Development Of Model Research Articles

Statistical and machine learning analysis for the application of microbially induced carbonate precipitation as a physical barrier to control seawater intrusion

Seawater intrusion in coastal aquifers is a significant problem that can be addressed through the construction of subsurface dams or physical cut-off barriers. An alternative method is the use of microbially induced carbonate precipitation (MICP) to reduce the hydraulic conductivity of the porous medium and create a physical barrier. However, the effectiveness of this method depends on various factors, and the scientific literature presents conflicting results, making it challenging to generalise the findings. To overcome this challenge, a statistical and machine learning (ML) approach is employed to infer the causes for the reduction in hydraulic conductivity and identify the optimum MICP parameters for preventing seawater intrusion. The study involves data curation, exploratory analysis, and the development of various models to fit the input data (k-Nearest Neighbours – kNN, Support Vector Regression – SVR, Random Forests – RF, Gradient Boosting – XgBoost, Linear model with interaction terms, Ensemble learning algorithms with weighted averages – EnL-WA and stacking – EnL-Stack). The models performed reasonably well in the region where permeability reduction is sensitive to carbonate increase capturing the permeability reduction profile with respect to cementation level while demonstrating that they can be used in initial assessments of the specific conditions (e.g., soil properties). The best performing algorithms were the EnL-Stack and RF followed by XgBoost and SVR. The MICP method is effective in reducing hydraulic conductivity provided that the various biochemical parameters are optimised. Critical biochemical parameters for successful MICP formulations are the bacterial optical density, the urease activity, calcium chloride concentration and flow rate as well as the interaction terms across the properties of the porous media and the biochemical parameters. The models were used to identify the optimum MICP formulation for various porous media properties and the maximum permeability reduction profiles across cementation levels have been derived.

Prediction of silicon content of alloy in ferrochrome smelting using data-driven models

Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R2 = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag

Role of virtual reality in advancing forensic medical examination in cases of occupational safety violations: a review

This article discusses the prospects for introducing innovative technologies in the form of virtual and augmented reality when conducting forensic examinations of certain categories of criminal cases, such as violations of labor protection rules. Problematic aspects are revealed when conducting inspections of crime scenes in hard-to-reach areas with possible risks to people.
 Based on a generalization of scientific works and practical experience of foreign countries, the application of innovations in scientific and technological progress in forensic examinations were analyzed.
 The authors proposed an algorithm for conducting forensic examination in the field of labor protection using virtual reality technology, as well as its further application in legal proceedings. The proposed algorithm can serve as basis for the development of more effective and safe methods for conducting forensic examinations in virtual reality.
 The development of detailed 3D injury models that simulate real-life accident scenarios will enable more accurate and objective injury assessments. Moreover, compared to traditional forensics, the use of virtual reality technology ensures high accuracy of measurements and coverage of the entire object under investigation, significantly reduces operating time, and stores all received information in its original state, which is an excellent opportunity to recreate the scene of an incident in real time.
 The introduction of virtual reality into forensic examination in cases of violation of labor protection rules will contribute to efficiency, objectivity, completeness, and ensuring the reliability of not only the conclusions of forensic examinations but also legal proceedings in general. Additionally, this direction of technology development represents significant benefits for the judicial system, contributing to the reduction of time and fair trials in the field of labor protection and in other categories of criminal cases.

Towards circular economy indicators: Evidence from the European Union.

The European Union (EU) is moving towards sustainable development, and a key role is played by circular economy (CE) models geared towards reducing pressure on natural resources, generating jobs and fostering economic opportunities. Indicators are able to aggregate a variety of information and their use, through the use of multi-criteria decision analysis (MCDA), allows the performance of alternatives to be monitored. This work aims to calculate the performance of the EU27 in the years 2019 and 2020 according to 15 CE indicators available on Eurostat. The results of the Analytic Hierarchy process show that the greatest impact on circularity is determined by the category 'competitiveness and innovation', which together with the category 'global sustainability and resilience' accounts for two thirds of the overall weight. The MCDA results show that Belgium prevails in both the baseline and alternative scenarios, ahead of Italy and the Netherlands respectively. In general, circular policies see western European countries excel, while the performance of eastern European countries is weaker. The implications of this work highlight the three main barriers to the development of CE models: (i) illegal waste management; (ii) lack of knowledge and low level of investment in circular technologies and (iii) low distribution of value among stakeholders. In this way, resource management based on circularity will enable Europe to meet the challenges of sustainability with less dependence on imported raw materials.

