Net Approach Research Articles

Predicting rapid intensification of tropical cyclones in the western North Pacific: a machine learning and net energy gain rate approach

In this study, a machine learning (ML)-based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs. It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms. This ensemble approach showed a noteworthy improvement of approximately 10% in the skill score of RI prediction when NGR was included. The findings of this study emphasize the potential of NGR in refining TC intensity prediction and underline the effectiveness of ensemble ML models in RI event detection.

The paths leading to harmonious and obsessive entrepreneurial passion

ABSTRACT Prior studies have typically examined the individual antecedents of entrepreneurial passion, but overlooked the complex interactions among those determinants. This study addresses this gap in the literature by using a fuzzy set qualitative comparative analysis to explore the combinations of educational, structural, and relational support and entrepreneurial self-efficacy that drive two types of entrepreneurial passion; namely, obsessive and harmonious passion. We tested our propositions by reference to a sample of 304 young individuals who had graduated from or were studying at business and engineering schools in France. Data were collected between November 2020 and January 2021. Our findings challenge the net effect approach and indicate that passion is, instead, shaped by several combinations of support mechanisms and individual traits. Specifically, obsessive and harmonious passion are triggered by one common path and two discrete configurations of factors. These findings have important implications for theory and practice.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis.

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

Net Zero Corridors—Enabling Urban Regeneration and Net Zero Cities

The net zero city can be a new priority if urban professionals show that it enables cost-effective urban regeneration through the new concept of net zero corridors. Net zero requires the integration of net zero technologies such as solar, batteries, and electric vehicles into transport and buildings. These technologies work well at the small, local scale, so if they can be developed into a corridor of net zero precincts, enabled by net zero transit systems, then urban regeneration can be achieved with reduced car dependence. The net zero corridor approach to urban regeneration enables a city’s historic economic and cultural roles, as well as pursuing the next economy’s climate agenda. Mid-tier transit, like trackless trams, are likely to be the best approach to enabling a corridor of net zero precincts with distributed energy systems. The paper sets out the new planning practice required to deliver such net zero corridors with community-based power, governance, local transport systems, and urban design to create strong place-based urban activation. This requires a particular focus on the type of urban fabric in each corridor. The paper illustrates how it can work based on a five-year research project in Australian cities. Four steps to deliver a net zero corridor are outlined. The paper is complemented by the Commentary on “Trackless Trams: An Emerging Transformative Opportunity,” by Peter Newman showing the personal journey to change the priorities of cities in transport and energy.

Colored Petri Nets for Modeling and Simulation of a Green Supply Chain System

Green supply chain is an emerging approach in supply chain management to reduce environmental impact of the process concerning the flow of goods and materials. As a discrete-event system, supply chain can be modeled using Petri Nets. Colored Petri Nets (CPNs) extend the classical Petri net formalism with data, time and hierarchy. These extensions makes it possible to deal with the green aspects of the supply chain.This paper deals with the Colored Petri Net approach to model and simulate a green supply chain system. The forward supply chain network starts from the raw material suppliers, to the manufacturers, wholesalers, retailers and to the final customers. The reverse supply chain network is made of collecting points, recycling plants, disassembly plants, and secondary material market. A government environment agency plays the role of regulation of the recycling process. A colored Petri net model of the green supply chain is developed and simulated with CPN Tools, a dedicated software for CPN models. The simulation results as well as the state-space analysis results validate the correctness of the model.

Bionic Hand Movements Recognition: A Unified Framework with Attention-Guided ROI Identification and the Bionic Fusion Net Approach

Bionic Hand Movements Recognition: A Unified Framework with Attention-Guided ROI Identification and the Bionic Fusion Net Approach

Comparative Analysis of Ridge, LASSO, and Elastic Net Regularization Approaches in Handling Multicollinearity for Infant Mortality Data in South Sulawesi

Infant mortality rate is a crucial indicator for assessing the health and infant care quality in a region. In the effort to reduce infant mortality rates, regression analysis serves as a tool to identify influential factors. However, regression analysis often encounters the challenge of multicollinearity, which involves high correlation among predictor variables. To address this issue, various regularization techniques can be applied, such as ridge regression, least absolute shrinkage and selection operator (LASSO), and elastic net. Ridge regression aims to control coefficient variance, while LASSO directs some coefficients to zero, functioning as variable selection. Elastic net combines the strengths of both methods by merging ridge and LASSO regularization. The objective of this research is to evaluate the performance of ridge regression, elastic net, and LASSO methods in handling multicollinearity issues, utilizing infant mortality rate data in South Sulawesi Province. The results indicate that the elastic net method outperforms both Ridge and LASSO methods. The best-performing model is obtained through elastic net with a coefficient of determination value of 60.81%, whereas ridge and LASSO methods yield coefficient of determination values of 54.11% and 58.18%, respectively. This demonstrates that the application of the elastic net method is capable of producing more accurate results in modeling the variables within the analysis of infant mortality rate data compared to other methods.

