Directed Acyclic Graph Approach Research Articles

Pay-for-performance and low back pain with interaction of overwork: findings from the cross-sectional Korean working conditions survey.

Pay-for-performance (PFP) is a type of incentive system where employees receive monetary rewards for meeting predefined standards. While previous research has investigated the relationship between PFP and health outcomes, the focus has primarily been on mental health. Few studies have explored the impact of PFP on specific physical symptoms like pain. Data from the Korean Working Conditions Survey (KWCS) was analyzed, encompassing 20,815 subjects with information on PFP and low back pain (LBP). The associations between types of base pay (BP) and PFP with LBP were examined using multivariate logistic regression models, taking into account a directed acyclic graph (DAG). The interaction of overtime work was further explored using stratified logistic regression models and the relative excess risk for interaction. The odds ratio (OR) for individuals receiving both BP and PFP was statistically significant at 1.19 (95% CI 1.04-1.35) compared to those with BP only. However, when the DAG approach was applied and necessary correction variables were adjusted, the statistical significance indicating a relationship between PFP and LBP vanished. In scenarios without PFP and with overtime work, the OR related to LBP was significant at 1.54 (95% CI 1.35-1.75). With the presence of PFP, the OR increased to 2.02 (95% CI 1.66-2.45). Pay-for-performance may influence not just psychological symptoms but also LBP in workers, particularly in conjunction with overtime work. The impact of management practices related to overtime work on health outcomes warrants further emphasis in research.

Inferring Causality Is Preference-Sensitive: We Need a Book of Who as Well as Why.

In multiple publications over 3 decades, most recently in The Book of Why, Judea Pearl has led what he regards as the 'causal revolution'. His central contention is that, prior to it, no discipline had produced a rigorous 'scientific' way of making the causal inferences from observational data necessary for policy and decision making. The concentration on the statistical processing of data, outputting frequencies or probabilities, had proceeded without adequately acknowledging that this statistical processing is operating, not only on a particular set of data, but on a set of causal assumptions about that data, often unarticulated and unanalysed. He argues that the arrival of the directed acyclic graph (DAG), a 'language of causation' has enabled this fundamental weakness to be remedied. We outline the DAG approach to the extent necessary to make the key point, captured in this paper's title regarding DAG's potential contribution to improved decision or policy making.

What Drives the Change in Employment in the US Logging Industry? A Directed Acyclic Graph Approach

Abstract Employment in the US logging industry has been declining over the past few decades and fell to a 20-year low following the 2008 economic recession. This study investigates the drivers of employment in the US logging industry from 2007 to 2017, using a directed acyclic graph (DAG). This approach is applied for the first time to disclose the contemporaneous causal relations among employment, wages, mechanization, production level, and product prices in the logging industry. Forecast error variance decomposition (VD) is further used to examine the long-run dynamic relationships between these variables. The results show that the product price directly affects employment and indirectly promotes employment through wages. The results of VD show that mechanization has an increasing long-term effect on employment.

What Drives the Change in Employment in the U.S. Logging Industry - a Directed Acyclic Graph Approach

What Drives the Change in Employment in the U.S. Logging Industry - a Directed Acyclic Graph Approach

Cybersecurity Enhancement of Power Trading Within the Networked Microgrids Based on Blockchain and Directed Acyclic Graph Approach

Power grid resilience, reliability, and sustainability can be improved significantly by decomposing the large grids into networked microgrids (NMGs). However, the optimal energy management problem and preserving the security in NMGs are more complicated and challenging. This paper aims to propose a secured stochastic energy management framework for NMGs based on the modified blockchain approach, utilizing the directed acyclic graph (DAG). Using the decentralized and transparent blockchain technology will help to have higher security and lower risks within the network, thus eliminating the financial fraud and cutting down the total operational cost. In order to address the issues arising in the traditional blockchain models, mainly due to the storage and high complexities of hash address calculations, this paper proposes a new modified blockchain technology based on the DAG method. Also, a novel data restoration technique is developed to provide a way to restore the data with appropriate accuracy. The unscented transform (UT) approach is employed to model the uncertainties of forecast error in hourly load demand, solar power output, and wind turbines power output. Finally, the proposed model is tested on an NMG system with four MGs, including two residential MGs (as the noncrucial loads), a commercial MG (as the intermediate level loads), and a hospital MG (as the crucial loads).

Prognostic Factors for Return to Sport After High Tibial Osteotomy: A Directed Acyclic Graph Approach

