Application of Design Structure Matrix to Simulate Surgical Procedures and Predict Surgery Duration

Background. The complexities of surgery require an efficient and explicit method to evaluate and standardize surgical procedures. A reliable surgical evaluation tool will be able to serve various purposes such as development of surgery training programs and improvement of surgical skills. Objectives. (a) To develop a modeling framework based on integration of dexterity analysis and design structure matrix (DSM), to be generally applicable to predict total duration of a surgical procedure, and (b) to validate the model by comparing its results with laparoscopic cholecystectomy surgery protocol. Method. A modeling framework is developed through DSM, a tool used in engineering design, systems engineering and management, to hierarchically decompose and describe relationships among individual surgical activities. Individual decomposed activities are assumed to have uncertain parameters so that a rework probability is introduced. The simulation produces a distribution of the duration of the modeled procedure. A statistical approach is then taken to evaluate surgery duration through integrated numerical parameters. The modeling framework is applied for the first time to analyze a surgery; laparoscopic cholecystectomy, a common surgical procedure, is selected for the analysis. Results. The present simulation model is validated by comparing its results of predicted surgery duration with the standard laparoscopic cholecystectomy protocols from the Atlas of Minimally Invasive Surgery with 2.5% error and that from the Atlas of Pediatric Laparoscopy and Thoracoscopy with 4% error. Conclusion. The present model, developed based on dexterity analysis and DSM, demonstrates a validated capability of predicting laparoscopic cholecystectomy surgery duration. Future studies will explore its potential applications to other surgery procedures and in improving surgeons’ performance and training novices.

Process Optimization for Post Disaster Reconstruction Project Based on Industrial Design Structure Matrix (DSM)

Post-disaster reconstruction projects face tighter time constraints and are in a more complex environment, making the implementation process of conventional projects unable to meet new requirements. This study decomposes the construction process and then determines the feed-forward and feedback relationship between activities in the post-disaster reconstruction environment. An information relationship diagram is established, and the relationship is transformed into a design structure matrix (DSM). Through DSM manipulation, a partitioned DSM is formed to express the activity process that is more suitable for reconstruction. This research shows that the activities sequence and content need to be changed to adapt to the reconstruction scenario, and some activities may even be canceled. Some suggestions can help construct the project faster, including closer cooperation between design and construction. The bidding scope includes design and construction and the use of more integrated project delivery methods. Finally, a reconstruction case in China illustrates the operability of analyzing and adjusting the implementation process through this framework.

Tension vector and structure matrix associated force sensitivity of a 6-DOF cable-driven parallel robot

This paper investigates the force sensitivity of 6-DOF cable-driven parallel robots (CDPRs) in order to propose a better force measurement device. Kinematics and dynamics for a CDPR of n-DOF are deduced and formulated, and algorithms for calculating the cable tension are developed. Then, by defining geometrical parameters related to the dimensions and configurations of the CDPRs, optimal methods for determining force sensitivity with respect to the structure matrix and twist vector of the 6-DOF CDPRs with two different moving platforms (i.e. a cubic-shaped, and a flat moving platform) are proposed. By using numerical examples integrated with external twists obtained from wind tunnel tests, simulations and analysis for the two type of 6-DOF CDPRs are carried out. The simulation results help identify the optimal dimensions that can be used to design 6-DOF-CDPR-based force measuring devices with high force sensitivity. Experiment validation is also conducted to verify the method proposed in this paper.

Surface Electromyography Based Core Muscle Fatigue Analysis During Repetitive Plank Using Multivariate Dimensionality Reduction Methods in Boys Aged 12-14