LINKING PLASMA PROTEOMICS TO BRAIN ACCELERATED/RESILIENT AGING IN THE ATHEROSCLEROSIS RISK IN COMMUNITY STUDY

Abstract Machine learning models are increasingly being used to estimate ‘brain age’ from neuroimaging data. The gap between chronological age and the estimated brain age (BAG) is potentially a measure of accelerated/resilient brain aging, and we estimated BAG based on an elastic net regression approach. Here, we report associations between this brain age measure and plasma protein levels (measured using an aptamer-based proteomic platform) in the Atherosclerosis Risk in Communities (ARIC) Study to determine whether BAG was associated with proteins linked to biologic aging. We used brain MRI scans from 1507 ARIC visit 5 participants: 938 were Cognitively Normal (CN), 495 had mild cognitive impairment (MCI) and 74 had dementia. The proteomic dataset contains 4877 plasma proteins in these same individuals. We fitted univariate linear regression models adjusting for age, race, smoking, education, sex, diabetes, hypertension and intra-cranial volume. A Bonferroni correction was applied to account for multiple testing to maintain an analysis-wise α=0.05. In total 31 proteins were significantly associated with BAG. Among these SEVP1, GDF-15, pleiotrophin, MMP7, Natriuretic-peptides and Hsp70 were found to be associated with accelerated BAG, whereas EGF-receptor, mast/stem-cell-growth-factor-receptor (KIT), Coagulation-factor-VII and cGMP-dependent-protein-kinase-1 were associated to resilient BAG. The analysis based only on CN participants produced 3 significant proteins: retinoblastoma-2 and SEVP1, which were associated with accelerated BAG and Coagulation-factor-VII, which was associated to resilient BAG. These results suggest circulating proteins implicated in biological aging, cellular senescence, angiogenesis and coagulation are associated with a neuroimaging measure of accelerated brain aging.

Mitigation approach of plastic and microplastic pollution through recycling of fishing nets at the end of life

Plastic and microplastic pollution in the marine environment is a global issue and lost or discarded fishing nets (FNs) represent a relevant part of the marine litter. This type of waste is very harmful to marine organisms due to ghost fishing and microplastics release. Furthermore, nets are difficult to dispose because they take up a lot of space at landfill sites and are expensive to transport. Therefore, the development of recovery and recycling strategies represents one of the solutions for this problem. In the present work, a multifilament, knotted, twisted waste FN, composed of nylon 6,6, was recycled realizing composite materials in combination with recycled expanded polystyrene (EPS) or poly(acrylonitrile-butadiene-styrene) (ABS) matrices. The composites were realized exploiting a cold mixing approach, mixing the FN, either as a pre-ground short fiber filler or as mats, with the polymers swollen in an organic solvent. Several compositions were explored by these approaches and the composites were characterized by means of thermal, spectroscopic, mechanical and morphological analyses. Finally, a qualitative assessment of the economic impact of fishing net recycling was conducted. Results showed that the proposed recycling approaches allow to realize composite materials with interesting properties, opening new routes for recycling the FNs at the end of life improving the circular economy of plastics and reducing their ecological, ecosystem service and social and economic impacts.

Tumor growth inhibition modeling in patients with second line biliary tract cancer and first line non-small cell lung cancer based on bintrafusp alfa trials.

