Penetration Rate Research Articles

A Short term Electricity Load Forecasting for Community Residents Based on Federated Learning and Considering Privacy Protection

INTRODUCTION: As the penetration rate of renewable energy increases and patterns of energy demand evolve, fluctuations on both the supply and demand sides of electricity are becoming more pronounced. Consequently, accurate forecasting of community residential electrical loads has become crucial. OBJECTIVES: Although the widespread adoption of smart meters among residents provides abundant data for model training, strict challenges arise during the training process due to the need for privacy protection and data security. METHODS: This paper proposes a privacy-preserving community residential short-term electric load forecasting method based on federated learning. Initially, the method applies shared random masking encryption to the sensitive data of community residents, ensuring data privacy while maintaining consistency with the original data after preprocessing. Subsequently, a private data aggregation scheme is established to perform dynamic clustering of the community’s electrical load. RESULTS: The clustered model then serves as the basis for establishing individual load forecasting models for each category of community residents to predict short-term electrical loads. Finally, an empirical analysis is performed using the electrical load data from 120 households across 6 communities in a city in Southern China. CONCLUSION: The analysis demonstrates that the proposed method can achieve the prediction of community residential electrical loads without sharing residents’ data, thus verifying the effectiveness of this approach.

Unveiling Disparities: A Case of Digital Divide in Nepal

This research investigates the digital divide in Nepal, a country witnessing rapid growth in digital adoption. Despite impressive internet penetration rates, disparities persist, rooted in socio-economic factors such as income inequality, gender imbalances, educational disparities, and rural-urban distinctions. Drawing on international literature and employing logistic regression analysis on data from the 2022 National Demographic and Health Survey, the study reveals a significant correlation between socio-economic status (SES) and internet access. Results indicate that individuals with higher SES have a 3.92 times greater likelihood of internet access. Additionally, gender disparities emerge, with females exhibiting higher access probabilities. Urban residents and those with higher education levels also show an increased likelihood of internet access. The findings highlight the persistent digital divide in Nepal and emphasize the need for targeted interventions to promote digital inclusivity. This research contributes to international discussions on digital disparities, offering insights into the multifaceted nature of the digital divide and urging for equitable digital opportunities.

Optimization of Rock Breaking Parameters by Combing Mechanical Specific Energy Theory and Random Forest Algorithm

Abstract As the key indicators to evaluate drilling efficiency, mechanical rate of penetration (ROP) and mechanical specific energy (MSE) are affected by many uncertain factors. In the actual drilling process, it is often necessary to adjust the parameters to achieve speed and efficiency. A dual-objective model is established considering the maximization of ROP and the minimization of MSE. Which combined with the theory of mechanical specific energy and the principle of multi-objective optimization, the model parameter weights are determined by the feature importance in the random forest regression model, and the value range of rock breaking parameter is optimized, realize the fusion of physical model and data model. The results show that, in the four strata of well X, the average ROP of T2k1 stratum is increased by 48.6% and the average MSE is decreased by 26.6%, the average ROP of T1b3 stratum is increased by 89.9% and the average MSE is decreased by 33.8%, the average ROP of the T1b2 stratum was increased by 41.3% and the average MSE was decreased by 39.0%, the average ROP of the T1b1 stratum was increased by 29.2% and the average MSE was decreased by 37.3%, which met the dual objectives of maximizing the ROP and minimizing the MSE. Compared with the traditional single-objective optimization method, the established dual-objective model is more in line with the needs of complex drilling engineering, and the fusion of physical model and data model can reduce certain human error.

