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There is a growing interest in current medical research to develop personalized treatments using a molecular‐based approach. The broad goal is to implement a more precise and targeted decision‐making process, relative to traditional treatments based primarily on clinical diagnoses. Specifically, we consider patients affected by Acute Myeloid Leukemia (AML), an hematological cancer characterized by uncontrolled proliferation of hematopoietic stem cells in the bone marrow. Because AML responds poorly to chemotherapeutic treatments, the development of targeted therapies is essential to improve patients' prospects. In particular, the dataset we analyze contains the levels of proteins involved in cell cycle regulation and linked to the progression of the disease. We evaluate treatment effects within a causal framework represented by a Directed Acyclic Graph (DAG) model, whose vertices are the protein levels in the network. A major obstacle in implementing the above program is represented by individual heterogeneity. We address this issue through a Dirichlet Process (DP) mixture of Gaussian DAG‐models where both the graphical structure as well as the allied model parameters are regarded as uncertain. Our procedure determines a clustering structure of the units reflecting the underlying heterogeneity, and produces subject‐specific estimates of causal effects based on Bayesian Model Averaging (BMA). With reference to the AML dataset, we identify different effects of protein regulation among individuals; moreover, our method clusters patients into groups that exhibit only mild similarities with traditional categories based on morphological features. [ABSTRACT FROM AUTHOR]

Specifying causal models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the causal structures; however, such models have not been previously published. The objective of this study was to develop and evaluate a directed acyclic graph (DAG) diagraming metal mixture exposure and cardiometabolic outcomes. We conducted a literature search to develop the DAG of metal mixtures and cardiometabolic outcomes. To evaluate consistency of the DAG, we tested the suggested conditional independence statements using linear and logistic regression analyses with data from the San Luis Valley Diabetes Study (SLVDS; n=1795). We calculated the proportion of statements supported by the data and compared this to the proportion of conditional independence statements supported by 1,000 DAGs with the same structure but randomly permuted nodes. Next, we used our DAG to identify minimally sufficient adjustment sets needed to estimate the association between metal mixtures and cardiometabolic outcomes (i.e., cardiovascular disease, fasting glucose, and systolic blood pressure). We applied them to the SLVDS using Bayesian kernel machine regression, linear mixed effects, and Cox proportional hazards models. From the 42 articles included in the review, we developed an evidence-based DAG with 74 testable conditional independence statements (43 % supported by SLVDS data). We observed evidence for an association between As and Mn and fasting glucose. We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health. [ABSTRACT FROM AUTHOR]

Zero-inflated count data arise in a wide range of scientific areas such as social science, biology, and genomics. Very few causal discovery approaches can adequately account for excessive zeros as well as various features of multivariate count data such as overdispersion. In this paper, we propose a new zero-inflated generalized hypergeometric directed acyclic graph (ZiG-DAG) model for inference of causal structure from purely observational zero-inflated count data. The proposed ZiG-DAGs exploit a broad family of generalized hypergeometric probability distributions and are useful for modeling various types of zero-inflated count data with great flexibility. In addition, ZiG-DAGs allow for both linear and nonlinear causal relationships. We prove that the causal structure is identifiable for the proposed ZiG-DAGs via a general proof technique for count data, which is applicable beyond the proposed model for investigating causal identifiability. Score-based algorithms are developed for causal structure learning. Extensive synthetic experiments as well as a real dataset with known ground truth demonstrate the superior performance of the proposed method against state-of-the-art alternative methods in discovering causal structure from observational zero-inflated count data. An application of reverse-engineering a gene regulatory network from a single-cell RNA-sequencing dataset illustrates the utility of ZiG-DAGs in practice. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As the number of variables under study increases, the graphical approach may become impractical, and the clarity of the representation is lost. Clustering of variables is a natural way to reduce the size of the causal diagram, but it may erroneously change the essential properties of the causal relations if implemented arbitrarily. We define a specific type of cluster, called transit cluster, that is guaranteed to preserve the identifiability properties of causal effects under certain conditions. We provide a sound and complete algorithm for finding all transit clusters in a given graph and demonstrate how clustering can simplify the identification of causal effects. We also study the inverse problem, where one starts with a clustered graph and looks for extended graphs where the identifiability properties of causal effects remain unchanged. We show that this kind of structural robustness is closely related to transit clusters. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high routing algorithm complexity, low QoS flow satisfaction and single link failure in current software-defined network (SDN) architecture, a multi-constraint QoS dual-path routing optimization algorithm based on software-defined network (SDN_ MCQDP) is proposed. The controller is used to obtain the global network state information, and generate a directed acyclic graph based on the destination node. In the multi-constraint QoS routing stage, the multi-constraint problem is transformed into a linear programming problem by the Lagrangian relaxation dual algorithm. The reverse link is used to delete redundant dual-path links that meet multi-constraint QoS and ensure data transmission after link failure. The algorithm is simulated and analyzed from the aspects of routing calculation time, link utilization, and QoS flow satisfaction. The results show that, compared with MODLARAC, QT, RMCDP_RD, and H_MCOP algorithms, SDN_MCQDP can effectively reduce the transmission delay and route calculation time, improve the link utilization, and still meet the QoS requirements after link failure. [ABSTRACT FROM AUTHOR]

