Intraoperative thermostatic nursing and failure mode and effects analysis enhance gastrectomies’ care quality

BACKGROUND Utilizing failure mode and effects analysis(FMEA)in operating room nursing provides valuable insights for the care of patients undergoing radical gastric cancer surgery.AIM To evaluate the impact of FMEA on the risk of adverse events and nursing-care quality in patients undergoing radical surgery.METHODS Among 230 patients receiving radical cancer surgery between May 2019 and May 2024,115 were assigned to a control group that received standard intraoperative thermoregulation,while the observation group benefited from FMEA-modeled operating room care.Clinical indicators,stress responses,postoperative gastroin-testinal function recovery,nursing quality,and the incidence of adverse events were compared between the two groups.RESULTS Significant differences were observed in bed and hospital stay durations between the groups(P<0.05).There were no significant differences in intraoperative blood loss or postoperative body temperature(P>0.05).Stress scores improved in both groups post-nursing(P<0.05),with the observation group showing lower stress scores than the control group(P<0.05).Gastrointestinal function recovery and nursing quality scores also differed significantly(P<0.05).Additionally,the incidence of adverse events such as stress injuries and surgical infections varied notably between the groups(P<0.05).CONCLUSION Incorporating FMEA into operating room nursing significantly enhances patient care by improving safety,expediting recovery,and reducing healthcare-associated risks.

Performance-driven assignment and mapping for reliable networks-on-chips

Network-on-chip(NoC) communication architectures present promising solutions for scalable communication requests in large system-on-chip(SoC) designs. Intellectual property(IP) core assignment and mapping are two key steps in NoC design, significantly affecting the quality of NoC systems. Both are NP-hard problems, so it is necessary to apply intelligent algorithms. In this paper, we propose improved intelligent algorithms for NoC assignment and mapping to overcome the drawbacks of traditional intelligent algorithms. The aim of our proposed algorithms is to minimize power consumption, time, area, and load balance. This work involves multiple conflicting objectives, so we combine multiple objective optimization with intelligent algorithms. In addition, we design a fault-tolerant routing algorithm and take account of reliability using comprehensive performance indices. The proposed algorithms were implemented on embedded system synthesis benchmarks suite(E3S). Experimental results show the improved algorithms achieve good performance in NoC designs, with high reliability.

Equality detection for linear arithmetic constraints

Satisfiability modulo theories (SMT) play a key role in verification applications. A crucial SMT problem is to combine separate theory solvers for the union of theories. In previous work, the simplex method is used to determine the solvability of constraint systems and the equalities implied by constraint systems are detected by a multitude of applications of the dual simplex method. We present an effective simplex tableau-based method to identify all implicit equalities such that the simplex method is harnessed to an irreducible minimum. Experimental results show that the method is feasible and effective.