Time Graph Research Articles

Theories of complicated identifications exchange

The main objective of this study is to determine the pressure derivatives diagnoses for the infinite-conductivity horizontal well flow regimes identification which are not well studied so far in literature. The pressure derivatives diagnoses for the uniform-flux horizontal well flow regimes identification are fully studied in literature and presented here for comparison purposes only. The horizontal well flow regimes: early-radial, early-linear, late-radial, late-linear, and pseudo-steady state (PSS) are being identified. The identifications of flow regimes which have characteristic trends on log-log graph of pressure derivatives versus time represent unique technique in reservoir characterization. Five pressure derivatives are studied in detail: primary pressure derivative (PPD) (i.e., PPD=∂PD/∂tDA), Slope of PPD, PPD ×tDA, Bourdet derivative (i.e., tDA × PPD), and Slope of Bourdet derivative. For the first time, the pressure derivatives have been computed at high-resolution to diagnose the five infinite-conductivity horizontal well flow regimes identifications (i.e., the wellbore is discretized into twenty thousand of segments). In field application, synthetic pressure drawdown test data for infinite-conductivity horizontal well are analyzed by using type-curve matching technique.The novelties in this study are the following:1.The identifications of infinite-conductivity early-radial and early-linear flow regimes by Slope of Bourdet derivative are equal to −0.5 and 0 respectively which are different to their uniform-flux counterparts 0 and 0.5 respectively. Therefore, the pressure derivatives diagnoses are unique for a certain wellbore condition and there is a complicated identifications exchange between uniform-flux and infinite-conductivity flow regimes. The identification of infinite-conductivity early-radial flow regime is used for uniform-flux spherical flow regime and vice versa, and the identification of infinite-conductivity early-linear flow regime is used for uniform-flux early-radial and late-radial-flow regimes and vice versa.2.Theories of developing infinite-conductivity early-radial flow regime by multi aligned and merged uniform-flux spherical flow regimes and developing infinite-conductivity early-linear flow regime by multi aligned uniform-flux early-radial flow regimes are presented. The new theories prove the existence of the complex identifications exchange between uniform-flux and infinite-conductivity flow regimes.3.Without type-curve matching technique, downhole flow or pressure profile measurement, or presence of uniform-flux early-linear flow regime, or determination of mt at late-radial, late-linear, or PSS flow regime are crucial to distinguish if the wellbore is under uniform-flux or infinite-conductivity condition for correct horizontal well test analysis, mt = segments number of the half-length wellbore.4.At infinite-conductivity late-linear flow regime, qD distribution over the wellbore is uniform for any well and reservoir configuration as at infinite-conductivity PSS flow regime, qD = dimensionless inflow rate.

Feedback control laws to ensure generalized mutual exclusion constraints in a network of partially observable timed event graphs

This paper deals with the control problem of partially-observable Discrete Event Systems (DESs) which are modelled by Networks of Timed Event Graphs (NTEGs) and subject to Generalized Mutual Exclusion Constraints (GMECs). A new mathematical methodology based on the use of Min-Plus formalisms to synthesize control laws that guarantee the concerned constraints is suggested. For this, the dynamic behavior of the NTEGs is described by linear equations in Min-Plus algebra and the GMECs are represented by weighted inequalities in Min-Plus algebra too. Sufficient conditions are supplied to verify the existence of feedback gains guaranteeing the satisfaction of GMECs. Eventually, to verify the analytical results, it is important to examine a real case study of manufacturing systems.

