Direct Graph Research Articles

Drug use and acute kidney injury: a Drug-Wide Association study (DWAS) in Denmark and Sweden

Abstract Background Knowledge of which medications may lead to acute kidney injury (AKI) is limited, relying mostly on spontaneous reporting in pharmacovigilance systems. We here conducted an exploratory drug-wide association study (DWAS) to screen for associations between dispensed drugs and AKI risk. Methods Using two large Danish and Swedish data linkages, we identified AKI hospitalizations occurring between April 1997 to December 2021 in Denmark and between March 2007 to December 2021 in Sweden. We used a case-time control design comparing drug dispensing in the three months prior to the AKI with earlier periods for the same patient. Odds Ratios (ORs) for the association between each drug and AKI were estimated using conditional logistic regression and adjusting for the presence of comorbidities. We sought replication of signals in both health systems and explored the plausibility of findings through pharmacovigilance system analysis in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database, appearance in the RESCUE list of medications that report AKI as a side effect, PUBMED evidence review and causality assessment through direct acyclic graphs. Results We included 20 622 adults in Denmark and 13 852 in Sweden hospitalized for AKI. In total, 16 unique medications were identified in both cohorts as associated to increased AKI occurrence. Of these, 10 medications had higher reporting odd ratios in the FAERS database, and 9 were listed by RESCUE or appeared in PUBMED. This analysis identified some medications with known AKI risks (i.e. likely true positives such as furosemide, penicillin, spironolactone, and omeprazole), medications that may have initiated in response to conditions that lead to AKI (i.e. false positives like metoclopramide provided to treat nausea/vomiting) and other candidates (e.g. opioids) that warrant further evaluation in subsequent studies. Conclusions This hypothesis-generating study identifies medications with potential involvement in AKI that require confirmation and validation.

Automated CT-based Decoupling of the Effects of Airway Narrowing and Wall Thinning on Airway Counts in Chronic Obstructive Pulmonary Disease.

We examine pathways of airway alteration due to wall thinning, narrowing, and obliteration at different COPD severity stages using CT-derived airway metrics. Ex-smokers (N = 649; age mean±std: 69 ± 6years; 52% male) from the COPDGene Iowa cohort (September 2013-July 2017) were studied. Total airway count (TAC), peripheral TAC beyond 7th generation (TACp), and airway wall thickness (WT) were computed from chest CT scans using previously validated automated methods. Causal relationships among demographic, smoking, spirometry, COPD severity, airway counts, WT, and scanner variables were analyzed using causal inference techniques including direct acyclic graphs (DAGs) to quantitatively assess multi-pathway alterations of airways in COPD. TAC, TACp, and WT were significantly lower (p < 0.0001) in mild, moderate, and severe COPD compared to the preserved lung function group. TAC (TACp) losses attributed to narrowing and obliteration of small airways were 4.59, 13.29, and 32.58% (4.64, 17.82, and 45.51%) in mild, moderate, and severe COPD, while the losses attributed to wall thinning were 8.24, 17.01, and 22.95% (12.79, 25.66, and 33.95%) in respective groups. Different pathways of airway alteration in COPD are observed using CT-derived automated airway metrics. Wall thinning is a dominant contributor to both TAC and TACp loss in mild and moderate COPD while narrowing and obliteration of small airways is dominant in severe COPD. This automated CT-based study shows that wall thinning dominates airway alteration in mild and moderate COPD while narrowing and obliteration of small airways leads the alteration process in severe COPD.

