Success Rate Research Articles

Enhancing the transferability of adversarial attacks with diversified input strategies

Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously.

Optimized wavelet and feature set of EEG signal for Parkinson disease classification

Parkinson disease (PD) is a type of neurodegenerative disorder that affects the motor movement of the patient. But each technique has its own advantages or disadvantages. In gene, speech and handwriting data model, the feature extraction and reduction is an important step for efficient classification. These two steps require proper attention for selection and also require high processing time as compared to other data model like images. Because in image modality, the deep learning algorithm can be applied that can perform all process and automate the classification. As compared to these domains, the signal produces better and best results. Because the electroencephalogram (EEG) signal are taken from the brain using electrodes and it helps to observe the brain signals effectively and immediately as compared to the other data modals. Hence, in this paper, the wavelet transform will be used to decompose the signals and statistical features will be extracted from the transformed signal. Here, the satin bower bird optimization will be used for both type of wavelet selection and feature reduction process for final classification. The reduced feature set will be classified using Ensemble Neural Network type including InceptionV3, DenseNet, MobileNet, Xception, and NasNet) recently proposed for medical image classification. The whole process will be realized using MATLAB R2021a software and its performance will be evaluated in terms of Accuracy and is compared against Automated Tunable Q-wavelet transform performance. The proposed ensemble method, employing EEG signal processing and neural networks, achieved a 97% success rate in discriminating PD datasets, surpassing Convolutional Neural Network (CNN) and Machine Learning (ML) classifications (88% –92%). Utilizing MATLAB R2021a, its superiority over Q-wavelet transform was evident, signifying improved PD dataset discrimination.

Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate.

Improved ant colony optimization algorithms for multi-objective investment decision model based on intelligent fuzzy clustering algorithm

With the continuous changes and development of financial markets, it has brought many difficulties to investment decision-making. For the multi-objective investment decision-making problem, the improved Ant colony optimization algorithms was used to improve the effectiveness and efficiency of the multi-objective investment decision-making. Therefore, based on intelligent Fuzzy clustering algorithm and Ant colony optimization algorithms, this paper studied a new multi-objective investment decision model, and proved the advantages of this method through comparative analysis of experiments. The experimental results showed that the improved Ant colony optimization algorithms has significantly reduced the system’s construction costs, operating costs and financial costs, all of which were controlled below 41%. Compared with the traditional Ant colony optimization algorithms, this method had lower values in policy risk, technical risk and market risk, and can effectively control risks. Meanwhile, the environmental, economic, and social benefits of this method were all above 58%, and the average absolute return rate and success rate in this experiment were 21.5450% and 69.4083%, respectively. Therefore, from the above point of view, the multi-objective investment decision model based on intelligent Fuzzy clustering algorithm and the improved Ant colony optimization algorithms can effectively help decision-makers to find the best investment decision-making scheme, and can improve the accuracy and stability of decision-making. This research can provide reference significance for other matters in the field of investment decision-making.

Building Research Capacity and Promoting Collaboration Through Grant Writing: Lessons From Ukrainian Universities

<p style="text-align: justify;">This study examines the state of grant writing practices in Ukrainian universities and proposes recommendations to enhance their capacity to secure external funding for research and development. Through a mixed-methods approach involving surveys and semi-structured interviews, data was gathered from researchers and faculty members. The study identifies challenges faced by Ukrainian universities in obtaining external funding and highlights important themes for improving grant-writing practices. These themes include promoting collaboration, providing resources and infrastructure, implementing language support policies, raising awareness of funding opportunities, offering institutional support, fostering a research culture, establishing internal grant review processes, leveraging international collaborations, and emphasising evaluation and learning. By addressing these themes, Ukrainian universities can improve their grant-writing practices, increase proposal competitiveness, and enhance success rates in securing external funding. This study provides valuable insights, complements existing literature, and offers practical recommendations for grant writing in Ukrainian universities.</p>

Predictors of Success of Endonasal Septal Perforation Repair: A 10-Year Experience.

