Poor Design Research Articles

Social Media Apps: A Paradigm for Examining Usability of Mobile Apps for Working Age Adults with Mild-Moderate Cognitive Disabilities

An estimated 79% of the US population used some form of social media regularly in 2019, highlighting a powerful platform for fostering connections with others. This aspect is particularly beneficial for people with cognitive disabilities (PwCDs) who may experience extreme social isolation. Studies have linked social media use amongst PwCDs to increased mental well-being and social capital, and PwCDs have reported corresponding increases in self-esteem and social connection. However, research also indicates that usability issues with social media user interfaces (UIs) often create barriers that deter participation of PwCDs. In this study, we investigate the usability of five social media apps for PwCDs, with the goal of identifying key UI features that facilitate/impede user performance and experience. The five apps selected for testing were: Facebook, Instagram, Pinterest, Snapchat, and Tumblr. Six to eight basic tasks were defined for each app, establishing a usability testing framework designed to compare similar tasks across apps. 19 working-age participants diagnosed with mild/moderate cognitive disabilities by their care provider were recruited and tested with each app across five different sessions. Mixed-methods data analysis and source of error analysis were performed for the resultant data. Snapchat and Pinterest were found to have the highest and lowest rates of task failure, respectively, consistent with the participant preference data on satisfaction and perceived usability. The fundamental task types with the highest rate of participant failure across all apps entailed either selecting or creating media, consistent with the identified qualitative theme that PwCDs tend to avoid using more active features in mobile apps due to their higher task complexity. Ten key types of usability issues were identified across all five apps, with the top issues identified as poor icon design and misleading icons, unclear user interface hierarchy, lack of user interface element separation and contrast, and similar task approaches with very different outcomes.

Effects of uncertainty in the design parameters during the design of horizontal gravity oil, gas and water separators

The design of bulk separators for oil, gas, and water vessel-type gravity separators significantly affects the cost of topside and subsea oil- and gas-processing installations. There are often many uncertainties during the design process, such as the separator performance, fluid properties, and variation and evolution of the inlet conditions. The industry employs mechanistic models, empirical correlations, numerical studies using Computational Fluid Dynamics, and extensive and expensive experimental campaigns with separator prototypes. However, owing to time, personnel, and information integration constraints in the design phase, design evaluation, and validation are often limited to performing comprehensive sensitivity studies. This often leads to suboptimal designs and early bottlenecks. This study proposes using stochastic mathematical programming and optimization to include the effects of uncertainties in the design parameters during the separator design process. The proposed solution involves the development of a three-phase horizontal separator design model. The model was implemented using Microsoft Excel Visual Basic for Applications. The study then considered the effects of the uncertainties in the oil, gas, and water flow rates and slug volume on the optimal design using stochastic optimization. Conservative, optimistic, and ultimate design parameters (P10, P50, and P90) were obtained for each scenario. The results of this study provide a method and tool to aid topside design teams while expanding on previous research in the literature regarding separator design.

Using a Fuzzy Model to Evaluate Risks caused by Variation Orders in Construction Projects

The construction industry is the main driving sector for any country's economy. However, it is too risky due to the many risks and challenges in this industry. One of the main challenges in construction projects is variation orders, as they lead to conflicts between project parties and cause poor project performance. This study aims to evaluate the risks associated with variation orders in construction projects using fuzzy risk assessment. The targeted population is the construction contractors in Palestine. A questionnaire survey was used to collect data on the severity and frequency of the 22 identified factors of variation orders. Then, a fuzzy logic system was developed to rate these factors. The factor risk level was determined by connecting the relationship between the severity and frequency indices using If-Then rules. The determination of risk level is a critical task in the proposed fuzzy system, which depends on the complex combinations of all If-Then rules. Risk is determined not only by the If-Then rules but also by the weight of each rule. This study shows that the factors with the highest risk levels are scope change by the client, client financial problems, unavailability of required materials, poor design documents, and modification of specifications. The results can serve as guidelines for project participants who need to prepare and implement a comprehensive and effective risk management plan to meet project goals. To improve project performance, construction parties are recommended to implement the proposed method to assess risks related to construction projects. Finally, the proposed risk model demonstrates the ability to evaluate risk levels by aggregating rules and combining the project risk factors using a fuzzy logic model and MATLAB.