Development of a novel nonlinear model and control strategy for soft continuum robots featuring hard magnetoactive elastomers

Magnetoactive soft continuum robots (MSCRs), capable of controllable steering and navigation, hold substantial promise for healthcare applications. However, advancements in MSCRs have been hindered by a limited understanding of MSCR dynamics and a lack of effective control methods. Addressing these gaps, this study presents a novel, time-dependent, and computationally efficient analytical model of MSCR, alongside a new optimal closed-loop control strategy for precise high-frequency trajectory tracking. A finite element (FE) model of the MSCR is initially developed, with its validity confirmed through rigorous laboratory measurements. Using the formulated FE model, a new and computationally efficient analytical model is subsequently developed to accurately predict the highly nonlinear response of MSCR. This model operates as a system of switched linear models, each of which is a reduced-order version of its corresponding high-order linear model extracted from the FE analysis. This innovative approach not only maintains the predictive accuracy of the FE model but also significantly reduces computational demands, operating in just a few seconds. The results highlight that the developed model can accurately predict the dynamic responses of the MSCR while significantly reducing the computational load by almost 80 orders of magnitude compared with the FE model on the same simulation platform. The proposed model has been effectively utilized to develop a novel optimal control strategy using the feedforward interval type-2 fractional-order fuzzy-PID method. A hardware-in-the-loop experimental test has been finally designed to demonstrate the superior performance of the MSCR under the proposed controller.

Integration of Febio as an Instructional Tool in the Undergraduate Biomechanics Curriculum.

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

A simple and efficient dialogue generation model incorporating commonsense knowledge

The performance of dialogue systems, artificial intelligence systems that communicate between a user and a machine, have rapidly improved owing to the development of the pre-trained language model that can perform well in various contexts. However, dialogue systems are often not well received by users because they generate universal and formulaic answers to user queries. As a result, users turn away from dialogue systems by reducing their interest in dialogue systems and lowering their expectations. To address this limitation, we propose a simple and efficient dialogue system that uses commonsense-based language models to facilitate more natural communication. Our model determines the context of human–machine conversations and then applies the relevant commonsense embedding to user queries. We quantitatively confirmed that our dialogue system performs significantly better on Korean datasets than non-commonsense-based dialogue systems.

Sarcasm Detection in Pidgin Tweets Using Machine Learning Techniques

Detecting sarcasm in social media is of growing importance for applications such as monitoring, consumer feedback, and sentiment analysis. However, detecting sarcasm in Pidgin tweets poses unique challenges due to the blend of English and Pidgin languages, along with local cultural references. Existing models for sarcasm detection in English lack appropriate annotated data for Pidgin. This scarcity hinders the development of effective machine learning models. This research aims to address these challenges and create a model for accurate sarcasm detection in Pidgin tweets. Logistic Regression, XGBoost, Random Forest, and Vanilla Artificial Neural Network (ANN) classifiers were assessed, focusing on accuracy, precision, recall, and F1-score metrics on sarcasm data collected by curating and pre-processing a dataset of Nigerian Pidgin tweets. The XGBoost model demonstrated notable performance, attaining an accuracy of 85.78%, precision of 88.57%, recall of 94.44%, and F1-score of 91.41%. These outcomes underscored the model's prowess in discerning sarcastic and non-sarcastic expressions. By unfolding the intricacies of language in the Nigerian context, this research into sarcasm identification in Nigerian Pidgin text data introduced a comprehensive pipeline encompassing data curation, exploratory analysis, culturally tailored pre-processing, model training, evaluation, and prediction.

A Review of Bayesian Spatiotemporal Models in Spatial Epidemiology

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies.

Development of a high-throughput scale-down model in Ambr® 250 HT for plasmid DNA fermentation processes.

Recent advances in messenger ribonucleic acid (mRNA) vaccines and gene therapy vectors have increased the need for rapid plasmid DNA (pDNA) screening and production within the biopharmaceutical industry. High-throughput (HT) fermentor systems, such as the Ambr® 250 HT, can significantly accelerate process development timelines of pDNA upstream processes compared to traditional bench-scale glass fermentors or small-scale steam-in-place (SIP) fermentors. However, such scale-down models must be qualified to ensure that they are representative of the larger scale process similar to traditional small-scale models. In the current study, we developed a representative scale-down model of a Biostat® D-DCU 30 L pDNA fermentation process in Ambr® 250 HT fermentors using three cell lines producing three different constructs. The Ambr scale-down model provided comparable process performance and pDNA quality as the 30 L SIP fermentation process. In addition, we demonstrated the predictive value of the Ambr model by two-way qualification, first by accurately reproducing the prior trends observed in a 30 L process, followed by predicting new process trends that were then successfully reproduced in the 30 L process. The representative and predictive scale-down Ambr model developed in this study would enable a faster and more efficient approach to strain/clone/host-cell screening, pDNA process development and characterization studies, process scale-up studies, and manufacturing support.

