Multi-layer networks which consist of high-frequency cells and low-frequency cells in the same coverage area, can ensure capacity and coverage simultaneously. However, the existing handover algorithms in multi-layer networks result in the waste of spectrum resources. In this paper, we propose a new handover algorithm to address this gap. To ensure coverage and capacity, the proposed algorithm utilizes inter-frequency handover based on coverage and user throughput, encourages inter-frequency handover but suppresses intra-frequency handover. We provide a theoretical basis for this algorithm, whose superior performance has been verified by our simulations. Compared with the traditional algorithms, the proposed algorithm balances the load ratio between high-frequency cells and low-frequency cells from 30/90 to 60/80; Meanwhile, the proposed algorithm increases user throughput by at least 54.3% for the handover users; Moreover, the proposed algorithm increases the average signal interference plus noise ratio by about 1.5 dB for the high-frequency cells and 5 dB for the low-frequency cells, which gives rise to the increase in system capacity for 10.5% and 11.57% for downlink and uplink, respectively; Besides, the proposed algorithm decreases call blocking ratio by 38% and end-to-end delay by 5.82%.

The introduction of chest pain centers (CPC) in China has achieved great success in shortening the duration of nursing operations to significantly improve the treatment and outcomes of patients with ST-segment elevation myocardial infarction (STEMI). The nursing handover period is still considered the high incidence period of adverse events because of the distractibility of nurses\' attention, potential interruption, and unclear responsibilities. Under the CPC mechanism, the nursing efficiency and patients\' outcome, whether affected by the nursing handover, is still a knowledge gap in research. This is also the aim of this study.
A retrospective study was conducted with data from STEMI patients from a tertiary hospital in the north of Sichuan Province from January 2018 to December 2019 through the Chinese CPC database. Patients are divided into handover and non-handover groups according to the time they presented in the Emergency Department. D2FMC, FMC2FE, FMC2BS, FMC2CBR, FMC2FAD, and D2W were selected to measure nursing efficiency. The occurrence of major adverse cardiovascular events, the highest troponin values within 72 h of hospitalization, and the length of hospitalization were selected to measure the patient outcomes. Continuous variables are summarized as mean ± SD, and t-tests of the data were performed. P-values < 0.05 (two-tailed) were considered statistically significant.
A total of 231 cases were enrolled, of which 40 patients (17.3%) were divided into the handover period group, and 191 (82.6%) belonged to the non-handover period group. The results showed that the handover period group took significantly longer on items FMC2BS (P < 0.001) and FMC2FAD (P < 0.001). Still, there were no significant differences in D2FMC and FMC2FE, and others varied too little to be clinically meaningful, as well as the outcomes of patients.
This study confirms that nursing handover impacts the nursing efficiency of STEMI patients, especially in FMC2BS and FMC2FAD. Hospitals should also reform the nursing handover rules after the construction of CPC and enhance the triage training of nurses to assure nursing efficiency so that CPC can play a better role.

This study aims to analyze the effect of using an intensive care unit point-of-care nursing handover checklist based on the situation, background, assessment, and recommendation (SBAR) communication technique.
An intensive care unit point-of-care nursing handover checklist was designed based on the SBAR technique, and standard point-of-care nursing handover procedures and effect assessment indicators were established to compare the occurrence of adverse handover events and nursing risks with those previously observed.
Before and after the application of the intensive care unit point-of-care SBAR checklist, the occurrence of missed items during the handover was 7.26 and 2.02%, inadequate preparation for handover was 28.33 and 5%, and nursing risks were 5 and 1.67%, respectively.
Based on the SBAR technique, the application of an intensive care unit point-of-care nursing checklist reduced the occurrence of adverse handover events and nursing risks.

Presently, lightweight devices such as mobile phones, notepads, and laptops are widely used to access the Internet throughout the world; however, a problem of privacy preservation and authentication delay occurs during handover operation when these devices change their position from a home mesh access point (HMAP) to a foreign mesh access point (FMAP). Authentication during handover is mostly performed through ticket-based techniques, which permit the user to authenticate itself to the foreign mesh access point; therefore, a secure communication method should be formed between the mesh entities to exchange the tickets. In two existing protocols, this ticket was not secured at all and exchanged in a plaintext format. We propose a protocol for handover authentication with privacy preservation of the transfer ticket via the Diffie-Hellman method. Through experimental results, our proposed protocol achieves privacy preservation with minimum authentication delay during handover operation.

The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.

