Prototype Solution Research Articles

Developing an educational resource for people experiencing eating disorders during the menopause transition: A qualitative co-design study

BackgroundThe pronounced changes in reproductive hormones, such as oestradiol and progesterone, that occur during the menopause transition can contribute to increased risk of eating disorder onset or exacerbate a pre-existing eating disorder. Despite this heightened risk, there is a lack of available education and support that takes into consideration the unique challenges of experiencing an eating disorder during the menopause transition. This research aimed to qualitatively explore the perspectives of people with a lived experience of an eating disorder during the menopause transition, and to co-design a support option that addressed their unmet needs.MethodsA Double Diamond co-design process was followed involving four phases: discover, define, develop, and deliver. Seventeen women in Australia with a lived experience of an eating disorder during the menopause transition participated in online workshops across the four phases to identify their unmet health educational needs in experiencing an eating disorder during this transition, develop potential solutions and ultimately deliver a prototype solution in the final phase. All online workshops were recorded, transcribed verbatim and analysed using qualitative thematic analysis. The findings from the previous phase informed the next leading to the prototype creation.ResultsQualitative thematic analysis identified six major themes across the four phases; lack of awareness of the intersection of menopause and eating disorders, lack of education, limited and stigmatising services, learning from lived experience, resource impact and resource development.ConclusionsFindings from this study provided preliminary acceptability of a novel online resource to address the unmet educational needs of people experiencing an eating disorder during the menopause transition. Overall positive feedback on the potential for the resource to improve knowledge and empower treatment-seeking was provided by women with lived experience.

INTELLECTUAL SUPPORT OF THE PROCESSES OF SEARCHING AND EXTRACTION OF PRECEDENTS IN CASE-BASED REASONING APPROACH

Context. The situational approach is based on the real-time decision-making methods for solving current problem situation. An effective tool for implementing the concept of a situational approach is an experience-based technique that widely known as сasebased reasoning approach. Reasoning by precedents allows solving new (latest) problems using knowledge and accumulated experience of previously solved problems. Since cases (precedents) describing a scenario for solving a certain problem situation are stored in the case library, their search and retrieval directly determine the system response time. In these conditions, there is a need to find ways of solving an actual scientific and practical problem aimed at optimizing case searching and extracting processes. The object of the paper is the processes of searching and extracting of cases from the case library. Objective. The purpose of the article is to improve the process of cases searching in CBR approach by narrowing down the set of cases permissible for solving the current target situation, and excluding from further analysis such cases that do not correspond to the given set of parameters of the target situation. Method. The research methodology is based on the application of rough set theory methods to improve the decision-making procedure based on reasoning by precedents. The proposed two-stage procedure for narrowing the initial set of cases involves preliminary filtering of precedents whose parameter values belong to the given neighborhoods of the corresponding parameters of the target situation at the first stage, and additional narrowing of the obtained subset of cases by the methods of rough set theory at the second stage. The determination of the R-lower and R-upper approximations of a given target set of cases within the notation of rough set theory allows dividing (segmenting) the original set of cases available for solving the current problem stored in case library into three subgroups (segments). The search for prototype solutions can be performed among a selected subset of cases that can be accurately classified as belonging to a given target set; which with some degree of probability can be attributed to the given target set, or within the framework of the union of these two subsets. The third subset contains cases that definitely do not belong to the given target set and can be excluded from further consideration. Results. The problem of presentation and derivation of knowledge based on precedents has been considered. The procedure for searching for precedents in case library has been improved in order to reduce the system response time required to find the solution closest to the current problem situation by narrowing the initial set of cases. Conclusions. The case-based reasoning approach is received the further development by segmenting cases in terms of their belonging to a given target set of precedents uses methods of the rough set theory, then the search for cases is carried out within a given segment. The proposed approach, in contrast to the classic CBR framework, uses additional knowledge derived from obtained case segment; allows modeling the uncertainty regarding the belonging / non-belonging of a case to a given target set; removing from further consideration cases that do not correspond to a given target set.

Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries.

Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad. To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix. The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%, and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.

Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence-based Screening for Diabetic Retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness. Artificial intelligence (AI) has been proposed to provide a novel opportunity to increase screening for DR. While it is paramount to ensure AI has adequate technical capabilities to perform accurate screening, it is also important to assess how to best implement such technology into clinical practice. Human-centered design offers a methodology to understand the real-world context and behaviors of individuals, engage stakeholders, and rapidly prototype and test solutions, enhancing usability and avoiding unintended consequences. This review describes the methodology of human-centered design, examining how it has been used within a variety of health care contexts, with a particular focus on how it has been used to implement an AI-based DR screening program. Further research is needed to understand the best strategies to implement and evaluate AI in health care.

Modeling and Identification of Electromechanical Actuators for the ILR-33 AMBER Rocket

The aim of the work was to develop a method for modeling, testing, and identifying electromechanical actuators for rocket applications. Works were performed using a prototype solution designed for the ILR-33 AMBER suborbital rocket, developed by Łukasiewicz Research Network – Institute of Aviation. A set of physical relationships was used to create system’s mathematical model, including Kirchhoff’s laws, Newton’s laws, and nonlinear friction models. System’s tests were then performed. A new method of results analysis was applied herein to gather unknown parameters for the model and to confirm an elevated level of convergence between both model and experiment in all cases analyzed. The identification approach proved itself to be effective and useful. A complex approach concerning modeling, testing and identification of such actuators was explained for the first time in this paper. The methods presented herein can be applied in other disciplines, wherever electromechanical actuator systems are used, and where their proper identification is necessary to ensure system reliability and safety. Presented solutions are simple to implement, and the test stands do not require expensive measurement equipment. The results obtained permit to create a high fidelity model at a reasonably low computational cost.

Strengthening Capacity for Tailored Immunization Programs Using Adult Learning Principles: A Case Study from Nigeria.

Introduction: Nigeria has the highest number of children who have not received any vaccines in Africa. The training-of-trainers (TOT) model used to train program managers (PMs) and health care workers (HCWs) is ineffective for adult learning and limits immunization programs' success. We incorporated adult learning principles (ALPs) in designing and delivering TOT for immunization PMs and HCWs to use data to engage communities for tailored immunization strategies.Methods: Our study was implemented in 3 local government areas (LGAs) of the Federal Capital Territory, Nigeria. A training curriculum was developed, integrating ALPs and technical and operational content based on best practices in delivering immunization training and the training needs assessment findings. State PMs (n=10), LGA PMs (n=30), and HCWs (n=42) were trained on the human-centered design for tailoring immunization programs (HCD-TIP) approaches using ALPs. We used interviews and surveys with purposively and conveniently sampled PMs and HCWs, respectively, and observations to assess participants' satisfaction, knowledge and competence, behavior changes, and results. The interviews were analyzed thematically, and surveys were statistically.Results: There was a high level of satisfaction with the training among LGA PMs (100%), state PMs (91%), and HCWs (85%), with significant knowledge and competence improvements post-training (P<.001). The trained participants conducted 2 HCD sessions with 24 undervaccinated communities and co-designed 24 prototype solutions for testing. Results showed increased coverage of the pentavalent vaccine first dose (54%) and third dose (188%) across 12 participating communities. Improved community colaboration, communication skills, and data-driven approaches were the most cited behavior changes in practice.Conclusion: The application of ALPs in training, use of HCD-TIP approaches and tools, and supportive supervision enhanced PMs' and HCWs' capacity for tailored interventions. Countries should consider adopting a holistic approach that focuses on using these approaches in immunization programs to strengthen the health system for equitable vaccine coverage.

