Usage Of Models Research Articles

Robustness, replicability and scalability in topic modelling

• We identify three key properties a topic model must exhibit for effective application in the social sciences: statistical robustness, descriptive power across all dimensions and reflection of reality. • We propose a simple approach for estimating the statistical robustness of topic models that is based on pairwise similarity scores between documents. • Applying that approach we find that the neural network-based Doc2Vec is more stable than the other topic models tested: Latent Dirichlet Allocation and Non-negative Matrix Factorisation. • We further propose a principal component analysis based approach for assessing the descriptive power of topic models. In applying that approach we find that Doc2Vec performs the best, but LDA does also perform well. • We provide grounds for the application of neural embeddings approaches in the social sciences and also how traditional visualisation techniques can be applied directly to dense vector representations. Approaches for estimating the similarity between individual publications are an area of long-standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mining approaches based on title and abstract text have appeared more frequently in recent years. In principle, topic models have great potential in this domain. But, in practice, they are often difficult to employ successfully, and are notoriously inconsistent as latent space dimension grows. In this manuscript we identify the three properties all usable topic models should have: robustness, descriptive power and reflection of reality. We develop a novel method for evaluating the robustness of topic models and suggest a metric to assess and benchmark descriptive power as number of topics scale. Employing that procedure, we find that the neural-network-based paragraph embedding approach seems capable of providing statistically robust estimates of the document–document similarities, even for topic spaces far larger than what is usually considered prudent for the most common topic model approaches.

A judgment-based model for usability evaluating of interactive systems using fuzzy Multi Factors Evaluation (MFE)

The study aimed to propose a judgment-based evaluation model for usability evaluating of interactive systems. Human judgment is associated with uncertainty and gray information. We used the fuzzy technique for integration, summarization, and distance calculation of quality value judgment. The proposed model is an integrated fuzzy Multi Factors Evaluation (MFE) model based on experts’ judgments in HCI, ISPD, and AMLMs. We provided a Fuzzy Inference System (FIS) for scoring usability evaluation metrics in different interactive systems. A multi-model interactive system is implemented for experimental testing of the model. The achieved results from the proposed model and experimental tests are compared using statistical correlation tests. The results show the ability of the proposed model for usability evaluation of interactive systems without the need for conducting empirical tests. It is concluded that applying a dataset in a neuro-FIS and training system cause to produce more than a hundred effective rules. The findings indicate that the proposed model can be applied for interactive system evaluation, informative evaluation, and complex empirical tests. Future studies may improve the FIS with the integration of artificial neural networks.

Coupling a Lagrangian–Eulerian Spark-Ignition (LESI) model with LES combustion models for engine simulations

In the United States transportation sector, Light-Duty Vehicles (LDVs) are the largest energy consumers and CO2 emitters. Electrification of LDVs is posed as a potential solution, but SI engines can still contribute to decarbonization. Car manufacturers have turned to unconventional engine operation to increase the efficiency of Spark-Ignition (SI) engines and reduce the carbon emissions of their fleets. Dilute, lean, and stratified-charge engine operation has the potential for engine efficiency improvements at the expense of increased cyclic variability and combustion instability. At such demanding engine conditions, the spark ignition event is key for flame initiation and propagation and for enhanced combustion stability. Reliable and accurate spark ignition models can help design ignition systems that reduce cyclic variability. Multiple computational spark-ignition models exist that perform well under conventional conditions, but the underlying physics needs to be expanded, for unconventional engine operation. In this paper, a hybrid Lagrangian–Eulerian Spark-Ignition (LESI) model is coupled with different turbulent flame propagation models for engine simulations. LESI relies on Lagrangian arc tracking and Eulerian energy deposition. The LESI model is coupled with the Well-Stirred Reactor (WSR), Thickened Flame Model (TFM), and g-equation model and used to simulate several cycles of a Direct-Injection Spark-Ignition (DISI) engine using a commercial Computational Fluid Dynamics (CFD) engine solver. The results showcase the successful coupling of LESI with the combustion models. Global engine metrics, such as pressure and Apparent Heat Release Rate (AHRR), for each simulation setup are compared to experimental engine results, for validation. In addition, results highlight the successful prediction of spark channel movement by comparing simulation images to experimental optical engine images. Finally, the successful coupling of LESI to combustion models, making it a usable model in the engine modeling community, is emphasized and future development details are discussed.

