Intuitive System Research Articles

Comprehensive modeling and parametric analysis of Multi-Mission Radioisotope Thermoelectric Generator

Radioisotope thermoelectric generators (RTGs) have been widely used as a promising power source for space mission, in which the Multi-Mission RTG (MMRTG) is the state of the art type. However, due to the scarcity of the 238 P u fuel and associated cost concerns, there exists an imperative need to increase the efficiency of RTGs. This requires intuitive, detailed and accurate system property predictions of the RTG system. In this work, a comprehensive finite element model of the MMRTG has been developed, by using a commercial software, COMSOL Multiphysics, including a full-size three-dimensional geometry model, temperature dependent materials and the thermoelectric and radiation heat transfer multiphysics fields involved. The calculation results agree well with reported experimental and simulated values. Through the obtained detailed temperature and voltage distribution, thermal and power properties of the MMRTG are characterized and the effects of non-uniform distributions of temperature and power generated in each TE couple are revealed. At last, the parametric analyses of operation conditions, thermal environment and key design factors are performed and several salient findings have been obtained. Through the analysis results, the design parameters and working mechanisms of the MMRTG are clarified, which can help the design and optimization of the future hundred-watt RTG. • The comprehensive FEM method is first implemented in the MMRTG modeling and analysis. • The non-uniform distributions of temperature and power in TE modules are revealed. • Suitable load and sufficient thermal inventory ensure adequate power output. • The insensitivity of performance to ambient temperature expands MMRTG’s application.

The next generation of evidence-based medicine.

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation 'deep' medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

건설현장 배치계획 작성 시스템 개발을 위한 현황 조사 및 핵심기능 도출

To this day, construction site layout planning (CSLP) practice is achieved through a manual approach centered on experience and intuition. Although studies of various approaches have been conducted to solve these problems, it is still difficult to apply them in practice because practitioners may feel counterintuitive and complicated. Therefore, it is necessary to develop a more intuitive and user-friendly CSLP system. As a first step to filling this gap, this study aims to identify the importance of CSLP and its current status in practice, and derive key functions for system development. To do this, literature review, brainstorming, focus group interview, and survey were conducted. As a result, 17 key functions were derived. If the CSLP system is successfully constructed based on the key functions derived from this study, human error can be expected to be improved while the layout planning decisions, and it will help to generate an effective layout solution in terms of productivity and safety.

Using diverging predictions from classical and quantum models to dissociate between categorization systems

Quantum probability theory has successfully provided accurate descriptions of behavior in the areas of judgment and decision making, and here we apply the same principles to two category learning tasks, one task using information-integration categories and the other using rule-based categories. Since information-integration categories lack verbalizable descriptions, unlike rule-based ones, we assert that an information-integration categorization decision results from an intuitive probabilistic reasoning system characterized by quantum probability theory, whereas a rule-based categorization decision results from a logical, rational probabilistic reasoning system characterized classical probability theory. In our experiment, participants learn to categorize simple, visual stimuli as members of either category S or category K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same likelihood rating for the other category during a transfer phase. Following the principle of complementarity in quantum theory, we expect the category likelihood ratings to exhibit order effects in the information-integration task, but not in the rule-based task. In the information-integration task, we found definitive order effects in the likelihood ratings. But, in the rule-based task, we found that the order effects in the likelihood ratings are not significant.