This paper presents the research undertaken with the goal of designing a consensus algorithm for cryptocurrencies with less latency than the current state-of-the-art while maintaining a level of throughput and scalability sufficient for real-world payments. The result is Nero, a new deterministic leaderless byzantine consensus algorithm in the partially synchronous model that is especially suited for Directed Acyclic Graph (DAG)-based cryptocurrencies. In fact, Nero has a communication complexity of O( n 3 ) and terminates in two message delays in the good case (when there is synchrony). The algorithm is shown to be correct, and we also show that it can provide eventual order. Finally, some performance results are given based on a proof of concept implementation in the Rust language. [ABSTRACT FROM AUTHOR]

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Motivated by a multimodal neuroimaging study for Alzheimer's disease, in this article, we study the inference problem, that is, hypothesis testing, of sequential mediation analysis. The existing sequential mediation solutions mostly focus on sparse estimation, while hypothesis testing is an utterly different and more challenging problem. Meanwhile, the few mediation testing solutions often ignore the potential dependency among the mediators or cannot be applied to the sequential problem directly. We propose a statistical inference procedure to test mediation pathways when there are sequentially ordered multiple data modalities and each modality involves multiple mediators. We allow the mediators to be conditionally dependent and the number of mediators within each modality to diverge with the sample size. We produce the explicit significance quantification and establish theoretical guarantees in terms of asymptotic size, power, and false discovery control. We demonstrate the efficacy of the method through both simulations and an application to a multimodal neuroimaging pathway analysis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis – cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) – we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed. This study examined the cross-sectional and longitudinal network structures of depression and post-traumatic disorder symptoms using undirected and directed methods. The most central depressive symptoms included sadness and depressed mood, whereas the most central post-traumatic disorder symptoms changed from irritability and hypervigilance to difficulty concentrating and avoidance of reminders over time. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. [ABSTRACT FROM AUTHOR]

A central question in high-dimensional mediation analysis is to infer the significance of individual mediators. The main challenge is that the total number of potential paths that go through any mediator is super-exponential in the number of mediators. Most existing mediation inference solutions either explicitly impose that the mediators are conditionally independent given the exposure, or ignore any potential directed paths among the mediators. In this article, we propose a novel hypothesis testing procedure to evaluate individual mediation effects, while taking into account potential interactions among the mediators. Our proposal thus fills a crucial gap, and greatly extends the scope of existing mediation tests. Our key idea is to construct the test statistic using the logic of Boolean matrices, which enables us to establish the proper limiting distribution under the null hypothesis. We further employ screening, data splitting, and decorrelated estimation to reduce the bias and increase the power of the test. We show that our test can control both the size and false discovery rate asymptotically, and the power of the test approaches one, while allowing the number of mediators to diverge to infinity with the sample size. We demonstrate the efficacy of the method through simulations and a neuroimaging study of Alzheimer's disease. A Python implementation of the proposed procedure is available at . [ABSTRACT FROM AUTHOR]

