Open Environment Research Articles

Effects of Transparent Communication on Employee Trust in Management in Tunisia

Purpose: The aim of the study was to assess the effects of transparent communication on employee trust in management in Tunisia. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that transparent communication significantly impacts employee trust in management. When management consistently provides clear, accurate, and timely information, it fosters an environment of openness and honesty. Employees are more likely to trust their leaders when they feel informed about organizational changes, goals, and challenges. Transparent communication also helps to reduce uncertainties and rumors, which can lead to anxiety and mistrust. Studies show that when employees trust their management, they exhibit higher levels of engagement, job satisfaction, and commitment to the organization. Moreover, transparency in communication can enhance collaborative efforts and innovation, as employees feel valued and respected, leading to a more positive workplace culture and improved overall organizational performance. Implications to Theory, Practice and Policy: Social exchange theory, organizational justice theory and leader-member exchange theory may be used to anchor future studies on assessing the effects of transparent communication on employee trust in management in Tunisia. From a practical standpoint, organizations should prioritize implementing comprehensive transparent communication strategies to foster employee trust. At the policy level, organizations should institutionalize transparent communication as a core value, embedding it in corporate governance frameworks and organizational policies.

European Culex pipiens Populations Carry Different Strains of Wolbachia pipientis.

The mosquito Culex pipiens occurs in two ecotypes differing in their mating and overwintering behavior: pipiens mate in open environments and diapause, and molestus also mate in small spaces and is active throughout the year. Cx. pipiens carry Wolbachia endosymbionts of the wPip strain, but the frequency of infection differs between studied populations. Wolbachia infection affects the host reproductive success through cytoplasmic incompatibility. wPip Wolbachia is divided into five types, wPip I-V. The type of wPip carried varies among Cx. pipiens populations. In northern European locations different wPip types are found in the two ecotypes, whereas in southern locations, they often carry the same type, indicating differences in hybridization between ecotypes. In this study, Cx. pipiens specimens of both ecotypes were collected from Sweden and compared to specimens from Norway, England, Italy, and the Netherlands, as well as Cx. quinquefasciatus from Mali and Thailand. The abundance varied, but all specimens were infected by Wolbachia, while the tested specimens of other mosquito species were often uninfected. The wPip strains were determined through the sequence analysis of Wolbachia genes ank2 and pk1, showing that Cx. pipiens ecotypes in Scandinavia carry different wPip strains. The observed differences in wPip strains indicate that hybridization is not frequent and may contribute to barriers against hybridization of the ecotypes in Sweden and Norway.

Serein: A parallel pipeline‐based DAG schema for consensus in blockchain

AbstractAs the core technology of blockchain, consensus mechanisms play a crucial role in ensuring the consistency and reliability of blockchain systems. In a decentralized and open system environment like blockchain, traditional consensus algorithms are often unsuitable due to their inability to tolerate arbitrary faults such as malicious node behaviour. Consequently, Byzantine fault tolerance consensus algorithms have become a focal point in blockchain systems. However, as Byzantine fault tolerance consensus algorithms have evolved, they still face significant challenges, particularly in addressing issues related to network latency and throughput. This paper proposes a parallel pipeline‐based DAG schema for consensus in blockchain, Serein. Firstly, the Serein algorithm achieves functional partitioning of nodes, enhancing their scalability. Secondly, it employs a pipeline structure, allowing each block to proceed without waiting for the previous block's result, thereby reducing block generation latency. Lastly, the Serein algorithm leverages the advantages of the DAG block structure to achieve concurrent block ordering and submission, improving system throughput. Experimental results demonstrate that the proposed Serein algorithm maintains robust performance under conditions of high transaction volume with multiple nodes, effectively enhancing consensus efficiency while ensuring Byzantine fault‐tolerant security.