A Hierarchical Framework of Decision Making and Trajectory Tracking Control for Autonomous Vehicles

Most of the existing research in the field of autonomous vehicles (AVs) addresses decision making, planning and control as separate factors which may affect AV performance in complex driving environments. A hierarchical framework is proposed in this paper to address the problem mentioned above in environments with multiple lanes and surrounding vehicles. Firstly, high-level decision making is implemented by a finite-state machine (FSM), according to the relative relationship between the ego vehicle (EV) and the surrounding vehicles. After the decision is made, a cluster of quintic polynomial equations is established to generate the path connecting the initial position to the candidate target positions, according to the traffic states of the EV and the target vehicle. The optimal path is selected from the cluster, based on the quadratic programming (QP) framework. Then, the speed profile is generated, based on the longitudinal displacement–time graph (S–T graph), considering the vehicle motion state constraints and collision avoidance. The smoothed speed profile is created through another QP formulation, in the space created by the dynamic-programming (DP) method. Finally, the planned path and speed profile are combined and sent to the lower level control module, which consists of the linear quadratic regulator (LQR) for lateral trajectory tracking control, and a double PID controller for longitudinal control. The performance of the proposed framework was validated in a co-simulation environment using PreScan, MATLAB/Simulink and CarSim, and the results demonstrate that the proposed framework is capable of addressing most ordinary scenarios on a structured road, with reasonable decisions and controlling abilities.

Análisis con Google Trends y Our World in Data sobre la salud mental mundial en el contexto de la pandemia por covid-19

IntroductionDuring the covid-19 pandemic, mental health services were unable to cope with the high demand from the population, so many people chose to search the Internet for information that could help them cope with the psychological process they were experiencing at the time. The aim of this study was to characterize the global search trend for the term «psychiatry» in the context of covid-19 using Google Trends and Our World in Data. MethodsDescriptive-cross-sectional study on global search trends for Psychiatry in the context of covid-19 under the terms «psychiatry», «depression», «anxiety», «stress», «insomnia» and «suicide» in the category of health, this was conducted over the period 2020-2021 and time graphs were generated. ResultsThe term «psychiatry» remained at a consistently high relative search volume (between 60 and 90), with a significant and gradual search in the month of April. The relative search volume for «depression», «anxiety» and «stress» remained constant with some non-significant fluctuations over the period 2020-2021. The term «insomnia» was predominant between January and June 2020, gradually declining in April and remaining constant until October 2021. Finally, the term «suicide» had a fluctuating RBV between 60 and 100 during this period. ConclusionsDuring the study period, the topics related to mental health and the speciality of psychiatry remained constant, with some fluctuating, but not outstanding variations.

A graph-based superframework for mixture model estimation using EM: an analysis of US wholesale electricity markets

A fully unsupervised graph-based superframework is proposed to handle the EM initialization problem for estimating mixture models on financial time series. Using a complex network approach that links time series and graphs, the graph-structured information derived from the observed data is exploited to produce a meaningful starting point for the EM algorithm. It is shown that structural information derived by complex graphs can definitely capture time series behavior and nonlinear relationships between different observations. The proposed methodology is employed to estimate Gaussian mixture models on US wholesale electricity market prices using two different configurations of the superframework. The obtained results show that the proposed methodology performs better than conventional initialization methods, such as K-means based techniques. The improvements are significant on the overall representation of the empirical distribution of log-returns and, in particular, on the first four moments. Moreover, this approach has a high degree of generalization and flexibility, exploiting graph manipulation and employing functional operating blocks, which can be adapted to very different empirical situations.

Computing Bend-Minimum Orthogonal Drawings of Plane Series–Parallel Graphs in Linear Time

AbstractA planar orthogonal drawing of a planar 4-graph G (i.e., a planar graph with vertex-degree at most four) is a crossing-free drawing that maps each vertex of G to a distinct point of the plane and each edge of G to a polygonal chain consisting of horizontal and vertical segments. A longstanding open question in Graph Drawing, dating back over 30 years, is whether there exists a linear-time algorithm to compute an orthogonal drawing of a plane 4-graph with the minimum number of bends. The term “plane” indicates that the input graph comes together with a planar embedding, which must be preserved by the drawing (i.e., the drawing must have the same set of faces as the input graph). In this paper we positively answer the question above for the widely-studied class of series–parallel graphs. Our linear-time algorithm is based on a characterization of the planar series–parallel graphs that admit an orthogonal drawing without bends. This characterization is given in terms of the orthogonal spirality that each type of triconnected component of the graph can take; the orthogonal spirality of a component measures how much that component is “rolled-up” in an orthogonal drawing of the graph.