Network intrusion detection plays a vital role in information network security protection. To solve the deficiency of the feature dimension reduction and the detection performance, we propose a novel intrusion detection model, referred to as DSAE-PSOCNN. The proposed model is a deep learning model which fuses with the improved Sparse Auto-Encoder (SAE) and optimization of Convolutional Neural Network (CNN) based on Particle Swarm Optimization (PSO). The intrusion data has problems such as high feature dimension and noise data, which may cause overfitting. The model of DSAE designs SAE based on Directed Acyclic Graph (DAG) structure to solve the above problems. It extracts differentially the characteristics of each attack type by analytic hierarchy process and gets the high correlation features. The hyper-parameters of CNN are optimized by using PSO algorithm in PSOCNN model to select independently the best CNN structure without the guidance of experience. Finally, the excellence of the proposed model is verified on the CIC-IDS2017 dataset. DSAE-PSOCNN model achieves an accuracy of 98.6% and it compares with the other three models. We conclude that DSAE-PSOCNN model outperforms the comparative models in the precision and recall rate. The suggested model provides an effective solution to the feature dimension reduction. [ABSTRACT FROM AUTHOR]

Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a posterior estimation in the Bayesian inference framework is used as a score function for TVDBN structure evaluation, and the alternating direction method of multipliers (ADMM) with L-BFGSB algorithm is used for optimal structure learning. Thorough simulation studies and a real case study are carried out to verify our proposed method's efficacy and efficiency. [ABSTRACT FROM AUTHOR]

• A total of 83 studies on Philosophy for Children (P4C) were reviewed. • Based on the studies a directed acyclic graph (DAG) was created. • The DAG highlights the variables and causal relations that affect the effectiveness of P4C interventions. • The study provides instructions on how to use the DAG for future research and how to create new DAGs from systematic reviews. Philosophy for Children is an educational program designed to cultivate critical, creative, and caring thinking. Despite empirical research since the 1970s, there is no unified theoretical model that explains the causal structures behind the program's effectiveness. To address this gap, our study seeks to answer two key questions: (i) What variables are identified in empirical research into the effectiveness of Philosophy for Children? (ii) Which causal relationships can be postulated between these variables? Based on a review of seven published systematic reviews and meta-analyses and 83 empirical studies, we construct a directed acyclic graph (DAG) that illuminates the variables and their causal relations which impact the effectiveness of Philosophy for Children. Additionally, our study provides a detailed description on how to use this novel methodology to create a causal model from a systematic analysis of empirical research. [ABSTRACT FROM AUTHOR]

In distributed computing, the uncertainty is the most challenging issue which is caused by the asynchrony of distributed entities’ communication and many other reasons such as geographical scattering of distributed entities, their mobil[1]ity, and etc. In this paper, IOTA, a DAG based Distributed ledger technology is used in order to cope with asynchronous communications and uncertainty. Moreover, IOTA private network is chosen to deal with other mentioned problems inside distributed computing. As a case study, a system is presented which could be implemented inside Tehran Polytechnic university to bring computational power of computers with low resources together in order to solve many problems which can be solved in distributed computing manner.

Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.

We introduce the theoretical foundations of the Tangle 2.0, a probabilistic leaderless consensus protocol based on a directed acyclic graph (DAG) called the Tangle. The Tangle naturally succeeds the blockchain as its next evolutionary step as it offers features suited to establish more efficient and scalable distributed ledger solutions. Consensus is no longer found in the longest chain but on the heaviest DAG, where PoW is replaced by a stake- or reputation-based weight function. The DAG structure and the underlying Reality-based UTXO Ledger allow parallel validation of transactions without the need for total ordering. Moreover, it enables the removal of the intermediary of miners and validators, allowing a pure two-step process that follows the propose-vote paradigm at the node level and not at the validator level. We propose a framework to analyse liveness and safety under different communication and adversary models. This allows providing impossibility results in some edge cases and in the asynchronous communication model. We provide formal proof of the security of the protocol assuming a common random coin.