Background: There is no consensus on optimal repair technique for nasal septal perforations (NSPs). Objective: To measure success rate and evaluate predictors of success for NSP repair. Methods: Medical records of patients who underwent NSP repair from 2010 to 2020 were reviewed. Included patients had at least 60 days of postsurgical follow-up. Surgical technique involves an endonasal approach; subperichondrial dissection with local flap mobilization; and multilayer closure using cartilage interposition graft, fascia graft, and mucoperichondrial flaps. A chi-squared test or Fisher exact test was used for statistical analysis. Results: Eighty-one repairs were performed with a closure rate of 86%. The median patient age was 46 years (range 13-77); 34.6% of perforations were ≥2 cm. Conchal (77.8%), rib (7.4%), or septal (7.4%) cartilage was used as graft material. A complication rate of 8.6% was reported. Perforation size or graft material had no impact on successful closure rate. Of patients with failed repairs, 55% had perioperative complications or conditions associated with poor healing. Conclusion: An endonasal approach for NSP repair showed a high success rate across diverse presentations; however, NSP repair was significantly more likely to be successful in patients without perioperative complications or pre-existing conditions associated with poor wound healing.

Retrospective comparison of first-line treatments for odontogenic sinusitis based on duration of symptoms.

To investigate the clinical efficacy of dental treatment and endoscopic sinus surgery (ESS), each primary/combined treatment modality, in patients with odontogenic sinusitis (ODS), according to its phase, acute or chronic. We retrospectively reviewed clinical data on 172 patients diagnosed with ODS. They were divided into two groups: acute (≤ 3 months; 90 patients) and chronic (> 3 months; 82 patients) ODS. The success rate and time to resolution of each primary/combined treatment modality were compared between the two groups. In both ODS groups, the success rate was highest with combined ESS and dental therapy, followed by ESS alone and dental therapy alone. ESS outperformed dental therapy (96.6% vs 65.5% for acute ODS, p = 0.011; 80.6% vs 56.5% for chronic ODS, p = 0.046) and led to quicker resolution of symptoms for acute ODS than dental therapy (0.9 vs 1.7 months, p = 0.012). In the comparison between ESS alone and combined therapy, no significant difference was observed for acute ODS, whereas combined therapy demonstrated a superior success rate for chronic ODS (100% vs 80.6%, p = 0.046). In our study, the clinical utility of dental treatment and/or ESS depended on the morbidity period of ODS. For chronic ODS, combined ESS and dental treatment seems to be an effective first-line treatment.

Evaluation of the Validity of Endoscopic Transpapillary Gallbladder Drainage for Acute Cholecystitis Based on the Tokyo Guidelines 2018.

We evaluated the validity of endoscopic transpapillary gallbladder drainage (ETGBD) as a bridging therapy prior to elective Lap-C for the patients with acute cholecystitis (AC). The Tokyo Guidelines 2018 recommend early laparoscopic cholecystectomy (Lap-C) for patients with AC, however, some patients require the preoperative drainage because of inadequate for early Lap-C du to background and comorbidities. We performed a retrospective cohort analysis using data from our hospital records from 2018-2021. In total, 71 cases of 61 patients with AC underwent ETGBD. The technical success rate was 85.9%. Patients in the failure group had more complicated branching of the cystic duct. The length of time until feeding was started and until WBC levels normalized, and the length of hospital stay were significantly shorter in the success group. The median waiting period for surgery was 39 days in the ETGBD success cases. The median operating time, amount of bleeding, and length of postoperative hospital stay were 134 min, 83.2g, and 4 days, respectively. In patients who underwent Lap-C, the waiting period for surgery and the operating time were similar between the ETGBD success and failure groups. However, the temporary discharge period after drainage and the length of postoperative hospital stay were significantly longer in the patients with ETGBD failure. Our study revealed that ETGBD has equivalent efficacy prior to elective Lap-C despite some challenges that lower its success rate. Preoperativ ETGBD can improve patient quality of life by eliminating the need for a drainage tube.

