Dynamic Situations Research Articles

A Research on Single-Vehicle Trajectory Planning Algorithm in MultiObstacle Environments

This work aims to investigate the optimal trajectory planning for single-vehicle scenarios in obstacle-filled environments. The first section of the paper provides background information and an overview of autonomous driving trajectory planning, emphasizing its crucial role in ensuring safety and efficiency. A review of previous literature shows a range of approaches and constraints for trajectory optimization, especially in complicated and dynamic situations. Then, the single-vehicle trajectory planning optimum control problem (OCP) is stated and, in order to solve it practically, it is transformed into a nonlinear programming (NLP) issue. A classification of numerical solution methods is presented, including direct and indirect approaches. Extensive trials conducted with different initial and terminal circumstances and with single and multiple obstacles have demonstrated that the model can reliably produce the desired outcomes. As a result, the study demonstrates the robustness and efficiency of the proposed algorithm.

A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications

The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper classifies the algorithms into two main categories: (1) global and local path-planning approaches in single UAVs; and (2) multi-UAV path-planning methods. It further analyses obstacle detection and avoidance methods, as well as their capacity to adapt, optimise, and compute efficiently in different operational environments. The outcomes highlight the advantages and limitations of each method, offering valuable information regarding their suitability for remote sensing applications, such as precision agriculture, urban mapping, and ecological surveillance. Additionally, this review also identifies limitations in the existing research, specifically in multi-UAV frameworks, and provides recommendations for future developments to improve the adaptability and effectiveness of UAV operations in dynamic and complex situations.

Personalized Model-Driven Interventions for Decisions From Experience.

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.

YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes

Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.

A Method for Predicting the Result of University Employment Education Transformation Based on Multi-Source Information Technology

In the data on the outcomes of higher education employment transformation, there is high correlation among multiple independent variables, leading to multicollinearity. If multicollinearity exists, it can complicate the establishment of predictive analysis models and the interpretation of relationships between variables for higher education employment transformation outcomes. The employment issues of college graduates have become a focal point of social concern. These issues not only reflect the employment status of college graduates but also indirectly indicate the quality of higher education and the level of talent cultivation. Therefore, a prediction method for the effectiveness of higher education employment transformation based on multi-source information technology is proposed. According to the theory of multi-source information technology, an employment data classification framework is constructed to integrate multi-source information. The matching operator function output values are calculated, weights are determined, and the final college student information collection results are output. The standard error between employment unit types and salary ranges is analyzed, clustering training samples are trained, and the distance from each employment information sample to the centroid is calculated to obtain preprocessing results of the transformation outcomes, thus avoiding the impact of multicollinearity on the results of higher education employment transformation. A model structure for analyzing factors influencing higher education employment is established, allowing for an understanding of the characteristics of different types of educational resources and enabling the prediction of the effectiveness of higher education employment transformation. Experimental results show that this method can accurately monitor the dynamic employment situation of university students. After applying this method, the standard error and probability error values are significantly reduced, proving to be significantly effective in predicting the outcomes of higher education employment transformation.

Navigating transitions: a qualitative study of nursing teams' experiences of educational and cultural transitions in Germany.

The global migration of health professionals in general and nurses in particular, has led to nursing shortages and socioeconomic impacts on health systems in both source and destination countries. Adding to the complexity of the situation is the fact that the nursing profession itself is evolving from a vocational to an academic one. Although nursing migration and academization have been studied from either an institutional or an individual perspective, there is a gap in the literature regarding how nursing teams experience these transitions. This study aims to explore how nursing teams navigate through the transitions of academization and internationalization of the current dynamic nursing landscape in Germany. Based on social constructivism epistemology this qualitative study involved face-to-face focus group discussions conducted at several hospital sites in Germany from September 2021 to May 2023. The focus group discussions were audio-recorded, transcribed, and analysed using content analysis; the four dimensions of transition theory according to Schlossberg's Four S (4S) framework (self, situation, support, and strategy) were used as a priori items to construct the coding framework. Nine focus group discussions were conducted with a total of 40 nurses from different educational and migrant backgrounds. The analysis showed that the transition experiences of the nursing teams were heterogeneous, with educational and national backgrounds playing an important role in how realistic their expectations of their professional roles and identities were. The dynamic situation characterized by a shortage of qualified nursing staff, increases the pressure on nursing teams and underscores the importance of employer-provided and peer support. Onboarding and communication are key strategies used depending on the duration of the employee turnover. This study provides insights into the challenges and coping strategies of nursing teams in the current dynamic scene of migration, academicization and professional socialisation in Germany. Extending the Schlossberg 4S framework from the individual to the team perspective provides a comprehensive view of the transitional experiences of nursing teams. Within each domain of the framework, the experiences of nursing teams are remarkably diverse. Educational background (vocational or academic) and origin (German or foreign trained) play an important role in shaping the transitional experiences of nursing teams.

