A Decision Support System for Selecting Between Designs for Dynamic Software Product Lines

When commissioning a system, a myriad of potential designs can successfully fulfill the system\u27s goals. Deciding among the candidate designs requires an understanding of how the design affects the system\u27s quality attributes and how much effort is needed to realize the design. The difficulty of the process compounds if the system to be designed includes dynamic run-time self- adaptivity, the ability for the system to self-modify its architecture at run-time in response to either external or internal stimuli, as the type and location of the dynamic self-adaptivity within the architecture must be co-decided. In this proposal, we introduce a Decision Support System, which contains a new Dynamic Software Product Line-centric cost / effort estimation technique, the Structured Intuitive Model for Dynamic Adaptive System Economics (SIMDASE), that will allow system designers / architects to select the most appropriate design for systems where the candidates can be structured as a Dynamic Software Product Line. We will focus on using the Decision Support System to select designs for a system where at least one component of the system is a low-level embedded system for use within the Internet of Things (IoT), particularly embedded systems whose purpose is to exist as things (either intelligent sensors or actuators). The Decision Support System we introduce is a multi-step process that begins with a high- level system architecture generated from the system requirements and goals. Candidate designs that can fulfill all goals / requirements of the high-level architecture are selected. Each design is then annotated using SIMDASE so that the effort, risk, cost and return on investment that can be expected from the realization of the design(s) can be compared in order to select the best design for a given organization

Formal Verification of Software Architecture

The majority of errors within a software project are introduced during the requirements and design phases of the project, yet, usually, these errors remain undetected until the implementation and test phases of the Software Development Life Cycle, when the errors are most costly and difficult to correct. The use of Formal Methods during implementation has made possible the earlier detection of errors. Using Formal Methods during the design phase when the software architecture is produced has significant impact by ensuring properties of the requirements are properly represented in the architecture enabling even earlier detection. Tools supporting formal verification of architecture are now mature enough to allow for verification at multiple levels of the architecture. The Architecture Analysis and Design (AADL) language and its verification tools, AGREE, BLESS and Resolute, provide an excellent platform for representing an architecture and its verification requirements. However, determining what is necessary to declare a requirement as sufficiently verified is not well understood

Another Look at Devaluation and the Trade Balance in China

This paper estimates the effect of Chinese Yuan devaluation on the trade balance of China.  For that a regression equation is developed in which domestic income, foreign income, domestic money supply, foreign money supply and real effective exchange rate are used as explanatory variable with trade balance as the dependent variable. In order to test the J-curve phenomenon the lagged values of exchange rata are also included.  Quarterly time series data from 1999 to 2016 are used.  Before estimating the model the time series properties of the data are diagnosed and an error correction model is developed and estimated.  The estimated results show that the contemporaneous effect of devaluation is positive, but the total effect is insignificant.  A J-curve pattern of adjustment of the trade balance is also detected

Designing for Reuse in an Industrial Internet of Things Monitoring Application

Abstract The Internet of Things (IoT) continues to experience rapid growth, and its influence is extending into previously unreached domains. However, some of these new domains impose specific limitations that complicate the design and implementation of IoT systems. Examples of such limitations are the exclusion of specific protocols, restrictions on the types of data that can be collected, requirements about what information can be transmitted to the public and controls around how that communication occurs. Capturing, representing and designing for these limitations as well as reuse is essential for the quick and successful deployment of such projects. In this paper, we present a case study of an IoT human in the loop monitoring system built for use within an industrial setting. We report our experiences with both designing the first deployment of the system as well as designing variation points into the software architecture to account for future iterations and deployment into other environments

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

missile-launch-interface: 1.0.0

Initial release of the software repository corresponding with publication in the ACM SPLC \u2715 conferenceNo description provided

collision_detection_aadl: 1.0.0

Initial release of collision detection model. (In partial fulfillment of the requirements of CPSC 8750 Clemson University, Spring 2013.

Measurement of Operator-machine Interaction on a Chaku-chaku Assembly Line

Assembly operations in the automotive industry represent a substantial proportion of overall manufacturing time and total manufacturing cost. With product complexity increasing year after year, humans continue to remain a cost-effective solution to the needs of flexible manufacturing. The human element is largely marginalized in Manufacturing 2.0 and necessitates a better understanding of the human\u27s impact on the future of manufacturing. The work herein illustrates a method through the use of the Industrial Internet of Things (IIoT) to capture ubiquitous data streams from human and automated machinery with the intention to make available the data necessary and elucidate the potential to deepen the understanding of the human impact on Industry 4.0 assembly systems

Measurement of Operator-machine Interaction on a Chaku-chaku Assembly Line

Assembly operations in the automotive industry represent a substantial proportion of overall manufacturing time and total manufacturing cost. With product complexity increasing year after year, humans continue to remain a cost-effective solution to the needs of flexible manufacturing. The human element is largely marginalized in Manufacturing 2.0 and necessitates a better understanding of the human\u27s impact on the future of manufacturing. The work herein illustrates a method through the use of the Industrial Internet of Things (IIoT) to capture ubiquitous data streams from human and automated machinery with the intention to make available the data necessary and elucidate the potential to deepen the understanding of the human impact on Industry 4.0 assembly systems