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The construction sector has been under growing public attention recently as one of the leading causes of climate change and its detrimental effects on local communities. In this regard, geopolymer concrete (GPC) has been proposed as a replacement for conventional concrete. Predicting the concrete’s strength before pouring is, therefore, quite useful. The mechanical strength of slag and corncob ash (SCA–GPC), a GPC made from slag and corncob ash, was predicted utilizing multi-expression programming (MEP). Modeling parameters’ relative importance was determined using sensitivity analysis. When estimating the compressive, flexural, and split tensile strengths of SCA–GPC with MEP, 0.95, 0.93, and 0.92 R 2-values were noted between the target and predicted results. The developed models were validated using statistical tests for error and efficiency. The sensitivity analysis revealed that within the mix proportions, the slag quantity (65%), curing age (25%), and fine aggregate (3.30%) quantity significantly influenced the mechanical strength of SCA–GPC. The MEP models result in distinct empirical equations for the strength characteristics of SCA–GPC, unlike Python-based models, which might aid industry and researchers worldwide in determining optimal mix design proportions, thus eliminating unneeded test repetitions in the laboratory.

Young modulus experiment is a frequent item in university physics experimental courses, and in the process of doing Young modulus experiment, we will encounter problems such as difficulty in adjusting the device, difficulty in reading, and easy to break the wire line. This teaching method is based on the finite element method, which allows students to simulate and model Young modulus in CAE software after understanding the principle of Young modulus experiment. Modeling can deepen students’ understanding of Young modulus experiment. Adjusting the Young modulus of the model, the length and diameter of the wire and other parameters can quickly solve different experimental results and cultivate students’ research thinking.

Virtual Reality(VR) technology builds a simulation environment through a computer,provides users with a three-dimensional dynamic view,and enhances the user's sensory experience,so that the user will get an immersive sense of immersion.With the rise and continuous development of virtual reality technology,people's visual and auditory experience has made great progress.With the development of multimedia technology,panoramic video emerges gradually.Compared with ordinary video,panoramic video has a wider viewing angle and richer visual information.The wide application of virtual reality technology and the development of panoramic video technology make virtual reality panoramic video(VR Video) become one of the most popular and concerned VR services.The information perception and acceptance behavior of users in the virtual reality environment have also been affected.Based on SMOTE algorithm,Bayesian network,logistic regression,and other statistical analysis methods and Few-shot Learning algorithm,this paper compares the differences in users' information memory degree,sense of reality,sense of participation,and other aspects when using VR head-mounted display and ordinary display iPad to watch panoramic video respectively.To explore the difference between the user's information acceptance effect and the sense of presence in the virtual reality environment and the traditional media environment.Experiments show that users with virtual reality headsets score an average of about 0.786 for message acceptance,compared with 0.634 for iPad users with regular displays.Among them,the score of positive information and non-positive information receiving effect in the virtual reality environment is 1.626 times and 1.245 times that of in the traditional environment respectively.However,it is noteworthy that in video A,which has the longest average scene duration,there is no significant difference in the information acceptance effect.In addition,the length of the user's visual residence time has a positive impact on the acceptance effect of information.After verifying that the sense of presence can be subdivided into the sense of reality and sense of participation,this paper shows that the score of users for the sense of presence in the iPad environment is always lower than that of in a VR environment through feature engineering and classification decision tree.After that,the correlation coefficient of the least square method is used to prove that presence has a positive effect on user information acceptance.Meanwhile,with the help of the Few-shot Learning algorithm,the average information memory number of video A－D is 9.20,9.13,8.83,and 10.57 when the sense of presence is strong,while the average information memory number of video A－D is 8.53,6.80,7.14 and 7.66 when the sense of presence is weak.It is proved that the user's sense of presence during information perception is beneficial to the user to obtain a better information acceptance effect.

Objective: To verify the efficiency and stability of the automated labeling and synthesis of dopamine transporters (DAT) imaging probe, 18F-N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4′-iodophenyl) nortropane (18F-FP-CIT), based on the AllinOne module. Meanwhile, to evaluate the safety and targeted ability of the probe and the imaging effect on the basal ganglia of rats. Methods: A fluorination reaction between nitrogen-[3-(methoxy)propyl]-2β-carbonmethoxy-3β-(4′-iodophenyl) nortropane, the reaction precursor, and the dried fluorine-18 ion (18F) was performed, under the catalysis of TBAOH and Kryptofix 222. The product of the reaction was separated and purified by high performance liquid chromatography (HPLC). All the procedure was established on the AllinOne module under the highest yield conditions to perform automated labeling and synthesis of 18F-FP-CIT. Quality control tests and Micro PET/CT imaging of rat brain with the automatically synthesized product were carried out. Results: The average synthesis time, efficiency after attenuation correction, and radiochemical purity of 20 batches of 18F-FP-CIT made by the AllinOne module was (72.9±8.9) min, 34.8%±5.7%, and 98.4%±2.0%, retrospectively. The results of sterility test, endotoxin test, toxicity test and residual solvent test of acetonitrile were all met the clinical practice standards. Micro PET/CT imaging showed uniform and obvious targeted uptake of 18F-FP-CIT in basal ganglia of normal rats, and the uptake of 18F-FP-CIT in basal ganglia was 2.6 times that in cerebral cortex, 3.7 times that in cerebellum, and 4.1 times that in skull, respectively. Conclusions: The AllinOne module can automatically label and synthesize DAT imaging probe 18F-FP-CIT stably, rapidly, with high-quality, which is expected to provide imaging technical support for the clinical diagnosis and treatment of neurodegenerative changes such as Parkinson′s disease (PD).