Control laws synthesis for timed event graphs subject to generalised marking constraints by Min-Plus algebra: application to cluster tools

This paper discusses the problem of control laws for Discrete Event Dynamic Systems (DEDSs) represented by Timed Event Graphs (TEGs) under Generalized Marking Constraints (GMCs). The behaviour of TEGs is described using linear equations while GMCs are expressed by weighted inequalities in Min-Plus algebra. We formulate the problem in terms of control linear Min-Plus models. Hence, an algebraic method to calculate control laws ensuring the respect of GMCs is suggested. And once sufficient conditions are satisfied, we propose casual feedbacks to guarantee these marking specifications. Besides, the proposed control strategy is applied to a dual-armed cluster tool, a well-known industrial application.

A double attention graph network for link prediction on temporal graph

Many data in the real world can be abstracted into the format of dynamic graph, and predicting the connection relationship between nodes in graph is a problem that is often faced in the researching. With the development of deep learning techniques, Graph Neural Networks(GNNs) have been widely used to solve graph-related problems and achieved great results. However, most of the works focus on static graph, or applied the method of static graph to coarse grained discrete time graph after slice dynamic graph into snapshot. As a kind of data that incorporates all the time process information, temporal graph contains a variety of information that worth to be explored. Several works have been done for temporal graph data, but there are still shortcomings. We believe that when researching the evolution of dynamic graph, the influence of the surrounding environment on each node in local time and space is decisive for the properties of the node, which has not been considered in the previous works. Therefore, we propose a novel general model: Double Attention Temporal Graph Network(DATGN). Through an activity based sampling algorithm, the significant nodes in the local time-structure space that associated with each node are sampled and generated as sequence, then the global information of node is aggregated through a global attention network. After which the information of the sampled sequences is aggregated by a local attention network that does not depend on edge relations, and the final representation vector of the node is obtained to make the prediction. In the experiment, we selected three data sets from different knowledge domains and compared them with the current state-of-art models. In the transductive and inductive link prediction tasks, DATGN both achieved the best results in terms of accuracy and operational efficiency than other baseline models, and we discussed the reasons of this improvement. The experiment results demonstrate that the local spatial–temporal network layer can capture the evolutionary pattern of the time sequence and improve the accuracy of link prediction.

Systems thinking activities used in K-12 for up to two decades

Infusing systems thinking activities in pre-college education (grades K-12) means updating precollege education so it includes a study of many systemic behavior patterns that are ubiquitous in the real world. Systems thinking tools include those using both paper and pencil and the computer and enhance learning in the classroom making it more student-centered, more active, and allowing students to analyze problems that have been heretofore beyond the scope of K-12 classrooms. Students in primary school have used behavior over time graphs to demonstrate dynamics described in story books, like the Lorax, and created stock-flow diagrams to describe what was needed to make a garden flourish. Middle school students have created larger stock-flow diagrams to study how composting helps to reduce pollution and have created small simulations to study population dynamics and the spread of epidemics. High school students have created/used numerous computer models to study systemic problems in mathematics, physical science, physics, biology, environmental science, global studies, and history. Some high schools developed modeling courses allowing students to create System Dynamics computer models to study problems of their choice, write technical papers explaining their models, and present their models and model results to an audience. This paper contains explanations of some of the systems thinking lessons that have been used with precollege students, some for just 5–6 years (especially the primary and middle school examples), others (especially the mathematics and system dynamics model courses for high school students) for decades.