An innovative tool to prioritize the assessment of investigational COVID-19 therapeutics: A pilot project.

As the COVID-19 pandemic unfolded, hundreds of investigational COVID-19 therapeutics emerged. Maintaining situational awareness of this extensive and rapidly evolving therapeutic landscape represented an unprecedented challenge for the Public Health Agency of Canada, as it worked to promote and protect the health of Canadians. A tool to triage and prioritize the assessment of these therapeutics was needed. The objective was to develop and conduct an initial validation of a tool to identify investigational COVID-19 therapeutics for further review based on an efficient preliminary assessment, using a systematic and reliable process that would be practical to validate, implement and update. Phase 1 of this pilot project consisted of a literature search to identify existing COVID-19 therapeutic assessment prioritization tools, development of the Rapid Scoring Tool (RST) and initial validation of the tool. No tools designed to rank investigational COVID-19 therapeutics for the purpose of prioritizing their assessment were identified. However, a few publications provided criteria to consider and therapeutic ranking methods, which helped shape the development of the RST. The RST included eight criteria and several descriptors ("characteristics"). A universal characteristic scoring scale from -10 to 10 was developed. The sum of all the characteristic scores yielded an overall benefit score for each therapeutic. The RST appropriately ranked therapeutics using a systematic, reliable and practical approach. Phase 1 was successfully completed. The RST presents several distinct aspects compared with other tools, including its scoring scale and method, and capacity to factor in incomplete or pending information. It is anticipated that the framework used for the RST will lend itself to use in other dynamic situations involving many interventions.

The influence of electrical/thermal fields on the microstructure and mechanical properties of Ti2AlNb alloy from the view of molecular dynamics

Abstract The mechanism of the electrical non-thermal effects on metals is still unclear. Simulations at the atomic level are used to obtain some causes of non-thermal electroplasticity. Molecular dynamics simulations are used to study the change of defects including vacancies, edge dislocations and screw dislocations in B2, α 2 and O phases of Ti2AlNb alloy in static or dynamic situations under pure thermal field and continuous/pulsed electric fields. External energy fields can restore most of these defects. Moreover, different energy input methods have the same restoration effect on defects in the same phase. Thus, the restoration of defects in Ti2AlNb alloy by an electric field is mainly based on the thermal effect. However, the uneven distribution of electro-induced atomic kinetic energy in uniaxial tension simultaneously reduces its deformation resistance. Non-thermal effects in the electrically-assisted processing of industrial-grade materials consist of the instantaneous atomic kinetic distribution imbalance induced by electrical pulses.

A Link’s Fineness Based on Spatial Relation for Multipoint Relays Selection Algorithm in Mobile Ad Hoc Networks Algorithm

The propagation of wireless mobile equipment has given rise to mobile ad hoc networks (MANET), which allow the establishment of networks deprived of infrastructure support. The improvements in these technologies have also resulted in the variety of link quality, scalability, stability and reachability limits. Principally, due to their incapability to guarantee node reliability, MANETs are weak in highly dynamic environment. Since high-speed nodes, low density, and rapid changes in the topology are directly damage networks performances and lifetime. In response, routing is an inspiring issue in these networks. This paper proposed a new routing scheme in MANETS. This technique is integrated in the optimized link state routing (OLSR). In the projected scheme, the author defines a link fineness based on spatial relation between mobile nodes, which exploits different parameters, speed, direction and acceleration, including the sent and received of hello packets. Correspondingly, the proposed selected multipoint relays (MPRs) based the projected methods is included in the algorithm. The scheme elected more stable MPRs with high link’s quality, more neighborhood degree and higher willingness as MPR that can increase the reachability and stability of the network even in an extremely dynamic situation. This version of OLSR is verified by the network simulator (NS3) under the Radom Waypoint mobility model. Results revealed an important improvement for SLF_OLSR. In addition, this scheme can be integrated into other protocols in MANETs.