Twilight of Rationality

Twilight of Rationality

PSAT160 The Dangers of Long-term Aromatase Inhibitor use in an Elderly Hispanic Population

Abstract The most common endocrine therapies for breast cancer are Aromatase Inhibitors (AIs), which can help reduce the recurrence of the disease and its mortality rate. However, because aromatase inhibitors prevent the aromatization of androgens into estrogen, they can have serious effects on bone health. This can lead to accelerated bone loss and an increased risk for fractures, especially among postmenopausal women. In 2017, the Journal of Bone Oncology (JBO) released a joint position statement advising that all patients with breast cancer on endocrine treatment, such as AIs which are known to accelerate bone loss, be placed on bisphosphonate therapy should they present 2 or more risk factors for fracture (1). The purpose of this study was to evaluate patients on AIs at a 5-star Medicare Advantage plan facility and document any discrepancies with regards to whether appropriate treatment is being given to prevent fractures. We hypothesized that we may have a large percentage of patients on AI that have 2 or more risk factors and are not on treatment. An electronic chart review was conducted of 398 patients on AIs between 6/9/2020 and 8/25/2020. Patients were evaluated for the following risk factors: current smoking, personal history of hip/vertebral fracture, current use of glucocorticoids, age > 65, BMD T-score < -1.5, and BMI < 20. Surprisingly, 1 out of every 2 patients treated with an AI was not being treated in accordance with the JBO's recommendations. Of the 398 patients evaluated, 191 presented 2 or more risk factors and were not on antiresorptive treatment. There are a few steps that can be taken to better manage these at-risk patients. First is raising awareness to providers about the dangers of long-term AI use. Additional changes to the EMR system may help as well, such as prompting the ordering provider to consider bisphosphonate therapy if risk factors are present. Lastly, the addition of a fracture reduction clinic can help streamline the referral process to an endocrinologist upon the prescribing of AI therapy. This would allow patients to be fully evaluated for fracture risks and prescribed treatment should they qualify. With the low costs of generic bisphosphonate therapy, if a fracture is prevented, providers can hope to save a significant amount in indirect costs. Through improved awareness of providers of the risks, a more intuitive EMR system, and the creation of a new automatic referral process, we believe that we can vastly improve patient morbidity and quality of life by reducing the chances of fracture in patients on long-term AIs.Reference: (1) Hadji et al., J Bone Oncol. 2017 Mar 23;7: 1-12. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m.

Indoor AR Navigation and Emergency Evacuation System Based on Machine Learning and IoT Technologies

In order to evacuate people safely and quickly in indoor disaster environments, it is necessary to estimate the current location of the individuals, detect disaster situations, predict disaster propagation, derive optimal individual escape paths, and implement a user-friendly and intuitive guidance system. In this study, we propose a machine-learning-based indoor augmented reality (AR) navigation and emergency evacuation system that can guide an optimal escape path for individual users. To detect emergency events and deliver sensing data, an Internet of Things (IoT)-enabled ad hoc network is considered. To deliver the sensing data safely and reliably to the server, we present a hybrid reinforcement-learning-based routing algorithm that combines direct and indirect <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning methods. Prediction of disaster propagation at multiple time scales is important to prevent dangerous situations. We propose a simple disaster area prediction method that ensembles elementary component gradient boosting machine models. User location is estimated by a deep neural network using the received signal strength from beacon nodes. To derive the optimum evacuation path for each individual, we propose a novel model-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning method, in which we consider the building structural model and disaster context information. The performance of the proposed system is experimentally evaluated for various disaster scenarios.

Loss context enhances preferences for generosity but reduces preferences for honesty: Evidence from a combined behavioural‐computational approach

AbstractThe current literature has revealed mixed evidence on whether loss (vs. gain) context promotes or curtails human prosociality. The current study (N=96) aimed to address this issue by examining whether gain/loss context has distinct effects on different prosocial preferences combining computational modelling with Dictator Game and Message Game. These interactive games allow for dissociating preferences for generosity and honesty, which have been respectively associated with intuitive and deliberative systems. Our behavioural and computational modelling results indicate that loss context enhances concerns for generosity but reduces concerns for honesty. These findings support an account under the framework of dual process model asserting that loss facilitates intuitive responses during social decision‐making, regardless of whether they are prosocial or proself. The current findings reconcile previous debates on the relationship between loss‐gain context and human prosociality and shed light on the design of institutions to promote human prosocial behaviours.