String representation of trivalent 2-stratifolds with trivial fundamental group

We give a Python program that is capable of computing and printing all distinct trivalent simple connected 2-stratifold graphs (see Gómez-Larrañaga et al. in Topol Proc 52:329–340, 2018) up to a chosen number of white vertices. Our algorithm uses the three basic operations described on (Gómez-Larrañaga et al. in Bol Soc Mat Mex 26:1301–1312, 2020) to construct new graphs from a set of graphs. We iterate this process to construct all the desired graphs. The algorithm includes an optimization that reduces repetitions at generating the graphs, this is done by recognizing equivalent white vertices of a graph under automorphism. We use a variation of the AHU algorithm to identify those vertices and as well to distinguish isomorphic graphs in linear time. The returned string from the AHU algorithm is also used as a hashing function to search for repeated graphs in amortized constant time.

Dynamic Spatiotemporal scheduling of hull parts under complex constraints in shipbuilding workshop

ABSTRACT The extremely competitive shipbuilding sector is going through a digital transformation in the context of Industry 4.0. The spatiotemporal problem has gained considerable attention during hull construction, particularly for blocks. There are fewer studies on intermediate products at lower levels, such as the dynamic spatiotemporal scheduling for hull parts under complex constraints in subassembly workshops. Therefore, this paper proposes a multi-queue two-level optimization method along with a dynamic spatiotemporal scheduling model based on the multi-directed acyclic graph (multi-DAG) and queueing theory. Firstly, a mathematical description of the complex constraints imposed by processing tasks and various resource types is provided. Then, a dynamic spatiotemporal scheduling model and an objective function are proposed to minimize the average time for parts in the system. At the sequencing level, different priority determination methods for queue events based on an improved genetic algorithm and the contribution ratio for release space strategy are proposed. At the service level, the allocation strategies for space and workers are constructed to improve utilization. Finally, several simulation experiments are conducted using the data gathered from the workshop. The proposed method is compared with the heuristic rules and combination rules using several queueing indicators and the space utilization vs. time graphs.

Tocodynamometric study of uterine contractions during parturition in queens.

Tocodynamometry is an approach in small animal obstetrics to diagnose initiation, progress, completion, and problems related to parturition in queens. The present study was conducted to monitor uterine contractions using tocodynamometry at full term pregnancy and during queening and to correlate uterine contractile pattern with progression of queening. Once the signs of impending parturition were noted, the pressure transducer of tocodynamometer was placed on the lateral abdomen to monitor queening. The recordings were plotted as graph of time against pressure in mmHg. Uterine contractile patterns were assessed in order to study their relation to the progression of queening. The initial uterine contractions had a baseline and peak pressures of 7-20 and 15-36.5 mmHg with mean time interval and frequency of 5.53 min and 1.91 peaks per 10 min, respectively, with mean change of amplitude of 15.35 mmHg. The progressive baseline and peak pressures of 15-32.5 and 24-62.3 mmHg increased to 20-55 and 30-89.2, respectively, with an increase in mean frequency from 3.88 to 5.25 peaks per 10 min and increase in mean change of amplitude from 25.21 to 33.87 mmHg, followed by decrease in mean time interval from 2.82 to 1.91 min in expulsive uterine contractions. Monitoring uterine contractions using tocodynamometer helps in understanding progression of queening and problems that may rise during parturition. It was observed that during queening, baseline and peak pressures of uterine contractions remain the same, irrespective of presentation of kittens at birth.

Simulation-based optimization of product integrations in line-less assembly systems based on digital twins

Line-less assembly systems enable increased resilience against uncertain events such as frequent integration of new product variants, material unavailability, personnel shortages and station failures. This can be achieved through the resolution of a general takt time, redundant workstations and flexible precedence graphs. Benefits arise in particular for use cases with a high number of product variants, parallel assembly of various products, and frequent integration of new products and product variants. This work presents a service-based framework for decision-support for product integrations in line-less assembly systems to meet these challenges. Based on a standardized data model serving as a basis for the digital twin, simulation models for system configurations are created automatically to reduce manual planning effort.The approach of simulation-based optimization is used to find feasible scenarios with least reconfiguration effort. Within the simulation, stochastic influences such as station failures and reactive scheduling decisions can be considered. The developed framework is verified by implementing all modules and a specific optimization model for the automated generation of scenarios after integrating a new product into the line-less assembly system. It can be shown, that recommended actions for adding, removing, or reconfiguring workstations are generated to support a production planner.