Design of a low-cost force insoles to estimate ground reaction forces during human gait

This paper proposes a low-cost electronic system for estimating ground reaction forces (GRF) during human gait. The device consists of one master node and two slave nodes. The master node sends instructions to slave nodes that sample and store data from two force insoles located at the participant’s feet. These insoles are equipped with 14 piezo-resistive FlexiForce A301 sensors (FSR). The slave nodes are attached to the ankles and feet of each participant. Subsequently, the start command is transmitted through the master node, which is connected to the USB port of a personal computer (PC). Once the walking session is completed, the information obtained by the slave nodes can be downloaded by accessing the access point generated by these devices through Wi-Fi communication. The GRF estimation system was validated with force platforms (BTS Bioengineering P6000, Italy), giving on average a fit measure equal to 68.71%±4.80% in dynamic situations. Future versions of this device are expected to increase this fit by using machine learning models.

Effects of a surface layer cross-flow and slope steepness on the rate of descent of dense water flows along a slope

The pathways and mixing in dense water overflows have been addressed in a range of numerical studies based on the DOME (Dynamics of Overflow Mixing and Entrainment) setup. These studies are motivated by overflows such as the flow of dense and cold water over the ridge between Iceland and Scotland. The main route of this water is through the Faroe-Shetland channel and further through the Faroe Bank Channel. After leaving the Faroe Bank Channel, the dense water continues along the slope along the Iceland-Faroe Ridge. Across this ridge there is also a strong flow of Atlantic water that may affect the pathway and mixing in the dense plume. In the DOME investigations, the slope steepness is assumed to be constant and there is no active background flow in the ambient water masses above the dense plume. With the situation along the Iceland-Faroe Ridge in mind, the setup from the DOME experiment is adjusted to study the effects of i) the slope profile, ii) flows crossing the slope on top of the along-slope flow of dense water, and iii) the topography near the overflow channel of dense water. It is found that the rate of descent is depending on slope steepness, and for a curved slope going from flat on top of a ridge to much steeper away from the ridge, dense water parcels near the ridge top or crest will sink slower than the faster flowing dense water further down along the slope. A cross-flow over the ridge, mimicking the flow of Atlantic water over the Iceland-Faroe Ridge, will force stronger mixing between the dense water and the ambient water above and dense water mixed up into the surface water can be allowed to flow out across the ridge. Furthermore, the dynamical situation along the slope will change and fluid parcels in the deeper part of the plume can experience a faster rate of descent. By removing a barrier in the topography downstream of the overflow channel, dense water can be steered towards the ridge and the starting point of the center of mass of the descending overflow water will be higher up on the slope.

Использование нечеткого метода распознавания образов для мониторинга запыленных помещений

ABSTRACT This article discusses a decision support system (DSS) based on fuzzy methods used to identify and process gas dynamic images obtained during monitoring and control of underground coal mine atmosphere processes. DSS supports ventilation operator solutions in complex and unforeseen gas dynamic situations. The work will analyze the quantitative and qualitative information used by the ventilation operator to make decisions. Based on these data, an analysis of the effectiveness of using fuzzy models and methods for interpreting and processing gas dynamic images will be carried out. Fuzzy models and methods of interpretation and processing of gas dynamic images will be proposed. In the future, based on these models and methods, the operator's decision support software will be developed.

Strategies for Effective Product Roadmap Development and Execution in Data Analytics Platforms

Manufacturing is only one of several industries going through a digital transformation in this era of digital disruption. Manufacturing businesses are racing to adopt IoT-based solutions in order to innovate, increase productivity, lower costs, and gain greater market share because of the enormous transformational potential offered by Internet-of-Things (IoT) & Big Data. It is being used by industrial companies all over the world to increase flexibility while achieving cost savings, reduced inefficiencies, and better performance. It is no longer a trend for the future. But putting Industry 4.0 technology support into practice is a tough endeavour that gets increasingly harder in the absence of a common method. These are produced during the course of business operations and are kept in databases, email communication, transaction logs, free form texts on (business) social media, and other places. Businesses want to integrate data analytics methods into their decision-making processes by utilisation of these data. The field of Big Data analyses has seen tremendous progress in the IT sector in recent years. It appears that in order to deal with the extremely dynamic situations of today, new methods for product route mapping are required. This article provides an overview of the literature in science on product road mapping in order to shed light on the current state of the art & pinpoint research gaps. In order to demonstrate the influence of Industry 4.0 technological innovations on the manufacturing sector, this study provides an organised and content-focused evaluation of the literature. This paper offers recommendations for converting a legacy manufacturing facility into an Industry 4.0-compliant smart plant. Research gaps encompass topics including how to include objectives or results in product roadmaps, how to match a roadmap with the item's vision, and how to include activities related to product discovery in product roadmaps.

