Usage Of Models Research Articles

Design and Application of Intelligent Library Intelligence Authentication System Based on Information Entropy Early Warning Models

Abstract Identifying the authenticity of library intelligence has consistently posed a significant challenge in intelligence management. This paper aims to optimize the D-S evidence theory to address the issues of uncertainty, imprecision, and high conflict in library intelligence evidence fusion. To achieve this, DSmT is employed to extend the D-S evidence theory by eliminating the process of assigning conflicting information with normalization coefficients. Then, through the fuzzy information pseudo-feature analysis method, the library intelligent intelligence information entropy feature quantity is extracted, and semantic feature matching is used to construct the library intelligent intelligence true and false information recognition system. Finally, the recognition performance of the system is examined using a dataset to analyze the intelligence authenticity recognition of mis-spliced bacterial colony genomic data from a biological experiment that has been entered into the library intelligence database. Finally, it was found that the average recognition rate of this paper's system in the four classical datasets is 96.57%, which is higher than the 89.61% and 90.02% of the two evidence fusion classification schemes. The results of this paper's algorithm for high-conflict evidence are 0.9506, 0.0558, and 0.0097, reflecting the accuracy of the evidence support results. Finally, 35 colony genome splicing error intelligence were identified, of which 32 were genuine errors, with a precision rate of 91% and a recall rate of 94%. The library intelligence authenticity recognition system designed in this paper has excellent performance and provides an effective path and usable algorithmic model for library intelligence authentication.

Construction and Validation of Alternative Usability Scale

Usability is the extent of a system being user-friendly. The aim of this research was to make a test to measure usability based on five-factor model of usability by Nielsen (1993). Total of 755 participants (Mage=26.1, SD=5.86) representing the users of 21 types of products or services gave responses. Psychometric analyses were done. The test with fifteen items did not fit the model of its parent theory as revealed by confirmatory factor analysis. Principal component analysis was conducted and some items were deleted. Finally, a test of 12 items with two subscales was made. The participants were given a survey in English but they were not its native speakers. The future research can take native speakers and more diverse products’ or services’ users for psychometric analyses of the 15-item version.

UI and UX Redesign for COVID-19 Response Mobile Application Using Usability ISO Model and Testing Tools

The aim of this study was to analyse the user-friendliness of the COVID-19 Response Mobile Application, namely PeduliLindungi. 107 respondents answered the questionnaire and concluded that the usability of the current version is 68%, which is categorised as ‘terrible' on the system usability scale, indicating that the main concern of users is the accessibility of the application. The main objective is to redesign the UI and UX of the PeduliLindungi mobile application to improve the usability and accessibility of the application. The quantitative method was used as a research tool to obtain the responses of PeduliLindungi users and measure the attributes of the application based on the ISO 9241-11 metrics: Efficiency, Effectiveness and Satisfaction. A revised version of the PeduliLindungi mobile application received a 70% usability rating and was rated "good" on the system usability scale. The study also identified specific usability characteristics from the ISO usability model widely used in usability engineering. A guide was created for developers to include UX considerations in the development of mobile applications

Evaluating Usability of IRAIVI Pregnancy Prediction Model Using System Usability Scale

Background: Barriers to utilize maternal healthcare services amongst pregnant women at community level differs substantial variation among slums and urban areas which is essential to recognize and resolve these issues within framework of district-level policy planning. IRAIVI pregnancy prediction model is developed to enhance optimal utilization of maternal healthcare services. The study was conducted to evaluate usability of IRAIVI using System Usability Scale amongst healthcare workforce. Methods: This model was developed with a set of predictors based on data collected during baseline and follow up visits from currently pregnant women as per study protocol. For evaluating efficiency of this model, System Usability Scale (SUS) was adopted and shared with 25 randomly selected experts in the field of public health. The questionnaire was shared via google form and responses recorded on Likert’s scale were then analysed using SPSS. Results: IRAIVI model's usability assessed using SUS found to be user-friendly, best learning curve, adaptable to new system and highly acceptance by healthcare workforce. The SUS score averaging at 84 demonstrates favourable usability of the model. Conclusion: This model has capability to accentuate maternal health services which in-turn can contribute for better ANC services in resource-constrained settings. Additionally, future opportunities can be explored through field studies in different settings.

