Capability-based software project scheduling with system dynamics and heuristic search

Software project management, a peculiar area in project management, is concerned with activities involved in ensuring that software is delivered on time and on schedule. Software project management is unique because software products are perceived as intangible and flexible. In software projects, the capability of people is the most important factor to determine whether a project will succeed of fail. During the software project management process, scheduling can be influenced by a lot of dynamics elements such as the skills of engineers, the growth of skills and experiences, cooperation, and leadership. Our objective is to help project managers to assign human resources automatically and realistically based on personnel/team capability. This thesis proposed a framework for scheduling and monitoring in software project management. Based on this framework, dynamic elements in software project management, especially personnel capability, are simulated in System Dynamics models. The genetic algorithms for previous work (i.e., task-based model and timeline-based model) are revised to reduce the computation in the new model. Experiments are also reported including the procedure on tuning GA parameters, results of several example tests, and discussion on the results. Experiments on comparison of GA and Hill-Climbing method help us build the confidence that GA is a good choice in software project scheduling problems

Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger

Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94\% objects within the $\pm$6mm error and measured 80\% of the structural strains within $\pm$0.1mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment.Comment: 8 pages, 6 figures, full-length version of a submission to IEEE RoboSoft 202

Impaired thymic iNKT cell differentiation at early precursor stage in murine haploidentical bone marrow transplantation with GvHD

IntroductionEarly recovery of donor-derived invariant natural killer T (iNKT) cells are associated with reduced risk of graft-versus-host disease (GvHD) and overall survival. Patients with severe GvHD, however, had much slower iNKT cell reconstitution relative to conventional T cells.MethodsTo characterize the delay of iNKT cell reconstitution and explore its possible causes, we used a haploidentical bone marrow transplantation (haplo-BMT) mouse model with GvHD. We found the delayed recovery of thymic and peripheral iNKT cell numbers with markedly decreased thymic NKT1 subset in GvHD mice. The defective generation of thymic iNKT precursors with egress capability contributed to the reduced peripheral iNKT cells in GvHD mice. We further identified intermediate NK1.1- NKT1 precursor subpopulations under steady-state conditions and found that the differentiation of these subpopulations was impaired in the thymi of GvHD mice. Detailed characterization of iNKT precursors and thymic microenvironment showed a close association of elevated TCR/co-stimulatory signaling provided by double positive thymocytes and macrophages with defective down-regulation of proliferation, metabolism, and NKT2 signature in iNKT precursor cells. Correspondingly, NKT2 but not NKT1 differentiation was favored in GvHD mice.DiscussionThese data underline the important roles of TCR and co-stimulatory signaling in the differentiation of thymic iNKT subsets under transplantation conditions

Capability-based software project scheduling with system dynamics and heuristic search

Software project management, a peculiar area in project management, is concerned with activities involved in ensuring that software is delivered on time and on schedule. Software project management is unique because software products are perceived as intangible and flexible. In software projects, the capability of people is the most important factor to determine whether a project will succeed of fail. During the software project management process, scheduling can be influenced by a lot of dynamics elements such as the skills of engineers, the growth of skills and experiences, cooperation, and leadership. Our objective is to help project managers to assign human resources automatically and realistically based on personnel/team capability. This thesis proposed a framework for scheduling and monitoring in software project management. Based on this framework, dynamic elements in software project management, especially personnel capability, are simulated in System Dynamics models. The genetic algorithms for previous work (i.e., task-based model and timeline-based model) are revised to reduce the computation in the new model. Experiments are also reported including the procedure on tuning GA parameters, results of several example tests, and discussion on the results. Experiments on comparison of GA and Hill-Climbing method help us build the confidence that GA is a good choice in software project scheduling problems.</p

A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks

For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into a series of relatively stable component sequences. For improving the prediction accuracy further by utilizing the current day solar radiation profile in one-hour-ahead predictions, similar solar radiation profile data were selected for training LSTM neural networks. Simulation results show that the hybrid model achieves better prediction performance than traditional prediction methods, such as the exponentially-weighted moving average (EWMA), weather conditioned moving average (WCMA), and only LSTM models

Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach

<div><p>Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases.</p></div

Part A of Genome S Generated from Possible-Assignment Matrix and 2D Task-Employee Assignment Array.

<p>Part A of Genome S Generated from Possible-Assignment Matrix and 2D Task-Employee Assignment Array.</p

An empirical performance study for validating a performance analysis approach: PSIM

Performance analysis gains more attention in recent years by researchers who focus their study on the early software development stages to mitigate the risk of redesign as problems emerge later. Previously we proposed PSIM (a Performance SImulation and Modeling tool) that integrated performance properties into software architecture specifications expressed in several major UML diagrams. PSIM models can be transformed into Colored GSPN (Colored Generalized Stochastic Petri Nets). As a result, the Colored GSPN models can be simulated to perform model-based performance evaluation. In this paper we briefly review the PSIM approach and apply it to model a web-based electronic conferencing system, called M-Net, to derive performance metrics. We then conduct runtime performance testing to the implementation of M-Net and compare the simulation data to runtime testing data. The comparison results show the effectiveness of the PSIM method in predicting system performance and identifying system performance bottlenecks.This is a manuscript of a proceeding published as Jinchun Xia, Yujia Ge and C. K. Chang, "An empirical performance study for validating a performance analysis approach: PSIM," 29th Annual International Computer Software and Applications Conference (COMPSAC'05), 2005, pp. 307-312 Vol. 2, doi: 10.1109/COMPSAC.2005.43. Posted with permission. © 2005 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work