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Infective endocarditis is a life-threatening condition and is associated with embolic events. We aimed to evaluate the association of vegetation size, multiplicity, and position with cerebral embolism and late mortality in patients with infective endocarditis.We retrospectively reviewed patients with infective endocarditis who were admitted to a single institution between November 2005 and August 2017. A total of 419 patients with infective endocarditis were included in the study, 273 of whom had undergone surgery. The primary endpoint was all-cause mortality, and the secondary endpoint was cerebral embolism. Multivariate Cox regression and logistic regression analyses were performed to identify independent risk factors for 30-day mortality, late mortality, and cerebral embolism.Age (hazard ratio [HR] 1.02; 95% confidence interval [CI], 1.00 to 1.04), renal failure (HR 4.21; 95% CI, 2.67 to 6.65), surgery (HR 0.31; 95% CI, 0.21 to 0.46), and Acute Physiology and Chronic Health Evaluation II score (HR 1.08; 95% CI, 1.01 to 1.15) were associated with late mortality. Vegetation size, multiplicity, and position were not significantly associated with late mortality, but a mitral vegetation size of greater than 10 mm (odds ratio 2.25; 95% CI, 1.32 to 3.84) was an independent risk factor for cerebral embolism.A vegetation size of greater than 10 mm and the mitral position were found to be significant risk factors for cerebral embolism, and for this group, early surgery might be considered to prevent cerebral embolism.

To compare unilateral versus bilateral antegrade cerebral perfusion (ACP) techniques on cerebral protection during acute type A aortic dissection repair.Using an institutional database, we retrospectively reviewed patients who underwent acute type A aortic dissection repair with selective ACP techniques from October 2008 to December 2019. Primary end point was the detection of neurologic dysfunctions. The secondary end point was mortality. For baseline adjustment, the propensity score matching method was used. Multivariable logistic regression analysis was performed to determine the predictor of neurologic events.Among 522 patients (aged 62.0 ± 14.9 years; 45.7% women), unilateral and bilateral ACP techniques were used in 357 (64.7%) and 165 (35.3%) patients, respectively. Transient (19.6% vs 21.2%;Despite additional supply, the patients undergoing bilateral ACP during acute type A aortic dissection repair did not have superior outcomes in neurologic and death events compared with the patients undergoing unilateral ACP.

본 연구는 흡착제 Zeolite 3A와 DETOX에 대하여 $H_2S$ 의 유입농도와 흡착온도를 공정변수로 하여 포화시간, 흡착량, 흡착속도 등의 $H_2S$ 흡착 제거특성을 평가하기 위하여 수행되었다. $H_2S$ 의 유입질량유속이 증가함에 따라 Zeolite 3A의 흡착용량은 증가되었으나 포화시간은 감소되었다. 한편 DETOX의 흡착용량과 포화시간은 $H_2S$ 의 유입질량유속의 증가에 따라 감소되었다. 흡착온도가 상승함에 따라 Zeolite 3A의 흡착용량과 포화시간은 감소한 반면에, DETOX에 대한 이들 값은 증가하였다. DETOX의 $H_2S$ 흡착용량은 Zeolite 3A의 2.5~16.4배 정도 높게 나타났다. 이는 흡착에서 활성화에너지장벽을 넘어설 충돌빈도는 흡착온도가 증가함에 따라 증가한 것에 기인한 것으로 해석된다. Zeolite 3A와 DETOX에 대하여 $H_2S$ 의 흡착속도는 $H_2S$ 의 유입질량유속과 흡착온도가 증가함에 따라 증가하였다. $H_2S$ 의 흡착속도는 Zeolite 3A가 DETOX의 4배로 나타났다. DETOX는 Zeolite 3A에 비하여 온도 308~318K에서 포화시간은 더욱 길어지고 흡착용량은 더욱 커진다. 바이오가스 중의 $H_2S$ 제거에 있어서 DETOX는 Zeolite 3A에 비하여 유리한 것으로 나타났다. 【This study examined the $H_2S$ removal characteristics, such as breakthrough time, adsorption capacity, and adsorption rate of adsorbents between Zeolite 3A and DETOX in terms of the $H_2S$ inflow concentration and adsorption temperature. The adsorption capacity of Zeolite 3A increased with increasing mass flow rate of hydrogen sulfide( $H_2S$ ) inflow, but the breakthrough time decreased. On the other hand, both the adsorption capacity and breakthrough time of DETOX decreased with increasing mass flow rate of $H_2S$ inflow. The adsorption capacity and breakthrough time of Zeolite 3A decreased with increasing adsorption temperature but those of DETOX increased. The adsorption capacity of DETOX was higher than that of Zeolite 3A by a factor of 2.5 - 16.4 because the collision frequency that overcomes the activation energy barrier increased with increasing adsorption temperature. For Zeolite 3A and DETOX, the adsorption rate of $H_2S$ increased with increasing mass flow rate of $H_2S$ inflow and adsorption temperature. The adsorption rate of $H_2S$ for Zeolite 3A was 4 times as much as that for DETOX. For the removal of $H_2S$ in biogas, DETOX had an advantage over Zeolite 3A because DETOX had a much longer breakthrough time and greater adsorption capacity in the temperature range of 308~318K than Zeolite 3A.】

Despite the rapid progress in the clinical application of laparoscopic surgery robots, many shortcomings have not yet been fully overcome, one of which is the lack of reliable haptic feedback. This study implemented a force-feedback structure in our compact laparoscopic surgery robot. The surgery robot is a master-slave configuration robot with 5 DOF (degree of freedom corresponding laparoscopic surgical motion. The force-feedback implementation was made in the robot with torque sensors and controllers installed in the pitch joint of the master and slave robots. A simple dynamic model of action-reaction force in the slave robot was used, through which the reflective force was estimated and fed back to the master robot. The results showed the system model could be identified with significant fidelity and the force feedback at the master robot was feasible. However, the qualitative human assessment of the fed-back force showed only limited level of object discrimination ability. Further developments are underway with this result as a framework.

For pt.I see ICNN'97, vol.2, p.1321-5 (1997). Call admission control (CAC) is a key technology of ATM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. Conventional approach to the ATM CAC requires network analysis in detail in all cases. The optimal implementation is said to be very difficult. Therefore, a neural approach has been employed, but it does not meet the adaptability requirements. It requires additional learning data tables and learning phase during CAC operation. The authors compare the neural network CAC method based on sparse distributed memory (SDM) which they proposed in pt.I with a conventional neural CAC method. The performance of our method is as good as those of the previous neural approaches without additional learning table or learning phases. Our method, however, shows better adaptability to manage changes in ATM network.