1239 Trends in Geospatial Availability of Neuro-oncology Clinical Trials in the United States Over Two Decades

INTRODUCTION: Advances in brain tumors treatments rely on successful neuro-oncology (NO) clinical trials. To date, data on NO trials completion in the US is scarce. Understanding these factors could help improve access to and the design of future clinical trials. METHODS: NO clinical trials registered in the US were analyzed (2000-2019). Univariate and multivariate analysis were used to compare success rates among trials. Geospatial analysis was performed to determine the trial density and failure rates across different states. Trial density was estimated by the number of trials/state population. RESULTS: A total of 2,610 neuro-oncology clinical trials were registered in the US during the study period, of which 1,257 met a primary endpoint – completion or trial failure. North Dakota had the highest trial density (6.03 trials/100,000 residents), followed by South Dakota (5.83) and several Northeastern states (3.90-5.60). The change in trial failure rates did not correlate with trial density by state (p = 0.777). The states with the greatest increase in trial failure rates were Nevada (23.1%), West Virginia, Alaska, and Idaho (all three at 20.0%). Florida showed a trend towards a statistically significant increase in failure rates to 10.9% (p = 0.09). CONCLUSIONS: The results of this study highlight the variations in success rates and geographic distribution of neuro-oncology clinical trials in the United States. Factors beyond trial density may be critical to trial success. The significant geographic variations in trial density and failure rates across states emphasize the need for targeted efforts to improve access participation to trials in certain regions. Further studies aimed at identifying the factors contributing to the increase in trial failure rates are needed to develop strategies to improve trial enrollment.

Stent Insertion for Inoperable Hilar Cholangiocarcinoma: Comparison Between Unilateral Radioactive Stent and Bilateral Normal Stent.

To comparatively analyze the clinical efficacy and safety of unilateral radioactive stent (RS) insertion versus bilateral normal stent (NS) insertion in patients with inoperable hilar cholangiocarcinoma (HC). Patients with inoperable HC were treated in our hospital from January 2016 to December 2020. The treatment approach included the insertion of either unilateral RS or bilateral NS, evaluating the efficacy and safety of therapy in 2 distinct groups. A total of 58 individuals experienced the insertion of a unilateral RS, whereas 57 patients underwent the insertion of bilateral NS. No statistically significant difference between the unilateral RS and bilateral NS groups was seen in the technical success rates (98.3% vs 94.7%, P = 0.598) and clinical success rates (98.2% vs 100%, P = 0.514). While there is no statistically significant difference in the rates of stent restenosis (19.3% vs 9.3%, P = 0.132) between the two groups, the unilateral RS group demonstrated substantially longer stent patency (202 vs 119 d, P = 0.016) and overall survival (229 vs 122 d, P = 0.004) compared with the bilateral NS group. Moreover, 8 patients (14.0%) in the unilateral RS group and 14 patients (25.9%) in the bilateral NS group had postoperative complications with no significant difference ( P = 0.116). When inserting stents for inoperable HC, both unilateral RS and bilateral NS insertion procedures have demonstrated favorable therapeutic efficacy. Nevertheless, inserting a unilateral RS provided a longer duration of stent patency and overall survival than implantation of bilateral NS.

PowerScout: Security-Oriented Power Delivery Network Modeling for Side-Channel Vulnerability Analysis

The growing complexity of modern electronic systems leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared hardware resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains which are not explicitly specified in the functional specification. Recent works have demonstrated that the exploitation of the side channel can compromise the system security such as information leakage and fault injection. In this work, we systematically investigate the PDN-based side channel as well as potential countermeasures. To facilitate this goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs a fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in the literature via simulation. Furthermore, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23 dB at mid-frequency range and inter-chip leakage by an average of 31.68 dB at mid- and high-frequency range. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control. In addition, the vulnerabilities are evaluated when hiding-based countermeasures are implemented. Based on the evaluation, we can understand the optimal defense configuration and explore the trade-off between information leakage mitigation and power supply stability.

The "Hitch-Stitch" for Preventing High Tracheal Stent Migration: Expanding Indications and Extended Experience.

Airway stenting is a standard treatment option for tracheo-bronchial obstruction and other conditions like tracheo-esophageal fistula (TEF). To prevent stent migration, a percutaneous fixation strategy called the "hitch-stitch" was described in 2016 as an efficient technique. We describe an extended experience of the "hitch-stitch" for silicone stents in high tracheal stenting situations and expanding indications to metal stents. Seventy-four percutaneous stitches were placed in 54 patients (36 males and 18 females). The mean age of the patients was 42 (±19) years. The indication for stenting was tracheal stenosis in 63/74 (85%) cases and TEF in 11/74 (15%) cases. Silicone stents were used in 56/74 (75.5%) cases while self-expanding metal stents (SEMS) were used in 18/74 (24.5%) cases. The majority of the stents (62/74, 85%) were high tracheal stents. The "hitch-stitch" as an anti-migration strategy had a high success rate, with distal stent migration in only 2 cases (2.7%) as the stitch had given way-these were repositioned and hitched with double stitches. Minor complications included delayed skin healing (2.8%) and suture site infection (1.4%), which was successfully managed with oral antibiotics. Stitch removal was easy with no complications. In this large series of percutaneous "hitch-stitch" as a stent migration prevention strategy, long-term data shows it is safe and effective in an expanded cohort of silicone stents. Its extended utility is the successful application to metal stents, especially in TEF, where the consequences can be deleterious. It is simple to do, with minimal extra requirements and not associated with any significant complications.