A Hybrid Synthetic Minority Oversampling Technique and Deep Neural Network Framework for Improving Rice Yield Estimation in an Open Environment

Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acquire spatial characteristics of rice and conducts research on yield estimation in an open environment. The study proposes a yield estimation framework that hybrids synthetic minority oversampling technique (SMOTE) and deep neural network (DNN). Firstly, the framework used the Pearson correlation coefficient to select 10 key vegetation indices and determine the optimal feature combination. Secondly, it created a dataset for data augmentation through SMOTE, addressing the challenge of long data collection cycles and small sample sizes caused by long growth cycles. Then, based on this dataset, a yield estimation model was trained using DNN and compared with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). The experimental results indicate that the hybrid framework proposed in this study performs the best (R2 = 0.810, RMSE = 0.69 t/ha), significantly improving the accuracy of yield estimation compared to other methods, with an R2 improvement of at least 0.191. It demonstrates that the framework proposed in this study can be used for rice yield estimation. Additionally, it provides a new approach for future yield estimation with small sample sizes for other crops or for predicting numerical crop indicators.

Research on Teaching Reform of Chemistry Experimental Course under New Engineering Background

With the proposal of new engineering education concept, new challenges are posed to the teaching reform of chemical experiment course. This study aims to explore the teaching reform path of chemistry experiment course under the new engineering background, so as to improve the students' practical ability and innovation ability. As an important part of engineering education, the teaching objectives of chemistry experiment should be consistent with the new engineering education concept, and pay attention to cultivating students' ability to solve practical problems and teamwork spirit. At present, there are some problems in chemistry experiment teaching, such as outdated curriculum system, too theoretical experiment content, lack of combination with engineering practice, and low participation of students. These problems restrict the improvement of teaching quality, and can not meet the needs of new engineering education for talent training. Therefore, the primary task of the reform is to update the experimental curriculum system and integrate more experimental projects with engineering background and cutting-edge technology, in order to improve the practicality and challenge of the experiment. In terms of reform strategies, it is suggested to implement the "student-centered" teaching mode, and encourage students to take the initiative to explore and innovate through project-driven and problem-oriented teaching methods. At the same time, strengthen the integration of experimental teaching and theoretical teaching, so that students can deepen the understanding of theoretical knowledge in the experiment, and use theoretical knowledge to solve practical problems. In addition, the information level of experimental teaching should be improved, and virtual simulation and other technologies should be used to expand the space-time dimension of experimental teaching and improve the teaching effect. The change of the role of teachers is also the key. Teachers should change from the teacher of knowledge to the guide and collaborator of students' learning. By building an open and interactive classroom environment, stimulate students' interest in learning and innovative thinking. At the same time, the establishment of a diversified evaluation system, in addition to the investigation of students' experimental skills, but also should pay attention to the evaluation of their innovative thinking, teamwork and solving practical problems. To sum up, the teaching reform of chemistry experimental curriculum under the background of new engineering should focus on the update of curriculum content, the transformation of teaching mode, the integration of teaching resources and the reform of evaluation system. Through the implementation of these strategies, it aims to build an experimental teaching system that can not only meet the needs of new engineering education, but also cultivate high-quality chemical engineering talents. Such reform is of great significance to enhance the competitiveness of chemical engineering education in China.

Long-Range Imaging of Alpha Emitters Using Radioluminescence in Open Environments: Daytime and Night-Time Applications.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates the successful detection of a 3 MBq alpha emitter in an open environment using a compact alpha camera. This camera incorporates a deep-cooled CCD and a low f-number lens system designed to minimize the blue shift effects of filters. Night-time imaging was achieved with a dual-filter system using a sandwich filter assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 m away within one minute, and the lowest detection limit can be calculated as 75 kBq. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling the detection of the alpha source at 70 cm within 10 min under indirect sunlight. This research highlights the viability of long-range optical detection of alpha emitters for environmental monitoring in real-world settings.