This analysis aimed to quantify tumor dynamics in patients receiving either bintrafusp alfa (BA) or pembrolizumab, by population pharmacokinetic (PK)-pharmacodynamic modeling, and investigate clinical and molecular covariates describing the variability in tumor dynamics by pharmacometric and machine-learning (ML) approaches. Data originated from two clinical trials in patients with biliary tract cancer (BTC; NCT03833661) receiving BA and non-small cell lung cancer (NSCLC; NCT03631706) receiving BA or pembrolizumab. Individual drug exposure was estimated from previously developed population PK models. Population tumor dynamics models were developed for each drug-indication combination, and covariate evaluations performed using nonlinear mixed-effects modeling (NLME) and ML (elastic net and random forest models) approaches. The three tumor dynamics' model structures all included linear tumor growth components and exponential tumor shrinkage. The final BTC model included the effect of drug exposure (area under the curve) and several covariates (demographics, disease-related, and genetic mutations). Drug exposure was not significant in either of the NSCLC models, which included two, disease-related, covariates in the BA arm, and none in the pembrolizumab arm. The covariates identified by univariable NLME and ML highly overlapped in BTC but showed less agreement in NSCLC analyses. Hyperprogression could be identified by higher tumor growth and lower tumor kill rates and could not be related to BA exposure. Tumor size over time was quantitatively characterized in two tumor types and under two treatments. Factors potentially related to tumor dynamics were assessed using NLME and ML approaches; however, their net impact on tumor size was considered as not clinically relevant.

Developing a DNAm Surrogate to Predict Cognitive Functioning among Older Adults

AbstractBackgroundBlood‐based epigenetic measures are being developed and used to predict aging‐related health outcomes. Specifically, researchers have developed “epigenetic clocks” based on predictions of chronological age and age‐related health declines by DNA methylation. However, these markers are meant to predict aging per se and are not disease specific. Recently, scholars have developed “DNAm surrogates” to predict specific outcomes. This methodological technique has yet to be applied to cognitive functioning despite its potential to improve prediction of cognitive health in later life and provide insight into the role of biological aging.MethodThis study developed a novel DNAm surrogate measure for cognitive functioning (DNAmCogScore) based on an elastic net approach linking using 833,865 CpG sites predicting the Telephone Interview Cognitive Status score (TICS) available in the Health and Retirement Study (HRS), a nationally representative sample of older adults in the United States. Additionally, we evaluate the association of this DNAm surrogate and other neuropathology biomarkers. We used used ridge regression to create DNAmCogScore with 50% of the sample for training and 50% for testing (N = 2009 for each subsample). The Irish Longitudinal Study of Aging (TILDA) will be used for validation (results forthcoming).ResultWe find a positive association and modest predictive value between DNAmCogScore and TICS (see Figure 1, r = .4). Table 1 shows the strong associations with the DNAmCogScore and demographic characteristics. Overall, we find a strong positive coefficient between DNAmCogScore with attenuation after controlling for demographic characteristics, from 1.12 in Model 1 to .20 in Model 3, but remaining statistically significant throughout. Table 3 shows modest negative correlations between DNAmCogScore and NfL, pTau181, and GFAP (ranging from ‐.24 to ‐.36) and no correlation with AB42:40.ConclusionThis study is among the first to create a DNA methylation surrogate measure of cognitive functioning. Our measure is associated with cognitive functioning, even after adjusting for key demographic characteristics. And it has the expected association with other more neuropathology biomarkers. Overall, it suggests that changes in DNA methylation may be a molecular‐level mechanisms by which biological aging impacts cognitive health, and this measure may provide useful prediction of cognitive impairment before the onset of symptoms.

Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol.

In low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility. This prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values. The current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. https://clinicaltrials.gov/, identifier (NCT05730387).

A novel fault detection and diagnostic Petri net methodology for dynamic systems

Faults can have significant, negative impacts on the operation and performance of simple and complex dynamic systems. Based on the integration of Bayesian network diagnostic features with Petri net formalism, the existing Bayesian-supported Petri net tool has demonstrated the flexibility of using the Petri net approach for diagnosing failure scenario of a dynamic system. However, studies on using the proposed hybrid Petri net approach for condition monitoring and early detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops are yet to be investigated. Thus, this paper presents a methodology to address this research gap using the operation of a water tank level control system as a case study. The method combines the constructed Generalised Stochastic Petri Net (GSPN) model of the system operation with its corresponding fault diagnostic Petri net model, created using the proposed modified Bayesian Stochastic Petri Net (mBSPN) formalism. The GSPN model establishes the causal relationships between the system’s components and/or subsystems. It further identifies deviations in the sensor measurements of the observable process variables characterising the system operation. The information provided by the sensors in the system model are then inputted into the mBSPN model to diagnose the root cause of the observed deviations. The obtained results demonstrated the capability of using the proposed integrated Petri net methodology for system condition monitoring, early fault detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops.

Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies

Significant progress has been made in preventing severe COVID-19 disease through the development of vaccines. However, we still lack a validated baseline predictive biologic signature for the development of more severe disease in both outpatients and inpatients infected with SARS-CoV-2. The objective of this study was to develop and externally validate, via 5 international outpatient and inpatient trials and/or prospective cohort studies, a novel baseline proteomic signature, which predicts the development of moderate or severe (vs mild) disease in patients with COVID-19 from a proteomic analysis of 7000 + proteins. The secondary objective was exploratory, to identify (1) individual baseline protein levels and/or (2) protein level changes within the first 2 weeks of acute infection that are associated with the development of moderate/severe (vs mild) disease. For model development, samples collected from 2 randomized controlled trials were used. Plasma was isolated and the SomaLogic SomaScan platform was used to characterize protein levels for 7301 proteins of interest for all studies. We dichotomized 113 patients as having mild or moderate/severe COVID-19 disease. An elastic net approach was used to develop a predictive proteomic signature. For validation, we applied our signature to data from three independent prospective biomarker studies. We found 4110 proteins measured at baseline that significantly differed between patients with mild COVID-19 and those with moderate/severe COVID-19 after adjusting for multiple hypothesis testing. Baseline protein expression was associated with predicted disease severity with an error rate of 4.7% (AUC = 0.964). We also found that five proteins (Afamin, I-309, NKG2A, PRS57, LIPK) and patient age serve as a signature that separates patients with mild COVID-19 and patients with moderate/severe COVID-19 with an error rate of 1.77% (AUC = 0.9804). This panel was validated using data from 3 external studies with AUCs of 0.764 (Harvard University), 0.696 (University of Colorado), and 0.893 (Karolinska Institutet). In this study we developed and externally validated a baseline COVID-19 proteomic signature associated with disease severity for potential use in both outpatients and inpatients with COVID-19.

Net effects explains the benefits to children from maternal fish consumption despite methylmercury in fish

In 2001 the U.S. Food and Drug Administration (FDA) issued precautionary advice to pregnant women to limit fish consumption over concern that the methylmercury content might harm their children’s neurodevelopment. This concern was based largely on results from an epidemiological study of mothers primarily exposed to methylmercury from consuming pilot whale. Subsequently, FDA and the World Health Organization/Food and Agriculture Organization (WHO/FAO) undertook independent assessments of fish consumption that considered net effects from both fish nutrients, primarily omega-3 fatty acids, as beneficial and methylmercury as harmful. Both assessments estimated that when mothers regularly consume fish during pregnancy, their children are likely to have improved neurodevelopment compared to children of non-fish eaters despite their exposure to methylmercury. These estimated improvements included gains of two to over five full scale IQ points from levels of maternal consumption that are achievable in most of the world. Consistent with those estimates, human research on fish consumption and child neurodevelopment from more than 200,000 mother-child pairs now collectively reports 51 beneficial associations with neurodevelopmental outcomes and three adverse associations, the latter with no discernable pattern. These associations include full scale IQ gains similar to, or somewhat higher than, those estimated by FDA and FAO/WHO. Also consistent with the FDA and FAO/WHO estimates, research has reported beneficial associations with fish consumption when pregnant women are exposed to methylmercury from fish in excess of the U.S. Environmental Protection Agency’s (EPA) Reference Dose (RfD). Our analysis evaluates how the net effects approach as utilized by FDA and FAO/WHO provides a holistic explanation for these results with implications for public health policy. This concordance of net effects modeling and empirical scientific evidence supports a clarification of current public health recommendations to focus on greater fish consumption by pregnant women for their children’s neurodevelopment.

Kernel methods are competitive for operator learning

We present a general kernel-based framework for learning operators between Banach spaces along with a priori error analysis and comprehensive numerical comparisons with popular neural net (NN) approaches such as Deep Operator Networks (DeepONet) [46] and Fourier Neural Operator (FNO) [45]. We consider the setting where the input/output spaces of target operator G†:U→V are reproducing kernel Hilbert spaces (RKHS), the data comes in the form of partial observations ϕ(ui),φ(vi) of input/output functions vi=G†(ui) (i=1,…,N), and the measurement operators ϕ:U→Rn and φ:V→Rm are linear. Writing ψ:Rn→U and χ:Rm→V for the optimal recovery maps associated with ϕ and φ, we approximate G† with G¯=χ∘f¯∘ϕ where f¯ is an optimal recovery approximation of f†:=φ∘G†∘ψ:Rn→Rm. We show that, even when using vanilla kernels (e.g., linear or Matérn), our approach is competitive in terms of cost-accuracy trade-off and either matches or beats the performance of NN methods on a majority of benchmarks. Additionally, our framework offers several advantages inherited from kernel methods: simplicity, interpretability, convergence guarantees, a priori error estimates, and Bayesian uncertainty quantification. As such, it can serve as a natural benchmark for operator learning.