A comprehensive and systematic literature review on intrusion detection systems in the internet of medical things: current status, challenges, and opportunities

The increasing number of medical devices in the Internet of Medical Things (IoMT) environment has raised significant cybersecurity concerns. These devices often have weak security features, poor design, and insufficient authentication protocols, making them vulnerable to cyberattacks and intrusions. To mitigate these threats, robust security measures are essential. This includes implementing strong security protocols, ensuring continuous security monitoring, enforcing regular updates, and maintaining a constant response plan. Additionally, designing an effective Intrusion Detection System (IDS) is crucial to safeguard patient data and devices. This paper systematically studies the current state of the literature and the essential methods for intrusion detection in the IoMT. Employing a selection process, the paper identifies 28 critical studies published between 2018 and April 2024. The intrusion detection mechanisms in the IoMT are divided into five categories: IDS based on artificial intelligence models, datasets used in IoMT for IDS, fundamental security requirements, intrusion detection processes, and evaluation metrics. This paper dissects the various mechanisms within each category in a meticulous and comprehensive analysis. Finally, the paper examines the challenges and open issues in developing IDSs in IoMT. By offering a roadmap for researchers to enhance IDSs in the IoMT, this paper has the potential to significantly impact the fields of computer engineering, cybersecurity, and healthcare, thereby contributing to the advancement of these crucial fields.

Merits and Demerits of Social Assistance Programs to Poor People

Social assistance programs have increasingly expanded and become a vital tool for poverty alleviation and promoting equitable society, particularly in developing countries. The merits of these programs are well documented in the literature including human capital development, poverty reduction and economic stability and growth. However, such programs are not without demerits; critics argue that social assistance programs have created dependence syndrome among developing countries, implementation challenges due to poor design, low coverage, and limited impacts on the community. This paper discusses the merits and demerits of social assistance programs in developing countries, offering suggestions on how these programs can be refined to foster equilibrium between supporting poor people and promoting sustainable socio and economic self-sufficiency.

Hydrological Parametric Investigation of Water Distribution Network for Sawale Village, Raigad District (Maharashtra): A GIS Approach

The water distribution network (WDN) is considered one of the most important infrastructural requirements for future cities worldwide. In order to meet the growing water demand of the developing population, it is essential to design a sustainable WDN. In recent decades, numerous researches have been conducted in this field considering various parameters, but poor WDN layout design leads to increasing project cost in terms of execution, maintenance, etc. Hence, the objective of this research work was to overcome the above drawbacks by designing a real time network for a rural emerging village near Rasayani, Maharashtra with the help of Geographical Information System (GIS) and hydraulic simulation using Environmental Protection Agency Network (EPANET). The area of Sawale village is surveyed both physically and using the software. The basic objective of this research is to determine the best possible way in which a given WDS can be optimized to meet the current water demand in Sawale village. EPANET is hydraulic simulation software that can be efficiently used for such purposes. The results show that hydraulic simulation together with the application of GIS helps to create a less time consuming, sustainable and optimized WDN.

Advancing Microscale Electromagnetic Simulations for Liquid Crystal Terahertz Phase Shifters: A Diagnostic Framework for Higher-Order Mode Analysis in Closed-Source Simulators