Modelling Cosmic Springs with Finsler and Generalised Finsler Geometries

We show that the equations of motion governing the dynamics of strings in a compact internal space can be written as dispersion relations, with a local speed that depends on the velocity and curvature of the string in the large dimensions. From a (3+1)-dimensional perspective these can be viewed as dispersion relations for waves propagating in the string interior and are analogous to those for current-carrying topological defects. This allows us to construct a unified framework with which to study and interpret the internal structure of various field-theoretic and fundamental string species, in a simple physically intuitive coordinate system, without the need for dimensional reduction or approximate effective actions. This, in turn, allows us to identify the precise conditions under which higher-dimensional strings and current-carrying defects are observationally indistinguishable, for macroscopic observers. Our approach naturally incorporates the description of so-called ‘cosmic springs’, whose dynamics are expressed in terms of an effective Finsler geometry, for circular loops, or generalised Finsler geometry, for non-circular configurations. This demonstrates the importance of these novel geometric structures and their utility in modelling complex physical phenomena in cosmology and astrophysics.

FLEXotendon Glove-III: Voice-Controlled Soft Robotic Hand Exoskeleton With Novel Fabrication Method and Admittance Grasping Control

This article presents a voice-controlled soft robotic hand exoskeleton for hand assistance. Cervical spinal cord injury results in a disruption of the nerve signals, causing paralysis and limited upper extremity function. The impairment significantly limits the completion of activities of daily living (ADL) due to the importance of hand function. Recently, there has been an increase in hand exoskeleton studies to provide active assistance for effective ADL completion. However, the design, control, and user interface (UI) of the devices remain a challenging issue. This article proposes a novel fabrication method for the wearable glove using high consistency rubber silicone and biomimetic tendon routing. The developed glove provides a robust, deformable, and optimal-fitting design for an individual’s hand with a weight of 131 g and five-degree-of-freedom motion for various ADLs. It provides a pinch force of 6.3 N and an object-level admittance controller was implemented for virtual grasping modalities. A smartphone app-based voice UI was developed to provide a truly portable and intuitive control system. Grasping experiments with a healthy participant were conducted to evaluate the system.

DIAGNOSTIC YIELD AND COMPLICATIONS USING ROBOTIC-ASSISTED NAVIGATION BRONCHOSCOPY FOR MIXED-DENSITY PULMONARY LESIONS

DIAGNOSTIC YIELD AND COMPLICATIONS USING ROBOTIC-ASSISTED NAVIGATION BRONCHOSCOPY FOR MIXED-DENSITY PULMONARY LESIONS

Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.

A multicriteria evaluation methodology for assessing the impact of COVID-19 in EU countries

Our purpose in this paper is to develop an integrated multicriteria evaluation methodology for assessing the impact of COVID-19 in the 27 countries of the European Union. Initially, a specialized and comprehensive set of normalized criteria metrics that capture several dimensions of the pandemic, such as infection, mortality, recovery and testing rates, is carefully specified. Then, by means of a well-established and intuitive weighting system, which directly takes into consideration the experts’ preferences, the gravity of each criterion is determined properly. Next, two of the most popular multicriteria ranking techniques, i.e. the TOPSIS and the PROMETHEE II, are simultaneously exploited in order to derive and integrate the obtained evaluations. Moreover, the value of the suggested decision support system is enriched through the introduction of a novel element in the field of multicriteria analysis, i.e. the ‘2-dimensions evaluation plane’, a data visualization concept which provides rich information to the decision maker, by fruitfully blending the ranking results of the utilized multicriteria methods. The validity of the proposed approach is verified through an indicative illustrative application on Coronavirus data for the European Union countries, deriving a wide spectrum of insightful conclusions. Finally, the flexibility of the suggested framework is also stressed, since it can be fully customized, both in terms of the selected criteria and its weights, and run repetitively under a specific time-step frequency, according to the evolution dynamics and implications of the underlying pandemic.