A Petri Net Approach for Business Process Modeling and Simulation

A business process is a set of activities executed in a specific sequence involving various actors. This relationship between actors, activities, and sequence is crucial for achieving the goals and objectives of organizations. Business process modeling involves determining the actors, activities, and execution sequence necessary to accomplish the organization’s objectives, permitting analysis of the overall process in order to identify and rectify potential failures, improve operations, or eliminate activities that do not generate value. In this study, Petri nets have been selected as the modeling tool due to their ability to provide graphical and mathematical representations of business processes, which offers a significant advantage over other tools. The objective of this research is to develop a BP modeling algorithm using Petri nets. It aims to simulate the behavior of each activity within a case study in which the mathematical representation of Petri nets is utilized to measure process performance. Additionally, a software artifact is created to assess the algorithm’s functionality and compare the performance of two different business process that share the same goal.

Potential Business Models of Carbon Capture and Storage (CCS) for the Oil Refining Industry in Thailand

The escalating concerns over climate change have propelled industries worldwide to seek innovative strategies for mitigating greenhouse gas emissions. Within the energy sector, Carbon Capture and Storage (CCS) technology emerges as a promising solution to curtail emissions and foster sustainable development aims for the net zero approach. This research delves into the role of government support in expediting CCS adoption for the maximum potential of 9.79 MtCO2 storage from six major refinery plants. The refineries mentioned above are anticipated to necessitate an initial capital investment of approximately 18,307 million THB. This research focuses on potential business model proposals appropriate for a country’s context, specifically, applying CCS technology to the Thai oil refining sector. To achieve the realization of CCS within the context of this study, a combination of three essential measures will be required: tax incentives, carbon credits, and grants. This process will commence with the implementation of tax incentives, followed by an increase in the carbon price within the country. Finally, the establishment of a dedicated fund, funded through deductions from oil excise tax revenue, will play a pivotal role in facilitating the necessary financial support for the emergence of CCS.

Ultrastrong and ductile additively-manufactured medium-carbon steel via modulating austenite stability

Selective laser melting (SLM) is a very promising near net shape approach to manufacture metallic complex-shaped parts. However, it is highly challenging to manufacture ultrahigh strength medium-carbon steels via SLM due to the cracking and low ductility issues. In this contribution, the mechanisms of crack and limited ductility in the conventional medium-carbon H13 steels by SLM were investigated, which was likely attributed to the poor stability of retained austenite (RA) and the resultant formation of brittle fresh martensite. A modified crack-free H13 steel for SLM was successfully additively manufactured via in-situ minor alloying of 316 L powders, leading to the substantially enhanced austenite stability with heterogeneity. The modified SLM-fabricated H13 steel exhibited an ultrahigh tensile strength (UTS) of ∼2.0 GPa with a fractured elongation of ∼19%. This work demonstrates the critical role of austenite stability in cracking behavior and ductility of the SLM-fabricated medium-carbon steels.

Measuring ancient technological complexity and its cognitive implications using Petri nets

We implement a method from computer sciences to address a challenge in Paleolithic archaeology: how to infer cognition differences from material culture. Archaeological material culture is linked to cognition, and more complex ancient technologies are assumed to have required complex cognition. We present an application of Petri net analysis to compare Neanderthal tar production technologies and tie the results to cognitive requirements. We applied three complexity metrics, each relying on their own unique definitions of complexity, to the modeled production processes. Based on the results, we propose that Neanderthal technical cognition may have been analogous to that of contemporary modern humans. This method also enables us to distinguish the high-order cognitive functions combining traits like planning, inhibitory control, and learning that were likely required by different ancient technological processes. The Petri net approach can contribute to our understanding of technology and cognitive evolution as it can be used on different materials and technologies, across time and species.