This work addresses a critical challenge in microscale computational electromagnetics for liquid crystal-based reconfigurable components: the inadequate capability of current software to accurately identify and simulate higher-order modes (HoMs) in complex electromagnetic structures. Specifically, commercial simulators often fail to capture modes such as Transverse Electric (TE11) beyond the fundamental transverse electromagnetic (TEM) mode in coaxial liquid crystal phase shifters operating in the terahertz (THz) regime, leading to inaccurate performance predictions and suboptimal designs for telecommunication engineering applications. To address this limitation, we propose a novel diagnostic methodology incorporating three lossless assumptions to enhance the identification and analysis of pseudo-HoMs in full-wave simulators. Our approach theoretically eliminates losses associated with metallic conductivity, dielectric dissipation, and reflection effects, enabling precise assessment of frequency-dependent HoM power propagation alongside the primary TEM mode. We validate the methodology by applying it to a coaxially filled liquid crystal variable phase shifter device structure, underscoring its effectiveness in advancing the design and characterization of THz devices. This work provides valuable insights for researchers and engineers utilizing closed-source commercial simulators in micro- and nano-electromagnetic device development. The findings are particularly relevant for microscale engineering applications, including millimeter-wave (mmW), sub-mmW, and THz systems, with potential impacts on next-generation communication technologies.

Finite element-based nonlinear dynamic optimization of nanomechanical resonators

Nonlinear dynamic simulations of mechanical resonators have been facilitated by the advent of computational techniques that generate nonlinear reduced order models (ROMs) using the finite element (FE) method. However, designing devices with specific nonlinear characteristics remains inefficient since it requires manual adjustment of the design parameters and can result in suboptimal designs. Here, we integrate an FE-based nonlinear ROM technique with a derivative-free optimization algorithm to enable the design of nonlinear mechanical resonators. The resulting methodology is used to optimize the support design of high-stress nanomechanical Si3N4 string resonators, in the presence of conflicting objectives such as simultaneous enhancement of Q-factor and nonlinear Duffing constant. To that end, we generate Pareto frontiers that highlight the trade-offs between optimization objectives and validate the results both numerically and experimentally. To further demonstrate the capability of multi-objective optimization for practical design challenges, we simultaneously optimize the design of nanoresonators for three key figure-of-merits in resonant sensing: power consumption, sensitivity and response time. The presented methodology can facilitate and accelerate designing (nano) mechanical resonators with optimized performance for a wide variety of applications.

Software Metric Based Impact Analysis of Code Smells ‐ A Large Scale Empirical Study

ABSTRACTContextCode smells are indicators of poor design and implementation choices that negatively affect software quality and maintainability. Moreover, it is difficult and time‐consuming to work with a long list of the code smells, as not all of those smells have equal impact on the system. So, understanding the individual impact of the code smells is significant while performing refactorings on a priority basis.ObjectiveDespite significant research efforts aimed at detecting and refactoring these code smells, understanding their individual impact on software quality metrics such as size, complexity, coupling, etc. remains still unclear.MethodologyTo mitigate this research gap, we present an empirical investigation on the impact analysis of code smells based on the 25 software quality metrics such as size, cyclomatic complexity, coupling, etc. To the best of our knowledge, this is the largest empirical study about the impact analysis of code smells with respect to the number of software metrics. Particularly for this study, we identify 13 code smells in 35 open‐source software systems, and analyze (1) the relationship between code smells and software metrics, (2) which code smells are highly impactful that affect the metrics, and (3) which impactful smells occur frequently in the systems.ResultsThe results show varying degrees of correlation‐based impact between specific code smells and software metrics, with some smells showing strong correlations with multiple metrics. Three categories of impact for the code smells have been identified, namely High, Moderate and Low, where Long Method, Anti Singleton, Complex Class, Large Class and Long Parameter List smells have high impact, but their frequencies are not high except Anti Singleton; Refused Parent Bequest, Spaghetti Code and Blob have moderate impact; and rest of the smells have low impact. We also observe that perceptions about the impact of code smells vary from developer to developer and they most cases refactor the smells based on their intuition.ConclusionOur findings will help them to refactor the smells on an objective‐based instead of an intuition‐based, which will be more significant to improve the software quality. For example, refactoring the smells having a high impact on the coupling between objects metric can be an objective. Furthermore, our results will not only assist developers in prioritizing refactoring activities but also provide researchers with valuable insights to innovate tools that prioritize refactoring based on the impact of code smells. These tools will help developers target the most impactful smells and thus enhance the overall quality and maintainability of software systems.