Design and Field Test of a Mobile Augmented Reality Human–Machine Interface for Virtual Stops in Shared Automated Mobility On-Demand

Shared automated mobility on-demand (SAMOD) is considered as a promising mobility solution in the future. Users book trips on-demand via a smartphone, and service algorithms set up virtual stops (vStop) where users then need to walk to board the automated shuttle. Navigation and identification of the virtual pickup location, which has no references in the real world, can be challenging. Providing users with an intuitive information system in that situation is essential to achieve high user acceptance of new automated mobility services. Our novel vStop human–machine interface (HMI) prototype for mobile augmented reality (AR) supports users with information in reference to the street environment. This work firstly presented the results of an online interview study (N = 21) to conceptualize an HMI. Secondly, the HMI was prototyped by means of AR and evaluated (N = 45) regarding user experience (UX), workload, and acceptance. The results show that the AR prototype provided high rates of UX especially in terms of high pragmatic quality. Furthermore, cognitive workload when using the HMI was low, and acceptance ratings were high. The results show the positive perception of AR for navigation tasks in general and the highly assistive character of the vStop prototype in particular. In the future, SAMOD services can provide customers with vStop HMIs to foster user acceptance and smooth operation of their service.

Analysis of Security Operational Characteristics of Distribution Network considering Electric Vehicle Load Access Forecast

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses “security margin percentage” as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization.

Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove

AbstractThe purpose of this study is to realize virtual interaction and manipulation control of a hexacopter based on hand gesture recognition from a designed data glove, to provide an intuitive and visual real-time simulation system for flight control algorithm verification and external control equipment testing. First, the hand gesture recognition from a designed data glove is studied, which can recognize different actions, such as mobile ready, grab, loosen, landing, take-off, and hover. Then, the design of virtual simulation system for hexacopter capture is completed, with the model design of hexacopter and manipulator, and the simulation software design with $CoppeliaSim$ . Finally, virtual simulation experiment of hexacopter grasping and virtual flight control experiment based on data glove are tested, respectively, and quantitatively described. The overall recognition rate is 84.3%, indicating that the data glove produced has the ability to recognize gestures, but its recognition performance is not superior. In gesture recognition, the recognition rate of static gestures is relatively higher than that of dynamic gestures. Among the static gestures, the hover gesture has the highest recognition rate. The average correct rate of static gestures can reach 94%. The lowest recognition rate of dynamic gestures is upward movement, and the average recognition rate of dynamic gestures is 76.1%. The research can be used to remotely operate hexacopter using a data glove in the future and improve the control performance through virtual interaction and manipulation simulation before actual application.

Power of Heuristics to Improve Health Information Technology System Design

Abstract Background Clinical decision-making can be prone to error if health system design does not match expert clinicians' higher cognitive skills. There is a gap in understanding the need for the importance of heuristics in clinical decision-making. The heuristic approach can provide cognitive support in designing intuitive health information systems for complex cases. Objective We explored complex decision-making by infectious diseases (ID) clinicians focusing on fast and frugal heuristics. We hypothesized that ID clinicians use simple heuristics to understand complex cases using their experience. Methods The study utilized cognitive task analysis and heuristics-based decision modeling. We conducted cognitive interviews and provided clinicians with a fast-and-frugal tree algorithm to convert complex information into simple decision algorithms. We conducted a critical decision method–based analysis to generate if–then logic sentences from the transcript. We conducted a thematic analysis of heuristics and calculated the average time to complete and the number of crucial information in the decision nodes. Results A total of 27 if–then logic heuristics sentences were generated from analyzing the data. The average time to construct the fast-and-frugal trees was 1.65 ± 0.37 minutes, and the average number of crucial pieces of information clinicians focused on was 5.4 ± 3.1. Conclusion Clinicians use shortcut mental models to reduce complex cases into simple mental model algorithms. The innovative use of artificial intelligence could allow clinical decision support systems to focus on creative and intuitive interface design matching the higher cognitive skills of expert clinicians.