A Model to Predict Future Biologic or Targeted Synthetic DMARD Switch at a Subsequent Clinic Visit in Rheumatoid Arthritis.

To understand factors leading to biologic switches and to develop a readily usable model with data collected in clinical care at preceding visits, with the overall aim to predict the probability of switching biologic at a subsequent clinic visit in patients with rheumatoid arthritis (RA). Participants were adults with RA participating in the CorEvitas RA registry. The study matched patients who switched biologics or targeted synthetic disease-modifying anti-rheumatic drugs (tsDMARDs) with control patients who had not switched biologics/tsDMARDs; the cohort was divided into a training and test set for prediction model development and validation. Using the training set, the best subset regression, lasso, and elastic net methods were used to determine the best potential models. Area under the ROC curve (AUC) was used for the final selection of the best model, and estimated coefficients of this model were applied to the test dataset to predict switching. A total of 5050 patients were included, of whom 3016 were in the training set and 2034 were in the test dataset. The average age was 59.6 years, the majority were female (3998, 79.2%), and the average duration of RA at the time of switch or control visit was 12.8years. The final model included prior Clinical Disease Activity Index (CDAI) by category, prior patient pain measurement, change in CDAI from baseline, age group, and number of prior biologics, all of which were significantly associated with switching biologics. The AUC was 0.690 for this model with the training dataset. The model was then applied to the test data with similar performance; the AUC was 0.687. We have developed a simple model to determine the probability of switching biologics for RA at the following clinic visit. This model could be used in practice to provide clinicians with more information about their patient's trajectory and likelihood of switching to a new biologic.

Government Agencies Impediments Towards Establishing Pertinence Agrotourism Prospect in Malaysia: Case Study in Jerantut Pahang

The purpose of this study is to find the impediments of government agencies towards establishing agrotourism prospect in the case study area. Qualitative interviews were done with semi-structured questions to the involved government agencies. The finding shows that there’s unclear line of communication between involved government agencies, no cross-industry knowledge transfer, undeveloped marketing skills and no usable agrotourism model. Thus, this study will present the current and proposed line of communication among all involved parties to ensure easy information transfer and sharing.

Conus-wide model calibration and validation for CRESTv3.0 – An improved Coupled Routing and Excess STorage distributed hydrological model

The Coupled Routing and Excess Storage (CREST) is a flood-centric distributed hydrological model that has been widely used by researchers, educators, and decision-makers around the world since 2011. With growing public concern about the impact of climate change on water security, hydrological models such as CREST need to continue improving to provide more accurate simulations, more output products, and reduced application difficulties for users. In this study, an improved version of the CREST model (version 3.0) was proposed to consider the groundwater component of the hydrological cycle. A CONUS-wide CREST model calibration was conducted to provide usable model parameters for CREST users worldwide. The calibration improved the overall NSCE score from −0.97 to 0.11 over 3206 gauge points in CONUS. The validation results indicated that the CREST model performed well in the eastern CONUS but not in the Rocky Mountains and the Mountain West. The newly added groundwater module reduced the overestimation and the steepness of the falling limb during flood events in the eastern CONUS but drastically reduced the simulated water quantities in the mountainous regions.

An Adaptive M-Learning Usability Model for Facilitating M-Learning for Slow Learners