Development of Hollow Fiber Membranes Suitable for Outside-In Filtration of Human Blood Plasma.

Hemodialysis (HD) is a critical treatment for patients with end-stage kidney disease (ESKD). The effectiveness of conventional dialyzers used there could be compromised during extended use due to limited blood compatibility of synthetic polymeric membranes and sub-optimal dialyzer design. In fact, blood flow in the hollow fiber (HF) membrane could trigger inflammatory responses and thrombus formation, leading to reduced filtration efficiency and limiting therapy duration, a consequence of flowing the patients' blood through the lumen of each fiber while the dialysate passes along the inter-fiber space (IOF, inside-out filtration). This study investigates the development of HF membranes for "outside-in filtration" (OIF) in HD. In OIF, blood flows through the inter-fiber space while dialysate flows within the fiber lumens, reducing the risk of fiber clogging and potentially extending treatment duration. For the OIF mode, the membrane should have a blood-compatible outer selective layer in contact with the patient's blood. We develop HFs for OIF via liquid-induced phase separation using PES/PVP (polyethersulphone/polyvinylpyrrolidone) blends. The fibers' surface morphology (SEM, scanning electron microscopy), chemistry (ATR-FTIR-attenuated total reflection-Fourier transform infrared spectroscopy, XPS-X-ray photoelectron spectroscopy), transport properties, and uremic toxin removal from human plasma are evaluated and compared to commercial HFs. These membranes feature a smooth, hydrophilic outer layer, porous lumen, ultrafiltration coefficient of 13-34 mL m2 h-1 mmHg-1, adequate mechanical properties, low albumin leakage, and toxin removal performance on par with commercial membranes in IOF and OIF. They offer potential for more efficient long-term HD by reducing clogging and systemic anticoagulation needs and enhancing treatment time and toxin clearance.

Workload analysis of noodle sheeting slitting machine operators using the heart rate variability method

Abstract One of the most most popular foods in the community is noodles. In the noodle processing process, poor design and work systems can increase the workload of machine operators. This study uses heart rate variability (HRV) monitoring to examine the workload of noodle machine operators. The operator’s heart rate was measured using a Garmin Pro device during each stage of the noodle moulding process: preparation, operation, and finishing. Software Kubios HRV Standard 3.5.0 was used to process the data, and the analysis parameters that were used were SDNN, RMSSD, LF, HF, and LF/HF. The data processing results show that the preparation stage bears the heaviest workload, as evidenced by the lowest average values of SDNN, RMSSD, and HF. Conversely, the operation stage shows the lightest workload, reflected by the highest average values of SDNN, RMSSD, and HF.

Hybrid CNN‐GNN Framework for Enhanced Optimization and Performance Analysis of Frequency‐Selective Surface Antennas

ABSTRACTFrequency‐selective surface (FSS) antennas are critical in modern communication systems, where optimizing their design for enhanced performance is essential. However, traditional methods often struggle with the complexity of FSS structures, leading to suboptimal designs. This paper addresses these limitations by proposing a novel CNN‐GNN hybrid network (CGHN) framework for FSS antenna optimization. The proposed methodology integrates convolutional neural networks (CNNs) for efficient feature extraction of spatial patterns within FSS designs and graph neural networks (GNNs) to model the relational dependencies between unit cells. This approach ensures that both local features and global interactions are captured, leading to more accurate and optimized antenna designs. The objective is to enhance the performance of FSS antennas by leveraging the complementary strengths of CNNs and GNNs, with an emphasis on improving design accuracy and efficiency. The novelty lies in the combination of CNN's localized pattern recognition with GNN's relational learning, which together enable a comprehensive understanding of the antenna's behavior. The proposed CGHN framework achieves a 96.78% accuracy rate in predicting optimal FSS designs, with a 23.84% boost in performance due to CNN‐driven feature extraction. Additionally, implementing stochastic gradient descent with gradient clipping increased the F1 score by 15%. Compared with existing techniques, the proposed method demonstrates significant improvements in both accuracy and efficiency, making it a superior choice for FSS antenna design optimization.