The intervention of Islamic Religious Education Teachers in Puberty Assistance: NVIVO Analysis

This research explores the role of Islamic Religious Education (PAI) teachers in accompanying students during puberty, a critical transition phase in the lives of Muslim adolescents that involves significant physical, emotional, and spiritual changes. Appropriate assistance during this period is crucial to help teenagers understand the changes occurring and carry out their religious responsibilities well. Using a qualitative approach with phenomenological methods, this research involved in-depth interviews with 10 PAI teachers from elementary and junior high schools. The analysis using NVivo 12 shows that the PAI teacher's role includes religious education, emotional guidance, spiritual formation, and social facilitation. Despite facing unique challenges, PAI teachers overcame them with various adaptive and responsive strategies. Through intensive personal guidance, key strategies that have proven effective include creating a supportive and open environment where students feel comfortable discussing sensitive topics. This research highlights the importance of a holistic and integrative approach in assisting puberty and provides practical recommendations such as [specific recommendations] to increase the effectiveness of this assistance. This research contributes to the Islamic religious education literature by providing new insights into the multifaceted role of PAI teachers and the challenges they face, as well as suggesting strategies for improving student support during puberty.

The influence of urban campus landscape on college students' exercise participation and mental health: an approach using deep learning and street view images

PurposeCollege student’s mental health issues have emerged as a significant public health concern. The urban campus environment, being the primary habitat for college students, plays a crucial role in influencing their mental health.Design/methodology/approachBased on survey data from 34 Chinese universities and 1173 college students in 2021, this study utilized deep learning and street view images to explore the relationship between various urban campus landscapes, college students' exercise participation, and mental health.FindingsThe study revealed substantial variations in campus landscape features, particularly in terms of spatial openness. While green campus landscapes (measured by the Green View Index and Normalized Difference Vegetation Index) showed no significant impact on exercise participation or mental health, the Sky View Factor did. Higher levels of campus openness and exercise frequency were associated with better mental health. The study also underscored that the influence of urban campus landscapes on college students' mental health was mediated by their exercise participation. Notably, spatial openness emerged as the most prominent differentiating factor among urban campus landscape attributes, significantly affecting students' exercise participation and mental health.Originality/valueThus, fostering open campus environments and reducing spatial constraints are vital steps in creating a sustainable urban landscape that can help alleviate potential negative effects on college students' mental health issues.

Machine learning-based bee recognition and tracking for advancing insect behavior research

The study of insect behavior, particularly that of honey bees, has a broad scope and significance. Tracking bee flying patterns grants much helpful information about bee behavior. However, tracking a small yet fast-moving object, such as a bee, is difficult. Hence, we present artificial intelligence, machine-learning-based bee recognition, and tracking systems to assist the researcher in studying the bee’s behavior. To develop a machine learning system, a labeled database is required for model training. To address this, we implemented an automated system for analyzing and labeling bee videos. This labeled database served as the foundation for two distinct bee-tracking solutions. The first solution (planar bee tracking system) tracked individual bees in closed mazes using a neural network. The second solution (spatial bee tracking system) utilized a neural network and a tracking algorithm to identify and track flying bees in open environments. Both systems tackle the challenge of tracking small-bodied creatures with rapid and diverse movement patterns. Although we applied these systems to entomological cognition research in this paper, their relevance extends to general insect research and developing tracking solutions for small organisms with swift movements. We present the complete architecture and detailed methodologies to facilitate the utilization of these models in future research endeavors. Our approach is a simple and inexpensive method that contributes to the growing number of image-analysis tools used for tracking animal movement, with future potential applications under less sterile field conditions. The tools presented in this paper could assist the study of movement ecology, specifically in insects, by providing accurate movement specifications. Following the movement of pollinators or natural enemies, for example, greatly contributes to the study of pollination or biological control, respectively, in natural and agro-ecosystems.