Experimental study on the behavior of polyamide multifilament subject to impact loads under different soaking conditions

This article studies the mechanical characterization of impact loads on polyamide fibers. Using synthetic ropes in mooring systems, these are subject to static loads, but dynamic loads are also expected. One of the dynamic loads that can occur on cables are sudden loads, which makes the analysis of impact loads important. In this study, impact cycles were applied to polyamide multifilaments until rupture with different impact masses, and considering the conditions: dry, after 6 hours of immersion in water and after 24 hours of immersion in water. The analysis of the immersed conditions allows us to interpret the plasticizing effect that moisture exerts in polyamide, through loss stiffness in the rupture test. The results show that the increase in immersion time represents decrease in the breaking strength, and also in the resistance to impact cycles. A curve parameterization is proposed that relates the number of impact cycles and the percentage of Yarn Break Load used in the impact, getting through the coefficient of determination the best model. For force versus time graphs, obtained in each impact cycle, the energy dissipation in the multifilament can be observed in two main mechanisms: the first is the elastic deformation in form of ricochets, the second is the plastic deformation by stretching/elongation. The force-time graphs of impact cycles and the number of impact cycles to failure are measures that show performance for impact dynamic loads. Attention should be the plasticizing effect caused by water, as it reduces the static and dynamic mechanical strength of polyamide.

Quality monitoring of resuscitative endovascular balloon occlusion of the aorta using cumulative sum analysis in Korea: a case series.

Resuscitative endovascular balloon occlusion of the aorta (REBOA) is a state-of-the-art lifesaving procedure. However, due to its high mortality and morbidity, including ischemia and reperfusion injury, well-trained medical staff and effective systems are needed. This study was conducted to investigate the learning curve for REBOA in Korea. To monitor this learning curve, we used cumulative sum (CUSUM) analysis and graphs of mortality and aortic occlusion time within 60, 90, and 120 minutes for consecutive patients. The procedures performed between July 2017 and June 2021 were divided into pre-trauma center (pre-TC; July 2017-February 2020) and TC (February 2020-June 2021) periods. REBOA was performed for 31 consecutive patients with trauma. The pre-TC (n=12) and TC (n=19) periods did not differ significantly with regard to Injury Severity Score, age, injury mechanism, initial systolic blood pressure, prehospital cardiopulmonary resuscitation (CPR), or CPR in the emergency department. At the 17th consecutive patient during the TC period, CUSUM failure graphs for mortality and aortic occlusion time exhibited a downward inflection, indicating an improvement in performance. The mortality and aortic occlusion time of REBOA improved, and these parameters can be monitored using CUSUM analysis at the hospital level.