Categorically distinct subsets allow flexible memory selection in hybrid search.

In many everyday situations, we search our visual surroundings for any one of many memorized items held in memory, a process termed hybrid search. In some cases, only a portion of the memorized mental list is relevant within a specific visual context, thus, restricting memory search to the relevant subset would be desirable. Previous research had shown that participants largely fail to "partition" memory into several distinct subsets, on a trial-by-trial basis. However, given the known role of semantic content in long-term memory organization, we hypothesized that semantically defined subset categories might serve as a more powerful means for flexible memory selection in dynamic hybrid search situations. Experiment 1 revealed that, indeed, semantic characteristics (i.e., object category), but not perceptual features (e.g., arbitrary color), can provide a firm basis for flexible memory partitioning. Experiments 2 and 3 further showed that such memory partitioning is costless and is independent of the nature of the surrounding visual distractors (i.e., a categorically homogeneous or heterogeneous display). These findings demonstrate that confining one's memory search to a currently relevant subset of items is highly effective when the different memory subsets are defined by clear semantic categories. The results underscore the importance of conceptual information in the organization of activated long-term memory and in forming the basis for a flexible trial-by-trial memory selection. Our findings further highlight the relationship between visual search and memory search, and they may shed light on the processes contributing to a successful construction of bounded episodes in long-term memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exposure to an acute stress impaired the metabolic plasticity of resilient rats by enhancing fatty acid β-oxidation in the ventral hippocampus

The concept of resilience has changed over time and nowadays it refers to the positive adaptation to life adversities, rather than to the absence of a pathological response normally occurring in susceptible people. Based on our previous data showing that the exposure to the chronic mild stress (CMS) paradigm differently affected bioenergetics in the ventral hippocampus of vulnerable and resilient animals, here we investigated whether resilience is a stable trait or if the energetic strategy set in motion to sustain resilience unveils a vulnerability feature in a more dynamic situation. To this aim, vulnerable and resilient rats after 6 weeks of CMS were subjected to a further acute, unfamiliar restraint stress (ARS) and metabolomic studies were conducted in the ventral hippocampus. We observed that exposure to a single novel challenge negatively affects the fuel utilization of resilient animals. Indeed, while they increase glycolysis to sustain the non-hedonic phenotype when exposed to CMS, they shift to fatty acid β-oxidation after ARS, as vulnerable animals following CMS, suggesting that the energy strategy that guarantees resilience is fragile and can be negatively modified by a different environmental condition. These results suggest that strengthening resilience to foster individuals to bounce back from stressful life events may represent a strategy to decrease vulnerability or prevent the risk of relapsing to a pathological state.

The innovative teaching strategies of educational soft skills cultivation affect students' performance and sense of efficiency

Introduction: Organization and time management are critical soft skills in education, as learning is frequently arranged around test and assignment deadlines. Guiding students to use long-term projects with milestones and smaller goals can help them improve their planning abilities. The Lack of self-confidence, fear of making errors, fear of being mocked, and lack of faith in own expertise or talents. This study is intended to identify how the instruction tactics for educational soft skills cultivation affect students' performance. Method: A total of 120 students were collected for this study. The data were split into two groups such as Group A and Group B, respectively. Interaction, flipping the classroom, decision-making, cooperation, engagement in professional work, and collaborative learning are all strategies of educational soft skills that are increasingly regarded as critical for success in dynamic work situations. The data were examined with SPSS software. Result: The findings indicate while there was no discernible increase in Group B, students in Group A showed a considerable improvement in soft skills, which encompass all strategies. Conclusion: The research reveals a positive association between academic achievement and the improvement of performance-related soft skills. The study emphasizes how crucial it is to include cutting-edge teaching techniques in the curriculum to help students develop critical soft skills.

AFTEA Framework for Supporting Dynamic Autonomous Driving Situation

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that fairness, accountability, transparency, and explainability (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment. This allows for unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an adaptability, fairness, transparency, explainability, and accountability (AFTEA) framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.