Low-cost dual-antenna GNSS-based heading and pitch angles estimation considering baseline length constraint

This contribution presents a vehicular heading and pitch angles estimation method based on the dual-antenna GNSS scheme, the baseline length is investigated as a priori nonlinear constraint to improve the estimation accuracy. The coordinates of the formed moving baseline are estimated first, the baseline length is exploited to enhance the ambiguity resolution process, and then its corresponding attitude angles are determined. The capability and reliability of the proposed algorithm are assessed by a real car-borne experiment with off-the-shelf, low-cost GNSS receivers. The driving trajectory contains both stationary periods and dynamic situations under the open sky and dense community environments. The experimental results show that, compared to the classical method that does not consider the baseline length constraint, the accuracy improvements of the proposed method on attitude angles range from 16.7% to 84.0% in different observation conditions, and the attitude errors of ambiguity fixed solutions are dominantly less than 5°.

Distribution and bioaccumulation of per- and polyfluoroalkyl substances (PFASs) in the Kuroshio Extension region of Northwest Pacific Ocean

Per- and polyfluoroalkyl substances (PFASs) are prevalently present in oceans, posing potential health risks to organisms and humans. However, information of PFAS distribution in remote open oceans is limited. In the Kuroshio Extension region of Northwest Pacific Ocean (6 stations), samples of 84 seawater (0–5800 m), 9 sediments, and 9 organisms were taken, and 25, 10, and 15 out of 29 PFASs were identified, respectively, with perfluorooctanoic acid (PFOA) and perfluorooctane sulfonates (PFOS) as the most dominant PFASs. In seawater, ΣPFASs concentration decreased from the Kuroshio region (4.47 ng/L) to the Oyashio region (3.15 ng/L), and decreased with increasing seawater depth under the function of biological and physical pumps. Additionally, 12 precursors and emerging PFASs, including perfluorooctane sulfonamide (FOSA, 0.20 ng/L), were detected. In sediment, PFASs (5.92–12.97 pg/g) were identified at depths exceeding 5000 m, including 3 precursors (e.g., FOSA, 0.82 pg/g). ΣPFASs contents were 27.12, 31.47 and 36.97 ng/g (dry weight) in brown algae (Phaeophyceae), barnacles (Balanus), and lanternfish (Myctophiformes), respectively, in which two precursors (e.g., FOSA, 0.09–0.12 ng/g) were also identified. A correlation with the trophic position was found for PFOA bioaccumulation. These findings provide useful information on PFAS distribution in the global open ocean environments.

Saccade size predicts onset time of object processing during visual search of an open world virtual environment

ObjectiveTo date the vast majority of research in the visual neurosciences have been forced to adopt a highly constrained perspective of the vision system in which stimuli are processed in an open-loop reactive fashion (i.e., abrupt stimulus presentation followed by an evoked neural response). While such constraints enable high construct validity for neuroscientific investigation, the primary outcomes have been a reductionistic approach to isolate the component processes of visual perception. In electrophysiology, of the many neural processes studied under this rubric, the most well-known is, arguably, the P300 evoked response. There is, however, relatively little known about the real-world corollary of this component in free-viewing paradigms where visual stimuli are connected to neural function in a closed-loop. While growing evidence suggests that neural activity analogous to the P300 does occur in such paradigms, it is an open question when this response occurs and what behavioral or environmental factors could be used to isolate this component. ApproachThe current work uses convolutional networks to decode neural signals during a free-viewing visual search task in a closed-loop paradigm within an open-world virtual environment. From the decoded activity we construct fixation-locked response profiles that enable estimations of the variable latency of any P300 analogue around the moment of fixation. We then use these estimates to investigate which factors best reduce variable latency and, thus, predict the onset time of the response. We consider measurable, search-related factors encompassing top-down (i.e., goal driven) and bottom-up (i.e., stimulus driven) processes, such as fixation duration and salience. We also consider saccade size as an intermediate factor reflecting the integration of these two systems. Main resultsThe results show that of these factors only saccade size reliably determines the onset time of P300 analogous activity for this task. Specifically, we find that for large saccades the variability in response onset is small enough to enable analysis using traditional ensemble averaging methods. SignificanceThe results show that P300 analogous activity does occur during closed-loop, free-viewing visual search while highlighting distinct differences between the open-loop version of this response and its real-world analogue. The results also further establish saccades, and saccade size, as a key factor in real-world visual processing.