Novel odor sensing intelligence and surveillance capabilities in controller-responder robots

The rise in global travel has led to an increased need for heightened security measures at airports. Despite the best efforts of airport security officers, in the past year, hundreds of kilograms of illegal drugs and thousands of agricultural invasive species have found their way into the country, posing a severe threat to public safety and the environment. Moreover, human threats pose a significant risk to civil aviation, reinforcing the need for advanced security technology. In response to these challenges, NOSI (Novel Odor Sensing Intelligence) and ROSI (Reconnaissance Operations Security Intelligence), intelligence surveillance systems consisting of semi-autonomous controller-responder robots, were developed as a proof of concept to supplement the efforts of security and K-9 (police dogs) operators at airports. NOSI is equipped with multi-channel gas sensors for odor detection, enabling it to identify illegal drugs and invasive species in the baggage handling process, while ROSI is equipped with computer vision to identify individuals already in the government’s database of persons of interest. These coordinated robots also provide travelers with important information pertaining to their journey and allow them to trigger emergency alerts. The robots were tested in a custom-designed test bed that replicated both the behind-the-scenes baggage handling and front-office customer service operations of an airport, thus simulating a realistic airport-like setting. Based on design criteria, NOSI and ROSI demonstrated success rates of 73.4 percent and 69.8 percent, respectively. Improvements in areas of robot stability, sensor accuracy, and feature expansion were documented for further development. In conclusion, the NOSI and ROSI framework can enhance the efficiency and accuracy of airport infrastructure monitoring and supplement the capabilities of human and K9 operators. Overall, this approach can potentially revolutionize operations in various infrastructures and represents the future of human-robot collaboration.

Reinforcement Learning-Based Switching Controller for a Milliscale Robot in a Constrained Environment

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employs the robot's inverse kinematic solutions to do an environment search of the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario to demonstrate the applicability of the proposed approach. The experimental results show an average success rate of 98.86\% calculated over 30 episodes for randomly generated trajectories.

Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS

The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classification algorithms. This study involves a comparative analysis of five algorithms: logistics regression, k-nearest neighbors (K-NN), support vector machine (SVM), decision tree, and random forest. They were applied to a set of data from TIMSS 2019 in Morocco, to determine their effectiveness in predicting student performance using prediction models such as logistics regression, KNN, SVM, decision-tree, and random forest, decision-makers can make data-driven decisions to enhance educational strategies and improve outcomes in mathematics education.

Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection

Link prediction, inferring the undiscovered or potential links of the graph, is widely applied in the real world. By facilitating labeled links of the graph as the training data, numerous deep learning-based link prediction methods have been studied, which have dominant prediction accuracy compared with nondeep methods. However, the threats of maliciously crafted training graphs will leave a specific backdoor in the deep model; thus, when some specific examples are fed into the model, it will make a wrong prediction, defined as a backdoor attack. It is an important aspect that has been overlooked in the current literature. In this article, we prompt the concept of a backdoor attack on link prediction and propose Link-Backdoor to reveal the training vulnerability of the existing link prediction methods. Specifically, the Link-Backdoor combines the fake nodes with the nodes of the target link to form a trigger. Moreover, it optimizes the trigger by the gradient information from the target model. Consequently, the link prediction model trained on the backdoored dataset will predict the link with a trigger to the target state. Extensive experiments on five benchmark datasets and five well-performing link prediction models demonstrate that the Link-Backdoor achieves the state-of-the-art attack success rate under both the white box (i.e., available of the target model parameter) and the black box (i.e., unavailable of the target model parameter) scenarios. In addition, we testify the attack under the defensive circumstance, and the results indicate that the Link-Backdoor still can construct a successful attack on the well-performing link prediction methods. The code and data are available at https://github.com/Seaocn/Link-Backdoor.