Evaluating Learning Management System based on PACMAD Usability Model: Brighten Mobile Application

During the pre-COVID-19 pandemic, mobile learning is just an optional or a supplementary module in learning process. However, when the pandemic hit the world in the middle of 2020, a large number of students were forced to move from traditional learning process to online learning. This has become a critical issue especially for new online learners. Usability of a mobile learning application is important in ensuring that learners are able to learn efficiently and effectively with ease. This study evaluates the usability of the Brighten mobile application; a Moodle-based Learning Management System (LMS) which is currently used by all Universiti Tenaga Nasional’s students. The evaluation is based on People at the Center of Mobile Application Development (PACMAD). The results indicate that Brighten mobile application is acceptable in terms of usability’s effectiveness, efficiency, learnability, memorability and error-tolerance. Learners’ satisfaction level shows a “marginally acceptable” result based on the SUS Adjective Rating Scale and the result for cognitive load shows that the highest cognitive load was in terms of the performance factor.

Mobile Application Usability Evaluation: A Study Based on Demography

An application or product is considered “usable” if it is pleasing, easy to use, and works as expected user interface. Most companies majorly focus on the application’s functional requirements but put minimal effort into user experience (usability). Consumers’ adaptation of these applications depends on the number of features and user interface. In this work, an android based application, “Houzcalls” is used for usability study using PACMAD usability model. This work is focused on the variations in PACMAD attributes based on the participants’ education and age. Participants are segregated into two major groups FG1 and FG2 based on their education. All the participants with more than 10 years of education are in FG1 while others in FG2. Each focal group is divided into four subgroups Under 25, 25-35, 36-45, and Over 45 based on their ages. The results have shown participants in FG1 have shown more Effectiveness, Efficiency, Satisfaction, Learnability, and Memorability. In contrast, they have committed fewer Errors and shown less Cognitive Load during usability testing as compared to FG2. These variations can also be seen age-wise as generally “Under 25 and 25-35” subgroups have shown better results than other subgroups. It is inferred from the study that application usability and acceptability can be increased by considering the general population during development which includes all groups of people based on education and age.

Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications.

Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

How Can Remote Sensing Reduce Required Human Intervention in Robotic Forest Regeneration

In this article, we introduce an alternative solution for forest regeneration based on unmanned ground vehicles (UGV) and describe requirements for external data, which could significantly increase the level of automation. Over the past few decades, the global forested area has decreased, and there is a great need to restore and regenerate forests. Challenges such as the lack of labor and high costs demand innovative approaches for forest regeneration. Mechanization has shown satisfactory results in terms of time-efficient planting, although its usage is limited by high operational costs. Innovative technologies must be cost-efficient and profitable for large scale usage. Automation could make mechanized forest regeneration feasible. Forest regeneration operations can be automated using a purpose built unmanned platform. We developed a concept to automate forest planting operations based on mobility platform. The system requires external data for efficient mobility in clear-cut areas. We developed requirements for external data, analyzed available solutions, and experimented with the most promising option, the SfM (structure from motion) technique. Earth observation data are useful in the planning phase. A DEM (digital terrain model) for UGV planter operations can be constructed using ALS (airborne laser scanning), although it may be restricted by the cost. Low-altitude flights by drones equipped with digital cameras or lightweight laser scanners provided a usable model of the terrain. This model was precise (3–20 cm) enough for manually planning of the trajectory for the planting operation. This technique fulfilled the system requirements, although it requires further development and will have to be automated for operational use.

A Usable Kinetic Model of Suspended Ion Exchange Processes for Natural Organic Matter Removal

AbstractThe suspended ion exchange (SIEX) process is an emerging technology for the removal of dissolved natural organic matter (NOM). To facilitate the SIEX selection, design, and operation, a usa...

Changes of Host Immunity Mediated by IFN-γ+ CD8+ T Cells in Children with Adenovirus Pneumonia in Different Severity of Illness.