Background: Post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) are two highly comorbid psychological outcomes commonly studied in the context of stress and potential trauma. In Hubei, China, of which Wuhan is the capital, residents experienced unprecedented stringent lockdowns in the early months of 2020 when COVID-19 was first reported. The comorbidity between PTSD and MDD has been previously studied using network models, but often limited to cross-sectional data and analysis. Objectives: This study aims to examine the cross-sectional and longitudinal network structures of MDD and PTSD symptoms using both undirected and directed methods. Methods: Using three types of network analysis – cross-sectional undirected network, longitudinal undirected network, and directed acyclic graph (DAG) – we examined the interrelationships between MDD and PTSD symptoms in a sample of Hubei residents assessed in April, June, August, and October 2020. We identified the most central symptoms, the most influential bridge symptoms, and causal links among symptoms. Results: In both cross-sessional and longitudinal networks, the most central depressive symptoms included sadness and depressed mood, whereas the most central PTSD symptoms changed from irritability and hypervigilance at the first wave to difficulty concentrating and avoidance of potential reminders at later waves. Bridge symptoms showed similarities and differences between cross-sessional and longitudinal networks with irritability/anger as the most influential bridge longitudinally. The DAG found feeling blue and intrusive thoughts the gateways to the emergence of other symptoms. Conclusions: Combining cross-sectional and longitudinal analysis, this study elucidated central and bridge symptoms and potential causal pathways among PTSD and depression symptoms. Clinical implications and limitations are discussed.

The area of study of motion control to Wireless Sensor Networks (WSN) was difficult. This study examines the problem of vehicular networks in the previously implemented IETF pathing algorithm standard to reduce power electricity WSN, the Routing Algorithm of Low power and Lossy Networks (RAL). RAL lacks motion capabilities because it was developed at the origin of static networks.In this study, we discuss the issue and suggest CoRAL as a RAL change that supports portability and should be based on the Corona mechanism.The researchers carried out an in-depth simulation analysis using the Contiki/Cooja emulator and compared the results with those of the traditional RAL emulator to demonstrate the efficacy of Co-RAL.We investigated the number of roots of Directed Acyclic Graphs (DAG), packet transmission rate, or network speed affecting system performance. According to the simulation results, Co-RAL reduces final time to 2.5 s, median power usage 50%, but alsothe packet delivery rate by 45% correlated with normal RAL.

Manufacturing scheduling improvement heuristics iterate over trial schedules to determine a satisfactory schedule. During each iteration, a performance measure (e.g. makespan) is calculated. The paper presents an efficient algorithm, Structural Perturbation Algorithm (SPA), that accelerates the calculation of the makespan. This means all scheduling improvement heuristics using SPA to calculate makespan for each trial schedule will run faster. To achieve this goal, the manufacturing system is modelled by a Directed Acyclic Graph (DAG). Schedule trials can be described as a perturbed DAG where multiple edges are added and deleted. The major contribution of this research is that SPA can handle multiple edge deletions/additions with a single pass which makes it more efficient in terms of time complexity than current approaches. SPA accomplishes this by partitioning the nodes into three regions based on the locations of the added and deleted edges. Then, SPA updates the length of the affected nodes in each region. The application of SPA is not limited to the scheduling problem. The SPA can be applied in other fields as long as the problem can be described as a Perturbed DAG. [ABSTRACT FROM AUTHOR]

Background: Observational studies suggest that ulceration is considered to be a negative prognostic factor for cutaneous melanoma. However, the impact of ulceration over different subgroups (e.g. AJCC Stage, thickness level) are controversial and its true causal effect on survival is lack of studies in the view of treating ulceration as an exposure. Objective: To explore the true causal effect of ulceration on melanoma's survival by adopting a combination of methods to discover proper adjustment set and confirming its correctness through a variety of means. Methods: A minimal sufficient adjustment set (MSAS) was found using directed acyclic graphs (DAG) to adjust the effect of causality. Sensitivity analysis was conducted to diagnose potential confounders in addition to MSAS. Coxmodels were built to analyze the causality in-depth and themodel was validated using a novel method. Lastly, stratified effects of ulceration were examined to illustrate its impact within subgroups. Results: Hazard ratio (HR) of ulceration after adjustment by MSAS variables was 1.99 (95% CI=1.88-2.09). The sensitivity analysis of propensity score matching and E-value both demonstrated that variables other than MSAS do not have great influence on ulceration and MSS relationship. The HR of ulceration in AJCC Stage, thickness level, invasion level and tumor extension were all monotonically decreased from 5.76 to 1.57, 4.03 to 1.78, 2.75 to 1.78 and 2.65 to 1.71 respectively. Conclusion: Ulceration in all subgroups were shown to have a significantly negative impact on MSS and its magnitude of effect was monotonically decreased as the disease progressed. The true effect of ulceration can be adjusted by MSAS and its correctness was validated through a variety of approaches. [ABSTRACT FROM AUTHOR]

