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To assess the feasibility of percutaneous pedicle screw fixation assisted by a fully automated orthopedic robotic system for the treatment of isthmic spondylolisthesis and evaluate its early postoperative outcome. Totally 20 patients with isthmic spondylolisthesis who underwent surgical procedure by the same medical group from March 2020 to March 2023 were retrospectively analyzed, including 10 patients in the robot-assisted group (RA group) and the other 10 patients in the conventional free-hand technique group (FH group). Accuracy of screw insertion was determined using the Gertzbein-Robbins Scale. The accuracy of the novel robotic system was evaluated by comparing the screw position in the preoperative planning and measuring the entry point deviation distance and the trajectory rotation. The differences in operative time, intraoperative blood loss, radiographic fluoroscopy time and fluoroscopic dosage, and length of hospital stay were compared between the two groups. The lumbar visual analog scale scores before and 7 days after operation were analyzed to evaluate the improvement of low back pain as the early postoperative outcome. A total of 84 pedicle screws were placed. In the RA group, 97.5% of screws were Grade A, and 2.5% were Grade B. In the FH group, 88.6% of screws were Grade A, 9.1% were Grade B, and 2.3% were Grade C. No statistical difference was found in the operation time between two groups. The RA group showed a significant reduction in intraoperative blood loss, radiographic fluoroscopy time and fluoroscopic dosage, and length of hospital stay compared to the FH group. The low back pain in both groups was significantly improved after the operation. The novel orthopedic robotic system-assisted percutaneous pedicle screw fixation, with accurate intraoperative screw placement, less surgical damage, less fluoroscopy and shorter length of hospital stay, can be safe and effective for the surgical treatment of isthmic spondylolisthesis.

To prevent the ternary cavity filter rods from deformation or falling out during transferring process, an adsorptive accelerating drum was designed for a PROTOS 1-8 filter cigarette maker. The effects of factors, including fluid inlet radius, the length of lateral fluid region, rotational speed, runner outlet hole diameter, and drum groove width, on the suction of the drum grooves and the stress on the filter rods were investigated with an ANSYS finite element analysis software in combination with the FLUENT and transient structure modules. It was found that the outlet hole diameter and the drum groove width were the primary influencing factors. The response surface optimization method was used to determine the optimal parameters for the accelerating drum, they were: outlet hole diameter 4.0 mm and drum groove width 7.4 mm. Three modified PROTOS 1-8 cigarette makers were tested with "Septwolves (Guanhai Demi-slim)" brand cigarettes in Longyan Tobacco Industrial Limited Corporation, the results showed that the number of filter rod falling out per maker reduced from 41 to 14 times/day. This method supports the promotion of operation stability of cigarette makers. [ABSTRACT FROM AUTHOR]

114 VOCs were continuously monitored online from January 1st to February 28th, 2021, to analyze the concentration levels and component characteristics of VOCs in Jiaxing, and evaluate ozone formation potential (OFP). The source of VOCs was explored by backward trajectory method and calculation of characteristic species pair ratio. Furthermore, health risk assessment of benzene series was carried out. The results showed that the average volume concentration of VOCs was 38.16x10-9, and the proportion of alkanes, oxygenated volatile organic compounds (OVOCs), halogenated hydrocarbons, olefins, aromatics and alkyns were 31.05%, 20.78%, 19.61%, 13.56%, 12.07% and 2.93%, respectively. The average volume concentration of VOCs during the Spring Festival was 16.82x10-9. The diurnal variation of alkanes, alkenes, aromatic hydrocarbons and halogenated hydrocarbons was consistent with VOCs, while OVOCs showed a fluctuating increase. As shown in OFP results, olefins and aromatic hydrocarbons contributed the most to OFP, and the crucial active species were toluene, propylene, m-p-xylene and ethylene, which were mainly affected by solvent volatilizing. During the Spring Festival, olefins and alkanes contributed the most to OFP, and the crucial active species were propylene, trans-2-butene and cis-2-butene, which were mainly affected by traffic sources. Combining the investigation of characteristic species pair ratio, backward trajectory and potential source contribution function, it can be concluded that VOCs in Jiaxing should be affected by transport and local emission. Among them, alkanes, halogenated hydrocarbons, olefins and aromatic hydrocarbons were jointly affected by regional transport and local emission, and OVOCs and alkynes were mainly local emission. In addition, the non-carcinogenic risk hazard quotient value of benzene series were within the safety threshold, whereas the carcinogenic risk value of benzene and m-xylene/p-xylene were beyond the safety threshold. The above findings can serve as a theoretical foundation for the control of VOCs pollution and the improvement of air quality during winter in Jiaxing. [ABSTRACT FROM AUTHOR]

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]