Analysis framework for evaluating PLC software: An application of Semantic Web technologies

The present study describes the development of control hardware and software for a mobile welding robot. This robot is able to move and perform welding tasks in a double hull structure. The control hardware consists of a main controller and a welding machine controller. Control software consists of four layers. Each layer consists of modules. Suitable combinations of modules enable the control software to perform the required tasks. Control software is developed using C programming under QNX operating system. For the modularizing architecture of control software, we designed control software with four layers: Task Manager, Task Planner, Actions for Task, and Task Executer. The embedded controller and control software was applied to the mobile welding robot for successful execution of the required tasks. For evaluate this imbedded controller and control software, the field tests are conducted, it is confirmed that the developed imbedded controller of mobile welding robot for shipyard is well designed and implemented.

Background In healthcare, quality improvement (QI) tools are predominantly used to address human, system and process factors to improve clinical care. We believe that QI tools can also be used to address similar factors in medical education, to facilitate improvement in learning outcomes and competencies for new junior doctors in a postgraduate medical education program in our anaesthesia and critical care unit. Methods A stepwise competency checklist was devised to guide the learning and monitor the percentage who had completed the required learning activities and tests at the end of each month. This was tabulated as monthly competency scores, and served as a measure of effectiveness of the education program. QI tools, namely the Fishbone diagram and Pareto chart, were used to identify modifiable root causes and prioritise interventions. Results Monthly competency scores ranged 30–50% at baseline, and improved to 60–75% after 6 months, with the implementation of a series of QI interventions. Conclusion QI tools were utilised to guide education interventions, with consequent improvement in the monthly competency scores of our junior doctors. Focused improvement cycles that are aligned to learning outcomes are key to the success of using QI tools in medical education.

Objective Quality measures in healthcare are crucial for improving outcomes and ensuring patient safety. This study investigated the evolution, implementation, and impact of Primary Care Quality (PCQ). The PCQ aims to facilitate nationwide quality benchmarking, serving as a tool for quality improvement (QI) and research. Design/Settings A descriptive design outlining the development and operationalisation of the PCQ, a national framework for automatic and systematic data collection and feedback. Results The national PCQ system is a tool for continuous QI in primary care in Sweden. PCQ has achieved extensive adoption, with over 97% of Swedish primary care centres, both private and public driven, utilising the platform for automatic data extraction from patient records and data visualisation. Quality indicators were developed through a structured approach involving primary care professionals, evidence-based clinical practices, and expert contributions from established knowledge organisations, reflecting the breadth of general practice. Data are automatically retrieved from medical records and visualised in real time, with the possibility of benchmarking at an aggregate level and identifying individuals locally at primary care centres. The PCQ has facilitated improvements by enabling quality dialogue among healthcare professionals and supporting continuous local QI. Regionally, the PCQ supports needs assessments and patient safety initiatives. Nationally, it establishes standardised indicators for quality measurement, enabling effective benchmarking and strategic healthcare planning. Conclusions The implementation of the national PCQ system provided a framework and tool for continuous QI in primary care. The system has influenced national standardization of primary care indicators, with quality improvement results demonstrated regionally and locally through the PCQ.

In order to solve the problem that it is difficult to achieve better multi-robot coordination operation using the original control system in multi-robot cooperative systems, this paper conducts system modeling, control framework design and software development and verification experiments for multi-robot cooperative systems. We firstly model the system composed of multiple robots and operated objects. The coordination between any two robots can be divided into two categories: loose coordination and tight coordination according to the constraint relationship between the robots and the objects, based on which the kinematics and dynamics analysis of the multi-robot system is carried out. A multi-robot cooperative control framework is designed, which contains modules for task planning, force and position assignment, sensor information acquisition, position/force control and synchronization clock. On the basis of the multi-robot cooperative control system, the robot cooperative control software is designed and developed, and the software system contains, with excellent characteristics. Finally, an experimental environment containing three large-load EFORT industrial robots was established and experiments were conducted using the developed multi-robot cooperative control software to verify the usability and reliability of the software system.