The host immunity of patients with adenovirus pneumonia in different severity of illness is unclear. This study compared the routine laboratory tests and the host immunity of human adenovirus (HAdV) patients with different severity of illness. A co-cultured cell model in vitro was established to verify the T cell response in vitro. Among 140 patients with confirmed HAdV of varying severity, the number of lymphocytes in the severe patients was significantly reduced to 1.91 × 109/L compared with the healthy control (3.92 × 109/L) and the mild patients (4.27 × 109/L). The levels of IL-6, IL-10, and IFN-γ in patients with adenovirus pneumonia were significantly elevated with the severity of the disease. Compared with the healthy control (20.82%) and the stable patients (33.96%), the percentage of CD8+ T cells that produced IFN-γ increased to 56.27% in the progressing patients. Adenovirus infection increased the percentage of CD8+ T and CD4+ T cells that produce IFN-γ in the co-culture system. The hyperfunction of IFN-γ+ CD8+ T cells might be related to the severity of adenovirus infection. The in vitro co-culture cell model could also provide a usable cellular model for subsequent experiments.

Architectural Interior Modeling Product Design Based on the Multimedia Three-Dimensional Hybrid Algorithm

With the continuous development of social economy, people’s aspiration for architecture is getting higher and higher. Designing more functional and aesthetically pleasing interior styling products is increasingly important research. Relying on the multimedia three-dimensional hybrid algorithm, the mathematical modeling of the relationship between visual comfort and chromaticity is modeled in this paper, to construct an economical and practical interior architectural design model that takes into account color comfort perception, that is, complete “design, conversion, editing and repair, and semantic and coordinate supplement” and other chain construction to realize usable interior architectural design models. The simulation experiment results show that the model based on the multimedia 3D hybrid algorithm has high practicability.

Fuzzy logic model of usability of websites of higher education institutions in the context of digitalization of educational services

Fuzzy logic model of usability of websites of higher education institutions in the context of digitalization of educational services

Combined Model of Technology Use and Medical Adherence in eHealth Technology Implementation

eHealth applications are expected to improve the effectiveness and efficiency of healthcare systems by providing improved medical information flow between medical providers and patients. Although the technology is expected to empower patients, lower treatment costs, and provide real-time collection of health data, individuals may be apprehensive about the use and efficacy of eHealth technologies. Medical professionals are often unaware of human factors technology acceptance or usability models which impact the use of medically focused technology, such as eHealth applications. Similarly, human factors professionals are often unaware of treatment adherence models which map the relationship of illness factors and individual differences to treatment protocols. The present paper presents a theoretical approach through which technology acceptance and usability models should be combined with medical treatment adherence models to ensure that eHealth applications are used properly and effectively.

P-039. Building clinically usable machine-learning models for outcome predicition in preeclampsia

P-039. Building clinically usable machine-learning models for outcome predicition in preeclampsia

Six Impossible Things Before Breakfast

Six Impossible Things Before Breakfast

Development and evaluation of a serious game to support learning among pharmacy and nursing students

Background and purposeSerious games (SGs) are a digital method that promotes learning through playability. DOSE is a SG that aims to improve the use and navigation of the British National Formulary, a standard medicines reference source. This research aimed to design and examine the playability and perceptions of DOSE within healthcare curricula. Educational activity and settingThe study used gaming psychology concepts to design DOSE and evaluated it across two cohorts of students from the undergraduate pharmacy and nursing courses at Kingston University. Pre- and post-game questionnaires were utilised to examine usability and student perceptions. SG playability and validity were measured against standard criteria using validated assessment frameworks. Cronbach's α was calculated to determine the internal reliability of the framework assessments. FindingsThe SG was validated for playability, functionality, and rationale (α ≥ 0.8). The majority (95%, n = 95) of pharmacy students reported they would use DOSE again with 58% (n = 58) indicating they would use the SG as a revision tool. DOSE was also perceived positively among the nursing cohort, with 84% (n = 118) indicating they would like to see DOSE embedded within the curriculum; 77% (n = 108) agreed DOSE had helped to improve their knowledge of pharmacology. SummaryDOSE playability and validity was reported by pharmacy students. Nursing students reported a significant increase in confidence using the BNF and overall high acceptability of DOSE as a SG. DOSE was found to be a usable SG model across both cohorts.

User-Environment Interaction: The Usability Model for Universal Design Assessment.

