The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.

Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms. Objective: The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system. Methods: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects. Results: Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05). Conclusions: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.

Advancing Metallic Lithium Anodes: A Review of Interface Design, Electrolyte Innovation, and Performance Enhancement Strategies.

Lithium (Li) metal is one of the most promising anode materials for next-generation, high-energy, Li-based batteries due to its exceptionally high specific capacity and low reduction potential. Nonetheless, intrinsic challenges such as detrimental interfacial reactions, significant volume expansion, and dendritic growth present considerable obstacles to its practical application. This review comprehensively summarizes various recent strategies for the modification and protection of metallic lithium anodes, offering insight into the latest advancements in electrode enhancement, electrolyte innovation, and interfacial design, as well as theoretical simulations related to the above. One notable trend is the optimization of electrolytes to suppress dendrite formation and enhance the stability of the electrode-electrolyte interface. This has been achieved through the development of new electrolytes with higher ionic conductivity and better compatibility with Li metal. Furthermore, significant progress has been made in the design and synthesis of novel Li metal composite anodes. These composite anodes, incorporating various additives such as polymers, ceramic particles, and carbon nanotubes, exhibit improved cycling stability and safety compared to pure Li metal. Research has used simulation computing, machine learning, and other methods to achieve electrochemical mechanics modeling and multi-field simulation in order to analyze and predict non-uniform lithium deposition processes and control factors. In-depth investigations into the electrochemical reactions, interfacial chemistry, and physical properties of these electrodes have provided valuable insights into their design and optimization. It systematically encapsulates the state-of-the-art developments in anode protection and delineates prospective trajectories for the technology's industrial evolution. This review aims to provide a detailed overview of the latest strategies for enhancing metallic lithium anodes in lithium-ion batteries, addressing the primary challenges and suggesting future directions for industrial advancement.

[ADAS and automation-a relevant contribution to maintaining mobility of older drivers?] / Fahrerassistenzsysteme und automatisiertes Fahren ­ ein relevanter Beitrag zum Erhalt der Mobilität im Alter?

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have a high level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be a priority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in a critical situation.

The impact of clock timing on VDT visual search performance under time constraint.

Introduction: Conducting Visual Display Terminal (VDT) visual search tasks under time constraint has broad applications in fields such as security checks, medical diagnostics, and rescue operations. While excessive time pressure can impair performance, moderate time pressure can motivate individuals to complete tasks and increase productivity. Investigating the positive impact of time pressure on visual search tasks has become a crucial area of study. Clock timing plays a vital role in the visual interface, influencing the perception of time pressure and impacting visual search performance. However, existing research has paid little attention to the induction of time pressure and the impact of clock timing in VDT visual interfaces on visual search performance. Hence, the objective of this study is to investigate the impact of clock timing on VDT visual search performance under time constraint. Methods: The content of the experimental tasks was determined through a pilot experiment. The formal experiment was conducted in two phases over six sessions. Participants were tasked with locating the letter "E" embedded within the distractor letter "F," displayed with a clock area above the interface. The first phase of experiments included conditions of no clock, 4-min clock timing, and 4-min countdown clock timing. In the second phase of the experiment, the clock display method was a countdown clock, with three experiments conducted featuring long time, medium time, and short time. Search speed and accuracy were used as primary performance evaluation metrics to examine the impact of clock timing methods and duration on visual search performance. Twenty-one undergraduate students participated in the formal experiment. Results: In the first phase of experiments, participants demonstrated significantly faster reaction times (RTs) in tasks where a clock display was present compared to tasks without (ANOVA, F(2, 60) = 4.588, P = 0.014). However, there were no significant differences in accuracy rates across different timing conditions (ANOVA, F(2, 60) = 0.146, P = 0.865), and no significant correlation between RTs and accuracy was found (Kendall's R = 0.11, P = 0.914). During the second phase, RTs decreased significantly as time constraints became more stringent (ANOVA, F(2, 60) = 7.564, P < 0.05). Conversely, accuracy rates decreased significantly under shorter time constraints (ANOVA, F(2, 60) = 4.315, P < 0.05), with a negative correlation observed between RTs and accuracy (Kendall's R = 0.220, P < 0.01). Discussion: Compared to conditions without clock displays, having clock displays significantly improved the speed of the visual search task, although the difference in accuracy was not statistically significant. In the context of shorter clock countdown limits, Shorter timing constraints resulted in faster search speeds but also led to reduced accuracy and increased fatigue. Overall, a correlation exists between search speed and accuracy in visual tasks, where higher speed often correlates with lower accuracy. These findings provide valuable insights into clock timing design for visual search interfaces under time pressure.