OBJECTIVE: This study examined the effect on pediatric nursing handover quality and efficiency when a standardized e-handover system was implemented.
BACKGROUND: Handover quality is an important aspect of nursing quality management; however, handover quality among nursing staff is poor.
METHODS: A prospective interventional study was carried out in a general pediatrics ward from December 2019 to November 2020. The tools included a standardized e-handover system. The intervention strategies included workflow remodeling and employee training on oral handover using the standardized e-handover system.
RESULTS: The omission frequency of critical handover elements decreased from 47.32% to 2.94% (p < .01), among which the omission frequencies of nine out of 16 key elements significantly decreased. Integrity also showed improvement. Specifically, the integrity of five types of critical information was significantly improved, including vital signs, signs and symptoms, laboratory test results, radiologic examination results, and treatment regimen (2.00 vs. 5.00, p < .01; 3.00 vs. 5.00, p < .01; 3.00 vs. 5.00, p < .01; 5.00 vs. 5.00, p = .009; 3.00 vs. 4.00, p < .01, respectively). Information accuracy was 100%. Workflow and efficiency significantly improved, communication duration with patient/family during work hours significantly increased (24.00 vs. 56.00, p < .01), and prehandover preparation duration significantly decreased (32.00 vs. 2.50, p < .01). Nurse handover satisfaction showed improvement (56.88 ± 15.08 vs. 74.31 ± 9.22, p < .01).
CONCLUSIONS: The standardized e-handover system effectively improved nurse handover quality, optimized workflow, increased work efficiency, and promoted teamwork.
CONCLUSIONS: Standardized e-handover systems have great potential for ensuring the safety of pediatric patients and improving the quality of handover.

OBJECTIVE: Currently, there is no standardized handover pattern for patients undergoing general anesthesia when being transferred to the postanesthesia care unit (PACU).
METHODS: A review of the literature.
METHODS: In this study, a review of the literature was conducted to analyze the PACU handover status, factors for poor handover, and commonly used handover patterns.
RESULTS: Important handover information was often omitted during the handover of PACU patients, and there were many factors influencing postoperative patient handover quality. This study analyzed and compared several commonly used handover patterns for patients. Among these, the Situation-Background-Assessment-Recommendation tool is relatively mature. However, there is currently no unified standardized patient handover pattern, and the validity and applicability of tools still need to be verified.
CONCLUSIONS: PACU is an important place for the recovery of surgical patients. Anesthesia providers need to provide PACU nurses with complete and comprehensive postoperative handover information. A standardized handover model for clinical nurses is needed to improve patient safety management and work efficiency.

Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head\'s buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency.

In patients undergoing major surgery, complete handover of intraoperative anesthesia care is associated with adverse postoperative outcomes including high mortality and more major complications. The purpose of this study was to explore the association between the intraoperative complete handover between anesthesiologists and the occurrence of postoperative delirium. This was a secondary analysis of the database of a previously published clinical trial. Seven hundred patients aged 65 years or older, who were admitted to the intensive care unit after noncardiac surgery, were included. Delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit twice daily during the first 7 postoperative days. Other postoperative outcomes were also monitored. The association between the intraoperative complete handover of anesthesia care and the development of postoperative delirium was analyzed with a logistic regression model. Of the 700 enrolled patients, 111 (15.9%) developed postoperative delirium within 7 days. After correction for confounding factors, intraoperative complete handover between anesthesiologists was associated with an increased risk of postoperative delirium (OR 1.787, 95% CI 1.012-3.155, P = 0.046). Patients with intraoperative complete handover also had higher incidence of non-delirium complications (P = 0.003) and stayed longer in hospital after surgery (P = 0.002). For elderly patients admitted to the intensive care unit after noncardiac surgery, intraoperative complete handover of anesthesia care was associated with an increased risk of postoperative delirium. Chinese Clinical Trial Registry ( http://www.chictr.org.cn ): ChiCTR-TRC-10000802.

OBJECTIVE: To investigate different professionals\' (nurse anaesthetists\', anaesthesiologists\', and postanaesthesia care unit nurses\') descriptions of and reflections on the postoperative handover.
METHODS: A focus group interview study with a descriptive design using qualitative content analysis of transcripts.
METHODS: One anaesthetic clinic at two hospitals in Sweden.
METHODS: Six focus groups with 23 healthcare professionals involved in postoperative handovers. Each group was homogeneous regarding participant profession, resulting in two groups per profession: nurse anaesthetists (n=8), anaesthesiologists (n=7) and postanaesthesia care unit nurses (n=8).
RESULTS: Patterns and five categories emerged: (1) having different temporal foci during handover, (2) insecurity when information is transferred from one team to another, (3) striving to ensure quality of the handover, (4) weighing the advantages and disadvantages of the bedside handover and (5) having different perspectives on the transfer of responsibility. The professionals\' perceptions of the postoperative handover differed with regard to temporal foci and transfer of responsibility. All professional groups were insecure about having all information needed to ensure the quality of care. They strived to ensure quality of the handover by: focusing on matters that deviated from the normal course of events, aiding memory through structure and written information and cooperating within and between teams. They reported that the bedside handover enhances their control of the patient but also that it could threaten the patient\'s privacy and that frequent interruptions could be disturbing.
CONCLUSIONS: The present findings revealed variations in different professionals\' views on the postoperative handover. Healthcare interventions are needed to minimise the gap between professionals\' perceptions and practices and to achieve a shared understanding of postoperative handover. Furthermore, to ensure high-quality and safe care, stakeholders/decision makers need to pay attention to the environment and infrastructure in postanaesthesia care.