National databases collect large amounts of clinical information, yet application of these data can be challenging. The authors present the NeuroPoint Alliance and Institute for Healthcare Improvement (NPA-IHI) program as a novel attempt to create a quality improvement (QI) tool informed through registry data to improve the quality of care delivered. Reducing the length of stay (LOS) and readmission after elective lumbar fusion was chosen as the pilot module. The NPA-IHI program prospectively enrolled patients undergoing elective 1- to 3-level lumbar fusions across 8 institutions. A three-pronged approach was taken that included the following phases: 1) Research Phase, 2) Development Phase, and 3) Implementation Phase. Primary outcomes were LOS and readmission. From January to June 2017, a learning system was created utilizing monthly conference calls, weekly data submission, and continuous refinement of the proposed QI tool. Nonparametric tests were used to assess the impact of the QI intervention. The novel QI tool included the following three areas of intervention: 1) preoperative discharge assessment (location, date, and instructions), 2) inpatient changes (LOS rounding checklist, daily huddle, and pain assessments), and 3) postdischarge calls (pain, primary care follow-up, and satisfaction). A total of 209 patients were enrolled, and the most common procedure was a posterior laminectomy/fusion (60.2%). Seven patients (3.3%) were readmitted during the study period. Preoperative discharge planning was completed for 129 patients (61.7%). A shorter median LOS was seen in those with a known preoperative discharge date (67 vs 80 hours, p = 0.018) and clear discharge instructions (71 vs 81 hours, p = 0.030). Patients with a known preoperative discharge plan also reported significantly increased satisfaction (8.0 vs 7.0, p = 0.028), and patients with increased discharge readiness (scale 0-10) also reported higher satisfaction (r = 0.474, p < 0.001). Those receiving postdischarge calls (76%) had a significantly shorter LOS than those without postdischarge calls (75 vs 99 hours, p = 0.020), although no significant relationship was seen between postdischarge calls and readmission (p = 0.342). The NPA-IHI program showed that preoperative discharge planning and postdischarge calls have the potential to reduce LOS and improve satisfaction after elective lumbar fusion. It is our hope that neurosurgical providers can recognize how registries can be used to both develop and implement a QI tool and appreciate the importance of QI implementation as a separate process from data collection/analysis.

There are numerous ways to improve the quality of healthcare services, and Quality Improvement (QI) tools play a central role in this. These tools are essential for identifying problems, reducing errors and costs, modifying practices, generating innovative ideas, acquiring and analysing data, visualising issues, and supporting decision-making. Using them effectively promotes healthcare quality, patient safety, and optimal resource utilisation. Despite the importance of QI tools, the lack of systematic and comprehensive data on the frequency and purpose of their use in healthcare facilities constitutes the main problem area of this study. This descriptive and cross-sectional study examines the frequency and purpose of QI tool usage among quality managers in hospitals across Türkiye. The study population comprised quality managers from 248 hospitals who fully completed the survey. It focused on the use of 18 widely recognised QI tools, including Brainstorming, Fishbone Diagram, Five Whys, Flowchart, Control Chart, PDCA Cycle, FMEA, Histogram, Scatter Diagram, Process Mapping, and others. The results indicated that the least known tools were the Swiss Cheese Model, Spaghetti Diagram, Six Thinking Hats, House of Quality, Mapping the Last Ten Patients, Tree Diagram, and Pareto Chart. Conversely, Brainstorming, Fishbone Diagram, Five Whys, and Flowcharts were the most frequently used. QI tools were primarily used for generating ideas, visualisation, identifying problems, and analysing them. Significant differences in tool usage were observed based on experience in healthcare and quality roles. The findings underscore the complementary nature of QI tools and the need for enhanced training and awareness.