Enhancing microbial desalination cell performance through co-culture optimization and response surface methodology (RSM) in high saltwater feed concentration

In response to contemporary water and energy supply challenges, the Microbial Desalination Cell (MDC) emerged as an innovative solution, generating electricity and desalting saline water through wastewater treatment. However, the accumulation of ions during the desalination process adversely affects the biocatalyst performance, imposing scalability limitations on the technology. This study focused on enhancing the system's performance by co-culturing Shewanella oneidensis MR–1 and Escherichia coli. Through the response surface methodology, the effects of organic matter concentration and saltwater feed salinity were systematically investigated by central composite design. The study emphasizes the significant impact of studied factors on system performance. Elevated sodium lactate, sodium acetate concentrations, and salinity positively influence the desalination rate, with NaCl concentration showing an interaction effect with lactate. Positive linear relationships for OCV are observed with lactate and acetate. All factors significantly impact power output, with lactate having the most potent positive effect. Additionally, positive contributions to COD removal are noted for lactate and NaCl concentration and a positive interaction effect. These achievements facilitated the development of a mathematical model to optimize system performance by leveraging co-cultured inoculum and strategic environmental manipulation, overcoming limitations from ion accumulation, and opening the path for further advancements in this field.

Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail. The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis. To construct risk models exhibiting heightened predictive efficacy, cox- and lasso-regression methodologies were employed, and the veracity of these models was substantiated across both internal and external datasets. Subsequently, an exploration into the distinctions within the tumor microenvironment (TME), immunotherapy responses, and enriched pathways among disparate risk cohorts ensued. Ultimately, cell experiments were conducted to validate the consequential impact of SMS in Head and Neck Squamous Cell Carcinoma (HNSCC). A total of 184 genes orchestrating glycosylation were delineated for subsequent scrutiny. Employing cox- and lasso-regression methodologies, we fashioned a 3-gene signature, proficient in prognosticating the outcomes for patients afflicted with HNSCC. Noteworthy observations encompassed distinctions in the Tumor Microenvironment (TME), levels of immune cell infiltration, and the presence of immune checkpoint markers among divergent risk cohorts, holding potentially consequential implications for the clinical management of HNSCC patients. The prognosis of HNSCC can be proficiently anticipated through risk signatures based on Glycosylation-related genes (GRGs). A thorough delineation of the GRGs signature in HNSCC holds the potential to facilitate the interpretation of HNSCC's responsiveness to immunotherapy and provide innovative strategies for cancer treatment.

Major Depressive Disorder as a Driver of Dual Systems Model Development During Adolescence and Emerging Adulthood Among Justice-Involved Youth: Is Salience Age-Graded?

The dual systems model is a psychological framework centered on differential development of sensation-seeking and impulse control during adolescence and emerging adulthood with implications for understanding antisocial behavior. However, there is a dearth of research which has examined mental illness as a driver of differential development of these constructs. This study examined major depressive disorder as a risk factor for elevated sensation-seeking and diminished impulse control and tested to determine whether the salience differed by age. The Pathways to Desistance data were analyzed. Mixed effects models examined the direct effect of major depressive disorder on dual systems outcomes and test for moderation by age. Findings indicated that major depressive disorder at baseline was associated with increased sensation-seeking and diminished impulse control. Relationships did not differ in salience based on age. Results suggest that treatment effective for addressing depression may have relevance for mitigating the impact of the disorder on cognition.

Covid-19 Pneumonia Level Detection Using Deep Learning Algorithm and Transfer Learning

This paper frames the improvement of a deep learning model intended for the exact discovery and order of pneumonia levels (Mild and Severe) in Coronavirus affirmed patients in light of chest X-beam pictures. The model's development includes the execution of two particular strategies. The primary strategy utilizes a convolutional neural network worked without any preparation to characterize pneumonia levels in Coronavirus affirmed patients. Conversely, the subsequent strategy uses transfer learning with pre-trainedd models for a similar order task. The essential goal of this study is to achieve raised degrees of precision, awareness, and particularity in separating pneumonia levels among Coronavirus affirmed patients by utilizing these two assorted techniques. Keywords—Chest X Ray Images, Transfer Learning, Convolutional Neural Network, Accuracy, Covid Pneumonia Levels.

