End-user Requirements Research Articles

Developing human factors design guidelines for marine electronics — the case of S-mode

Human factors issues with navigation equipment have been identified as a challenge to safe and efficient maritime operations. A reason behind the issues is the lack of guidance from regulatory agencies, particularly regarding interface design. The International Maritime Organisation (IMO) has taken measures to address the situation, an example of which is the development of Circular MSC.1/Circ.1609 Guidelines for the Standardisation of User Interface Design for Navigation Equipment. This article presents a case study on the development of MSC.1/Circ.1609. An in-depth analysis was conducted using multiple sources of data to identify influential factors behind key events and provide recommendations for similar future initiatives. The data used in the study include interviews with major stakeholders, official reports of the development work, and personal records of members of the development team. The study finds the development of MSC.1/Circ.1609 to be affected by the politics of decision-making at the IMO, the difficulties associated with developing implementable technical regulations, and the rigid nature of IMO working procedures. The findings suggest that successful human factors regulations should address the requirements of both end-users and implementors, which are equipment manufacturers in this case. This study also identifies characteristics of IMO working principles that should be considered to improve the application of human factors in the maritime field.

Elemental and experimental analysis of modified stent's structure under uniaxial compression load

Additive manufacturing has enabled the fabrication of lightweight complex metamaterials that possess high energy absorption and impact resistance properties. Stents, a typical 3D auxetic material, have significant self-expanding behavior, and their mechanical properties can be finely tuned over a wide range. In this study, we systematically analyzed three distinctive elastic-plastic regions using experimental, numerical simulations, and theoretical analysis, focusing on investigating the energy absorption capability of a designed structure by varying tessellated unit cell numbers in two section views in X- and Y-direction. Two batches of 5 specimens each were 3D printed using FDM techniques. The results showed that designing a self-expanding stent with innovative capabilities was possible, with the yield stress ranging between 1.5 MPa and 2.0 MPa and extended effective elastic moduli derived from the deformation mode of tessellated unit cells. The maximum energy absorption for all structures ranged between 7.1J and 18J, with similar capabilities observed for the designed stents. However, increasing unit cells along the X-direction resulted in a significant increase in SEA, while the Y-direction remained unchanged. Therefore, these structures have a significant influence on areas requiring energy absorption. In addition, they are the ideal class of energy absorbers for cushioning applications. Furthermore, their energy-absorption capacity can be easily tailored to meet specific end-use requirements by varying their structural parameters using unit cell tessellation.

Caching Scalable Videos in the Edge of Wireless Cellular Networks

By pre-fetching popular videos into the local caches of edge nodes, wireless edge caching provides an effective means of reducing repeated content deliveries. To meet the various viewing quality requirements of multimedia users, scalable video coding (SVC) is integrated with edge caching, where the constituent layers of scalable videos are flexibly cached and transmitted to users. In this article, we discuss the challenges arising from the different content popularity and various viewing requirements of scalable videos, and present the diverse types of cached contents as well as the corresponding transmission schemes. We provide an overview of the existing caching schemes, and summarize the criteria of making caching decisions. A case study is then presented, where the transmission delay is quantified and used as the performance metric. Simulation results confirm that giving cognizance to the realistic requirements of end users is capable of significantly reducing the content transmission delay, compared to the existing caching schemes operating without SVC. The results also verify that the transmission delay of the proposed random caching scheme is lower than that of the caching scheme which only provides local caching gain.

Adaptation of a Scientific Decision Support System to the Productive Sector—A Case Study: MOPECO Irrigation Scheduling Model for Annual Crops

Despite the great number of models developed in research projects, only a small percentage have been successfully transferred to the productive sector. The PRIMA programme supported by Horizon 2020, the European Union Framework Programme for Research and Innovation, aims to reverse this situation. The SUPROMED project funded by PRIMA sought to develop an online platform composed of several models adapted to the requirements of end users for increasing the economic and environmental sustainability of Mediterranean agricultural systems. MOPECO, in its research version, was designed to maximize the profitability of irrigated farms in water-scarce regions. A simplified version of this model (MOPECO irrigation scheduling) was included in the SUPROMED platform for improving irrigation efficiency, providing farmers with a useful irrigation scheduling software. This paper shows the approach to adapt and transfer MOPECO to the productive sector. The tool was validated in three different demosite areas across the Mediterranean, involving local stakeholders in the design, validation, and dissemination of the software. The simplified tool reached similar or higher yields than farmers using less water. Thus, the average water saved was around 16%, while the average yield increased around 10% in the plots located in the three demosites of the project (Eastern Mancha in Spain, Bekaa valley in Lebanon, and Sidi Bouzid in Tunisia). This fact decreased the water footprint and increased the profitability of farms. The high applicability of the tool has generated interest among many technicians, farmers, and advisory enterprises. Furthermore, regional and national governmental extension services have shown interest in spreading the use of the tool across their territories, validating the methodology used for adapting and transferring a scientific model to the productive sector.

Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles

With the development of the sixth-generation (6 G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.

An automatic alignment of the business process and business value models: a novel MDA method

With the massive development of end-users requirements, the model alignment has become an essential stage in software engineering, especially in the model driven architecture (MDA) approach, to absorb the end-user’s need. The purpose is to facilitate the alignment of new models from existing ones. Our contribution in this paper is to deal with the MDA higher abstraction lever by focusing on the automatic alignment of the business value with the business process models for the information system (IS). For our case, the data-flow diagram (DFD) illustrates the business process model, and the E<sup>3</sup>value model illustrates the business value model. However, the ATLAS-transformation language (ATL) ensures automatic alignment. The main goal is to facilitate and accelerate IS implementation while enhancing its quality.

User-Driven Design and Development of an Underwater Soft Gripper for Biological Sampling and Litter Collection

Implementing manipulation and intervention capabilities in underwater vehicles is of crucial importance for commercial and scientific reasons. Mainstream underwater grippers are designed for the heavy load tasks typical of the industrial sector; however, due to the lack of alternatives, they are frequently used in biological sampling applications to handle irregular, delicate, and deformable specimens with a consequent high risk of damage. To overcome this limitation, the design of grippers for marine science applications should explicitly account for the requirements of end-users. In this paper, we aim at making a step forward and propose to systematically account for the needs of end-users by resorting to design tools used in industry for the conceptualization of new products which can yield great benefits to both applied robotic research and marine science. After the generation of the concept design for the gripper using a reduced version of the House of Quality and the Pugh decision matrix, we reported on its mechanical design, construction, and preliminary testing. The paper reports on the full design pipeline from requirements collection to preliminary testing with the aim of fostering and providing structure to fruitful interdisciplinary collaborations at the interface of robotics and marine science.

Updating the WHO target product profile for next-generation Mycobacterium tuberculosis drug susceptibility testing at peripheral centres.

There were approximately 10 million tuberculosis (TB) cases in 2020, of which 500,000 were drug-resistant. Only one third of drug-resistant TB cases were diagnosed and enrolled on appropriate treatment, an issue partly driven by a lack of rapid, accurate drug-susceptibility testing (DST) tools deployable in peripheral settings. In 2014, World Health Organization (WHO) published target product profiles (TPPs) which detailed minimal and optimal criteria to address high-priority TB diagnostic needs, including DST. Since then, the TB community's needs have evolved; new treatment regimens, changes in TB definitions, further emergence of drug resistance, technological advances, and changing end-users requirements have necessitated an update. The DST TPP's revision was therefore undertaken by WHO with the Stop TB Partnership New Diagnostics Working Group. We describe the process of updating the TPP for next-generation TB DST for use at peripheral centres, highlight key updates, and discuss guidance regarding technical and operational specifications.

Preliminary Experimental of Biomass Gasification Process with Open Core Throat Less Downdraft Gasifier

Gasification has been considered a promising approach to converting biomass into useful gaseous products. Downdraft gasifiers are famous for engine applications and used for thermal applications. The product gas has to meet the individual requirements of different end users or applications. The downdraft gasifier based on open core throat less design has been developed according to its suitability for small-sized biomass. The preliminary experiment investigated gasifying characteristics by studying the temperature distribution within the gasifier. This study used three kinds of biomass: Eucalyptus wood chip, Mimosa wood twig, and wood pellet. The results showed that the temperature distribution within the gasifier for each zone corresponds with the character of the gasifying process within the downdraft gasifier. Moreover, the temperature profiles for a gasifying process for all three biomass tests were in the same pattern. The maximum temperature was in the combustion zone (T3). The maximum temperature at the combustion zone for the Eucalyptus wood chip is the highest compared with the maximum temperature at the combustion zone of the Mimosa wood twig and wood pellet. The temperature at the combustion zone (T3) of the Eucalyptus wood chip, Mimosa wood twig, and the wood pellet were 838, 783, and 620 °C, respectively. The temperature at the combustion zone results in the temperature at the reduction zone. The average temperature at the reduction zone for gasifying all three biomass feeds ranges from 470-514 °C. While the temperature at the pyrolysis zone and the gas exit are not significantly different for all three biomass feeds. These results showed that the reaction within the gasifier could carry out continuously with a suitable temperature in each zone as per the design.

Pathway map development for medical device event reporting in operating theatres: a human factors approach to improving the existing system

ObjectivesThis study aimed to develop the actual pathway to reporting and information transfer in operating theatres in relation to medical technology malfunction/failure. This with the aim of understanding the differences...

Medigan: a Python library of pretrained generative models for medical image synthesis.

Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1)limited availability of (synthetic) datasets and (2)generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i)execution, (ii)visualization, (iii)search & ranking, and (iv)contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a)enabling community-wide sharing of restricted data, (b)investigating generative model evaluation metrics, and (c)improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.

Improving satellite-based monitoring of the polar regions: Identification of research and capacity gaps

We present a comprehensive review of the current status of remotely sensed and in situ sea ice, ocean, and land parameters acquired over the Arctic and Antarctic and identify current data gaps through comparison with the portfolio of products provided by Copernicus services. While we include several land parameters, the focus of our review is on the marine sector. The analysis is facilitated by the outputs of the KEPLER H2020 project. This project developed a road map for Copernicus to deliver an improved European capacity for monitoring and forecasting of the Polar Regions, including recommendations and lessons learnt, and the role citizen science can play in supporting Copernicus’ capabilities and giving users ownership in the system. In addition to summarising this information we also provide an assessment of future satellite missions (in particular the Copernicus Sentinel Expansion Missions), in terms of the potential enhancements they can provide for environmental monitoring and integration/assimilation into modelling/forecast products. We identify possible synergies between parameters obtained from different satellite missions to increase the information content and the robustness of specific data products considering the end-users requirements, in particular maritime safety. We analyse the potential of new variables and new techniques relevant for assimilation into simulations and forecasts of environmental conditions and changes in the Polar Regions at various spatial and temporal scales. This work concludes with several specific recommendations to the EU for improving the satellite-based monitoring of the Polar Regions.

Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEE-II and SLE Strategy.

The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study's approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.

Sociotechnical factors of sustainable digital health systems: A system dynamics model

BackgroundThe introduction of digital health technology in a healthcare facility results in emergent and recursive interactions between new technology and existing social systems, technologies, and operating environments. ObjectivesThe study aims at developing a sociotechnical system dynamics model of digital health systems to assess the influence of sociotechnical factors on the sustainable use of an electronic Health Information Management System (eHMIS). MethodsA total of 40 face-to-face interviews in 18 healthcare facilities and two separate sessions of focus group discussions were conducted to study eHMIS implementation in Ethiopia. A system dynamics modelling of digital health systems implementation is developed, tested and verified with a case of eHMIS implementation in Ethiopia. ResultsThe long-term sustainability of the digital health system requires acceptance of technology, improved information quality to make appropriate decisions and better end-user satisfaction. The ‘individuals’ intention to use’ drives technology acceptance in the early years of the eHMIS use and social factors in the later stage. Besides, the role of technology to improve work performance determines the level of users’ satisfaction. The effort of improving ‘information quality’ is strongly influenced by the quality systems, i.e., reliable, easy to use, and capable to meet end-user requirements. ConclusionsThe system's acceptance was improved through effective training and communication, whereas the ‘information quality’ was enhanced through ‘system quality’ and users’ satisfaction. The ability of technology to improve work performance and reduce the burden on end-users has increased the satisfaction of end-users.

A Stackelberg game scheme for pricing and task offloading based on idle node-assisted edge computational model

Mobile edge server (MES) can meet the low latency requirements of End user (EU) tasks, but the MES computing resources are limited. This paper proposes an IDs-assisted MES (MES-IDs) computing model to expand MES. However, how to find a task offload strategy to minimize EU calculation costs and improve MES and ID benefits on a task completion basis. This paper proposes a three-way optimization calculation method based on the Stackelberg game to reduce the problem complexity, which decomposes into EUs-BS and BS-IDs problems. The EUs-BS problem design pricing scheme obtains the EU task optimal offload rate. The BS-IDs problem uses nonlinear pricing methods to find the optimal task offload problem between MES and ID on the resource unit price, a multi-constrained nonlinear convex optimization problem. We propose an immune Metropolis-based particle swarm loss optimization algorithm (PIMA) to obtain an approximate optimal solution for the nonlinear convex optimization problem by adjusting the resource price. Numerical simulation results show that compared with GSP and PSI algorithms, the PIMA algorithm shows more robust stability and can effectively reduce task completion delay and computational cost.

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests are needed. Test sites are designed to carry out two things, i.e., proof of design and manufacturing tests. The team responsible for designing test sites considers several parameters like deployment cost, test time, test coverage, etc. In this study, an automated test site using a supervised machine learning algorithm for testing an ultra-high frequency (UHF) transceiver is presented. The test site is designed in three steps. Firstly, an initial manual test site is designed. Secondly, the manual design is upgraded into a fully automated test site. And finally supervised machine learning is applied to the automated design to further enhance the capability. The manual test site setup is required to streamline the test sequence and validate the control and measurements taken from the test equipment and unit under test (UUT) performance. The manual test results showed a high test time, and some inconsistencies were observed when the test operator was required to change component values to tune the UUT. There was also a sudden increase in the UUT quantities and so, to cater for this, the test site is upgraded to an automated test site while the issue of inconsistencies is resolved through the application of machine learning. The automated test site significantly reduced test time per UUT. To support the test operator in selecting the correct component value the first time, a supervised machine learning algorithm is applied. The results show an overall improvement in terms of reduced test time, increased consistency, and improved quality through automation and machine learning.