From the Publisher: Richard Heeks provides the first critical analysis of the development of India's software industry and the impact on it of the recent policy of liberalisation in the areas of trade, state intervention and foreign investment. The study is located at the intersection of three major trends witnessed in the ninetiesthe increasing importance of computer software in all aspects of business; the mounting global search for outsourcing services and manufacture on the part of multinational companies; and the growing dominance of neo-liberal policy models inspired by the World Bank and the IMF. The author starts with a detailed history of the government's role in India's industrial development. Dr Heeks then examines important issues concerning the software industry such as the division of labour within exports, the relation between export and domestic markets, the role of multinationals, and the impact of imports. Finally, he also studies the development of India's computer industry because of the intimate connection between software and hardware. Basing his arguments on a wealth of facts and figures and probing many misconceptions, Dr Heeks concludes that liberalisation has brought in only limited benefits, and argues that a successful software industry requires essential state interventions of a promotional nature. To this end, he makes recommendations for the future growth of this industry both in India and in other developing countries. Besides being of considerable interest to academics and professionals in the areas of business finance, management, information systems, economics, industrial development and computers, this book will be essential reading for all computer manufacturers, software companies, bureaucrats and those engaged in framing economic and industrial policies.

e18638 Background: The SOA is a key part of any clinical trial as it highlights activities to be performed during trial execution. Such a schedule provides a roadmap and calendar of tests, measurements, and evaluations. Normally, such a schedule is created after a protocol has been mostly created. Our aim is to demonstrate that the SOA can have a broader usage: that of a quality improvement (QI) tool used during protocol creation, thus helping to ensure a high-quality protocol at the design. When creating a protocol conflicting uses of the Protocol contribute to operational tension. For example, Trial Operations, which wishes to minimize the complexity of the trial, may debate with Clinical Science, who wish to collect the greatest amount of scientific tests and data. The more complex a set of requirements in the protocol, there is a corresponding drop in available sites able to meet those needs. Additionally potential sites may comment about being burdened with tests that are unnecessary to the core protocol aims, thus creating unnecessary delays in the trial. All these issues may be addressed by using the SOA as a QI tool. Methods: This research was completed at a major, international pharmaceutical organization for a Phase III oncology clinical trial. A team was created composed of: Clinical Scientist Lead , Stats/Data Lead, Trial Operations Led, Medical Monitor, Medical Leader, and Lean Expert. Methodologically, this was led by the Lean Expert and each team member was sent an electronic version of a draft SOA for review. They inserted questions and comments that were visible to the entire team. After this, an SOA issues log was created in an SOA tracking system. This system categorized and ranked issues for discussion by the entire team during the creation process of both the SOA and the protocol. The process used a “House of Quality” framework, allowing for the aggregation of protocol requirements so that they could be addressed in total, rather than individually. Results: The process was successful in 3 critical dimensions. From a time metric, the SOA was produced 3 to 6 months faster, enabling numerous efficiencies such as providing the development team with information both faster and at the time the team was creating the final protocol. Second, from an economic standpoint, the labor efficiency of the trial was improved as it required 33% less individuals to produce the SOA and protocol resulting in a protocol at record speed. Finally, protocol quality was increased as in this method the team worked jointly together, nearly simultaneously, to complete both the SOA and the protocol. This reduced the number of Protocol Amendments by 50%. Conclusions: Using this method, there was an increase in quality, a reduction in cost, and a faster time to the sites. This reduced the commercialization timeline by 3-6 months which is a significant cost savings. But most importantly, this allows for better treatment faster to be in the hands of oncologists to treat patients.

A three-step approach is proposed to substantially reduce the high cost of development of control software for an FMS on the shop floor. First, by introducing the concept of multiple computing processes into Automata Theory, a finite capacity machine (FCM) is presented as an extended finite machine. An FCM is used to model the control of a flexible manufacturing system, which avoids the state explosion problem. Secondly, a structured adaptive supervisory control (SASC) model meeting the desired control objectives for an FMS is derived from a composed FCM. The SASC model can be constructed systematically. Finally, generic software components for an SASC model are described, which allow the SASC model to be cost-effectively transferred into shop-floor applicable control software.

Diagnostic Emulation is the application of microprogramming to the emulation of an operational computer to support software development and verification for that computer. A conventional technique, Interpretive Computer Simulation (ICS), has been used for many years in support of such software development and verification efforts. The ICS method is becoming less cost effective. For the development of attitude control software for NASA's High Energy Astronomical Observatory (HEAO) diagnostic emulation was considered as an alternative. This paper describes the motivation for using an ICS or diagnostic emulator to validate command and control software, the potential advantages of microprogramming, and the analysis which led to the development of the HEAO Diagnostic Emulator rather than the use of conventional techniques. Also discussed are the emulator implementation tradeoff analysis, the resulting program design, and the interface between the microprogram and the assembly language modules. The results to date are described, including the capability, speed, and reliability of the program and initial user response.