From melt- to crystal-rich magmatic systems during rift localization: Insights from mineral chemistry in Central Afar (Ethiopia)

Magmatism plays a key role in accommodating and localizing extension during continental breakup. However, how the crustal magmatic systems evolve at the continental-ocean transition is poorly understood. We address these questions by studying the evolution of the magmatic system in the rift of Central Afar (Ethiopia), currently marking the transition from continental rifting to oceanic spreading. We focus on the voluminous and widespread Upper Stratoid Series (2.6–1.1 Ma) and the following Central Afar Gulf Series (1.1–0.6 Ma), the latter corresponding to localization of volcanism in narrow magmatic segments. We carried out the first systematic study of major and trace element mineral chemistry for these two Series and integrated it with geothermobarometry estimates and geochemical modeling, to reconstruct the evolution of the magmatic system architecture during rift localization. The Upper Stratoid magmas evolved by fractional crystallization in a melt-rich, moderately zoned, middle-lower crustal (10–18 km) magmatic system, from where they rose directly to the surface. Polybaric plagioclase convection and dissolution of a plagioclase-rich crystal mush is recorded in the phenocryst texture and chemistry. The Central Afar Gulf magmas evolved at similar depth in a more complex and dynamic storage system, with magma rising and mixing through multiple, relatively small, crystal-rich and interconnected reservoirs. Our study documents the transition during the continental breakup, from an overall stable and melt-rich magmatic system feeding the voluminous and homogeneous Upper Stratoid eruptions to a more dynamic, interconnected and crystal-rich situation feeding small-volume eruption while the rift localizes.

ВЛИЯНИЯ ЛОЯЛЬНОСТИ ПЕРСОНАЛА НА ПРОЦЕСС ЭФФЕКТИВНОГО ФУНКЦИОНИРОВАНИЯ ОРГАНИЗАЦИИ

The study is devoted to the urgent problem of studying the influence of staff loyalty on the process of effective functioning of an organization in the dynamic turbulent socio-economic situation of recent years in the Russian Federation. In a period of insufficient personnel caused by demographic reasons and at the same time high rates of staff turnover, the study of the influence of staff loyalty on the process of effective functioning of the organization comes to the fore. A theoretical analysis of the influence of staff loyalty on the process of effective functioning of the company was carried out. The study presents the main aspects of the influence of staff loyalty on the process of effective functioning of the organization. Attitudinal and behavioral approaches to the definition of “loyalty” of personnel are outlined. A formulation is given to the basic concept of “loyalty” of personnel, which is revealed as the quality of an employee’s personality, determined by the willingness to adhere to the principles and established rules of the company, and the attitude towards the performance of job duties at a high level to maintain the functioning of the organization. The criteria for staff loyalty are highlighted, for example, such as: level of staff satisfaction with the conditions and results of work; emotional attachment to the organization (external and internal manifestations); degree of trust between management and employees. A description is given and the results of a practical study of the influence of staff loyalty on the process of effective functioning of the organization are presented. Of particular empirical importance are practical recommendations for the HR manager to determine the influence of staff loyalty, based on the use of methods for assessing the level of loyalty; research into the relationship between loyalty and company performance; analysis of received materials during feedback; monitoring of key indicators of the company’s operational efficiency and effectiveness.

A Process-Based Standardization of an Intraoperative Escalation Protocol in Anesthesiology.

Prompt recognition and management of critical events is pivotal for the provision of safe anesthetic care. This requires a well-functioning team that focuses on effective communication, timely decision-making, and escalation of potential complications. We believe that variation in bedside care leads to "near-misses," adverse outcomes, and serious safety events (SSEs). The principles of an escalation culture have been used successfully in other highly reliable industries such as aviation, military, and manufacturing. We discuss here the introduction of a unique and compelling thought-process for developing an intraoperative escalation protocol that is specifically tailored for our institution. Inspired by a critical intraoperative event, this departmental protocol was developed based on an analysis of multispecialty literature and expert opinion to decrease the incidence of SSEs. It includes a stepwise approach and incorporates patient-specific information to guide team members who encounter dynamic clinical situations. The implementation of the protocol has facilitated continuous quality improvement through iterative education, improving communication, and enhancing decision-making. Concurrently, we have plans to incorporate technology and electronic decision support tools to enhance real-time communication, monitor performance, and foster a culture of safety.