Highly Robust and Self-Adhesive Soft Strain Gauge via Interface Design Engineering.

Highly robust soft strain gauges are rapidly emerging as a promising candidate in the fields of vital signs and machine conditions monitoring. However, it is still a key challenge to achieve high-performance strain sensing in these sensors with mechanical/electrical robustness for long-term usage. The multilayer structural design of sensors enhances sensing performance while the interfacial connection of heterogeneous materials between different layers is weak. Herein, inspired by the efficient perception mechanism of scorpion slit sensilla with tough interface interconnections, the synergy of ultra-high electrical performance and mechanical robustness is successfully achieved via interface design engineering. The developed multilayer soft strain gauge (MSSG) exhibits a strain sensitivity beyond 105, a lower detection limit of 8.3 µm, a frequency resolution within 0.1 Hz, and cyclic stability over 63 000 strain cycles. Also, the tough interface improves the level of heterogeneous integration in the MSSG which allows to endure different stresses. Furthermore, an MSSG-based wireless strain monitoring system is developed that enables applications on different complex dynamic surfaces, including accurate identification of human throat activity and monitoring of rolling bearing conditions.

Enhancing safety in conditionally automated driving: Can more takeover request visual information make a difference in hazard scenarios with varied hazard visibility?

Autonomous driving technology has the potential to significantly reduce the number of traffic accidents. However, before achieving full automation, drivers still need to take control of the vehicle in complex and diverse scenarios that the autonomous driving system cannot handle. Therefore, appropriate takeover request (TOR) designs are necessary to enhance takeover performance and driving safety. This study focuses on takeover tasks in hazard scenarios with varied hazard visibility, which can be categorized as overt hazards and covert hazards. Through ergonomic experiments, the impact of TOR interface visual information, including takeover warning, hazard direction, and time to collision, on takeover performance is investigated, and specific analyses are conducted using eye-tracking data. The following conclusions are drawn from the experiments: (1) The visibility of hazards significantly affects takeover performance. (2) Providing more TOR visual information in hazards with different visibility has varying effects on drivers' visual attention allocation but can improve takeover performance. (3) More TOR visual information helps reduce takeover workload and increase human-machine trust. Based on these findings, this paper proposes the following TOR visual interface design strategies: (1) In overt hazard scenarios, only takeover warning is necessary, as additional visual information may distract drivers' attention. (2) In covert hazard scenarios, the TOR visual interface should better assist drivers in understanding the current hazard situation by providing information on hazard direction and time to collision to enhance takeover performance.

Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La1-xSrxCoO3-δ/La1-xSrxMnO3-δ Bilayers Using Deep Learning Models.