With the growing adoption of Industry 4.0 concepts in production systems, new challenges arise in engineering control software. Highly distributed control with tight real-time constraints and safety regulations results in increasingly complex software. Current research focuses on increasing the abstraction with new architectures and modularization of software. The presented PhD research addresses modeling of the interactions between control software components, and of the emergent behavior of these compositions. Such behavior models can support the initial implementation, and facilitate (semi-)automated testing and monitoring of control software. Finally, visualizing behavior in a model can enhance understandability of existing control software, when software developers need not access abstracted hierarchy levels to deduct their functionality. This work aims at optimizing the benefit of behavior models in developing control software: Modeling the expected behavior directly for new software will allow using them throughout the software life-cycle. For legacy software, the initial development effort of behavior models will be minimized by automatically capturing behavior models from the implementation. The approach is evaluated in case studies and user studies to integrate experiences from the industrial domain into this software engineering research.

The partograph and WHO safe childbirth checklist (SCC), which include on-admission, before-birth, after-birth and discharge components, are quality improvement (QI) tools in intrapartum care that have long been recommended for use. However, poor-quality intrapartum care remains a significant barrier to improving maternal and newborn health outcomes in countries like Ethiopia, largely due to total non-use or poor adherence to these QI tools. eHealth-based interventions present promising strategies to enhance healthcare quality. Therefore, we conducted implementation research to examine the effect of ADHERE (Antenatal care, chilDbirtH carE and postnatal ca.RE), an innovative eHealth system with a clinical decision support element, on adherence to QI tools during intrapartum care in health facilities in Ethiopia. A quasi-experimental study was conducted in 3 hospitals and 6 health centers (5 intervention and 4 control) in Ethiopia, guided by the Implementation Research Logic Model. Data were collected from labor and delivery charts by trained data collectors. A total of 2,190 charts (1,076 baseline [538 per arm] and 1,114 end-line [554 control and 560 intervention arms] charts) were included in the analysis. Difference-in-difference (DiD) analysis was used to estimate the effect of the intervention. The partograph adherence score and SCC completion rates were compared between the two arms. Statistically significant differences were determined using a p-value < 0.05 cutoff. Baseline mean partograph scores in the control and intervention arms were 4.11 and 4.23, respectively. Baseline SCC adherence rates at admission, before-birth, after-birth, and discharge were 25.8%, 24.9%, 24.9%, and 24.3% in the control arm, respectively, and 49.6%, 47.8%, 45.9%, and 45.5% in the intervention arm, respectively. The ADHERE intervention increased partograph adherence score by 4.27 units (DiD = 4.27, 95%CI [1.80, 5.09]). The ADHERE intervention increased SCC adherence by 9.3%-points (DiD = 0.093, 95%CI [0.036, 0.151]) at admission, 22.3%-points (DiD = 0.223, 95%CI [0.121, 0.325]) before-birth, and 15.2%-points (DiD = 0.152, 95%CI [0.072, 0.231]) after-birth. Conversely, the ADHERE intervention did not significantly change SCC adherence at discharge (DiD = -0.025, 95%CI [-0.071, 0.021]). The lack of significant improvement may be due to providers less attention to discharge evaluation because of work overload. The ADHERE intervention significantly improved adherence to partograph and SCC at admission, before-birth, and immediately after-birth during intrapartum care. However, it did not significantly improve adherence to the discharge checklist. Therefore, ADHERE-assisted implementation of QI tools can potentially prevent adverse birth outcomes related to low-quality intrapartum care and could be considered for scale up across health facilities in Ethiopia. Poor-quality childbirth care remains a significant barrier to improving health outcomes in countries like Ethiopia. eHealth interventions present promising strategies to enhance healthcare quality. We conducted implementation research to see the effect of a clinical decision support intervention on childbirth care in health facilities in Ethiopia. The study was conducted in three hospitals and six health centers: five received the new intervention and four continued with the usual care. We followed a step-by-step guide during project implementation. The data were gathered from 2,190 patient charts. We compared how childbirth care practices changed between the two groups over the two time periods (before- and after-intervention). We followed scientific procedure to declare that the differences were real. The intervention increased the correct use of partograph by 4.27 units as compared to without intervention. The intervention increased essential childbirth practices at admission by 9.3% points, practices during labor by 22.3% points, and practices immediately after birth by 15.2% points. Conversely, the intervention did not change essential childbirth practices at discharge from the facility. The intervention clearly helped health workers use the partograph correctly and follow key childbirth care steps when the woman arrived, during labor, and right after birth. Therefore, eHealth-assisted provision of key childbirth care can potentially prevent bad birth outcomes related to low-quality childbirth care and could be considered for scale-up across health facilities in Ethiopia.