Improving transparency and quality assurance: Operationalising semi-automated data provenance tracking in a Trusted Research Environment

We present a prototype solution for improving transparency and quality assurance of the data linkage process through a data provenance dashboard designed to assist data analysts, researchers and information governance teams in authenticating and auditing data workflows within a trusted research environment (TRE). Building on prior research (Scheliga, et al., 2022), this work describes our first operationalised prototype tested in a real-world setting. The prototype development involved four stages: (1) Co-design of interfaces with end users for provenance data collection and visualisation, producing a low-fidelity design; (2) Extension and refinement of the Safe Haven Provenance (SHP) ontology (https://tre-provenance.github.io/SHP-ontology/releases/v0.2/index-en.html); (3) Design and implementation of mechanisms for semi-automated collection of data linkage provenance using the SHP ontology; (4) Implementation and user evaluation of the dashboard. A participatory design process with data analysts, researchers and information governance teams resulted in a low-fidelity prototype and was validated via public consultations to ensure it met public trust. The resulting prototype dashboard (https://tre-provenance.github.io/) was built as an offline desktop app to match the deployment TRE requirements. The interactive dashboard displays the data linkage information extracted from a knowledge graph described using the SHP ontology (e.g., modifications of datasets, data release) and results of rule-based validation checks (e.g., checking extracted data against researchers' specification). User evaluations confirmed the dashboard would contribute to better quality of data linkage. This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking from ingress to egress in a single tool.

The Development of a Prototype Solution for Detecting Wear and Tear in Pedestrian Crossings

Crosswalks play a fundamental role in road safety. However, over time, many suffer wear and tear that makes them difficult to see. This project presents a solution based on the use of computer vision techniques for identifying and classifying the level of wear on crosswalks. The proposed system uses a convolutional neural network (CNN) to analyze images of crosswalks, determining their wear status. The design includes a prototype system mounted on a vehicle, equipped with cameras and processing units to collect and analyze data in real time as the vehicle traverses traffic routes. The collected data are then transmitted to a web application for further analysis and reporting. The prototype was validated through extensive tests in a real urban environment, comparing its assessments with manual inspections conducted by experts. Results from these tests showed that the system could accurately classify crosswalk wear with a high degree of accuracy, demonstrating its potential for aiding maintenance authorities in efficiently prioritizing interventions.

A Conceptual Educoach Multi-Sided Business Model: Online Tutoring Platform to Improve Career Opportunities of B40s and Unemployed Graduates as Digital Entrepreneurs

The paper discusses the development of EduCoach, an online tutoring platform aimed at supporting B40 university students in Malaysia, who come from families with monthly incomes below RM 4,850. EduCoach connects these students with qualified tutors for personalized, one-on-one academic assistance, aiming to enhance academic outcomes and student satisfaction. Despite competition from similar platforms, EduCoach emphasizes ensuring high-quality tutoring to stand out. It also offers tutors, especially B40 working adults and the unemployed, a chance to earn extra income and gain teaching experience, albeit with the need to manage their time effectively. Additionally, note sellers can monetize their academic materials, provided they maintain high standards of quality and organization. The platform adopts a design thinking approach to empathize with users, define problems, and prototype solutions, ensuring a user-friendly and effective service. EduCoach addresses the educational challenges faced by university students while offering flexible income opportunities for tutors and note sellers.

Design and Construction of an Automated Boat that Removes Debris from the Ocean

Water bodies have a huge aquatic ecosystem within them. Any high concentration of suspended solids can cause numerous issues for aquatic life by blocking light from reaching submerged vegetation, hence there is a need for a solution to remove this debris from the water bodies. This project design and construction of an automated boat to remove debris from the ocean is focused on developing a robust and efficient prototype solution for autonomously collecting and managing marine Debris, thereby contributing to environment conservation. The two-channel remote control with receiver will enable the boat to navigate predefined routes, identify and collect debris using specialized mechanism and return to a designated collective point. The Two-Channel remote control system allows for precise maneuverability and control over the boat’s function. Through a combination of engineering principles, propellers, conveyer belt, tread mill arrangement and shafts. this project aims to address the critical issues of ocean pollution while showcasing the potential for remote controlled boat in maritime environmental management. The anticipated outcomes include a functional prototype, comprehensive operational documentation and preliminary performance assessments, setting the stage for potential scalability and deployment in real-world Ocean clean up initiative