Inadequate resources and facilities with zero latency affect the efficiencies of task scheduling (TS) and resource allocation (RA) in the fog paradigm. Only the incoming tasks can be completed within the deadline if the resource availability in the cloud and fog is symmetrically matched with them. A container-based TS algorithm (CBTSA) determines the symmetry relationship of the task/workload with the fog node (FN) or the cloud to decide the scheduling workloads (whether in the fog or a cloud). Furthermore, by allocating and de-allocating resources, the RA algorithm reduces workload delays while increasing resource utilization. However, the unbounded cloud resources and the computational difficulty of finding resource usage have not been considered in CBTSA. Hence, this article proposes an enhanced CBTSA with intelligent RA (ECBTSA-IRA), which symmetrically balances energy efficiency, cost, and the performance-effectiveness of TS and RA. Initially, this algorithm determines whether the workloads are accepted for scheduling. An energy-cost–makespan-aware scheduling algorithm is proposed that uses a directed acyclic graph (DAG) to represent the dependency of tasks in the workload as a graph. Workloads are prioritized and selected for the node to process the prioritized workload. The selected node for processing the workload might be a FN or cloud and is decided by an optimum efficiency factor that trades off the schedule length, cost, and energy. Moreover, a Markov decision process (MDP) was adopted to allocate the best resources using the reinforcement learning scheme. Finally, the investigational findings reveal the efficacy of the presented algorithms compared to the existing CBTSA in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

Border Gateway Protocol (BGP), the default inter-domain routing protocol on the Internet, lacks inherent mechanisms to validate the prefix ownership and integrity of inter-domain routes exchanged among multiple domains, resulting in BGP hijack attacks. Conventional security approaches such as RPKI and BGPSec are centralized and complex by nature, and require changes to existing routing infrastructure. In recent times, blockchain based solutions are proposed for validating the routing information exchanged across different domains in a decentralized manner. However, because of lower transaction throughput, longer confirmation time and huge storage overhead, the existing solutions are not suitable for validating the routing information exchanged among domains, where a large number of prefix allocations and BGP route advertisements are recorded as transactions on the blockchain. This work proposes an Inter-domain Prefix and Route Validation (IPRV) framework for validating prefix ownership and inter-domain routes exchanged among the domains on the Internet. IPRV leverages (a) Fast and Scalable Directed Acyclic Graph-based Distributed Ledger (FSD2L) to record transactions corresponding to the prefix allocations and BGP route advertisements made by different domains on the Internet, and (b) Route Validation Nodes (RVNs) which maintain FSD2L to provide prefix and route validation services to the BGP routers within a domain. IPRV framework is implemented and verified using docker containers, and the simulations performed on large inter-domain networks showed that the proposed IPRV framework using RVNs and FSD2L achieves high transaction throughput while minimizing the storage consumption of the FSD2L. [ABSTRACT FROM AUTHOR]

There are 3 common variable types in orthopedic research-confounders, colliders, and mediators. All 3 types of variables are associated with both the exposure (eg, surgery type, implant type, body mass index) and outcome (eg, complications, revision surgery) but differ in their temporal ordering. To reduce systematic bias, the decision to include or exclude a variable in an analysis should be based on the variable's relationship with the exposure and outcome for each research question. In this article, we define 3 types of variables with case examples from orthopedic research. Please visit the followinghttps://youtu.be/V-grpgB1ShQfor videos that explain the highlights of the article in practical terms. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput. [ABSTRACT FROM AUTHOR]

