drug delivery platform, AMB@PDAP-Fe (APPF), from the magnetotactic bacterium AMB-1 and realize MRI-visualized tumor-microenvironment-responsive photothermal–chemodynamic therapy. The Fe3+ in PDAP-Fe is reduced by the GSH at the tumor site and is released in the form of highly active Fe2+, which catalyzes the generation of ROS through the Fenton reaction and inhibits tumor growth. At the same time, the significant absorption of the mineralized magnetosomes in AMB-1 cells in the NIR region enables them to efficiently convert near-infrared light into heat energy for photothermal therapy (PTT), to which PDAP also contributes. The heat generated in the PTT process accelerates the process of Fe2+ release, thereby achieving an enhanced Fenton reaction in the tumor microenvironment. In addition, the magnetosomes in AMB-1 are used as an MRI contrast agent, and the curing process is visualized. This tumor microenvironment-responsive MTB-based multi-drug delivery platform displays the potency to combat tumors and demonstrates the utility and practicality of understanding the cooperative molecular mechanism when designing multi-drug combination therapies. [ABSTRACT FROM AUTHOR]