A robust integrated navigation optimization method for USV in signal occlusion environment

Unmanned surface vehicles (USV) can use global navigation satellite systems (GNSS) and inertial navigation systems (INS) for combined positioning and navigation. However, buildings such as port facilities and bridges blocking GNSS signals will increase the error in the discriminator output in the GNSS vector tracking loop and reduce positioning accuracy. Meanwhile, due to the cumulative error in the inertial navigation system, the credibility of the navigation results when the signal is blocked is further reduced. In this regard, this study proposes a robust integrated navigation optimization method. Specifically, the RTS smoothing optimized Kalman filter is used to constrain the carrier phase error and code phase error output by the discriminator, which can dynamically adjust the gain of the vector tracking loop, thereby improving the signal tracking capability. Simultaneously, the prediction results of the gated recurrent unit (GRU) network optimized based on the attention mechanism are combined with the inertial navigation system to improve navigation accuracy. Furthermore, an adaptive Kalman filter is utilized as the integrated navigation filter. The actual path of the carrier refers to the navigation solution of the existing receiver. In the open environment, the proposed optimization method reduces horizontal positioning error and speed error by 44.7% and 37.1% respectively compared with existing methods. Simultaneously, it can effectively improve the robustness of positioning in signal obstruction environments. The proposed integrated navigation method provides new possibilities for optimizing USV navigation solutions.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Segmentation Point Simultaneous Localization and Mapping: A Stereo Vision Simultaneous Localization and Mapping Method for Unmanned Surface Vehicles in Nearshore Environments

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments.

Mediating role of capabilities in organisational agility-performance relationship in J&K, India

PurposeThis study examines the impact of organisational agility on the performance of higher education institutions. It also aims to explore the mediating role of various capabilities, namely, service innovation, technology capability, learning capability, and collaborative knowledge creation, in the agility-performance relationship.Design/methodology/approachThe sample of this study includes teaching staff of two prominent state universities of J&K, India. The study employs Exploratory Factor Analysis (EFA) and Structured Equation Modelling (SEM) for scale purification and hypotheses testing.FindingsThe results show that organisational agility positively influences performance of higher education institutions. Capabilities, namely, service innovation, technology capability, learning capability and collaborative knowledge creation, partially mediate the organisational agility-performance relationship.Research limitations/implicationsThe sample comprises teaching staff of two established universities of J&K, India.Practical implicationsFor sustenance in the turbulent environment, higher educational institutions should employ practices like regular modification of academic programs, increasing the speed of logistics activities, investing in high-ended software and hardware facilities, and developing an open environment for the exchange of ideas by employees. Policy formulation should encourage adopting agile practices in higher education institutions for enhanced performance.Originality/valueThis paper focuses specifically on organisational agility and its impact on the performance of higher educational institutions. The study contributes to the agility literature by testing the mediating roles of service innovation, technology capability, learning capability and collaborative knowledge creation collectively in the relationship between organisational agility and performance, concluding that all these capabilities except service innovation enhance the effect of organisational agility on performance.

Comparative study of laser-induced graphene fabricated in atmospheric and interface-confined environments

Laser-induced graphene (LIG) has found widespread applications in various carbon-based electronics, such as sensors, optoelectronics, and energy storage devices, due to its low cost, high efficiency, and large-scale fabrication potential. Here, two laser-induced carbonization techniques are investigated in terms of structural integrity (uniformity and densification) and electrical property by a combined experimental and theoretical approach; these techniques, namely interface and surface ablation, operate in confined and ambient environments, respectively. The property of LIG for each laser-induced carbonization technique is systematically studied using optical and electrical characterization methods. The results show that the naturally confined and oxygen-free environment of interface ablation can fabricate more compact and intact LIG than surface ablation in an open and oxygen environment, thus enabling a 7.6 times reduction of the square/sheet resistance. The underlying mechanism for these improved properties is the compressive thermal stress and higher carbonization efficiency of interface ablation. The better structural integrity and electrical property of interface-ablated graphene enable a four-fold improvement in the specific areal capacitance of supercapacitors. These findings present that interface ablation is a more effective and facile method to enable the scalable preparation of intact and highly conductive LIG for its potential use in high-performance carbon-based electronics.