197. The Clinical Impact of Early Detection of ESBL-Producing Enterobacterales with PCR-Based Blood Culture Assays

Abstract Background Bloodstream infections (BSIs) due to extended spectrum beta-lactamase (ESBL) producing Enterobacterales can cause significant morbidity and mortality. The first-line treatment for these is a carbapenem. ESBL-encoding genes such as blactx-m can be detected by ePlex® Blood Culture Identification (BCID) panels by utilizing polymerase chain reaction (PCR)-based techniques. Our primary objective was to assess the impact of BCID on time to appropriate therapy; secondary objectives were to assess the clinical impact on mortality, 30-day readmission, and length of stay (LOS). Methods This study was a retrospective multicenter observational study of hospitalized patients with ESBL-producing Enterobacterales positive blood cultures. Patients with ESBLs identified in the final blood culture by conventional methods pre-ePlex® BCID (Jan 1 to Sep 30, 2019) were compared to patients with ESBLs identified by ePlex® BCID (Jan 1 to Sep 30, 2021). Patients who expired or were discharged prior to Gram stain results were excluded. Time to appropriate therapy was defined as the time from Gram stain result to ordering a carbapenem. In-hospital mortality, 30-day readmission rate, and LOS were analyzed for each cohort. T-test and Chi-square test were performed. Inclusion and exclusion criteria for pre-ePlex BICD (2019) and post-ePlex BCID (2021) groups Results ESBL BSIs in 503 patients across 11 hospitals were identified and a total of 281 patients (pre-ePlex® BCID n=130; ePlex® BCID n=151) were included. A significant decrease in the median time to carbapenem order was observed (44.5 hours in the pre-ePlex® BCID group vs. 8 hours in the ePlex® BCID group (p< 0.001)). No significant difference was observed in in-hospital mortality, 30-day readmissions, or LOS. The mortality rates were 6.9% and 6.6%, the 30-day readmission rates were 7.7% and 12.6%, and the LOS were 8 days and 10 days, respectively in the pre- and post-ePlex® BCID groups. Comparison of outcome measure results between the pre-ePlex BICD (2019) and post-ePlex BCID (2021) groups. Box and whisker graphs for time from Gram stain result to ordering carbapenems (hours) Conclusion Implementing ePlex® BCID to detect ESBL BSIs decreased the median time to escalation to carbapenems by 36 hours. Despite the decrease in time to appropriate therapy, no change in in-hospital mortality, 30-day readmission, and LOS was observed. Disclosures All Authors: No reported disclosures.

A case study on an innovative seismic performance evaluation procedure for irregular RC buildings

Assessing the structural capacity of an irregular structure requires an appropriate and robust representation of the analytical model. Conventional procedures are flawed because they lack the reversal effects of higher modes. Therefore, more robust procedures have been tested to accurately capture the capacity of irregular building-type structures. Some of these may be classified as adaptive pushover techniques in which the lateral load vector is revised instantaneously. These adaptive techniques generally refer to SRSS as a modal combination rule to superpose the maximum values of the modal quantities. However, this results in an overestimated capacity value. Furthermore, the reversal effects of the modes are not included in the evaluation process. Hence, in this research, a force-based adaptive multimode pushover method was tested on a plan-irregular building using a code developed and implemented into ZEUS-NL. First, an eigenvalue analysis was used to obtain the modal quantities of the considered building, and subsequently, the modal story shear forces were obtained using the modal story forces. Furthermore, the lateral load patterns were calculated at each iterative step. Finally, the structure was pushed using this calculated lateral load pattern up to a previously calculated target displacement value using a three-step adaptive pushover analysis. The first step is to run an adaptive single-mode pushover analysis considering the first mode. After comparing the fundamental period with a constant threshold limit, it is then decided whether to run an adaptive multimode pushover analysis considering the first two modes or to run two adaptive multimode pushover analyses considering the contribution of both the second and third modes. Neither SRSS nor CQC was used to obtain the maximum response quantities; instead, the use of the envelope response is suggested. After evaluating the capacity diagram of the considered building, the story drifts were determined. The outcomes of the adaptive pushover analyses were later checked with the nonlinear dynamic results. It can be concluded that the result of the tested procedure is in good correlation with the dynamic results. Furthermore, the top displacement–time traces were plotted to check which ground motion record reveals the maximum values. Using the ground motion record, interstory drift ratio–time graphs were developed for each story level and compared with the code limit. Base shear–time history traces were then obtained and compared with the tested FAP. Lastly, story shear–interstory drift relations were plotted to investigate the absorbed energy level as an indicator of damage. As a result, for irregular RC structures, the tested procedure was found to be more accurate. However, in its current form, the procedure reflects the seismic behavior of irregular midrise buildings.