Development and internal validation of a prediction model for patients with hematologic diseases of fall risk: a cohort study

ABSTRACT Background To develop and internally validate a prediction model for identifying patients with hematologic diseases of fall risk. Research design and methods This is a prospective cohort study from a prospective collection of data for 6 months. We recruited 412 patients with hematologic diseases in medical institutions and home environment of China. The outcome of the prediction model was fall or not. These variables were filtered via univariable logistic analysis, LASSO, and multivariable logistic analysis. We adopt an internal validation method of K-fold cross validation. The area under the ROC curve and the H-L test were used to evaluate the discrimination and calibration of the model. Results Five influencing factors were identified multivariable logistic regression analysis. The established model equation is as follows: the H-L goodness-of-fit test of the model p > 0.05. The area under the ROC curve of train is 0.957 (95% CI: 0.936 ~ 0.978), and the area under the ROC curve of test is 0.962 (95% CI: 0.884 ~ 1), so the model calibration and discriminant validity are good. Conclusion Our equation has good sensitivity and specificity in predicting the fall risk of patients with hematologic diseases, and has certain positive significance for clinical assessment of their fall risk. Trial registration number ChiCTR2200063940

Model of sociology training introduced by the fourthgeneration Federal State Educational Standard

Today a new level-system of higher education is formed in Russia through significant changes that meet the new requirements for specialists in economics, social sectors, and all spheres of public life. The new system is to incorporate the developments of the Soviet educational model and the experience of recent decades. The article considers a new model of sociological training implied in the reform of the higher education system and in the fourthgeneration Federal State Educational Standard (FSES). One of the central ideas of the new system is to provide students with the opportunity to choose a field of study not upon entering a university, but at the third year of study, which would increase the awareness of professional choice, help graduates to more successfully integrate into the labor market due to a broader educational base and ensure the flexibility of educational programs, preserving the “fundamental core” in each. Thus, the first two years are for general (for the enlarged group of areas) training, and then specialized (sociological) training starts. However, the draft of the new standard proposed by the Ministry of Education and Science, which is based on updated, enlarged groups of areas, would not improve the professional training of sociologists. The mandatory general part for all areas in the group would inevitably reduce the professional part of training, thus, depriving the higher sociological education of connections with practice and destroying the focused training of specialists, which contradicts the demands of society and the labor market, and the goals of the country’s social-economic development. There are also other problems: an unpredictable extension of the time to enter the labor market; not identified mechanisms for distributing student quotas between areas within the group, for funding an extended period of study (20 %-40 % increase in cost) and an increased teaching load. The current pilot project designed for three years would not allow to make valid conclusions about the suggested changes as students will not even complete the basic level of higher education. Therefore, it is necessary to return to the FSES for each area of training, to further develop regulatory and legal grounds for the transition from the Bologna system with a focus on the values and goals of the Russian higher education, and to ensure a broad public-p rofessional discussion about the planned reforms.

Reusing Convolutional Neural Network Models through Modularization and Composition

With the widespread success of deep learning technologies, many trained deep neural network (DNN) models are now publicly available. However, directly reusing the public DNN models for new tasks often fails due to mismatching functionality or performance. Inspired by the notion of modularization and composition in software reuse, we investigate the possibility of improving the reusability of DNN models in a more fine-grained manner. Specifically, we propose two modularization approaches named CNNSplitter and GradSplitter, which can decompose a trained convolutional neural network (CNN) model for N -class classification into N small reusable modules. Each module recognizes one of the N classes and contains a part of the convolution kernels of the trained CNN model. Then, the resulting modules can be reused to patch existing CNN models or build new CNN models through composition. The main difference between CNNSplitter and GradSplitter lies in their search methods: the former relies on a genetic algorithm to explore search space, while the latter utilizes a gradient-based search method. Our experiments with three representative CNNs on three widely used public datasets demonstrate the effectiveness of the proposed approaches. Compared with CNNSplitter, GradSplitter incurs less accuracy loss, produces much smaller modules (19.88% fewer kernels), and achieves better results on patching weak models. In particular, experiments on GradSplitter show that (1) by patching weak models, the average improvement in terms of precision, recall, and F1-score is 17.13%, 4.95%, and 11.47%, respectively, and (2) for a new task, compared with the models trained from scratch, reusing modules achieves similar accuracy (the average loss of accuracy is only 2.46%) without a costly training process. Our approaches provide a viable solution to the rapid development and improvement of CNN models.

Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon

In this paper, we theoretically develop and numerically validate an asymmetric linear bilateral control model (LBCM) , in which the motion information (e.g., position and speed) from the immediate leading vehicle and the immediate following vehicle of a subject vehicle in an automated vehicle platoon are weighted differently to determine the control inputs to the subject vehicle. The novelty of the asymmetric LBCM is that using this model all the follower vehicles in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time gap to improve platoon operational efficiency while maintaining local and string stability. We theoretically analyze the local stability of the asymmetric LBCM using the condition for asymptotic stability of a linear time-invariant system and prove the string stability of the asymmetric LBCM using a space gap error attenuation approach. Then, we evaluate the efficacy of the asymmetric LBCM by simulating a closely coupled cooperative adaptive cruise control (CACC) platoon of fully automated trucks in various non-linear acceleration and deceleration states. We choose automated truck platooning as a case study since heavy-duty trucks experience higher delays and lags in the powertrain system, and limited acceleration and deceleration capabilities than passenger cars. To evaluate the platoon operational efficiency of the asymmetric LBCM, we compare the performance of the asymmetric LBCM to a baseline model, i.e., the symmetric LBCM, for different powertrain delays and lags. Our analyses showed that the asymmetric LBCM can handle any combined powertrain delays and lags up to 0.6 sec while maintaining a constant desired time gap during a stable platoon operation, whereas the symmetric LBCM fails to ensure stable platoon operation as well as maintain a constant desired time gap for any combined powertrain delays and lags over 0.2 sec. These findings demonstrate the potential of the asymmetric LBCM in improving platoon operational efficiency and stability of an automated truck platoon.

Development and clinical empirical validation of the chronic critical illness prognosis prediction model.

The evolution of critical care medicine and nursing has aided and enabled the rescue of a large number of patients from numerous life-threatening diseases. However, in many cases, patient health may not be quickly restored, and the long-term prognosis may not be optimistic. In this study, we aimed to develop and validate a prediction model for accurate, precise, and objective identification of the severity of chronic critical illness (CCI) in patients. We used a retrospective case-control and prospective cohort study with no interventions. Patients diagnosed with CCI admitted to the ICU of a large metropolitan public hospital were selected. In the case-control study, 344 patients (case: 172; control:172) were enrolled to develop the prognosis prediction model of chronic critical illness (PPCCI Model); 88 patients (case:44; control: 44) in a prospective cohort study, served as the validation cohort. The discrimination of the model was measured using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). Age, prolonged mechanical ventilation (PMV), sepsis or other severe infections, Glasgow Coma Scale (GCS), mean artery pressure (MAP), heart rate (HR), respiratory rate (RR), oxygenation index (OI), and active bleeding were the nine predictors included in the model. In both cohorts, the PPCCI model outperformed the Acute Physiology And Chronic Health Evaluation II (APACHE II), Modified Early Warning Score (MEWS), and Sequential Organ Failure Assessment (SOFA) in identifying deceased patients with CCI (development cohort: AUC, 0.934; 95%CI, 0.908-0.960; validation cohort: AUC, 0.965; 95% CI, 0.931-0.999). The PPCCI model can provide ICU medical staff with a standardized measurement tool for assessing the condition of patients with CCI, enabling them to allocate ward monitoring resources rationally and communicate with family members.

Evaluation of machine learning algorithms for noninvasive intracranial pressure estimation using near infrared spectroscopy as a covariate.

Intracranial pressure (ICP) is a vital parameter that is continuously monitored in patients with severe brain injury and imminent intracranial hypertension. To estimate intracranial pressure without intracranial probes based on transcutaneous near infrared spectroscopy (NIRS). We developed machine learning based approaches for noninvasive intracranial pressure (ICP) estimation using signals from transcutaneous near infrared spectroscopy (NIRS) as well as other cardiovascular and artificial ventilation parameters. In a patient cohort of 25 patients, with 22 used for model development and 3 for model testing, the best performing models were Fourier transform based Transformer ICP waveform estimation which produced a mean absolute error of 4.68 mm Hg (SD = 5.4) in estimation. We did not find a significant improvement in ICP estimation accuracy by including signals measured by transcutaneous NIRS. We expect that with higher quality and greater volume of data, noninvasive estimation of ICP will improve.