Integration of strategic environmental assessment (SEA) in motorway/highway planning and construction: A case study for sustainable infrastructure development in Pakistan

Pakistan has constantly prioritized the development of roads and motorways as they are they are reflective of nation’s economic and physical condition. However, poor highway planning, design, and construction can have a negative impact on the environment and can lower the quality of life for inhabitants. Therefore, eco-friendly roadway/motorway planning, and construction approaches are crucial for reducing environmental, social, and economic damages. Currently, environmental impact assessment (EIA) is the prevalent method for assessing and evaluating the environmental impact of road infrastructure projects. Still, this conventional approach focuses primarily on development projects and lacks strategic insight. Hence, the world is now focusing on integrating and implementing SEA as a realistic approach for executing a sustainability plan in every area, including the infrastructure and transportation sector. Pakistan invests a considerable portion of its annual budget on infrastructure, particularly roadways; therefore, it must incorporate the changing trends and parameters into the planning and developing phase. thus, current research aims to incorporate SEA into the strategic planning of motorway/highway development by conducting a desktop study using secondary sources, including research articles and EIA reports, to analyze the environmental and socioeconomic effects of motorway/highway by identifying the direct, indirect, and commutative impacts. The alternatives are assessed using impact criteria and indicators to choose the best sustainable option for achieving the required objectives. our study is a pioneer work to integrate SEA into the planning process for highway construction, which would act as a baseline and an advisory tool for the decision-makers to incorporate SEA in Pakistan’s strategic development planning.

Dental students’ perceptions of integrating online learning in the dental curriculum

IntroductionFollowing the worldwide lockdown due to COVID-19, education globally switched from classroom to online learning. This transition impacted the teaching techniques used in dental education. Thus, the objective of this study was to explore the perceptions and attitudes of undergraduate dental students’ experience with online learning. The benefits and drawbacks of integrating online learning into dental teaching were assessed as well as the factors that enhanced the online learning experience.MethodsThis is a cross sectional descriptive study employing a survey design. A web-based survey was distributed to the undergraduate dental students at Jordan University of Science and Technology. All dental undergraduate students of different academic levels were eligible to participate (n = 1500). The responses were collected anonymously through August 2022. Descriptive statistics and the Pearson Chi-square test were utilized.ResultsA total of 984 undergraduate dental students participated in this questionnaire. Females and year four students dominated the study population (64.7%, 31.3%). Internet connection was not a barrier to online learning for most students (61.4%). The preference for online learning over on campus for most students was due to time and money saving, better comprehension, and less social anxiety (n = 421, 45.7%). Drawbacks of online learning for most students (60%) were the difficulty of maintaining focus and the poor design of lectures (76.3%). The low cost of the internet was a factor that significantly encouraged students to favour online learning (P value = 0.01). Active interaction during lectures was a main factor that encouraged students to stay focused during online sessions (P value < 0.001). For a significant number of students, the lecturers demonstrated superior teaching skills during online sessions in comparison to the classroom (P < 0.001).ConclusionDespite the positive attitude of undergraduate dental students towards online learning, many improvements are required. Enhancing online lecture design which can be facilitated by implementing comprehensive training and workshop programs for lecturers. Actively engaging students during lectures is necessary to help students regain focus during online sessions. Finally, decreasing lecture times to match students’ attention spans is fundamental. By focusing on interface design and providing structured training for lecturers, a more effective online learning environment can be achieved.