The PPOS protocol mitigates the detrimental effects of high BMI on embryo and clinical pregnancy outcomes

BackgroundThe impact of high body mass index (BMI) on embryo and pregnancy outcomes in women using the PPOS (progestin-primed ovarian stimulation) protocol during their first frozen embryo transfer (FET) cycles is not clear. This study is to investigate the impact of BMI on oocyte, embryo, and pregnancy outcomes in patients who underwent the PPOS protocol.MethodsThis retrospective study included the first FET cycle of 22,392 patients following the PPOS protocol. The impact of BMI on oocyte and pregnancy outcomes was assessed across different BMI groups, using direct acyclic graph to determine covariates, followed by the application of multiple linear and logistic regressions to further validate this influence.ResultsThe high BMI groups exhibited a higher number of oocytes; however, no significant differences were observed in good-quality embryos, clinical pregnancy rate, and implantation rate. Nevertheless, the high BMI groups demonstrated a significantly elevated miscarriage rate (9.9% vs. 12.2% vs. 15.7% vs. 18.3%, P < 0.001), particularly in late miscarriages, resulting in lower live birth rates (LBR, 41.1% vs. 40.2% vs. 37.3% vs. 36.2%, P = 0.001). These findings were further confirmed through multiple liner and logistic regression analyses. Additionally, several maternal factors showed significant associations with adjusted odds ratios for early miscarriage. However, women with a BMI ≥ 24 who underwent hormone replacement cycle or hMG late stimulation protocol for endometrial preparation experienced an increased risk of late miscarriage.ConclusionsBy utilizing the PPOS protocol, women with a high BMI exhibit comparable outcomes in terms of embryo and clinical pregnancies. However, an elevated BMI is associated with an increased risk of miscarriage, leading to a lower LBR. Adopting appropriate endometrial preparation protocols such as natural cycles and letrozole stimulation cycles may potentially offer benefits in reducing miscarriages.

Optimal L(d,1)-Labeling of Certain Direct Graph Bundles Cycles over Cycles and Cartesian Graph Bundles Cycles over Cycles

An L(d,1)-labeling of a graph G=(V,E) is a function f from the vertex set V(G) to the set of nonnegative integers such that the labels on adjacent vertices differ by at least d and the labels on vertices at distance two differ by at least one, where d≥1. The span of f is the difference between the largest and the smallest numbers in f(V). The λ1d-number of G, denoted by λ1d(G), is the minimum span over all L(d,1)-labelings of G. We prove that λ1d(X)≤2d+2, with equality if 1≤d≤4, for direct graph bundle X=Cm×σℓCn and Cartesian graph bundle X=Cm□σℓCn, if certain conditions are imposed on the lengths of the cycles and on the cyclic ℓ-shift σℓ.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The Archimedes Optimization Algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

Dominant point-based sequential and parallel algorithms for the multiple sequential substring constrained-LCS problem

The Longest Common Subsequence (LCS) problem is a well-known and studied problem in computer science and bioinformatics. It consists in finding the longest subsequence that is common to two or more given sequences. In this paper, we address the problem of finding all LCS for the Sequential Substring Constrained-LCS (SSCLCS) problem, called the Multiple SSCLCS problem. To solve this problem, we first propose a dominant point-based sequential algorithm, designed on a new Leveled Direct Acyclic Graph (DAG) that gives the correct evaluation order of subproblems to avoid redundancy due to overlap. Depending on whether the constraints may overlap or not, it requires O ( S | Σ | K + 1 + r + n | Σ |) and O ( S | Σ | K + 1 + n | Σ |) time with \(O(Max\_level+n|\Sigma |) \) space. S is the number of partial SSCLCS in a node, K is the number of DAG levels, n is the length of sequences, r is the total length of constraints, \(Max\_level \) is the number of nodes in the largest level of the DAG, and | Σ | is the length of the alphabet. Then, we derive a coarse-grained multicomputer parallel solution requiring \(O\left(\frac{S|\Sigma |^{K+1}+r+n|\Sigma |}{p}\right) \) and \(O\left(\frac{S|\Sigma |^{K+1}+n|\Sigma |}{p}\right) \) execution time, \(O(Max\_level+n|\Sigma |) \) memory space and O ( K ) communication rounds. p is the number of processors. Experimental results showed that the parallel algorithm is respectively 14.43 × and 19.19 × faster than the sequential algorithm on 32 and 64 processors.