Mobile devices have evolved from communication tools to versatile platforms for various purposes, including learning. Usability is crucial for practical mobile learning applications, ensuring ease of use and expected performance. However, existing research on mobile educational apps has primarily focused on typical learners, neglecting the specific requirements of slow learners who face cognitive limitations. In this work, we fill this research gap by proposing an adaptable learning-oriented usability model (ALUM) for mobile learning apps specifically tailored to support slow learners. The research conducts a detailed usability analysis and systematic review to identify the problems users face and investigate how slow learners respond to learning apps in terms of efficiency, effectiveness, satisfaction, and learning outcomes. Twenty-four participants classified as slow learners evaluated the usability of 25 HTML-based learning apps. The evaluation revealed critical deficiencies in existing learning apps concerning the needs of slow learners, particularly in user-friendliness and learnability, leading to their dissatisfaction. We propose a model that leverages a hybrid recommendation system to address these challenges. The model incorporates a navigational graph, ontology, and item matrix to provide personalized topic recommendations, tailoring the content and delivery of educational materials based on individual needs and preferences. By enhancing the learning experience for slow learners, the proposed model aims to improve their learning outcomes. This research bridges the gap between academic research and practical applications in interactive mobile technologies. The adaptable learning-oriented usability model presented in this paper offers a framework for supporting slow learners, emphasizing its essential components and their interactions to enhance the learning outcomes for this user group.

The recalibration and redevelopment of a model to calculate patients' probability of completing a colonoscopy following an abnormal fecal test.

Fecal immunochemical testing (FIT) is an effective screening tool for colorectal cancer. If an FIT is abnormal, a follow-up colonoscopy is necessary to remove polyps or find cancers. We sought to develop a usable risk prediction model to identify patients unlikely to complete a colonoscopy following an abnormal FIT test. We recalibrated and then redeveloped a prediction model in federally qualified health centers (FQHCs), using a retrospective cohort of patients aged 50-75 with an abnormal FIT test and clinical data. Logistic and Cox regressions were used to recalibrate and then redevelop the model. The initial risk model used data from eight FQHCs (26 clinics) including 1723 patients. When we applied the model to a single large FQHC (34 clinics, 884 eligible patients), the model did not recalibrate successfully (c-statistic dropped more than 0.05, from 0.66 to 0.61). The model was redeveloped in the same FQHC in a cohort of 1401 patients with a c-statistic of 0.65. The original model developed in a group of FQHCs did not adequately recalibrate in the single large FQHC. Health system, patient characteristics or data differences may have led to the inability to recalibrate the model. However, the redeveloped model provides an adequate model for the single FQHC.

Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement

A variety of surrogate modeling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up reliable design-ready surrogates of modern microwave components. The difficulty of the modeling task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across extended ranges of material, geometry, and operational parameters, is far from easy. As a matter of fact, conventional modeling techniques are merely capable of building models for microwave structures featuring a relatively small number of designable parameters within reduced ranges thereof. One possible way of mitigating these obstacles may be the employment of the recently proposed two-stage performance-driven modeling approach. Therein, the surrogate model domain is narrowed down to the section of the space where the vectors of adequate quality are located, thereby permitting significantly reducing the cost of acquiring the training data. Seeking even further cost reduction, this work introduces a novel modeling framework, which exploits problem-specific knowledge extracted from the circuit responses to achieve substantial cost-savings of training data acquisition. In our methodology, the modeling procedure targets response features instead of the complete responses. The response features are the characteristic locations of the circuit response, such as relevant minima or maxima over selected frequency bands. The dependency of the coordinates of the said features on circuit dimensions is considerably less nonlinear than is observed for the complete characteristics, which enables sizable reduction of the data acquisition cost. Numerical validation of our procedure involving three microwave structures corroborates its remarkable efficiency, which allows for setting design-ready surrogates using only a handful of samples.

An extended taxonomy of advanced information visualization and interaction in conceptual modeling

Conceptual modeling is integral to computer science research and is widely adopted in industrial practices, e.g., business process and enterprise architecture management. Providing adequate and usable modeling tools is necessary to adopt modeling languages efficiently. Meta-modeling platforms provide a rich and mature set of functionalities for realizing state-of-the-art modeling tools. These tools, albeit their stability and rich set of features, often lack a modern look and feel considering (i) how they visualize the models, and (ii) how modelers interact with the models. Current web technologies enable much richer, advanced opportunities for visualizing and interacting with conceptual models. However, a structured and comprehensive overview of possible information visualization and interaction techniques linked to conceptual models and modeling tools must be established. This paper aims to fill this gap by presenting an extended taxonomy of advanced information visualization and interaction in conceptual modeling. We present a generic taxonomy that is afterward contextualized within the specific domain of conceptual modeling. The taxonomy serves orientation in the vast developing field of information visualization and interaction and hopefully sparks innovation if future modeling tool development.