Modeling mousetrap car motion with polynomial functions of time

Spring powered cars speed up and then slow down. This paper presents three different methods for modelling this motion. These models express the car’s position and velocity as polynomial functions of time. They can be constructed from only one distance and two time measurements. Mousetrap cars are ideal candidates to be modelled in this way because it is obvious when the spring stops acting to accelerate these cars. This paper presents a spreadsheet which generates positive vs. time and velocity vs. time graphs using these polynomial functions. Students can use this spreadsheet to learn more about the motion of their cars. This tool also enables students to view and evaluate several different representations of that motion.

Extending the Chemistry of Reaction between BODIPY and Cyanide Ions: An Application in Selective Sensing of Fluoride and Cyanide Ions.

A novel colorimetric BODIPY-based probe for selective detection of fluoride and cyanide has been developed. The color of the solution significantly changes upon addition of fluoride and cyanide ions with detection limits of 2.2 × 10-7 and 1.8 × 10-7 M calculated by UV-vis absorption method for F- and CN- respectively. An unprecedented phenomenon about the interaction of cyanide ions with the probe was discovered which has not been reported yet. The green color of the paper strip in the presence of cyanide ions changes with time. This observation indicates that unlike fluoride, the cyanide ion interaction with the probe is beyond mere deprotonation of the phenolic group rather envisaged as nucleophilic addition reaction. The phenomenon was also observed in the solution phase and subsequently the reaction order and rate constant of the reaction were determined from absorption versus time graph which were found to be first order and 0.3465 s-1 respectively. The emission spectra also showed different behavior of interaction with time for the two ions. The rate of the reaction was found to be independent of the solvent polarity. The plausible mechanism of the reaction between cyanide and fluoride ions with the probe was proposed based on 1H NMR titration experiments and mass spectrometry.

Social engagement intervention increases dorsal attention network functional connectivity in socially‐isolated older‐old adults: I‐CONECT

AbstractBackgroundSocial engagement represents a promising intervention to improve or maintain cognitive function, however the neurobiological mechanism through which social engagement impacts cognitive aging remains unclear. Large‐scale cognitive brain networks facilitate cognitive function and represent a set of likely candidates for being affected by social engagement interventions.Using resting‐state functional magnetic resonance imaging (rs‐fMRI) data from the Internet‐Based Conversational Engagement Clinical Trial (I‐CONECT), we investigated the effect of social engagement intervention on functional connectivity within large‐scale cognitive brain networks, specifically the default mode network (DMN), salience network (SAL), frontoparietal network (FPN), and dorsal attention network (DAN).MethodDue to COVID‐19 pandemic, out of 186 randomized participants in the trial, 15 participants were able to complete rs‐fMRI both at baseline and six‐month follow‐up: six participants from the intervention arm (five women; mean age±SD: 79.0±3.8 years) and nine participants from the control arm (six women; 81.2±5.2 years). Nine participants had mild cognitive impairment; six had normal cognition.Intervention effect was calculated in separate regression models for within‐network connectivity of each brain network, with intervention group predicting connectivity at six‐month follow‐up, controlling for baseline connectivity, age, sex, site, and in‐scanner motion.ResultIn examining the intervention effect on DAN connectivity, the overall regression model was statistically significant (R2= 0.867, p = 0.004; Table 1): The experimental group had significantly higher DAN connectivity at follow‐up (b = 0.25, 95% CI: 0.03 – 0.47, p = 0.031) compared with the control group, (for group x time graph using residualized connectivity values, see Figure 1). Overall regression models for DMN, SAL, and FPN networks were not statistically significant (p > 0.1).ConclusionAs social engagement interventions emerge as an effective approach to improving cognition in older adults, this work provides novel insight into the impact of such an intervention on large‐scale cognitive brain network connectivity. Specifically, participants who underwent the intervention showed greater DAN connectivity at follow‐up than control participants. The voluntary directed attention facilitated by the DAN is an integral part of social engagement, and this work suggests that DAN merits additional focus in investigations of social behavior in cognitive aging. These findings must be interpreted with caution, due to small sample size.