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We report the phase-connected timing ephemeris, polarization pulse profiles, Faraday rotation measurements, and Rotating-Vector-Model (RVM) fitting results of twelve millisecond pulsars (MSPs) discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in the Commensal radio Astronomy FAST survey (CRAFTS). The timing campaigns were carried out with FAST and Arecibo over three years. Eleven of the twelve pulsars are in neutron star - white dwarf binary systems, with orbital periods between 2.4 and 100 d. Ten of them have spin periods, companion masses, and orbital eccentricities that are consistent with the theoretical expectations for MSP - Helium white dwarf (He WD) systems. The last binary pulsar (PSR J1912−0952) has a significantly smaller spin frequency and a smaller companion mass, the latter could be caused by a low orbital inclination for the system. Its orbital period of 29 days is well within the range of orbital periods where some MSP - He WD systems have shown anomalous eccentricities, however, the eccentricity of PSR J1912−0952 is typical of what one finds for the remaining MSP - He WD systems.

Energy consumption and carbon emission levels in the production process constitute an important basis for the selection of production equipment. The energy consumption and carbon emission levels of the same product produced by different kinds equipment vary greatly from one tool to another. Unfortunately, traditional modes of selection only give qualitative results, so that it is difficult to provide a quantitative reference to enable enterprises to choose appropriate modes of production in the context of the green development concept (GDC). In order to solve this problem, a calculation method for multiple energy consumption and carbon-emission objectives for commodity production is proposed. The focus of this paper is to research the difference between the energy consumption and carbon emission levels of the same product produced by different kinds of equipment. The energy consumption and carbon emissions of different kinds of equipment can be calculated by gray wolf algorithm. The results show that the proposed method can calculate the optimal values of energy consumption and carbon emissions in the same kinds of products produced by different equipment, which can provide assistance for enterprises in choosing the production equipment that best conforms to the green development concept.

The follow-up timing observations were carried out for 24 pulsars discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) in Commensal Radio Astronomy FAST Survey (CRAFTS). We report their phase-connected timing ephemeris, polarization pulse profiles and Faraday rotation measurements. With their spin periods spanning from 2.995 ms to 4.34 s, their period derivative were determined to spread between 7.996(8) × 10−21 s/s and 9.83(3) × 10−15 s/s, which imply that they have characteristic ages from 1.97 × 106 yr to 5.93 × 109 yr. It is inferred that PSRs J0211+4235 and J0518+2431 are beyond the ‘traditional death line’. PSR J0211+4235 is beyond the ‘death valley’. The death line model of Zhang et al (2000) also cannot explain the radio presence of PSR J0211+4235. This suggests that radiation theory needs to be improved. Besides, ten of the 22 canonical pulsars show nulling phenomena. Moreover, PSR J1617+1123 exhibits variation of emission and J0540+4542 shows subpulse drifting. The DM of five pulsars is larger than the estimated by the YMW16 electron density model, which could suggest that electron density models need updates for higher Galactic latitude regions. PSRs J0447+2447 and J1928−0548 are isolated millisecond pulsars. With their flux densities spanning from 5(1) μJy – 553(106) μJy, some of these new pulsars found by FAST are distant, dim, and low-$\dot{E}$ ones and are suitable for testing pulsar emission theories.

Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

Blanks, an important raw material for the manufacturing industry, are semi-finished products for further processing. The energy consumption and processing efficiency in the process of blank production and use can be determined to a great extent in the blank design stage. The design of appropriate blank dimensions is an important means of realizing ecological civilization. Current blank designs seldom consider the production conditions of enterprises. In order to design energy-saving and efficient blanks on the basis of the actual conditions of an enterprise, this paper establishes the blank dimension optimization design model from the perspective of a business compass. With energy savings and efficiency as the goals, and the blank production and use-process equipment parameters as variables, the blank dimensions were optimized by an NSGA-II algorithm, and the results showed that the energy efficiency and processing efficiency of the designed blank dimensions were significantly better than for the existing blank dimensions in the process of enterprise operation.