Multi-head co-training: An uncertainty-aware and robust semi-supervised learning framework

Co-training, an advanced form of self-training, allows multiple base models to learn collaboratively, leading to superior performance in semi-supervised learning tasks. However, its widespread adoption is hindered by high computational costs and intricate design choices. To address these challenges, we present Multi-Head Co-Training, a streamlined and efficient framework that consolidates individual models into a multi-head structure, adding minimal extra parameters. Each classification head in this unified model collaborates with others via a “Weak and Strong Augmentation” strategy, with diversity organically introduced through robust data augmentation. Consequently, our approach implicitly promotes diversity while incurring only a minor increase in computational overhead, making co-training more accessible. We validate the effectiveness of Multi-Head Co-Training through an empirical study on standard semi-supervised learning benchmarks. For example, our method achieves up to a 3.1% accuracy improvement on the semi-supervised CIFAR dataset compared to recent methods.Recognizing the necessity for more practical performance metrics beyond accuracy, we assess our framework from three additional perspectives: robust generalization, uncertainty, and computational efficiency. To evaluate robust generalization, we expand the conventional SSL experimental setting to a more comprehensive open-set semi-supervised learning scenario. For uncertainty assessment, we conduct experiments on model calibration and selective classification benchmarks. For example, our method achieves up to a 4.3% accuracy improvement on the open-set semi-supervised CIFAR dataset. Our extensive experiments confirm that our proposed framework better captures prediction confidence and uncertainty, rendering it more suitable for SSL deployment in open environments. The code is available at https://github.com/chenmc1996/Multi-Head-Co-Training.

Closed-Loop Control of Melt Pool Temperature during Laser Metal Deposition.

Laser metal deposition (LMD) is a technology for the production of near-net-shape components. It is necessary to control the manufacturing process to obtain good geometrical accuracy and metallurgical properties. In the present study, a closed-loop control method of melt pool temperature for the deposition of small Ti6Al4V blocks in open environment was proposed. Based on the developed melt pool temperature sensor and deposition height sensor, a closed-loop control system and proportional-integral (PI) controller were developed and tested. The results show that with a PI temperature controller, the melt pool temperature tends to the desired value and remains stable. Compared to the deposition block without the controller, a flatter surface and no oxidation phenomenon are obtained with the controller.

Simulation-Based Enhancement of Patient Safety During Intrahospital Transport of Trauma Patients With COVID-19: A Helipad Scenario.

Trauma resulting from accidents, violence, or war claims over five million lives annually, with traffic accidents and falls being predominant causes. The COVID-19 pandemic has posed unprecedented challenges in trauma care. Even though the number of injuries decreased during the lockdown period, the transportation of trauma patients became even more challenging due to concerns aboutinfection control and the need for enhanced protective measures. This simulation workshop was conducted in a controlled environment to test and refine protocols for the safe transport of trauma patients with COVID-19. Our goal was to develop comprehensive guidance on the intrahospital transportation of these patients, ensuring the highest level of patient care and safety. We detail a five-step approach from preparation to the pre-landing, initial assessment, patient transfer, and patient handover phases, emphasizing adherence to protocols, equipment readiness, and personal protective equipment (PPE) use. The primary issues we encountered were related to time management, the decision-making process for applying lifesaving procedures in an open environment, and the risk of cross-infection. The workshop underscored the importance of swift and coordinated care, balancing life-saving interventions with efficient transport to a definitive care facility.