Reactivation of Abandoned Oilfields for Cleaner Energy Generation: Three-Dimensional Modelling of Reservoir Heterogeneity and Geometry

With the changing picture of global energy supplies and the shift toward the energy transition, it has never been more important to look for alternative sources of energy. Globally there are tens of thousands of abandoned oil fields with considerable reserves left behind. These have the potential to be reactivated to become an energy supply that is cleaner than conventional oil and gas. This can be achieved by the use of in situ combustion and the subsequent exploitation of the inherent increase in temperature and pressure to produce geothermal energy, allied to sequestration of the mixture of produced fluids. In situ combustion (ISC) has conventionally been used as an enhanced oil recovery technique, with a high failure rate that has been recently attributed to poor reservoir selection and project design. We suggest that the failure of many earlier ISC projects is due to insufficient appreciation of how the subsurface geology affects the process. With the use of computer numerical modelling, we aim to ascertain how the geometry and heterogeneity of the reservoir control the success of the process. Here we employ simple three-dimensional sector models to assess a variety of different petrophysical heterogeneities, within a set of different reservoir geometries, on the temperature, velocity, propagation stability and enthalpy rate. These models illustrate that the biggest impact on success of the ISC process for geothermal energy generation, as a function of temperature and enthalpy, is the location of the wells relative to the heterogeneities and the scale of heterogeneities. Metre-scale heterogeneities do not have a significant effect on this. Instead, the biggest contributor to the propagation stability and direction of the fire front is the presence of a large-scale (10 s to 100 s of metres) heterogeneities, such as channels, or the geometry of a tilted fault block; both have a strong control over the direction of the propagation, and therefore are important factors with regards to well placement.

Study on the Design Quality Indicators for Building Evaluation of Taiwan Campus

Campus buildings play a crucial role in education. Designing campus buildings in Taiwan to meet high-quality standards is crucial due to the frequent seismic activity and the evolving requirements of modern education. Architects often rely on past experiences and regulations, which may only partially meet user needs, leading to suboptimal designs. This study examines the application of the Design Quality Indicator (DQI) framework to evaluate and enhance the design quality of campus buildings. We reviewed literature and cases to compile suitable DQI metrics, and then conducted the survey to assess various dimensions, including functionality, build quality, and impact. We also collaborated with property management experts to evaluate 60 campus buildings. The results indicate that teaching and dormitory buildings generally achieve higher levels of user satisfaction and operational efficiency. Conversely, administrative buildings show deficiencies in accessibility and innovation. Key issues identified include inadequate indoor environmental quality and insufficient adaptability to future needs. Recommendations for improvement focus on addressing these low-scoring areas through better design integration, flexible layouts, and sustainable technologies. This study highlights the need for continuous evaluation and adaptation in the design of campus buildings to meet the evolving requirements of the educational sector and ensure safety in earthquake-prone regions. Future research could explore the performance of buildings post-occupancy evaluation to assess the potential of incorporating DQI early in the design process, providing valuable insights for the field.

Group discussions: an active learning resource for school and kindergarten leaders?

ABSTRACT Background In higher education programmes, group discussions are commonly used as a form of active learning to support students in their learning and knowledge development beyond what is provided by traditional lectures. However, such student-centred learning approaches and methods have received scant attention in empirical research. Purpose This study, which used an action research design, investigated how group discussions, as an extension of lectures, can contribute to students’ learning. The findings are intended to support comprehensive insights into, and further development of, the authors’ and others’ higher-education teaching practices. Method The study context was a National Kindergarten and School Leadership Programme offered by one Norwegian university, where the co-author of this article was located. Our research question was: How do students perceive group discussions about a lecture as contributing to their learning; and what can we learn from this about our own teaching practices? The core data were students’ reflection notes, with observational data used as background material. Data were analysed thematically. Findings The analysis indicated that group discussions as extensions of lectures can provide learning opportunities that promote the students’ learning, both collectively and individually. Through group discussions, learning opportunities emerge in the interaction between theoretical knowledge addressed in the lecture and practical, experience-based knowledge based on the students’ ideas from their organisational contexts. In this interaction, learning experiences are produced that provide the students with increased understanding and new perspectives about how they can drive development in their practice contexts and how they, as school leaders, can act in new ways. Conclusion The findings offer insight into how students’ experiences of group discussions, based on lectures, contribute to their learning. However, they also reveal that organisational and structural conditions related to poor quality task design and some elements of the discussions themselves can inhibit learning potential.