Pregnancy Apps for Self-Monitoring: Scoping Review of the Most Popular Global Apps Available in Australia.

Digital health tools, such as apps, have the potential to promote healthy behaviours, especially self-monitoring, which can facilitate pregnancy management and reduce the risk of associated pregnancy health conditions. While pregnancy apps are popular amongst pregnant women, there is limited information about the overall quality of their content or self-monitoring tools and the number of behaviour change techniques (BCTs) that they include. The aim of this study was thus to assess the quality of pregnancy apps for self-monitoring, and their usage of BCTs. We identified pregnancy apps by web scraping the most popular global apps for self-monitoring in the Apple App Store and Google Play Store available in Australia. The app quality was evaluated using the scorecard approach and the inclusion of BCTs was evaluated using the ABACUS tool. We identified 31 pregnancy apps that met our eligibility criteria. We found that pregnancy apps tended to score the highest in the domains of 'cost and time', 'usability', and 'technical', and lowest on 'clinical' and 'end-user requirements'. Additionally, the majority of apps contained minimal BCTs. Based on our findings, we propose a digital health scorecard visualisation that would break down app quality criteria and present them in a more accessible way to clinicians and pregnant users. We conclude that these findings highlight the shortcomings of available commercial pregnancy apps and the utility of a digital health scorecard visualisation that would empower users to make more informed decisions about which apps are the most appropriate for their needs.

Dynamic load balancing upon the replication and deletion of cloud services

In the last decade, the considerable increase of the cloud services use has led to the need to have search and selection techniques that match both the requirements of end users and those of the system. Indeed, to select a cloud service that meet the needs of both system and user is a challenge, due to the several conflicting criteria problem for the user on one hand, and for the system, i.e., the load balancing between Virtual Machines (VMs), on the second hand. Therefore, the main challenge, in this context, is how to ensure the user requirements by maintaining the system performance constraint. To deal with this challenge, we present in this paper an approach based on the cloud service replication on one or more VMs when the number of the user requests will be important at a given moment. This allows better load balancing between VMs by distrusting the users’ requests over them. In addition, it allows to select the best cloud service according to the users need. However, the cloud services replication introduces the problem of the storage space saturation. Thus, our second contribution is to select and delete the cloud service replicas without degradation of the load balancing. The two proposed contributions are based on the MCDM techniques in order to select the VMs that can receive the replica of the cloud service and to select those, which their storage space is overloaded in order to delete the replica cloud service. The experimental results, based on Cloudsim simulator, show that our proposal can effectively achieve good performance (load balancing) and improve the response time.

Effectiveness of EU-funded R&I programmes from the perspective of the European railway sector stakeholders

The paper provides a methodology for measuring the adherence of Research & Innovation (R&I) actions in railways to the stakeholders’ requirements and expected benefits. After collecting the stakeholders’ requirements, the R&I objectives are extracted from high-level documents such as Europe's Rail Joint Undertaking Master Plan and Multi Annual Work Programme. Lastly, after standardization of the requirements and the technology concepts, a top-down analysis is performed. The results show that network management and vehicle technologies have the highest benefit for the stakeholders, and ERRAC and Regulatory Bodies’ requirements are well addressed. However, the end-users requirements are only partially fulfilled. The necessity of increasing multimodal transport and cooperation with other transport modes is shown, while stronger focus can be placed on the employees and education/training.

Optimized Strategy for Inter-Service Communication in Microservices

In the last decade, many enterprises have moved their software deployments to the cloud. As a result of this transmission, the cloud providers stepped ahead and introduced various new technologies for their offerings. People cannot gain the expected advantages from cloud-based solutions merely by transferring monolithic architecture-based software to the cloud since the cloud is natively designed for lightweight artifacts. Nowadays, the end user requirements rapidly change. Hence, the software should accommodate those accordingly. On the contrary, with Monolithic architecture, meeting that requirement change based on extensibility, scalability, and modern software quality attributes is quite challenging. The software industry introduced microservice architecture to overcome such challenges. Therefore, most backend systems are designed using this architectural pattern. Microservices are designed as small services, and those services are deployed in the distributed environment. The main drawback of this architecture is introducing additional latency when communicating with the inter-services in the distributed environment. In this research, we have developed a solution to reduce the interservice communication latency and enhance the overall application performance in terms of throughput and response time. The developed solution uses an asynchronous communication pattern using the Redis Stream data structure to enable pub-sub communication between the services. This solution proved that the most straightforward implementation could enhance the overall application performance.