The aims of the study were: 1. To analyse the discriminative power of neuromuscular components for classifying the pre and post muscle fatigued states. 2. To examine whether the modification of neural recruitment strategies become more/less heterogeneous due to fatigue. 3. To research the effect of Erector Spinae (ES) muscle activity collectively with Rectus Abdominis (RA) and External Oblique (EO) muscle activity to identify the reduced spine stability during fatiguing Plank. Material and methods. Twelve boys (age – 12-14 years, height 148.75 ± 10 cm, body mass 38.9 ± 7.9 kg) participated in the study. Multivariate Discriminant Analysis (DA) and Principal Component Analysis (PCA) were applied to identify the changes in the pattern of the electromyographic signals during muscle fatigue. In DA the Wilks’ lambda, p-value, canonical correlation, classification percentage and structure matrix were used. To evaluate the component validity the standard limit for Kaiser-Meyer-Olkin (KMO) was set at ≥0.529 and the p-value of Bartlett’s test was ≤0.001. The eigenvalues ≥1 were used to determine the number of Principal Components (PCs). The satisfactory percentage of non-redundant residuals were set at ≤50% with standard value >0.05. The absolute value of average communality (x̄ h2) and component loadings were set at ≥0.6, ≥0.4 respectively. Results. Standardized canonical discriminant analysis showed that pre and post fatigued conditions were significantly different (p = 0.000, Wilks’ lambda = 0.297, χ2 = 24.914, df=3). The structure matrix showed that the parameter that correlated highly with the discriminant function was ES ARV (0.514). The results showed that the classification accuracy was 95.8% between fatigued conditions. In PCA the KMO values were reduced [0.547Pre fatigue vs. 0.264Post fatigue]; the value of Bartlett’s sphericity test was in pre χ2 = 90.72 (p = 0.000) and post fatigue χ2 = 85.32 (p = 0.000); The Promax criterion with Kaiser Normalization was applied because the component rotation was non-orthogonal [Component Correlation Matrix (rCCM) = 0.520 Pre fatigue >0.3Absolute<0.357Post fatigue]. In pre fatigue two PCs (cumulative s2 – 80.159%) and post fatigue three PCs (cumulative s2 – 83.845%) had eigenvalues ≥1. The x̄ h2 increased [0.802 Pre fatigue vs. 0.838 Post fatigue] and the percentage of nonredundant residuals reduced [50% Pre fatigue vs. 44% Post fatigue] from pre to post fatigue. Conclusions. The variability and heterogeneity increase in the myoelectric signals due to fatigue. The co-activity of antagonist ES muscle is significantly sensitive to identify the deteriorating spine stability during the fatiguing Plank. Highly correlated motor unit recruitment strategies between ES and RA, providing supportive evidence to the concept of shared agonist-antagonist motoneuron pool or “Common Drive” phenomenon during fatigue.

Application of Service Modular Design Based on a Fuzzy Design Structure Matrix: A Case Study from the Mining Industry

The development of customized service is an important way to transform and upgrade China’s mining industry. However, in practice, there remain problems, such as the slow market response speed of service providers and the contradiction between the large-scale development of service providers and the personalized service needs of service demanders. This paper uses the theory and method of service modular design to solve these problems and explores the process-based service modular design method. Service modular design depends largely on the determination of the relationship between service activities and the reasonable division of modules. However, previous research has rarely made use of modular design methods and modeling tools in the mining service context. At the same time, evaluations of the relationship between service activities relying on knowledge and those relying on experience have been inconclusive. Therefore, this paper proposes a service modularization design method based on the fuzzy relation analysis of a design structure matrix (DSM) that solves the optimal module partition scheme. Triangular fuzzy number and fuzzy evidence theory are used to evaluate and fuse the multidimensional and heterogeneous relationship between service activities, and the quantitative processing of the comprehensive relationship between service activities is carried out. On this basis, the service module structure is divided, followed by the construction of the mathematical programming model with the maximum sum of the average cohesion degree in the module and the average coupling degree between modules as the driving goal. The genetic algorithm is used to solve the problem, and the optimal module division result is obtained. Finally, taking the service modular design of SHD coal production enterprises in China as an example, the feasibility of the proposed method is verified.

CDCN: A New NMF-Based Community Detection Method with Community Structures and Node Attributes

Community discovery can discover the community structure in a network, and it provides consumers with personalized services and information pushing. It plays an important role in promoting the intelligence of the network society. Most community networks have a community structure whose vertices are gathered into groups which is significant for network data mining and identification. Existing community detection methods explore the original network topology, but they do not make the full use of the inherent semantic information on nodes, e.g., node attributes. To solve the problem, we explore networks by considering both the original network topology and inherent community structures. In this paper, we propose a novel nonnegative matrix factorization (NMF) model that is divided into two parts, the community structure matrix and the node attribute matrix, and we present a matrix updating method to deal with the nonnegative matrix factorization optimization problem. NMF can achieve large-scale multidimensional data reduction processing to discover the internal relationships between networks and find the degree of network association. The community structure matrix that we proposed provides more information about the network structure by considering the relationships between nodes that connect directly or share similar neighboring nodes. The use of node attributes provides a semantic interpretation for the community structure. We conduct experiments on attributed graph datasets with overlapping and nonoverlapping communities. The results of the experiments show that the performances of the F1-Score and Jaccard-Similarity in the overlapping community and the performances of normalized mutual information (NMI) and accuracy (AC) in the nonoverlapping community are significantly improved. Our proposed model achieves significant improvements in terms of its accuracy and relevance compared with the state-of-the-art approaches.