Development of Detailed and Reduced Chemical Kinetic Models for Ammonia Gas Turbines

Abstract The power generation sector has been recently moving towards decarbonization and there is an increased interest in replacing conventional fossil fuels with fuels that produce reduced/zero carbon emissions. One such fuel is ammonia (NH3). However, ammonia is hard to ignite, has a low flame speed, and produces a significantly large amount of nitrogen oxide (NOx) emissions. Hence, using 100% ammonia as fuel in gas turbines requires significant modifications and the development of novel combustors. Blending hydrogen with ammonia, however, helps in having better control over the combustion properties. Before utilizing hydrogen-blended ammonia in an actual gas turbine combustor, thorough simulation studies are required to evaluate its performance, possible hazards, and emissions. The literature lacks well-validated chemical kinetic models for the combustion of hydrogen-blended ammonia for undiluted mixtures at gas turbine-relevant conditions (~20 bar). Hence, in this work, we develop a detailed chemical kinetic model for hydrogen blended ammonia combustion and validate it with a wide range of experimental data for both dilute and undiluted mixtures relevant to gas turbine operating conditions. The detailed chemical kinetic mechanism was reduced to a smaller version (32 species mechanism) without significant loss in accuracy using the direct relation graph with error propagation (DRGEP) and full species sensitivity analysis. The resultant mechanism can predict a wide range of experimental results with the least cumulative error and will be a valuable tool in CFD simulations that will enable the development of gas turbines for zero-carbon power generation.

Использование алгоритмов Process Mining для выявления паттернов поведения студентов

This work is devoted to studying the possibilities of using process analytics methods (Process Mining) to analyze student activity based on digital traces that students leave in learning management systems (LMS). This work examines the specifics of process mining algorithms that can be used to analyze educational processes, namely, heuristic and inductive algorithms are considered as the most effective for building models and suitable for use for the purpose of analyzing educational data. The work involved creating a way to use process analytics algorithms to identify clusters of students with similar behavior patterns. The development of a process analysis algorithm was carried out on the basis of the event log of the distance learning system of Kostroma State University. As a result of the work, models of student behavior were built and visualized, including the identification and clustering of students with similar behavior, the construction of heuristic networks, Petri nets, a direct sequence graph, a BPMN model and a decision tree. An analysis of the resulting models was carried out, which showed that the developed method makes it possible to study the behavioral patterns of students. The proposed method of using intellectual analysis of educational processes can be used to solve issues of increasing the productivity of the educational process, early detection of problems, especially in the context of changing student behavior in the system, as well as the development and optimization of educational programs. In addition, the limitations of this system have been identified, which may hinder its implementation and application in the educational environment of universities.

Association between perception of harm and valence of shoulder movement images with disability levels related to chronic shoulder pain.

Recent studies highlight an interplay between pain perception and emotional responses. This necessitates a thorough investigation into how beliefs and motivational influences respond to visual stimuli of movements. Such an analysis is crucial for understanding the extent to which these factors contribute to disability levels associated with shoulder pain. We aimed to investigate the relationship between the perception of harm and the valence in images depicting shoulder movements and determine how these perceptions are linked to disability levels associated with shoulder pain. This cross-sectional study recruited 42 individuals with chronic shoulder pain. Participants were presented with 58 shoulder movements images. Each participant evaluated these images for emotional valence and arousal using the self-assessment manikin. For every image, they provided their level of avoidance, fear, and perception of harm in a numerical scale. We measured disability levels and pain catastrophizing using the Shoulder Pain and Disability Index and the Pain Catastrophizing Scale. A direct acyclic graph was used. Multiple linear regression analysis was conducted with shoulder disability as the dependent variable and perception of harm and valence as independent variables, adjusted for the confounders catastrophizing and arousal. This analysis resulted in a significant model (F4,37 = 11.44; adjusted R2 = 0.547; P < 0.01). The perception of harm to shoulder movement (β = 0.11; P < 0.001; 95% confidence interval = 5.6-11.8) was significantly associated with the level of shoulder disability, whereas valence did not show a significant association (β = 0.26; P = 0.15; 95% confidence interval = 1.7-10.8). The perception of harm associated with shoulder movements images during daily activities was associated with disability. Individuals who believe that shoulder movements are harmful have greater disability.

ITRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its ϵ-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

The study of acute toxicity and some clinical parameters of a new chelated compound

The parameters of acute toxicity of a chelate complex containing microelements: iron, manganese, copper, selenium, iodine was studied on outbred male rats. Toxic doses were determined by constructing a direct regression graph. Thus, groups of animals were formed that were administered an aqueous solution of the chelate complex in doses, ml/kg: 6.94; 8.89; 11.11 and 11.67. The response to the solution was assessed using generally accepted clinical and hematological methods. At the same time, the dynamics of temperature, live weight, mortality and peripheral blood parameters were taken into account. During the experiment, toxicity indicators were determined. The toxic effect in experimental animals manifested itself in the form of inhibition of activity, shortness of breath, decreased reactions to external stimuli, decreased body temperature, ruffled coat, and weight loss. In the blood, a significant increase in the number of leukocytes and erythrocytes was noted in rats receiving subsidies of the chelate microelement complex in doses of 8.89 ml/kg and 11.11 ml/kg. It was concluded that the introduction of a chelate complex of microelements led to stimulation of erythropoiesis and leukopoiesis, and according to the results of a study of toxicity indicators, the presented formulation corresponds to the 3rd hazard class. The parameters of acute toxicity of the chelate complex of microelements were determined, taking into account the survival of animals, LD50 was established - 907 ± 0.63 mg/kg; LD16 – 765 mg/kg; LD84 – 1076 mg/kg. It was concluded that the toxicity indicators of the chelate complex of microelements according to GOST 12.1.007–76 correspond to the 3rd hazard class - “moderately hazardous substances”. Considering the potential hematopoietic and antioxidant effect of trace elements in the chelate complex and the relatively low toxicity with other seleniumcontaining organic and inorganic additives and preparations, the use of this complex opens up opportunities and prospects for testing as a radioprotective agent.

The impact of education level and socioeconomic status on the association between depressive symptoms and memory in an older population in Latin America: An exploratory analysis from the Brazilian Longitudinal Study of Aging (ELSI-BRAZIL)

PurposeThe globally increasing older population raises concerns about age-related conditions, including cognitive impairment and depressive symptoms. In Latin America, nearly one-third of the population is affected by either of these conditions. However, data investigating the association between cognitive impairment and depressive symptoms, particularly in Brazil, are limited to small-scale studies that have not carefully examined the critical effects of variables such as education level and socioeconomic status on this relationship. We aimed at exploring this association in a representative population-based cohort. MethodsWe used the Brazilian Longitudinal Study of Aging (ELSI-BRAZIL) database to examine the relationship between depressive symptoms and cognitive impairment in Brazilian older adults, adjusted for potential confounders. Direct acyclic graphs and multivariable linear regression were used to build our model. Depressive symptoms were measured using a short version of the Center for Epidemiologic Studies Scale (CES D-8), and combined memory recall test as a surrogate of cognitive impairment. ResultsThe study included 8280 participants. Only education level was identified as a confounder for the relationship between memory loss and depressive symptoms. After adjusting for age, sex, and education level, there was strong evidence for a negative association between depressive symptoms and memory performance. For every 5-unit increase in the CES D-8 score, there was a reduction in memory capacity, translating to a loss of approximately one word in the combined words recall test (mean − 0.18, 95% CI -0.22; −0.15, P < 0.001). In addition, we found strong evidence for an interaction between socioeconomic status and depressive symptoms. Subjects belonging to medium socioeconomic status (SES) showed more pronounced memory decline, when compared to those with lower SES (mean − 0.28, 95% CI -0.42 to −0.14, P < 0.001). ConclusionsIn adults aged over 50, after adjusting for sex, age, and educational level, a 5-unit increase in CES D-8 score is associated with loss of one point in the combined memory recall test. This association seems to be confounded by educational level and significantly modified by socioeconomic status.