Universal Design (UD) aims to provide designed environments that allow users to fully participate in all kinds of activities. Especially, the design of Sport and Leisure buildings should support and encourage the participation of mobility and sensory impaired people in any physical and social activity. Yet, the variety of physical and social users' needs calls for different approaches to investigate, analyze and assess how the environment fulfills users' needs and expectations. This paper presents a new analytical model that: a) investigates how people with mobility, visual, and hearing impairments interact with specific architectural features; b) links the examined user-environment interaction with the user's personal assessment of the spatial experience. The study employs the literature review of the existing analytical models, which are based on the concept of user-environment interaction and framed around empirically deducted basic human needs. These models address the issue of user-environment fit by focusing on the identification of environmental barriers. Also, some of these models are too descriptive and cannot inform the practice in creative design processes. The proposed analytical model, which is built upon the theoretical concepts of affordances and usability, aims to develop a qualitative evaluation method for identifying environmental facilitators by linking the design of architectural characteristics with the influenced perception of users of the physical and social aspects of the built environment. The model consists of three groups of elements: (1) users' physical abilities; (2) architectural features and (3) usability criteria. The inter-relations of each element across the groups develop the narrating scenarios that can be investigated from the user's perspective. This new model does not only advance the understanding of the spatial experiences of persons with mobility and sensory impairments but also offers new insights for exploring UD solutions by identifying the architectural features that enlarge the spectrum of possible user-environment interactions.

Development of a practically usable prediction model for quality of life of ICU survivors: A sub-analysis of the MONITOR-IC prospective cohort study.

As the goal of ICU treatment is survival in good health, we aimed to develop a prediction model for ICU survivors' change in quality of life (QoL) one year after ICU admission. This is a sub-study of the prospective cohort MONITOR-IC study. Adults admitted ≥12 h to the ICU of a university hospital between July 2016-January 2019 were included. Moribund patients were excluded. Change in QoL one year after ICU admission was quantified using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and Short-Form 36 (SF-36). Multivariable linear regression analysis and best subsets regression analysis (SRA) were used. Models were internally validated by bootstrapping. The PREdicting PAtients' long-term outcome for Recovery (PREPARE) model was developed (n = 1308 ICU survivors). The EQ-5D-models had better predictive performance than the SF-36-models. Explained variance (adjusted R2) of the best model (33 predictors) was 58.0%. SRA reduced the number of predictors to 5 (adjusted R2 = 55.3%, SE = 0.3), including QoL, diagnosis of a Cardiovascular Incident and frailty before admission, sex, and ICU-admission following planned surgery. Though more long-term data are needed to ascertain model accuracy, in future, the PREPARE model may be used to better inform and prepare patients and their families for ICU recovery.

An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks

Deep Neural Networks are well-known to be vulnerable to Adversarial Examples. Recently, advanced gradient-based attacks were proposed (e.g., BPDA and EOT), which can significantly increase the difficulty and complexity of designing effective defenses. In this paper, we present a study towards the opportunity of mitigating those powerful attacks with only pre-processing operations. We make the following two contributions. First, we perform an in-depth analysis of those attacks and summarize three fundamental properties that a good defense solution should have. Second, we design a lightweight preprocessing function with these properties and the capability of preserving the model's usability and robustness against these threats. Extensive evaluations indicate that our solutions can effectively mitigate all existing standard and advanced attack techniques, and beat 11 state-of-the-art defense solutions published in top-tier conferences over the past 2 years.

Feature selection algorithm for usability engineering: a nature inspired approach

AbstractSoftware usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

Experimental investigation and prediction of compressive strength of high-strength concrete containing waste ceramic powder using gene expression programming

The high volume of carbon dioxide produced in cement plants and the feasibility of reusing waste materials from factories in recent years has become one of the main concerns of research centers and environmental associations. Therefore, the main purpose of this research is to evaluate the feasibility of reusing waste materials with a focus on waste ceramic powder (WCP), as a semi-active aluminosilicate material that can be replaced as a percentage of cement used in concrete. WCP, in addition to activating the potential of using a waste material in concrete, can also reduce cement consumption. Therefore, in this research, cement replacement percentages between 0 and 50% in three water to cement ratios of 0.3, 0.4 and 0.5 in 24 concrete mixtures have been used to perform compressive strength tests. In order to provide a usable computational model, the gene expression programming (GEP) method has been used to predict the compressive strength of the samples. The results of experimental research indicate that in the ratio of water to cement 0.3 at the age of 90 days, the sample containing 20% of WCP has reached a compressive strength of 72.57 MPa. This result is almost equal to the control sample and therefore the use of this percentage is recommended for this ratio of water to cementitious materials. Finally, the results indicate the very good performance of the GEP method by increasing the number of chromosomes and increasing the correlation coefficient between experimental and numerical data up to 98%. Therefore, the GEP method has a significant advantage over other methods by providing an analytical relationship and high accuracy.