Perovskite oxides are gaining significant attention for use in next-generation magnetic and ferroelectric devices due to their exceptional charge transport properties and the opportunity to tune the charge, spin, lattice, and orbital degrees of freedom. Interfaces between perovskite oxides, exemplified by La1-xSrxCoO3-δ/La1-xSrxMnO3-δ (LSCO/LSMO) bilayers, exhibit unconventional magnetic exchange switching behavior, offering a pathway for innovative designs in perovskite oxide-based devices. However, the precise atomic-level stoichiometric compositions and chemophysical properties of these interfaces remain elusive, hindering the establishment of surrogate design principles. We leverage first-principles simulations, evolutionary algorithms, and neural network searches with on-the-fly uncertainty quantification to design deep learning model ensembles to investigate over 50,000 LSCO/LSMO bilayer structures as a function of oxygen deficiency (δ) and strontium concentration (x). Structural analysis of the low-energy interface structures reveals that preferential segregation of oxygen vacancies toward the interfacial La0.7Sr0.3CoO3-δ layers causes distortion of the CoOx polyhedra and the emergence of magnetically active Co2+ ions. At the same time, an increase in the Sr concentration and a decrease in oxygen vacancies in the La0.7Sr0.3MnO3-δ layers tend to retain MnO6 octahedra and promote the formation of Mn4+ ions. Electronic structure analysis reveals that the nonuniform distributions of Sr ions and oxygen vacancies on both sides of the interface can alter the local magnetization at the interface, showing a transition from ferromagnetic (FM) to local antiferromagnetic (AFM) or ferrimagnetic regions. Therefore, the exotic properties of La1-xSrxCoO3-δ/La1-xSrxMnO3-δ are strongly coupled to the presence of hard/soft magnetic layers, as well as the FM to AFM transition at the interface, and can be tuned by changing the Sr concentration and oxygen partial pressure during growth. These insights provide valuable guidance for the precise design of perovskite oxide multilayers, enabling tailoring of their functional properties to meet specific requirements for various device applications.

Human-centred explanations for artificial intelligence systems.

As Artificial Intelligence (AI) systems increase in capability, so there are growing concerns over the ways in which the recommendations they provide can affect people's everyday life and decisions. The field of Explainable AI (XAI) aims to address such concerns but there is often a neglect of the human in this process. We present a formal definition of human-centred XAI and illustrate the application of this formalism to the design of a user interface. The user interface supports users in indicating their preferences relevant to a situation and to compare their preferences with those of a computer recommendation system. A user trial is conducted to evaluate the resulting user interface. From the user trial, we believe that users are able to appreciate how their preferences can influence computer recommendations, and how these might contrast with the preferences used by the computer. We provide guidelines of implementing human-centred XAI.

This paper presents a formal description of explanatory discourse for Human-Centred Explainable Artificial Intelligence (XAI) and demonstrate the use of this formalism as the basis for designing user interface for a recommender system. The recommender system is evaluated through a user trial. The paper concludes with guidelines for developing Human-Centred XAI.

A suite of designed protein cages using machine learning and protein fragment-based protocols.

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, self-assembling protein cages. For the generation of docked conformations, we emphasize a protein fragment-based approach, while for sequence design of the de novo interface, a comparison of knowledge-based and machine learning protocols highlights the power and increased experimental success achieved using ProteinMPNN. An analysis of design outcomes provides insights for improving interface design protocols, including prioritizing fragment-based motifs, balancing interface hydrophobicity and polarity, and identifying preferred polar contact patterns. In all, we report five structures for seven protein cages, along with two structures of intermediate assemblies, with the highest resolution reaching 2.0 Å using cryo-EM. This set of designed cages adds substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Bi-Directional H-Bonding Modulated Soft/Hard Polyethylene Glycol-Polyaniline Coated Si-Anode for High-Performance Li-Ion Batteries.

Ultrahigh-capacity silicon (Si) anodes are essential for the escalating energy demands driven by the booming e-transportation and energy storage field. However, their practical applications are strictly hampered by their intrinsically low electroconductivity, sluggish Li-ion diffusion, and undesirably large volume change. Herein, a high-performance Si anode, comprised of a modulated soft/hard coating of polyethylene glycol (PEG) (as Li-ion conductor) and polyaniline (PANI) (as electron conductor) on the surface of Si nanoparticles (NPs) through H-bonding network, is introduced. In this design, the abundant ─OH groups of soft PEG allow it to uniformly cover Si NPs while the hard PANI binds to PEG through its ─N─H group, thus constructing a tight connectin between Si and PEG-PANI (PP). Consequently, the elastic PP allows Si@PP to accommodate the huge volume expansion while possessing fine electronic/ionic conductivity. Therefore, the Si@PP anode exhibits a high initial Coulombic efficiency of 90.5% and a stable capacity of 1871 mAh g-1 after 100 cycles at 1 A g-1 with a retention of 85.7%. Additionally, the Si@PP anode also demonstrates a high areal capacity of 3.01 mAh cm-2 after 100 cycles at 0.5 A g-1 . This work reveals a scalable interface design of multi-layer multifunctional coatings for high-performance electrode materials in next-generation Li-ion batteries.