Stop Using Black-Box Models: Application of Explainable Artificial Intelligence for Rate of Penetration Prediction

Summary Rate of penetration (ROP) prediction plays a crucial role in optimizing drilling efficiency and reducing overall costs in the petroleum industry. Although modern artificial intelligence (AI) models have shown promising performance in this task, their lack of interpretability hinders their practical applications. This study introduces the neural basis model (NBM), a self-explainable model, for ROP prediction. The adopted NBM is benchmarked against some well-known methods using a publicly available data set, demonstrating its promising performance. The key advantage of the NBM lies in its ability to provide clear explanations, where the influence of the input drilling parameters on the predicted ROP can be clearly visualized and analyzed. The study also compares the models’ performance under two scenarios—continuous learning and all-for-one. The results indicate that the models’ performance under the continuous learning scenario, in which the models are iteratively updated with new data from the same well, outperforms that under the all-for-one scenario, where models are trained on data from previously drilled wells and applied to new wells. The discrepancy is probably due to the absence of detailed formation characteristics from the data set. Therefore, the model trained in other wells could not generalize well on new wells. Currently, in petroleum engineering, AI is gradually playing an increasingly important role; however, the majority of AI-related works often directly employ black-box models, which lack interpretability and might cause serious risks if deployed in practice. This work introduces an approach to using neural networks to build self-explainable AI (XAI), with the aim of promoting the application of XAI in the petroleum industry.

A highly accurate and robust prediction framework for drilling rate of penetration based on machine learning ensemble algorithm

The rate of penetration (ROP) is a key indicator of drilling efficiency. Many researchers have explored the application of machine learning in ROP prediction. However, few studies have focused on the robustness of the constructed models, and developing a ROP prediction model that can achieve both high accuracy and strong robustness remains a challenge. This paper introduces a novel machine learning approach to constructing a ROP prediction model through ensemble learning algorithms. The model is based on field data from oilfields, incorporating 16 input parameters that influence ROP. First, the feasibility of the collected dataset is verified using correlation analysis. Then, ROP prediction models are developed based on various machine learning algorithms, including Decision Tree Regression (DTR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). By comparing the performance of these models under noise levels of 0%, 1.7%, and 5.1%, RF, LGBM, XGB, and SVR are selected as base learners. These base learners are then combined to construct multiple ensemble models, and the performance of the optimal ensemble model is evaluated under varying noise levels. The results show that the prediction error of the optimal model remains within 10%, and R2 is greater than 0.96. Finally, the Shapley Additive Explanations (SHAP) method is applied to perform interpretability analysis on the optimal ROP prediction model, examining the impact of different input factors on the model's predictive performance. Compared to single models and other ensemble models, the proposed ensemble model not only achieves higher accuracy but also demonstrates strong robustness and generalization capability.

LSTM Recurrent Neural Network Based Method for Identification of PDC Drill Bit Operating Conditions and Its Application

Abstract PDC drill bit underground operating conditions identification is one of of the difficulties during the drilling operation. It is of great significant for the drilling improvement and accurate construction formation to accurately identify the complex operating conditions during drilling the operation such as PDC bit wearing and stick-slip vibration, etc. In this paper, data of relationship between torque, rate of penetration (ROP) signal and weight on bit (WOB), rev were obtained through lab simulation experiment, after which, a relationship database of torque, ROP signal and WOB, rev was established, a single-layer LSTM recurrent neural network (LSTM-RNN) was constructed, the number of neurons, batch size and optimizer type of LSTM layer were optimized. With BPTT algorithm, LSTM model was trained and tested on the training set containing 6000 groups of data, and achieved the goal of identifying the bit operation conditions against the input of WOB, torque, rev and ROP signal parameters. Based on this model, the PDC bit underground operating conditions identification software was compiled, and was connected to the logging unit to realize real-time identification of bit underground operating conditions, and field test application was successfully carried out in Mahu 1 Well Region. The research showed that the convergence speed was fast and the time cost was at the lowest when the number of neurons of LSTM model was 50, and that the optimized Adam optimizer model could meet the requirements of convergence speed and prediction accuracy with the lowest loss value. The classification accuracy of this model on the test set reached 94% and the accuracy of the prediction method was also verified by field test application. To sum up, the method of LSTM-RNN based PDC bit underground operating conditions real-time identification can better meet the requirements of accurate identification of PDC bit underground operating conditions in the practical drilling, and has guiding significance for the judgement of PDC bit operating conditions on site and formulation of scientific construction decisions.