The Development of a Prototype Solution for Collecting Information on Cycling and Hiking Trail Users

Hiking and cycling have gained popularity as ways of promoting well-being and physical activity. This has not gone unnoticed by Portuguese authorities, who have invested in infrastructure to support these activities and to boost sustainable and nature-based tourism. However, the lack of reliable data on the use of these infrastructures prevents us from recording attendance rates and the most frequent types of users. This information is important for the authorities responsible for managing, maintaining, promoting and using these infrastructures. In this sense, this study builds on a previous study by the same authors which identified computer vision as a suitable technology to identify and count different types of users of cycling and hiking routes. The performance tests carried out led to the conclusion that the YOLOv3-Tiny convolutional neural network has great potential for solving this problem. Based on this result, this paper describes the proposal and implementation of a prototype demonstrator. It is based on a Raspberry Pi 4 platform with YOLOv3-Tiny, which is responsible for detecting and classifying user types. An application available on users’ smartphones implements the concept of opportunistic networks, allowing information to be collected over time, in scenarios where there is no end-to-end connectivity. This aggregated information can then be consulted on an online platform. The prototype was subjected to validation and functional tests and proved to be a viable low-cost solution.

Development of a Prototype Solution for Reducing Soup Waste in an Institutional Canteen

Food waste has gained increasing attention and debate, given its economic, environmental, social, and nutritional implications. One-third of food intended for human consumption is wasted. Although it is present at all stages of the food supply chain, it is in the final stages of consumption, such as households and food services, that the problem becomes most evident. This work builds on a previous study by the same authors, which identified computer vision as a suitable technology for identifying and quantifying food waste in institutional canteens. Based on this result, this paper describes the proposal and implementation process of a prototype demonstration. It is based on a Raspberry Pi 4 platform, a ResNet-50 model adapted with the Faster Region-Convolutional Neural Network (Faster R-CNN) model, and an algorithm for feature extracting. A specially built dataset was used to meet the challenge of detecting soup bowls and classifying waste in their consumption. A web application was developed to visualize the data collected, supporting decision making for more efficient food waste management. The prototype was subjected to validation and functional tests, and proved to be a viable, low-cost solution.

A Local-First Approach for Green Smart Contracts

Shared code in blockchains, known as smart contracts , stands to replace important parts of our digital governance and financial infrastructure. The permissionless execution of smart contracts is tightly coupled to cryptocurrencies and Proof-of-Work blockchains. As a result, smart contracts inherit the environmental impact of Proof-of-Work blockchains, such as its energy consumption, carbon footprint, and electronic waste. The four concepts of relaxed consistency, strong identities, probabilistic consensus, and the use of liabilities instead of assets may change the status quo. This work explores the integration of these concepts to decouple smart contracts from Proof-of-Work blockchains. By means of a local-first approach, which may expose users to inconsistent ephemeral contract states, the architecture of smart contracts can be transformed to become green. Because such contract states may be dropped, we base the interactions between users on liabilities. We propose a novel paradigm for smart contract architectures, named Green Smart Contracts, that is based on a local-first approach. Furthermore, we present and implement a prototype solution for this paradigm. We validate the need for a mechanism to resolve consistency violations by replaying the contract calls of a real smart contract. Our simulation shows that violations occur more often (13% of contract invocations) when using liabilities than when using a traditional blockchain (3% of contract invocations). However, we additionally validate that they can be avoided using a consensus mechanism, and our experiments show that a publish-subscribe messaging pattern uses the fewest messages to do so, though it may not be applicable for use cases that disallow the inherent imbalance in the messaging between peers. Our carbon emission estimation shows that a Green Smart Contract approach lowers carbon emissions by 52.31% when compared with the messaging behavior of a typical peer-to-peer blockchain with 1000 nodes.