A proposed disease classification system for duck viral hepatitis

The nomenclature of duck viral hepatitis (DVH) was historically not a problem. However, 14 hepatotropic viruses among 10 different genera are associated with the same disease name, DVH. Therefore, the disease name increasingly lacks clarity and may no longer fit the scientific description of the disease. Because one disease should not be attributed to 10 genera of viruses, this almost certainly causes misunderstanding regarding the disease-virus relationship. Herein, we revisited the problem and proposed an update to DVH disease classification. This classification is based on the nomenclature of human viral hepatitis and the key principle of Koch's postulates (“one microbe and one disease”). In total, 10 types of disease names have been proposed. These names were literately matched with hepatitis-related viruses. We envision that this intuitive nomenclature system will facilitate scientific communication and consistent interpretation in this field, especially in the Asian veterinary community, where these diseases are most commonly reported.

Modeling of natural intelligence and dynamics of human thinking with the use of significant combinary space

To create artificial intelligence, it is necessary to identify the properties of natural and develop a way to model it. There are many definitions of artificial intelligence in the literature, but there is no exact definition of this science yet. Differ-ent authors model natural intelligence differently. For example, artificial intelligence is defined as the ability of a digital computer to respond to information coming to its input devices, almost as a certain person reacts in the same infor-mation environment. This approach is based on the principle of self-organization of the model and is called heuristic. Human intelligence is also seen as an intuitive system. The creative process is accompanied by various manifestations of emotions, and decision-making in natural in-telligence is carried out in conditions of uncertainty of various kinds. Studies show that in the problems of this class it is related to: 1) incomplete input and current information; 2) with fuzzy input information; 3) with vaguely developed rules for processing and evaluating information. Significant combinatorial spaces, in particular significant information spaces, were used to model the dynamics of human thinking. The latter has a combinatorial nature and exists in two states: tranquility (convolute) and dynamics (deployed), which deployed from convolute. Collapsed is given by an information sign that contains the properties of the expanded space. Information is primarily related to the functioning of the human brain and is in the subconscious or consciousness in the form of images, fragments of speech and so on. The transfer of information (thoughts) is car-ried out with the help of deployed information space through the speech space, through gestures, movements, through writing, graphics. Depending on the type of uncertainty, the classification of natural intelligence is given. We believe that the con-cept of intelligence is associated with such operations as information processing and evaluation. Based on this, human intelligence is conditionally divided into three levels: 1) a person follows the rules, which are clearly formulated and described without analysis of their accuracy (learning rules); 2) the individual analyzes information for accuracy and develops its own rules of conduct under different conditions (rules of self-study); 3) the ability for independent of exist-ing rules of analysis, processing and evaluation of information for accuracy (rules of intuition). Partial realization of artificial intelligence is carried out through the use of self-tuning algorithms and modeling of self-organization processes in nature.

Analysis of a User Interface Based on Multimodal Interaction to Control a Robotic Arm for EOD Applications

A global human–robot interface that meets the needs of Technical Explosive Ordnance Disposal Specialists (TEDAX) for the manipulation of a robotic arm is of utmost importance to make the task of handling explosives safer, more intuitive and also provide high usability and efficiency. This paper aims to evaluate the performance of a multimodal system for a robotic arm that is based on Natural User Interface (NUI) and Graphical User Interface (GUI). The mentioned interfaces are compared to determine the best configuration for the control of the robotic arm in Explosive Ordnance Disposal (EOD) applications and to improve the user experience of TEDAX agents. Tests were conducted with the support of police agents Explosive Ordnance Disposal Unit-Arequipa (UDEX-AQP), who evaluated the developed interfaces to find a more intuitive system that generates the least stress load to the operator, resulting that our proposed multimodal interface presents better results compared to traditional interfaces. The evaluation of the laboratory experiences was based on measuring the workload and usability of each interface evaluated.