Real-time lithology identification from drilling data with self & cross attention model and wavelet transform

Accurate lithology identification during drilling operations is pivotal for optimizing drilling efficiency and informed decision-making. Traditional methods, such as mud logging, Logging While Drilling (LWD), and Measurement While Drilling (MWD), often face challenges such as delays and inaccuracies, lacking consistent utilization of real-time engineering data across all drilling sites. Consequently, these methods frequently overlook the complex interactions of influencing factors, resulting in underutilized geological information that could significantly enhance operational outcomes. This paper introduces the SelfAttention-CrossWT (SACWT) model, an innovative approach that integrates self-attention and cross-attention mechanisms with Wavelet Transform (WT) to utilize real-time drilling parameters, including Weight on Bit (WOB) and Rate of Penetration (ROP). The SACWT model effectively harnesses both temporal and frequency-domain characteristics of drilling data, facilitating comprehensive extraction of latent patterns and dynamics, which substantially improves lithology identification accuracy. The output of the model is the real-time lithology classification at the drill bit's depth. The performance of the SACWT model is compared with Long Short-Term Memory (LSTM) networks and a Self-Attention (SA) model that does not utilize frequency-domain data. When applied to a dataset from the Volve oil field, the SACWT model achieved identification accuracies of 87% and 91% in two separate test wells, outperforming traditional methods. These results underscore the model's efficacy and the significant potential of utilizing real-time drilling data for enhanced lithological characterization. This breakthrough offers a robust framework for future exploration and development operations, providing a more precise, efficient, and cost-effective approach to geological analysis.

Studies on drug delivery from dissolving microneedle and its uptake in tissue

Abstract In this study, a mathematical model for transdermal drug administration using a dissolving conical microneedle (MN) is developed. The drug is absorbed in the blood compartment after passing through the skin’s viable epidermis. Reversible reaction kinetics then allow the drug to enter the tissue compartment. An unsteady reaction equation is used to predict the drug’s distribution into the skin following MN’s dissolution. An unsteady reaction mechanism is used to determine the concentrations in the blood and tissue compartments. Using the fourth-order Runge–Kutta method, the governing equations with their initial conditions are solved numerically. The MN is expected to dissolve completely in 108.1 s, according to the predicted results, while the concentration in the skin peaks at 94.4 s. The concentrations in the tissue and blood compartments, however, reach their respective maximal amounts later on. In this investigation, the impacts of the drug’s mass fraction, volume fraction, and penetration rate cannot be completely ruled out. A thorough sensitivity analysis has been carried out for some of the parameters involved. Our findings closely align with the findings reported in the literature.

Evaluation of the safety and efficacy of skin penetration enhancer Putocrin®.

Substances that can efficiently enhance skin penetration while exerting no adverse effect are useful for drug and cosmetics formulation. To investigate the safety and enhance skin penetration efficacy of Putocrin®, a combination containing 2% isosorbide dimethyl ether, 1% pentanediol, and 0.5% inositol. An invitro keratinocyte cell assay using 3-(4,5-dimethylthiazolyl-2)-2,5 diphenyltetrazolium bromide (MTT), and an invitro EpiKutis® skin study adopted hematoxylin and eosin staining, immunostaining, and liquid chromatography-mass spectrometry (LC-MS) analysis were carried out to investigate the safety of Putocrin®. A pigskin-Franz cell system experiment applied high-performance liquid chromatography (HPLC) to compare the skin penetration efficiency of fluorescein isothiocyanate (Fitc)-labeled tranexamic acid with or without the assistance of Putocrin®. The safety and efficacy of Putocrin® was further evaluated on zebrafish embryos. The MTT assay showed that Putocrin® at concentration ≤2.5% did not significantly affect cell viability. The invitro EpiKutis® skin study revealed that 2.5% Putocrin® did not affect skin morphology, filaggrin content, ceramide/protein, or fatty acid/protein ratios, but significantly increased loricrin content by 86.00% (p < 0.001). The pigskin-Franz cell penetration experiment demonstrated that Fitc-labeled tranexamic acid could barely penetrate the skin (with penetration rate of 1.121%), while Putocrin® significantly enhanced the penetration rate up to 83.983%, which was close to unlabeled tranexamic acid (90.013%). The zebrafish embryo study showed that 2.5% Putocrin® did not exert observable toxicity and obviously assisted the skin penetration of Fitc-labeled tranexamic acid into fish embryos. These results indicate the strong enhancing skin penetration potency of Putocrin®. This study demonstrated the safety as well as the strong enhancing skin penetration potency of Putocrin® for cosmetics formulation use.

Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

Designing of Ecological Model Predictive Control Algorithm for Electric Trucks Platoons with Optimal Energy Consumption

Abstract In response to the problem of motor vehicle exhaust emissions pollution, the development of new energy in China’s truck industry will be an inevitable trend. At present, the penetration rate and market share of pure electric trucks are constantly increasing. The research on electric truck platoons has strong practical significance. This article focuses on the ecological control strategy of pure electric truck platoons. Firstly, a platoon model of four electric trucks is established in Trucksim. The leading vehicle designs an ecological optimal speed dynamic programming algorithm, and in terms of following vehicle tracking control, the battery SOC state motor speed is considered to be improved, Designed an ecological model predictive control algorithm for electric truck platoon with optimal energy consumption. Using real road slope data in the mining area, a Trucksim/Matlab/Simulink joint simulation was conducted. The simulation results showed that compared with traditional constant speed cruise control, the proposed ecological predictive cruise control algorithm for pure electric commercial vehicle platoons saved 9.2577% of the overall battery SOC.

Comparison of acidified and conventional low acid thermal processing of white mushroom in glass jars as influenced by reciprocating agitation

The objective of this study was to evaluate the quality advantage that can be gained by combining acidified low-acid food thermal processing (ALTP) (pH < 4.6) and reciprocating agitation thermal processing (RATP) of mushroom. Mushroom was selected as a low acid food which is conventionally subjected to low-acid food conventional thermal processing (LACP) (pH > 4.6). Glass jars were used as containers. LACP was established to target a commercial sterilization process (Fo value of 5.0 min at 121.1 °C), while ALTP was established to get an equivalent pasteurization process (Fo value of 10.0 min at 90 °C). Heat penetration data was obtained to establish the above targeted processes and to assess the influence of processing conditions on the heating rate index and heating lag factor. Once processed, the associated quality parameters were evaluated and compared. Process variables included three processing temperatures (115, 120, 125 °C for LACP and 90, 95 and 100 °C for ALTP) and three RATP conditions (0, 1.0 and 2.0 Hz agitation). The results revealed higher rates of heat penetration and lower processing times for both LACP and ALTP processes as the reciprocation frequency increased. Quality retention advantages were realized as the process temperature was increased in each case. The best advantage was delivered with ALTP. Although the rate of heat transfer was the highest at 2.0 Hz, the best quality was observed at a moderate agitation frequency (1.0 Hz) as product texture breakdown was avoided. This is the first study that combined ALTP and RATP and compared them with LACP.

Assessing the validity of social media in fighting COVID-19 in Africa

The severe outbreak of the COVID-19 pandemic has had a significant impact on global health, the economy, and society as a whole. To this effect, various measures both national and international were taken to respond to the pandemic. Among these measures was the rapid involvement of social media which played a vital role in the context of information dissemination, coordination of medical resources, promotion of public health campaigns, and population management and disease tracing. Besides these facilities put forward by social media, there were some weaknesses like misinformation, lack of guidance, and information leakage. Based on these numerous controversies brought about by social media during the pandemic periods, we aim to examine in this study the socio-economic effect of social media in the fight against Pandemics with particular attention on the case of the COVID-19 outbreak in Africa. Using Driscoll and Kraay's fixed effects and the Lewbel-2SLS estimators on 30 African countries, the results reveal that social media in Africa through the penetration rate of Facebook, Twitter, Instagram, Pinterest, and YouTube contribute significantly in disseminating COVID-19 in Africa. The findings equally reveal the mixed effect of other determinants of COVID-19 in this nexus. While Diabetes prevalence, the proportion of people aged 65 + , and population density contribute to the increased spread of the disease, GDP per capita and Hand Washing facilities instead prove a reducing effect of the disease. Sensitivity analysis reveals that this effect is homogenous for regional considerations of the overall sample. Based on this, appropriate suggestions are made to the various governments regarding the use of social media to prevent pandemics in the future.