Transmission of 10 Gbps C-band-signal-based Radio Over Fiber for Next Generation Communication Systems

Rapid development of 5G networks encourages researchers to improve the radio-over-fiber (RoF) technique in order to reach 10 Gbps data transmission rates, to increase bandwidth and range, while reducing latency and implementation cost. This paper evaluates an analog radio-over-fiber (ARoF) technique that is compatible with long-distance communication systems. We demonstrate a long distance transmission of a 28 GHz 64 QAM signal via a single mode fiber (SMF) after modulating it with the use of two parallel Mach-Zehnder modulators, without any optical amplifiers. The results show that our prototype solution is capable of transferring data over distances of up to 140 km, via SMF, with a 10 Gbps data rate. The error vector magnitude (EVM) was found to be 7.709%. The proposed system offers exceptional capabilities in terms of supporting high bitrates, while ensuring that EVM remains within the 3GPP limits. Compared to other works, the proposed solution proves to be superior in terms of performance, making it an ideal choice for next generation long-haul communication systems.

Suitable solutions and EU regulatory framework of electric light mobility vehicles for last-mile delivery: an overview

Due to current environmental European regulations in terms of pollutant emissions, electric cargo cycles are receiving growing interest compared to heavy-duty vehicles as a cheaper and healthier alternative for last-mile delivery of goods in urban and suburban areas. Within the PNRR Next Generation EU program, the Sustainable Mobility Center aims to design innovative e-cargo bikes considering technical features along with rider behaviour and infrastructure characteristics. The market offers plenty of solutions, which may differ in technical and design features. Nowadays a standardized terminology, widely shared among researchers for those light mobility vehicles does not exist. Several authors employ different terms or introduce classifications based upon the vehicle type of use or even on the vehicle layout (e.g., payload position or number of wheels). Concurrently, EU regulatory framework is undergoing rapid changes and appears unclear. Certain categories of light mobility vehicles might be ruled out from the type-approval system established by the regulation EU 168/2013. For those vehicles, harmonised standards are under drafting or approval. This situation requires a considerable effort among manufacturers in the identification of the proper product directives or regulations and the associated mandatory requirements and testing procedures for the introduction of a new vehicle model in the market. Within this context, an overview of the EU regulatory framework for these light mobility vehicles is presented. A literature evaluation with a structured and coded methodology of scientific databases, company websites and commercial reports is performed, evaluating commercial and prototype solutions of e-cargo bikes from a technical and legislative perspective. Furthermore, a case study is addressed, deepening the national legislation which rules the use of e-cargo bikes in Italy. The information provided in this work may be useful among manufacturers as a starting point for the design process of these types of vehicles. Moreover, this paper lays the foundations for a widely recognized taxonomy of e-cargo bikes based upon distinctive features.

Rapid Prototyping of Anomalous Reflective Metasurfaces Using Spray-Coated Liquid Metal.

Reconfigurable intelligent surfaces (RISs) have the potential to improve wireless communication links by dynamically redirecting signals to dead spots. Although a reconfigurable surface is best suited for environments in which the reflected signal must be dynamically steered, there are cases where a static, non-reconfigurable anomalous reflective metasurface can suffice. In this work, spray-coated liquid metal is used to rapidly prototype an anomalous reflective metasurface. Using a pressurized air gun and a plastic thin-film mask, a metasurface consisting of a 6 × 4 array of Galinstan liquid-metal elements is sprayed within minutes. The metasurface produces a reflected wave at an angle of 28° from normal in response to a normal incident 3.5-GHz electromagnetic plane wave. The spray-coated liquid-metal metasurface shows comparable results to an anomalous reflective metasurface with copper elements of the same dimensions, demonstrating that this liquid-metal fabrication process is a viable solution for the rapid prototyping of anomalous reflective metasurfaces.