Background: High tibial osteotomy (HTO) is increasingly used in young and physically active patients with knee osteoarthritis. These patients have high expectations, including return to sport (RTS). By retaining native knee structures, a return to highly knee-demanding activities seems possible. However, evidence on patient-related outcomes, including RTS, is sparse. Also, time to RTS has never been described. Furthermore, prognostic factors for RTS after HTO have never been investigated. These data may further justify HTO as a surgical alternative to knee arthroplasty. Purpose: To investigate the extent and timing of RTS after HTO in the largest cohort investigated for RTS to date and to identify prognostic factors for successful RTS. Study Design: Case-control study; Level of evidence, 3. Methods: Consecutive patients with HTO, operated on between 2012 and 2015, received a questionnaire. First, pre- and postoperative sports participation questions were asked. Also, time to RTS, sports level and frequency, impact level, the presymptomatic and postoperative Tegner activity score (1-10; higher is more active), and the postoperative Lysholm score (0-100; higher is better) were collected. Finally, prognostic factors for RTS were analyzed using a logistic regression model. Covariates were selected based on univariate analysis and a directed acyclic graph. Results: We included 340 eligible patients of whom 294 sufficiently completed the questionnaire. The mean follow-up was 3.7 years (± 1.0 years). Out of 256 patients participating in sports preoperatively, 210 patients (82%) returned to sport postoperatively, of whom 158 (75%) returned within 6 months. We observed a shift to participation in lower-impact activities, although 44% of reported sports activities at final follow-up were intermediate- or high-impact sports. The median Tegner score decreased from 5.0 (interquartile range [IQR], 4.0-6.0) presymptomatically to 4.0 (IQR, 3.0-4.0) at follow-up (P < .001). The mean Lysholm score at follow-up was 68 (SD, ± 22). No significant differences were found between patients with varus or valgus osteoarthritis. The strongest prognostic factor for RTS was continued sports participation in the year before surgery (odds ratio, 2.81; 95% CI, 1.37-5.76). Conclusion: More than 8 of 10 patients returned to sport after HTO. Continued preoperative sports participation was associated with a successful RTS. Future studies need to identify additional prognostic factors.

FastRule: Efficient Flow Entry Updates for TCAM-Based OpenFlow Switches

With an increasing demand for flexible management in software-defined networks (SDNs), it becomes critical to minimize the network policy update time. Although major SDN controllers are now optimized for rapid network update at the control plane, there is still room for data plane optimization in terms of update time, when using TCAM-based physical SDN commodity-off-the-shelf switches. A slow update directly affects network performance and creates bottlenecks. To minimize the flow entry update time, a dependency graph, a kind of directed acyclic graph (DAG), can be used for the access management of flow entries at the switch. Thanks to the DAG, unnecessary entry movements, which are the main factor slowing down flow entry updates, can be avoided. However, existing algorithms show limitations when updates become very frequent. We propose a new flow entry update algorithm, called FastRule, that exploits a greedy strategy with an efficient data structure to accelerate flow entry update with a DAG approach. Moreover, we also adjust our algorithm for other flow table layouts to make it scalable. We elaborate on the correctness of FastRule and test our algorithm using a hardware switch. Compared with existing algorithms, the evaluation shows that our algorithm is about 100x faster than state-of-the-art solutions with a flow table of 1k size.

A Directed Acyclic Graph (DAG) Ensemble Classification Model

In this paper, a hierarchical ensemble classification approach that utilizes a Directed Acyclic Graph (DAG) structure is proposed as a solution to the multi-class classification problem. Two main DAG structures are considered: (i) rooted DAG, and (ii) non-rooted DAG. The main challenges that are considered in this paper are: (i) the successive misclassification issue associated with hierarchical classification, and (i) identification of the starting node within the non-rooted DAG approach. To address these issues the idea is to utilize Bayesian probability values to: select the best starting DAG node, and to dictate whether single or multiple paths should be followed within the DAG structure. The reported experimental results indicated that the proposed DAG structure is more effective than when using a simple binary tree structure for generating a hierarchical classification model.

Causality among Foreign Direct Investment and Economic Growth: A Directed Acyclic Graph Approach

With the aim of examining the causal structure between foreign direct investment (FDI) and economic growth, this study derives inductive causal inference using the directed acyclic graph approach, which makes no a priori causal assumptions. There are three major findings of this study. First, economic growth causes FDI inflows for developing countries, whereas FDI induces economic growth for developed countries. Second, trade is an important intermediary to facilitate the interaction between FDI and other factors. Third, the stock market is found to be an intermediary that amplifies the influence on FDI from many causal variables of FDI for developed countries.

An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining

Traditional pattern growth-based approaches for sequential pattern mining derive length-(k+1) patterns based on the projected databases of length-k patterns recursively. At each level of recursion, they unidirectionally grow the length of detected patterns by one along the suffix of detected patterns, which needs k levels of recursion to find a length-k pattern. In this paper, a novel data structure, UpDown Directed Acyclic Graph (UDDAG), is invented for efficient sequential pattern mining. UDDAG allows bidirectional pattern growth along both ends of detected patterns. Thus, a length-k pattern can be detected in [log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> k + 1] levels of recursion at best, which results in fewer levels of recursion and faster pattern growth. When minSup is large such that the average pattern length is close to 1, UDDAG and PrefixSpan have similar performance because the problem degrades into frequent item counting problem. However, UDDAG scales up much better. It often outperforms PrefixSpan by almost one order of magnitude in scalability tests. UDDAG is also considerably faster than Spade and LapinSpam. Except for extreme cases, UDDAG uses comparable memory to that of PrefixSpan and less memory than Spade and LapinSpam. Additionally, the special feature of UDDAG enables its extension toward applications involving searching in large spaces.

Maximum weight k-independent set problem on permutation graphs

Based on a Directed Acyclic Graph approach, an O(kn 2) time sequential algorithm is presented to solve the maximum weight k-independent set problem on weighted-permutation graphs. The weights considered here are all non-negative and associated with each of the n vertices of the graph. This problem has many applications in practical problems like k-machines job scheduling problem, k-colourable subgraph problem, VLSI design and routing problem.