Rheological and mechanical evaluation of a novel fast curing cold asphalt concrete made with asphalt emulsion, by-products and magnetic induction

This work studies the feasibility of a new cold asphalt concrete with 5 % voids with fast curing, developed with asphalt emulsion and magnetic induction. Until now, it was impossible to produce fast curing cold asphalt concretes due to the impossibility of compacting and evaporating the water in the asphalt emulsion quickly. Therefore, the aim of this work is to solve the impossibility of compacting the cold asphalt concrete and thus reduce the long curing times. For this purpose, at laboratory level, an experimental cold asphalt concrete was designed with magnetic induction and compared with two reference hot asphalt concretes manufactured according to Spanish standards without magnetic induction (one with 50–70 grade bitumen and another with a PMB45/80–65 type polymer-modified bitumen). The research was carried out in two stages. First, the new cold asphalt concrete was rheologically characterized, the binder recovered from the asphalt concrete was analyzed according to the tests of ring and ball, penetration, dynamic shear rheometer (DSR) and multiple stress creep and recovery (MSCR). Secondly, the new cold concrete asphalt was mechanically characterized, the mechanical performance of the cold asphalt concrete was analyzed by evaluating stability and deformation, dry and wet indirect tensile strengths (ITS), wheel tracking test, stiffness modulus and resistance to raveling dry and wet conditions. Based on the rheological analysis results, the asphalt emulsion recovered from the cold asphalt concrete is extremely soft with a low softening point and a high penetration rate. On the other hand, the values of viscosity, phase angle, stiffness and non-recoverable creep compliance are between those of the bitumen 50/70 and the PMB45/80–65, but closer to bitumen 50/70. As for their mechanical performance, the novel cold asphalt concrete studied presents better resistance to water damage and particle loss, both in dry and wet conditions, but also high permanent deformations and low ITS, probably due to the fact that the residual binder of the emulsion used is softer than the others.

Critical lines dynamic identification for grid-connected wind power system under N-k contingency based on Oscillating equations

Abstract As the penetration rate of wind power and other renewable energy sources increases, power systems are more prone to experiencing multiple component failures, known as N-k contingency Identifying and then closely monitoring and protecting critical components during N-k contingency can effectively prevent widespread cascading failures. The primary task in identifying critical components is to model the power system; A refined model better captures the time-varying characteristics of electrical components. Therefore, a dynamic model of line power flow decay and oscillation based on swing equations is established; it introduces time-varying saturated cut-set and transfer margin indices to identify critical lines. Simulations on the IEEE-118 nodes system, along with MATCASC cascading failure simulations, demonstrate that targeting identified critical lines as deliberate attack points in the grid results in a normalized remaining load demand decrease to 50% of the original.

Study on mixed traffic characteristics around highway on-ramp bottleneck using a microscopic simulation model