This study explores dynamic relationships among Chinese housing prices for the years 2010–2019. With monthly data from 99 major cities in China, we use the vector error correction model and directed acyclic graph to characterize contemporaneous causality among housing prices from different tiers of cities. The PC algorithm identifies the causal pattern and the LiNGAM algorithm further identifies the causal path, from which we perform innovation accounting analysis. Complex housing price dynamics are found in the price adjustment process following price shocks, which is not only dominated by the top tiers of cities. This suggests that policies on housing prices in the long run might need to be planned from a national perspective. [ABSTRACT FROM AUTHOR]

Background: Censorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan-Meier or restricted assumptions like the Cox regression model. On the other hand, Machine learning algorithms commonly rely on the high dimensionality of data and ignore the censorship attribute. In addition, these algorithms are more sophisticated to understand and utilize. We propose a novel approach based on the Bayesian network to address these issues.Methods: We proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learned the structure of the directed acyclic graph. The likelihood approach conducted parameter learning. We conducted a simulation study to assess the performance of our model in comparison with the Kaplan-Meier and Cox proportional hazard regression. We defined various scenarios according to the sample size, censoring rate, and shapes of survival and censoring distributions across time. Finally, we fit the model on a real-world dataset that includes 760 post gastrectomy surgery due to gastric cancer. The validation of the model was explored using the hold-out technique based on the posterior classification error. Our survival model performance results were compared using the Kaplan-Meier and Cox proportional hazard models.Results: The simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan-Meier, especially in the late survival times. In the real-world data, the structure of the dynamic Bayesian network model satisfied the finding from Kaplan-Meier and Cox regression classical approaches. The posterior classification error found from the validation technique did not exceed 0.04, representing that our network predicted the state variables with more than 96% accuracy.Conclusions: Our proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightforward interpretation, handling of high-dimensional data, and few assumptions. [ABSTRACT FROM AUTHOR]

Objective: The present study examines the network structure and, using Bayesian network analysis, estimates the directional pathways among symptoms of posttraumatic stress disorder (PTSD), major depressive disorder (MDD), and levels of alcohol and cannabis use. Method: A sample of 1471 adults in the United States, who reported at least one potentially traumatic event, completed the PTSD Checklist (PCL-5), Patient Health Questionnaire (PHQ-9), and the Alcohol/Cannabis Use Disorders Identification Test (AUDIT/CUDIT). A regularized partial correlation network provided estimates of symptoms clusters and connections. Directional pathways within the network were then estimated using a directed acyclic graph (DAG). Results: Symptoms clustered in theoretically consistent ways. Risky behavior demonstrated the highest strength centrality and bridge strength. Neither alcohol nor cannabis use appeared central in the network, and DAG results suggested that MDD and PTSD symptoms are more likely to lead to substance use than the other way around. Conclusions: Results suggest that cannabis use is largely connected to alcohol use. Consistent with prior research, risky behavior appeared to be the primary bridge between substance use and PTSD. The direction of associations between substance use and psychological symptoms requires further attention.

Background: Early-life factors such as preterm birth or very low birthweight (VLBW) are associated with increased cardiovascular disease risk. However, it remains unknown whether this is due to an increased risk of obesity (unhealthy central adiposity) because studies have predominantly defined obesity based on BMI, an imprecise adiposity measure. Objective: Investigate if adolescents born preterm with VLBW have a higher risk of unhealthy central adiposity compared to term-born peers. Study Design: Cross-sectional analysis of data from a prospective cohort study of 177 individuals born preterm with VLBW (<1500 g) and 51 term-born peers (birthweight ≥2500 g). Individuals with congenital anomalies, genetic syndromes, or major health conditions were excluded. Height, weight, waist circumference, skin fold thickness, and dual energy X-ray absorptiometry body composition were measured at age 14 years. We calculated BMI percentiles and defined overweight/obesity as BMI ≥85th percentile for age and sex. We estimated the preterm-term differences in overweight/obesity prevalence and adiposity distribution with multivariable generalized linear models. Results: There was no difference in small for gestational age status or overweight/obesity prevalence. Compared to term, youth born preterm with VLBW had lower BMI z-score [ β -0.38, 95% confidence limits (CL) -0.75 to -0.02] but no differences in adiposity apart from subscapular-to-triceps ratio (STR; β 0.18, 95% CL 0.08 to 0.28). Conclusions: Adolescents born preterm with VLBW had smaller body size than their term-born peers and had no differences in central adiposity except greater STR.