Closed-loop control of a 3D printed soft actuator with integrated flex sensors and SMA wires

This article presents a soft SMA-driven actuator capable of achieving desired bending angles. To create the actuator, flex sensors and Shape memory alloy (SMA) wires were embedded into the thermoplastic polyurethane (TPU) matrix of the sample during the 3D printing process. By deactivating and activating the two SMA wires according to sensing signals from the flex sensors, the actuator sample can track the input bending angle. This approach enabled closed-loop control and improved the overall efficiency of the system. Additionally, the proposed manufacturing process provides a simple and cost-effective solution for the rapid prototyping and custom design of smart materials with complex structures and high resolutions.

A Study on Automated Problem Troubleshooting in Cloud Environments with Rule Induction and Verification

In a vast majority of cases, remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems suffers from accurate recommendations, which could be supplied in a timely manner for recovery of performance degradations. This is hard to realize by furnishing those abnormality definitions with appropriate expert knowledge, which varies from one environment to another. At the same time, in many support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams, making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the lack of a systematic approach to adopting AI Ops significantly impacts the mean-time-to-resolution (MTTR) rates of problems/alerts. This would imply building, maintaining, and continuously improving/annotating a data store of insights on which ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study aligns with this vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied to predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up-to-date analysis demonstrating how root conditions are discovered accurately for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer experts a Dempster–Shafer theory-based rule verification framework as a what-if analysis tool to test their hypotheses about the underlying environment.

Modelling and Simulation of a Hydraulically Operated Solar Tracker

The work deals with the design of the hydraulically operated solar tracker used for a photovoltaic (PV) platform. The tracking mechanism is a dual-axis, azimuthal type, the primary axis, that of the daily/diurnal movement, being positioned vertically, which provides a high stability of the entire structure. The actuation in both movement subsystems (daily and elevation) is achieved by using linear actuators, in the subsystem for the daily movement a stroke amplification mechanism being interspersed between the actuator and the rotating part of the support pillar, which constitutes an element of novelty/originality regarding the mechanical device of the solar tracker. The sun tracking is conducted by an open-loop control strategy, based on a predefined step-by-step algorithm, which was designed so that to capture as much as possible incident solar radiation with a minimum energy consumption for achieving the orientation. The modeling and simulation of the solar tracker is carried out in mechatronic concept, by using the virtual prototyping solutions ADAMS (for the mechanical device model) and EASY5 (for the control system model) of MSC Software.

Digital twin and digital thread within the product lifecycle management

In today's VUCA-defined age, accelerated by the rapid digital transformation brought on by the COVID-19 pandemic, the business sector needs to be able to adapt to expected and unexpected changes. Consistently improving the product quality and managing the whole product life cycle, starting from the new product development (NPD) process, and the related data, are important aspects of keeping a company competitive in a global landscape. This paper highlights the growing potential of Industry 4.0, particularly the digital thread (DTH) and digital twin (DT), in addressing these challenges. The research revealed existing gaps in clarity and practical guidelines for digital twin prototypes (DTP) and DTH in the existing literature. Moreover, additional issues within product life cycle management (PLM) were stated, regarding frequent data loss and the complexity of product audits in the perspective of a continuous improvement. Therefore, a definition and novel framework for DTP and DTH implementation was proposed, after analyzing and comparing state of the art literature. This framework is additionally based on product audits carried out during the design stage of the product development process using requirements engineering and quality control information added within the suggested framework. Preliminary tests with DTP and DTH commercial off-the-shelves and prototype solutions were carried out with a consumer product. The tests enabled delivering a better understanding of DTPs, their potential, and their implications for decision-making and implementation.