A New Manipulating Method of Metal Micro-Components Based on Electrochemistry

This article presents a new manipulating method of metal micro-components based on electrochemistry aiming at the problems of inaccurate release and easy damage to manipulated objects by conventional micro gripping tools and vacuum adsorption tools. This method uses a small-sized micropipette to manipulate large-sized metal micro-components. The reliable picking up is achieved by forming a strong chemical bond between the small-sized pipette and the manipulation object. The accurate and non-destructive release is achieved by converting the chemical bonding force into a more easily overcome capillary force through electrolysis. The electrochemical and the force models of the metal microcomponents based on electrochemistry are simulated by COMSOL and Abaqus, to obtain optimal electrochemical parameters and manipulation strategies. The experiments of picking up and releasing were performed to demonstrate the performance of the proposed method. The results show that this method can enable reliable and accurate manipulation of large-size objects with small-size tools and have a high success rate and efficiency.

Current Management of Acute and Posttraumatic Critical Bone Defects.

Limb reconstruction in patients with critical-sized bone defects remains a challenge due to the availability of various technically demanding treatment options and a lack of standardized decision algorithms. Although no consensus exists, it is apparent from the literature that the combination of patient, surgeon, and institutional collaborations is effective in providing the most efficient care pathway for these patients. Success relies on choosing a particular surgical approach that manages infection, soft tissue defects, stability, and alignment. Recent systematic reviews demonstrate high success rates with the following management options: Ilizarov bone transport, Masquelet (induced membrane) technique, cancellous bone grafting, and vascularized bone grafts.

Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs

Graph neural network (GNN) with a powerful representation capability has been widely applied to various areas. Recent works have exposed that GNN is vulnerable to the backdoor attack, i.e., models trained with maliciously crafted training samples are easily fooled by patched samples. Most of the proposed studies launch the backdoor attack using a trigger that is either the randomly generated subgraph e.g., erdős-rényi backdoor (ER-B) for less computational burden or the gradient-based generative subgraph e.g., graph trojaning attack (GTA) to enable a more effective attack. However, the interpretation of how is the trigger structure and the effect of the backdoor attack related has been overlooked in the current literature. Motifs, recurrent and statistically significant subgraphs in graphs, contain rich structure information. In this article, we are rethinking the trigger from the perspective of motifs and propose a motif-based backdoor attack, denoted as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Motif-Backdoor</i> . It contributes from three aspects. First, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interpretation</i> , it provides an in-depth explanation for backdoor effectiveness by the validity of the trigger structure from motifs, leading to some novel insights, e.g., using subgraphs that appear less frequently in the graph as the trigger can achieve better attack performance. Second, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Effectiveness</i> , Motif-Backdoor reaches the state-of-the-art (SOTA) attack performance in both black-box and defensive scenarios. Third, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficiency</i> , based on the graph motif distribution, Motif-Backdoor can quickly obtain an effective trigger structure without target model feedback or subgraph model generation. Extensive experimental results show that Motif-Backdoor realizes the SOTA performance on three popular models and four public datasets compared with five baselines, e.g., Motif-Backdoor improves the attack success rate (ASR) by 14.73% compared with baselines on average. In addition, under a possible defense, Motif-Backdoor still implements satisfying performance, highlighting the requirement of defenses against backdoor attacks on GNNs. The datasets and code are available at https://github.com/Seaocn/Motif-Backdoor.

Transitions in Video Laryngoscope Technology to Improve First-Pass Success.

Prehospital intubation is a high-risk, relatively low frequency procedure. Provider experience plays a key role in first-pass success rates, especially in the setting of a difficult airway. While strong foundational knowledge is necessary to equip providers with an adequate understanding of intubation procedures and the skill set needed to manage a difficult airway, effective equipment may provide an extra boost in first-pass success for novice airway providers. First-pass success is correlated with decreased adverse events and should be maximized in the prehospital setting. After evaluating overall first-pass success of 66% to 83% from 2016 to 2020, AHN LifeFlight enacted changes in education, training, and video laryngoscopy equipment to successfully increase first-pass success to over 90%.