The impact of diversity on job performance: evidence from private universities in Egypt

Employees from diverse backgrounds have been claimed to perform better; nevertheless, this link has been studied sparingly in the Egyptian setting. This paper addresses this gap by experimentally analyzing the link between employee performance and gender, educational background, and ethnic background. To investigate this link, a usable model was created, and data was collected from 269 academic staff members at private Egyptian institutions. PLS analysis was used, and the result revealed that educational and ethnic backgrounds have a positive effect on employees’ job performance whereas, the impact of gender background on employee’s job performance was found to be insignificant. Furthermore, the research findings disclose that the education and ethnic background of the employees mediates the relationship between employees’ job performance and gender diversity. Future research is yet to validate these results.

Examining differences in perceived usability of Google Drive among public library users

PurposeThis study aims to explore differences in the usability of the Google Drive application based on demographic characteristics, computer skills and use frequency of Google Drive among public library users of Lahore.Design/methodology/approachThe study adopted a quantitative approach and survey-based research method to achieve the study's objectives. The cloud usability model (user perspective) has been used as a theoretical lens to guide the study objectives. It comprises five dimensions of usability – capable, personal, reliable, valuable and secure.FindingsThe findings of the study revealed that the usability of Google Drive varied statistically significantly on the basis of depending on the respondents gender, age, academic qualification, computer skills and Google Drive use frequency.Practical implicationsIt is a valuable study since it and adds knowledge to existing literature and has implications for practice.Originality/valueThe findings might be helpful for cloud support teams including Google Drive as they can notice the demographic and other differences among users' perceived usability of Google Drive and can enhance certain features of usability which leads attributes to increase its usage among users.

A new model for cost estimation construction project using Hybrid importance regression ensemble method

Cost estimating entails gathering and evaluating historical data, as well as using quantitative models, methodologies, tools, and databases to forecast the cost of a program in the future. At the early stages of the building design process, the cost is considered one of the most important elements in making decisions. During the design phase of a project, cost estimating is quite essential. To compelet a construction project successfully, it is critical to design a usable model and method for cost estimation in construction projects. For the above reason, this study has developed a hybrid method to conduct an accurate cost estimation in construction projects in Iraq. This study also conducted a rigorous survey to find five main influential factors with thirty-six sub-factors in the Iraqi market. It was evaluated through previous studies, questionnaires, and surveys of twenty projects to build a matrix factors database for construction projects. This work gathered the construction cost factors from relevant research and expert views. In the second step, the researcher ranked the factors within the Importance Regression Ensemble Method then the K-Nearest Neighbor Method was applied to specify the effect of the near-effective factors on the cost. The outcome of this study will be helpful to construction professionals in estimating a construction project effectively.

LRAP: Layered Ring Based Adaptive and Personalized Usability Model for Mobile Commerce Apps

Usability is one of the most important characteristics of software applications, especially when it comes to mobile shopping applications. There is a great deal of shift from traditional shopping to online shopping because it benefits both parties i.e., customers as well as businessmen. In such a scenario, the usability factor can play a very vital role in the business industry. If a client stops using a mobile shopping app because it is not user-friendly, it can badly damage the annual revenues especially when hundreds of alternatives are available and there is tough competition. Therefore, to keep existing customers intact and to attract new customers, it is very important to provide a user-friendly mobile app to customers. In this paper, we consider a large variety of online customers with diverse requirements. background and constraints and evaluate the usability of existing mobile e-commerce apps to identify the actual problems people are facing with existing applications and do a systematic review of existing shopping apps. Then, we propose a personalized and adaptive usability model for mobile commerce apps considering the neglected user type i.e., illiterates and people with tactile disabilities. The proposed model LRAP is a layered approach from generalization to specification and it can be considered an extension of the famous PACMAD usability model. Besides this, we also suggest a combining score tool which will be helpful in measuring the usability of any app.