Evaluation of Air Pollution Control Management In Jakarta by Crossed-Impact Matrix Multiplications Applied to Classification (MICMAC)

Abstract The rapid activity in Jakarta due to high economic growth increases air pollution, which impacts health problems and environmental, social, and ecological disturbances. The research objectives are to determine factors for controlling air pollution, score and evaluate the matrix of direct influence (MDI), draw a direct and indirect influence graph, and make rating and classification comparisons. The information collection was carried out by applying the Center Gather Dialog strategy to decide the factors that impact contamination control. Information evaluation was carried out utilizing the interpretive structural modelling (ISM) with matrice d’ impacts cruises multiplication appliqué a classement (cross-impact matrix multiplication applied to classification, abbreviated as MICMAC). The results show that the factor that influences air pollution control is high concentrations resulting from activities in the transportation sector and industrial sector. Economic growth has an indirect effect because economic growth is correlated in the same direction as air pollution. The transportation sector consists of motorized vehicle activities such as cars, two-wheeled motorbikes, aeroplanes, trains, and ships that use oil fuel, which contributes to the high concentration of air pollution in Jakarta. Controlling air pollution must involve all parties, including government responsibility, public awareness, transportation, and industrial actors. All parties should implement and comply with regulations effectively and efficiently. Air pollution monitoring activities must be carried out appropriately and regularly.

Developing a New Skeletal Mechanism for Propane, Butane, and LPG Fuels

ABSTRACT This study aimed at developing a new skeletal mechanism for LPG fuel capable of predicting combustion in a wide range of engine operating conditions with high fidelity. A novel reduction schematic was developed and applied to the Lawrence Livermore National Laboratory (LLNL) detailed mechanism (980 species, 4972 reactions) concerning NOx and soot formation as well. A two-level reduction method was used in which each level included Direct Relation Graph Error Propagation (DRGEP), DRGEP with isomer lumping, and DRGEP with sensitivity analysis. As a result, the skeletal mechanism was obtained with 88 species and 727 reactions and was successfully verified by comparing the results of the ignition delay/temperature and laminar flame speed/equivalence ratio for the combustion of propane, butane, and fuel mixture (90% propane/10% butane) with the experimental data. The obtained discrepancy of the ignition delay/temperature curves for the fuel mixture was around 5%. Additionally, a zero-dimensional engine calculation was conducted, and the results indicated that the mechanism was successful in predicting the engine combustion pressure with an error of around 5%.

A causal trait model for explaining foliar water uptake capacity

AbstractQuestionsPlants largely vary in their capacity for foliar water uptake (FWU), that is, the capacity to increase leaf water content by directly absorbing water from leaf‐wetting events. Climate change will reduce leaf wetting and increase drought events. Therefore, we need a better understanding of the underlying traits and mechanisms that facilitate FWU.LocationSeasonally dry tropical montane grasslands in Brazil (Campos de Altitude).MethodsWe measured FWU and leaf traits related to wettability, surface conductance, water potential and water storage on up to 55 plant species. By using Direct Acyclic Graph theory and Bayesian modelling, we tested how those leaf traits affect FWU.ResultsWe found that stomatal conductance largely explained interspecific variation in FWU, which was also favoured in species with hydrophilic leaves, high cuticular conductance, more negative leaf water potentials, low dry‐matter content, isohydric behaviour, and more elastic cell walls.ConclusionsDue to the existence of trade‐offs, not all species exhibit an optimal combination of traits that favours FWU. Instead, co‐occurring species have achieved a similar capacity for FWU through distinct trait combinations. Consequently, species engaged in FWU may exhibit differential vulnerabilities to climate change as they can cope with drought using other strategies beside FWU.

Design and Validation of Structural Causal Model: A focus on SENSE-EGRA Datasets

Designing and validation of causal model correctness from a dataset whose background knowledge is gotten from a research process is not a common phenomenon. In fact, studies have shown that in many critical areas such as healthcare and education, researchers develop models from direct acyclic graphs without testing them. This phenomenon is worrisome and is bound to cast a dark shadow on the inference estimates that many arise from such models. In this study, we have design a novel application-based SCM for the first time using the background knowledge gotten from the American university of Nigeria (AUN), Yola, on the letter identification subtask of Early Grade reading Assessment (EGRA) program on Strengthen Education in Northeast Nigeria (SENSE-EGRA) project dataset, which was sponsored by the USAID. We employed the conditional independence test (CIT) criteria for the model’s correctness validation testing, and the results shows a near perfect SCM.