Causal directed acyclic graphs (DAGs) serve as intuitive tools to visually represent causal relationships between variables. While they find widespread use in guiding study design, data collection and statistical analysis, their adoption remains relatively rare in the domain of psychology. In this paper we describe the relevance of DAGs for health psychology, review guidelines for developing causal DAGs, and offer recommendations for their development. A scoping review searching for papers and resources describing guidelines for DAG development was conducted. Information extracted from the eligible papers and resources ( n  = 11) was categorised, and results were used to formulate recommendations. Most records focused on DAG development for data analysis, with similar steps outlined. However, we found notable variations on how to implement confounding variables (i.e., sequential inclusion versus exclusion). Also, how domain knowledge should be integrated in the development process was scarcely addressed. Only one paper described how to perform a literature search for DAG development. Key recommendations for causal DAG development are provided and discussed using an illustrative example.

Objectives: Hospital-acquired bloodstream infections (HA-BSI) in the intensive care unit (ICU) are common life-threatening events. We aimed to investigate the association between early adequate antibiotic therapy and 28-day mortality in ICU patients who survived at least 1 day after the onset of HA-BSI., Methods: We used individual data from a prospective, observational, multicentre, and intercontinental cohort study (Eurobact2). We included patients who were followed for ≥1 day and for whom time-to-appropriate treatment was available. We used an adjusted frailty Cox proportional-hazard model to assess the effect of time-to-treatment-adequacy on 28-day mortality. Infection- and patient-related variables identified as confounders by the Directed Acyclic Graph were used for adjustment. Adequate therapy within 24 hours was used for the primary analysis. Secondary analyses were performed for adequate therapy within 48 and 72 hours and for identified patient subgroups., Results: Among the 2418 patients included in 330 centres worldwide, 28-day mortality was 32.8% (n = 402/1226) in patients who were adequately treated within 24 hours after HA-BSI onset and 40% (n = 477/1192) in inadequately treated patients (p < 0.01). Adequacy within 24 hours was more common in young, immunosuppressed patients, and with HA-BSI due to Gram-negative pathogens. Antimicrobial adequacy was significantly associated with 28-day survival (adjusted Hazard Ratio (aHR), 0.83; 95% CI, 0.72-0.96; p 0.01). The estimated population attributable fraction of 28-day mortality of inadequate therapy was 9.15% (95% CI, 1.9-16.2%)., Discussion: In patients with HA-BSI admitted to the ICU, the population attributable fraction of 28-day mortality of inadequate therapy within 24 hours was 9.15%. This estimate should be used when hypothesizing the possible benefit of any intervention aiming at reducing the time-to-appropriate antimicrobial therapy in HA-BSI., (Copyright © 2024 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Background/objectives: Problematic online gaming and gambling tend to co-occur. The exact mechanisms underlying this phenomenon and the potential effects of gender differences remain unknown. This study aimed to identify the early clustering patterns of problematic online gaming and gambling in a community sample of young adults without a lifetime history of psychiatric treatment., Methods: Data were collected through an online survey and analyzed using partial correlations and Bayesian networks., Results: Altogether, 1441 individuals (aged 18-40 years, 51.4% females) participated in the survey. Both problematic online behaviors were weakly interrelated, suggesting that they serve as distinct constructs. Men's networks appeared to be more complex and had significantly higher global connectivity. Moreover, men and women differed with respect to the specific nodes that bridged both constructs. In men, the bridge nodes were "being criticized because of betting or being told about gambling problems", "loss of previous interests due to gaming", "deceiving other people because of gaming", and "health consequences of gambling". Among women, the bridge nodes were "feeling guilty because of gambling", "loss of previous interests because of gaming", "social consequences of gaming", and "continued gaming problems with other people". In men, the strongest edge was found between "borrowing money/selling anything to gamble" and "financial problems because of gambling", while in women, the strongest edge appeared between "betting more than afforded to be lost" and "tolerance symptoms of gambling"., Conclusions: The findings indicate that problematic online gaming and gambling tend to emerge in different ways among men and women. Therapeutic interventions should be planned considering gender differences.