A metamodeling approach for safe control software

OBJECTIVES:Increasing numbers of patients experience a prolonged stay in intensive care. Yet existing quality improvement (QI) tools used to improve safety and standardize care are not designed for their specific needs. This may result in missed opportunities for care and contribute to worse outcomes. Following an experience-based codesign process, our objective was to build consensus on the most important actionable processes of care for inclusion in a QI tool for adults with prolonged critical illness.DESIGN:Items were identified from a previous systematic review and interviews with former patients, their care partners, and clinicians. Two rounds of an online modified Delphi survey were undertaken, and participants were asked to rate each item from 1 to 9 in terms of importance for effective care; where 1–3 was not important, 4–6 was important but not critical, and 7–9 was critically important for inclusion in the QI tool. A final consensus meeting was then moderated by an independent facilitator to further discuss and prioritize items.SETTING:Carried out in the United Kingdom.PATIENTS/SUBJECTS:Former patients who experienced a stay of over 7 days in intensive care, their family members and ICU staff.INTERVENTIONS:None.MEASUREMENTS AND MAIN RESULTS:We recruited 116 participants: 63 healthcare professionals (54%), 45 patients (39%), and eight relatives (7%), to Delphi round 1, and retained 91 (78%) in round 2. Of the 39 items initially identified, 32 were voted “critically important” for inclusion in the QI tool by more than 70% of Delphi participants. These were prioritized further in a consensus meeting with 15 ICU clinicians, four former patients and one family member, and the final QI tool contains 25 items, including promoting patient and family involvement in decisions, providing continuity of care, and structured ventilator weaning and rehabilitation.CONCLUSIONS:Using experience-based codesign and rigorous consensus-building methods we identified important content for a QI tool for adults with prolonged critical illness. Work is underway to understand tool acceptability and optimum implementation strategies.

Nonferrous metal industry is the foundation of China’s substantial economy and plays a key role in national economy and defense construction. Industrial software is crucial for the high-quality development of the nonferrous metal industry and is associated with the in-depth implementation of national software development strategies. Currently, the industrial software development in the nonferrous metal industry is significantly restricted by the lack of knowledge models. Hence, we propose a method for constructing an intelligent model library for the nonferrous metal industry. Considering meta-model-driven engineering, we define the nonferrous metallurgical meta-model and its attributes, and propose a meta-modeling method based on the MODELING architecture. Additionally, we design an overall architecture for the intelligent model library based on industrial Internet, an agile model-development environment integrating multiple languages, and a meta-model encapsulation system based on multi-scenario black-box reuse. Moreover, a meta-model full lifecycle management platform is constructed based on a five-layer two-dimension classification standard and the domain knowledge graph. The intelligent model library for the nonferrous metal industry is developed based on the long-term accumulation of nonferrous metallurgy process mechanism, operating experiences, and intelligent methods. The good role of intelligent model library in improving the intelligent level in engineering applications is presented through the application of two typical nonferrous metallurgical scenarios. The intelligent model library of the nonferrous metal industry provides a core knowledge support for the development of industrial software and plays a fundamental role in promoting smart manufacturing and strengthening the nonferrous metal industry.