A Survey of Multiple Kernel Graph Clustering Algorithms

In recent years, algorithms based on multi-kernel learning and graph clustering have garnered significant attention in the field of data mining. This article reviews four advanced multi-kernel graph clustering algorithms: nearest neighbor linear kernel weighted multi-kernel graph clustering, multi-kernel graph clustering based on simultaneous global and local structure preservation, multi-kernel graph clustering based on direct consensus relationship graph learning, and custom non-negative matrix factorization multi-kernel graph tensor clustering. These algorithms have their own characteristics and, by combining multi-kernel learning, graph clustering, and other advanced technologies, they improve the accuracy and efficiency of clustering. However, they also face challenges such as high computational complexity and parameter settings, necessitating further research and optimization. These algorithms have wide application prospects in fields such as image recognition, text classification, and bioinformatics, providing new solutions for handling complex data structures.

Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network.

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles. Short force-field-based energy minimization is applied to refine final conformers. All computation-intensive stages of the algorithm are GPU-optimized. The procedure (termed GINGER) is benchmarked on a commonly used test set of bioactive three-dimensional (3D) conformers from the PDB. We demonstrate highly competitive results in conformer recovery and throughput rates suitable for giga-scale compound library processing. A web server that allows interactive conformer ensemble generation by GINGER and their viewing is made freely available at https://www.molsoft.com/gingerdemo.html.

Development of a reduced mechanism for methane combustion in OpenFOAM: A computational approach for efficient and accurate simulations

The primary aim of this study is to develop a more efficient reaction mechanism for accurately simulating methane combustion, with a specific focus on ignition delay, laminar flame speed, and 2-D simulated flames, while also reducing computational time. Ten reduced reaction mechanisms for methane combustion were evaluated, with only one, "SK30," meeting the required accuracy standards. However, SK30 proved to be computationally intensive when simulating a 2-D premixed flame at a microscale. To address this challenge, a two-step reduction process was implemented. Firstly, an automated algorithm utilized direct relation graphs and sensitivity analysis with ignition delays as a reference to streamline the mechanism while maintaining accuracy. Subsequently, the second step involved identifying key reactions that had a more significant impact on flame speed than ignition delay through sensitivity analysis. Any missing reactions were then added judiciously, prioritizing the retrieval of the missing but important reactions to overall accuracy while minimizing computational cost. This process resulted in a novel mechanism comprising 25 species and 132 reactions for methane-air combustion. The validity of this mechanism was confirmed through comparison with a benchmark model, demonstrating satisfactory agreement in 1-D flame speed and 2-D premixed flame modeling. Most notably, the new mechanism substantially reduced processing time, achieving a 50 % speedup compared to SK30.

On State Transition Probability and Performance of Direct Acyclic Graph Based Ledgers

Direct acyclic graph (DAG) based ledgers with multi-chain structures aim to solve the technical bottlenecks associated with classical blockchain technologies in the Internet of Things (IoT). The basic working principle of DAG-based ledgers is to validate new transactions by previous transactions in order to be added to the system. During the tip selection process in the unsteady regime, the state transition probability refers to the probability of a transaction changing from the initial state to an arbitrary state. The state transition probability plays an indispensable role in the performance and security analysis of the IoT relying on DAG-based ledgers. In this paper, we derive the exact expression and an approximate expression of the state transition probability, which both are in closed form. In addition, we propose and analyze three performance metrics, i.e., the expected cumulative weight, the expected number of steps, and the confirmation failure probability, which are derived from the state transition probability and greatly enrich the performance analysis and evaluation of the IoT. Markov chain Monte Carlo (MCMC) simulations are carried out to verify the derived analytical results and provide insight into the IoT using DAG-based ledgers.