Studies on Strength Properties of RC Beam Column joint With Basalt Reinforcement

In recent earthquakes it Is observed that the beam column joint is more exposed to lateral loads due to which the joints undergoes severe deformations leading to yielding of the joints and the overall structure. . Poor design practices for beam-column joints are compounded by the high demand imposed by adjacent flexural members (beams and columns) as they mobilize their inelastic capabilities to dissipate load energy. Unsafe design and details in the joint region put the entire structure at risk, even if other structural elements meet the design requirements. Therefore the new material that is basalt rebar is used as reinforcement to observe the structural behaviour of the beam column joint. The experiment is conducted and the results are taken. The comparative study is done by using both steel specimens and basalt reinforced specimens. The detailing is done as per the seismic codes. The results are tabulated and graph is plotted.

Cautiously optimistic: paediatric critical care nurses’ perspectives on data-driven algorithms in low-resource settings—a human-centred design study in Malawi

BackgroundPaediatric critical care nurses face challenges in promptly detecting patient deterioration and delivering high-quality care, especially in low-resource settings (LRS). Patient monitors equipped with data-driven algorithms that monitor and integrate clinical data can optimise scarce resources (e.g. trained staff) offering solutions to these challenges. Poor algorithm output design and workflow integration, however, are important factors hindering successful implementation. This study aims to explore nurses’ perspectives to inform the development of a data-driven algorithm and user-friendly interface for future integration into a continuous vital signs monitoring system for critical care in LRS.MethodsHuman-centred design methods, including contextual inquiry, semi-structured interviews, prototyping and co-design sessions, were carried out at the high-dependency units of Queen Elizabeth Central Hospital and Zomba Central Hospital in Malawi between March and July 2023. Triangulating these methods, we identified what algorithm could assist nurses and used co-creation methods to design a user interface prototype. Data were analysed using qualitative content analysis.ResultsWorkflow observations demonstrated the effects of personnel shortages and limited monitor equipment for vital signs monitoring. Interviews identified four themes: workload and workflow, patient prioritisation, interaction with guardians, and perspectives on data-driven algorithms. The interviews emphasised the advantages of predictive algorithms in anticipating patient deterioration, underlining the need to integrate the algorithm’s output, the (constant) monitoring data, and the patient’s present clinical condition. Nurses preferred a scoring system represented with familiar scales and colour codes. During co-design sessions, trust, usability and context specificity were emphasised as requirements for these algorithms. Four prototype components were examined, with nurses favouring scores represented by colour codes and visual representations of score changes.ConclusionsNurses in the LRS studied, perceived that data-driven algorithms, especially for predicting patient deterioration, could improve the provision of critical care. This can be achieved by translating nurses’ perspectives into design strategies, as has been carried out in this study. The lessons learned were summarised as actionable pre-implementation recommendations for the development and implementation of data-driven algorithms in LRS.

Suboptimal distributed cooperative control for linear multi-agent system via Riccati design.

In this paper, we propose a suboptimal distributed cooperative control scheme for the continuous-time linear multi-agent system (MAS) with a specified global quadratic cost functional over both undirected and directed graph scenarios. For undirected graphs, we first derive the cost functional for a given strictly linear feedback distributed protocol. It is shown that the cost functional is upper bounded by a quadratic form of the MAS's initial state, and the minimum upper bound can be derived by solving a parametric algebraic Riccati equation (PARE) depends solely on the algebraic connectivity of the graph and is independent of the largest eigenvalue compared with the existing work. Based on this, a suboptimal distributed design method is proposed, where the resulting cost functional is less than a specified positive scalar. Then, we extend the theoretical results and design method to the directed graph scenario by introducing the row and column Laplacian matrices associated with the directed graph. Finally, numerical examples are provided to verify the effectiveness of the obtained results.