A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.

BACKGROUND: Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration. OBJECTIVE: This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. METHODS: This study used a data set of 861 PSE reports from a large academic hospital's maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier's predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. RESULTS: The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. CONCLUSIONS: This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm.

Multi-interface Combination of Bimetallic Selenide and V4C3Tx MXene for High-Rate and Ultrastable Sodium Storage Devices.

Sodium-ion batteries (SIBs) have great potential as electrochemical energy storage systems; however, their commercial viability is limited by the lack of anode materials with fast charge/discharge rates and long lifetimes. These challenges were addressed by developing a multi-interface design strategy using FCSe (FeSe2/CoSe2) nanoparticles on V4C3Tx MXene nanosheets as conductive substrates. The heterogeneous interface created between the two materials provided high-speed transport of sodium ions, suppressed the chalking-off of nanoparticles, and improved the cycling stability. Additionally, the Fe-Co bonds generated at the interface effectively relieved mechanical stress, further enhancing the electrode durability. The C@FCSe@V4C3 electrode exhibited high-speed charging and discharging characteristics, and maintained a high specific capacity of 260.5 mAh g-1 even after 15,000 cycles at 10 A g-1, with a capacity retention rate of 50.2% at an ultrahigh current density of 20 A g-1. Furthermore, the composite displayed a good cycling capability in the fast discharge and slow charge mode. This demonstrates its promising commercial potential. This multi-interface design strategy provides insights and guidance for solving the reversibility and cycling problems of transformed selenide anode materials.

Design of aided augmentative and alternative communication systems for children with vision impairment: psychoacoustic perspectives.

Children with complex communication needs often have multiple disabilities including visual impairments that impact their ability to interact with aided augmentative and alternative communication (AAC) systems. Just as the field benefited from a consideration of visual cognitive neuroscience in construction of visual displays, an exploration of psychoacoustics can potentially assist in maximizing the possibilities within AAC systems when the visual channel is either (a) not the primary sensory mode, or (b) is one that can be augmented to ultimately benefit AAC outcomes. The purpose of this paper is to highlight background information about psychoacoustics and present possible future directions for the design of aided AAC system technologies for children with visual impairments who rely on auditory information to learn and utilize AAC.

A Vehicle Simulation Study Examining the Effects of System Interface Design Elements on Performance in Different Vibration Environments Below 3 Hz.

OBJECTIVE: This study aimed to explore the relationship between system interface elements' design features and interaction performance in simulated vehicle vibration environments. BACKGROUND: Touch screens have been widely used in vehicle information systems, but few studies have focused on the decline of touchscreen interaction performance and task load increase when driving on unpaved roads. METHOD: The interaction performance (reaction time and task accuracy rate) with vibration frequencies below 3 Hz (1.5, 2.0, and 2.5 Hz) and different interface design elements was investigated employing a touch screen computer and E-prime software. RESULTS: The results indicate that vehicle vibration (below 3 Hz) can significantly reduce interaction performance with a vehicle information system interface. CONCLUSION: An appropriate increase in the physical size of the interface design features (visual stimulus materials and touch buttons) can help to mitigate this negative effect of vibration. APPLICATION: The results and findings of this study can be utilized for the design of information system interfaces as it relates to the vibration scenario of unpaved roads.

A Suite of Designed Protein Cages Using Machine Learning Algorithms and Protein Fragment-Based Protocols.

Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, while methods for their creation remain challenging and unpredictable. In the present study, we apply new computational approaches to design a suite of new tetrahedrally symmetric, self-assembling protein cages. For the generation of docked poses, we emphasize a protein fragment-based approach, while for de novo interface design, a comparison of computational protocols highlights the power and increased experimental success achieved using the machine learning program ProteinMPNN. In relating information from docking and design, we observe that agreement between fragment-based sequence preferences and ProteinMPNN sequence inference correlates with experimental success. Additional insights for designing polar interactions are highlighted by experimentally testing larger and more polar interfaces. In all, using X-ray crystallography and cryo-EM, we report five structures for seven protein cages, with atomic resolution in the best case reaching 2.0 Å. We also report structures of two incompletely assembled protein cages, providing unique insights into one type of assembly failure. The new set of designed cages and their structures add substantially to the body of available protein nanoparticles, and to methodologies for their creation.

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers.

Motivation: Augmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load. Description: A novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design. Results: Experimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting. Conclusion: In this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.

A novel autonomous vehicle interface for older adults with cognitive impairment.

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

Usability and Acceptability of Clinical Dashboards in Aged Care: Systematic Review.

BACKGROUND: The use of clinical dashboards in aged care systems to support performance review and improve outcomes for older adults receiving care is increasing. OBJECTIVE: Our aim was to explore evidence from studies of the acceptability and usability of clinical dashboards including their visual features and functionalities in aged care settings. METHODS: A systematic review was conducted using 5 databases (MEDLINE, Embase, PsycINFO, Cochrane Library, and CINAHL) from inception to April 2022. Studies were included in the review if they were conducted in aged care environments (home-based community care, retirement villages, and long-term care) and reported a usability or acceptability evaluation of a clinical dashboard for use in aged care environments, including specific dashboard visual features (eg, a qualitative summary of individual user experience or metrics from a usability scale). Two researchers independently reviewed the articles and extracted the data. Data synthesis was performed via narrative review, and the risk of bias was measured using the Mixed Methods Appraisal Tool. RESULTS: In total, 14 articles reporting on 12 dashboards were included. The quality of the articles varied. There was considerable heterogeneity in implementation setting (home care 8/14, 57%), dashboard user groups (health professionals 9/14, 64%), and sample size (range 3-292). Dashboard features included a visual representation of information (eg, medical condition prevalence), analytic capability (eg, predictive), and others (eg, stakeholder communication). Dashboard usability was mixed (4 dashboards rated as high), and dashboard acceptability was high for 9 dashboards. Most users considered dashboards to be informative, relevant, and functional, highlighting the use and intention of using this resource in the future. Dashboards that had the presence of one or more of these features (bar charts, radio buttons, checkboxes or other symbols, interactive displays, and reporting capabilities) were found to be highly acceptable. CONCLUSIONS: A comprehensive summary of clinical dashboards used in aged care is provided to inform future dashboard development, testing, and implementation. Further research is required to optimize visualization features, usability, and acceptability of dashboards in aged care.

The Influence of Interface Design and External Frame on the Energy Absorption Performance of the Semi-Auxetic Structure.

In this article, four new semi-auxetic structures are designed by changing the way of interface connection and adding external frames. These structures were fabricated by fused deposition modeling, which is an additive manufacturing technology. The effects of interface design and external frame on deformation mode and energy absorption performance of semi-auxetic structure under quasi-static compression are studied. It was found that the deformation modes of framed and frameless structures are different. The specific energy absorption of the semi-auxetic structure is increased by ∼52% compared with the frameless hexagonal honeycomb structure. In addition, Abaqus was used to establish finite element models of the four new semi-auxetic structures and the frameless hexagonal honeycomb structure. It can be found that the simulation results were consistent with the experimental results.

Protocol of Validation Mechanisms Within the Design of an Electronic Case Report Form for the MULTI-SITA Project.

With the wide diffusion of web technology, dedicated electronic Case Report Forms (eCRFs) became the main tool for collecting patient data. The focus of this work is to thoroughly consider the data quality in every aspect of the design of the eCRF, with the result of having multiple steps of validation that should produce a diligent and multidisciplinary approach towards every step of data acquisition. This goal affects every aspect of the system design.