Design Students Between Traditional And Digital Learning: Usability Of Learning Management Systems And Preferences For Learning Environment Based On Course Type

Due to the educational transformation from traditional to digital learning in Jordan, caused by the COVID-19 pandemic, the purpose of this study was to contribute to this transformation in design education. Considering the practical nature of graphic and interior design education, this study aims to evaluate the usability of the Moodle-Learning Management System based on design students' assessments. It also aims to explore design students' preferences for learning environments; (Traditional, Online, Blended) based on course types in design programs (non-design courses, theoretical design courses, and practical/ studio-based courses). A quantitative approach was employed using a designed questionnaire developed from the Shackel usability model (1991) to conduct the research study. A total of (218) undergraduate graphic and interior design students from the design department at Yarmouk University participated in this study, and all the questionnaires were collected and analyzed in SPSS. The findings of this study have indicated that both the practical nature of design education and the late adoption of digital learning affect the usability evaluation of LMS and design students’ preferences for the learning environment. It was concluded that there is a need for implementing new pedagogies to enhance design students' experience, satisfaction, and acceptance of digital learning.

Towards industrial robots as a service (IRaaS): Flexibility, usability, safety and business models

Industrial robots form an integral part of today's manufacturing industry, due to their high versatility, precision, and fatigue proof nature. Yet, many small and medium sized enterprises (SMEs) still predominantly rely on manual labor. The main barriers that prevent SMEs from utilizing robots to a larger degree are described to be the large initial investment, uncertainty about costs (total cost of ownership), and lack of expertise. An opportunity to eliminate these barriers can be found in servitisation. While paradigms such as software as a service (SaaS) or Robot as a Service (RaaS) already exist, these focus mostly on software (functionality) via cloud computing. In this paper, a new paradigm based on software and hardware is proposed as Industrial Robots as a Service (IRaaS), which is composed of four elements: Flexibility (Plug and Produce, mobility), Usability (Easy Programming, Intuitive Interaction), Safety (Standards, Strategies), and Business Models (Time-based, Usage-based). To provide an overview of the current state-of-the-art a scoping survey is performed on each of the four key elements from an IRaaS perspective.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Advanced 3D Models of Human Brain Tissue Using Neural Cell Lines: State-of-the-Art and Future Prospects.

Human-relevant three-dimensional (3D) models of cerebral tissue can be invaluable tools to boost our understanding of the cellular mechanisms underlying brain pathophysiology. Nowadays, the accessibility, isolation and harvesting of human neural cells represents a bottleneck for obtaining reproducible and accurate models and gaining insights in the fields of oncology, neurodegenerative diseases and toxicology. In this scenario, given their low cost, ease of culture and reproducibility, neural cell lines constitute a key tool for developing usable and reliable models of the human brain. Here, we review the most recent advances in 3D constructs laden with neural cell lines, highlighting their advantages and limitations and their possible future applications.

BattX: An equivalent circuit model for lithium-ion batteries over broad current ranges

Advanced battery management is as important for lithium-ion battery systems as the brain is for the human body. Its performance is based on the use of fast and accurate battery models. However, the mainstream equivalent circuit models and electrochemical models have yet to meet this need well, due to their struggle with either predictive accuracy or computational complexity. This problem has acquired urgency as some emerging battery applications running across broad current ranges, e.g., electric vertical take-off and landing aircraft, can hardly find usable models from the literature. Motivated to address this problem, we develop an innovative model in this study. Called BattX, the model is an equivalent circuit model that draws comparisons to a single particle model with electrolyte and thermal dynamics, thus combining their respective merits to be computationally efficient, accurate, and physically interpretable. The model design pivots on leveraging multiple circuits to approximate major electrochemical and physical processes in charging/discharging. Given the model, we develop a multipronged approach to design experiments and identify its parameters in groups from experimental data. Experimental validation proves that the BattX model is capable of accurate voltage prediction for charging/discharging across low to high C-rates.