In the early mixed traffic environment, connected and automated vehicles (CAVs) tend to follow simple control rules for the sake of traffic safety and stability. Comprehensive analysis of overall mixed traffic performance and vehicle to vehicle interactions around highway on-ramp bottleneck is a basis for further traffic management and control. And yet CAVs in existing researches are mainly in the form of cooperative vehicular platoon, and mixed traffic scenarios within simulation software are not applicable for in-depth investigation. The purpose of this paper is to develop a microscopic simulation model to study in detail lane-based mixed traffic performance around the highway on-ramp. This model firstly considers vehicle characteristics of individual human vehicles (HVs) and CAVs by combining different microscopic traffic models. After the hypothetical on-ramp bottleneck structure is constructed, various simulation scenarios are developed under varying traffic volumes and changing market penetration rates (MPRs) of CAVs. Simulation results show that mixed traffic flow is improved in terms of congestion patterns and traffic capacity. For oscillating and nearly homogeneous congested traffic, congestion degrees are greatly reduced when CAV MPR above 30 % and 50 % respectively. Mixed traffic capacity is increased by 19.3 % when a smaller desired time gap set for CAVs, while it shows no significant change in a larger value. In the HV-HV interactions, traffic conflicts are slightly influenced by CAVs. Comparatively, conflict situations in HV-CAV interactions get worse, the proportion of time to collision (TTC) greater than 2 s monotonically declining with the rise in CAV MPR. It is concluded that traffic characteristics overall show a good momentum when MPR of CAV and the desired time gap of CAVs in mixed traffic are appropriate.

Is digitalization essential for abating carbon emission growth in South Asia?

Output generation processes across South Asia are deemed unclean (highly emission-intensive) due to excessive reliance on fossil fuels. Thus, decarbonizing the growth processes within this region has critical emphasis among policymakers. However, since the South Asian countries are yet to be ready to undergo the renewable energy transition, it is unlikely that “their annual carbon discharge levels will subside” anytime soon. Under such circumstances, these countries need to limit the rate at which their carbon emission levels rise each year. Therefore, the impacts of digitalization on annual carbon emission growth rates in selected South Asian countries are assessed in this study. Overall, considering internet penetration rate as a proxy of digital technology adoption, the results confirm that policies aimed at digitalizing the concerned South Asian economies not only exert emission growth rate-reducing impacts via a direct channel, but also indirectly reduce emission growth rates by making natural resources sectors more productive, expediting the energy sector greening processes, greening the financial sectors, making corruption controlling measures within institutions more effective, and limiting urbanization-induced environmental quality-worsening effects. Therefore, it is relevant for the South Asian government to emphasize on digitalization while simultaneously ensuring that green digital technologies are adopted within the major macroeconomic sectors across this region.

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Adaptive Speed Optimization Strategy at Signalized Intersections Based on the Penetration Rate of Connected Automated Vehicles

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Prevalence and Risk Factors for Swallowing Dysfunction in Patients With Laryngomalacia Treated Medically and Surgically

AbstractObjectiveTo identify the prevalence, pattern, and nature of swallowing dysfunction (SwD) in a consecutive cohort of patients with laryngomalacia (LM), and to determine factors associated with a higher burden of SwD.Study DesignThis was a retrospective review of consecutive patients diagnosed with LM by 2 pediatric otolaryngologists between 2013 and 2022 and a minimum of 3‐month follow‐up.SettingTertiary care pediatric otolaryngology referral center.MethodsConsecutive cohort of patients less than 3 years old with LM diagnosed on flexible laryngoscopy were reviewed. Patients with incomplete follow‐up, lack of swallowing assessment, and genetic conditions or syndromes were excluded. All patients underwent at minimum a systematic clinical swallowing evaluation by a speech‐language pathologist specialized in pediatric dysphagia. Patients with concerning clinical exams underwent instrumental swallow evaluation (Videofluoroscopic Swallow Study [VFSS] or Flexible Endoscopic Evaluation of Swallowing [FEES]). The prevalence of abnormalities of clinical swallowing evaluation, instrumental swallow evaluation data, and details of management were collected.ResultsTwo hundred and twelve patients met criteria and were included in the final analysis. One hundred and fifteen patients (54%) had an instrumental assessment (VFSS or FEES). Of the instrumental assessments performed, 96 (69%) were abnormal. Of the total patient cohort, 55 (26%) had laryngeal penetration and/or aspiration. One hundred and seventeen (55%) had clinical or instrumental indications for intervention, with 18 (8%) requiring tube feeding. Patients with severe LM and those treated surgically had a statistically significant higher rate of penetration and aspiration.ConclusionPatients with LM have a high burden of dysphagia requiring medical intervention. The authors advocate for routine and systematic assessment of all patients with LM for swallowing dysfunction.