Expert Users Research Articles

An ML-Enhanced Laser-Based Methane Slip Sensor Using Wavelength Modulation Spectroscopy.

Natural gas (NG) is a promising alternative to diesel for sustainable transport, potentially reducing GHG and air quality emissions significantly. However, the GHG benefits hinge on managing methane slip, the unburned methane in the exhaust of NG engines, which carries a significant global warming potential. The CH4 slip from NG engines is highly dependent on engine type and operation, and effective greenhouse gas emission mitigation requires that the actual operation of real-world engines is monitored. This requires suitable instrumentation for online robust CH4 measurement in engine exhaust. Traditional methane slip measurement methods need frequent calibration, may not be suited to dynamic operational conditions, carry significant costs, or require expert users. Furthermore, the significant computational demands associated with calibration-free spectroscopic methods and the prevalent noise uncertainty underscore the urgent requirement for innovative sensors. These sensors must not only respond rapidly but also have low uncertainty in their readings. This paper presents a machine learning (ML)-enhanced, laser-based methane slip sensor using wavelength modulation spectroscopy (WMS) for rapid, accurate, and calibration-free CH4 measurements for application in the exhaust of NG engines. The sensor utilizes a distributed feedback (DFB) laser diode emitting around 1.65 μm propagated through a multipass optical cell. An ML-based approach is used to invert the recorded WMS signal, which reduces computational cost and uncertainty due to noise vulnerabilities inherent in traditional measurement inversion approaches. A Gaussian process regression (GPR) model, trained on measured and simulated WMS signals, was selected for its high predictive accuracy, where it achieved a mean absolute percent error (MAPE) of 0.24%. For exhaust measurement on an in-use natural gas marine vessel, a mean absolute difference of 3.95% was observed, relative to simultaneous reference Fourier transform infrared spectroscopy measurements. The ML-based WMS inversion system marks a significant advancement in methane slip measurement, offering real-time monitoring capabilities with reduced computational demands. Its development supports the realization of NG environmental benefits for transport by providing accurate CH4 slip data, which are essential for engine performance optimization, regulatory adherence, and sustainable policy decisions.

Bridging the gap: user expectations for conversational AI services with consideration of user expertise

Bridging the gap: user expectations for conversational AI services with consideration of user expertise

HiRegEx: Interactive Visual Query and Exploration of Multivariate Hierarchical Data.

When using exploratory visual analysis to examine multivariate hierarchical data, users often need to query data to narrow down the scope of analysis. However, formulating effective query expressions remains a challenge for multivariate hierarchical data, particularly when datasets become very large. To address this issue, we develop a declarative grammar, HiRegEx (Hierarchical data Regular Expression), for querying and exploring multivariate hierarchical data. Rooted in the extended multi-level task topology framework for tree visualizations (e-MLTT), HiRegEx delineates three query targets (node, path, and subtree) and two aspects for querying these targets (features and positions), and uses operators developed based on classical regular expressions for query construction. Based on the HiRegEx grammar, we develop an exploratory framework for querying and exploring multivariate hierarchical data and integrate it into the TreeQueryER prototype system. The exploratory framework includes three major components: top-down pattern specification, bottom-up data-driven inquiry, and context-creation data overview. We validate the expressiveness of HiRegEx with the tasks from the e-MLTT framework and showcase the utility and effectiveness of TreeQueryER system through a case study involving expert users in the analysis of a citation tree dataset.

Visual Analysis of Multi-outcome Causal Graphs.

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process. First, a progressive visualization method is proposed for comparing multiple state-of-the-art causal discovery algorithms. The method can handle mixed-type datasets comprising both continuous and categorical variables and assist in the creation of a fine-tuned causal graph of a single outcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causal graphs. In our visual analysis approach, analysts start by building individual causal graphs for each outcome variable, and then, multi-outcome causal graphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causal graphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.

Innovative Applications of IoT in Smart Home Systems: Enhancing Environmental Monitoring with Integrated Sensor Technologies and MQTT Protocol

The paper propose a novel MQTT-based configuration for enhancing the security and reliability of smart home systems. Our approach focuses on implementing Transport Layer Security (TLS), client authentication, access control lists (ACLs), and regular security audits to address potential security vulnerabilities. Experimental results show that the implementation of TLS improved data transmission security by 85%, while client authentication reduced unauthorized access attempts by 90%. Additionally, the system's scalability was tested by connecting up to 100 devices simultaneously, maintaining a stable message delivery rate with a latency increase of only 5%. A comparative analysis with existing commercial smart home systems, such as Google Nest and Amazon Alexa, highlights the advantages of our MQTT-based solution in terms of customization and flexibility. However, it also notes the need for user expertise in managing security configurations. These results indicate that our MQTT-based smart home system can provide a secure, scalable, and customizable alternative to commercial solutions, making it suitable for users with specific security requirements and technical capabilities.

CRITERION-BASED METHOD FOR QUALIFYING THE STRATEGY OF INCREASING THE NOMINAL POWER OF NUCLEAR REACTORS

In the materials of the article, examples the analysis of known approaches to the substantiation (qualification) of strategies for operating VVER/PWR nuclear reactors at increased nominal power levels is presented. It has been established that the main shortcomings of the existing approaches to qualifying upgrades of the nuclear reactor core lie in the dependence of qualification results on the implementation methods of the reactor operation strategy in high nominal power modes, as well as on the effects of differences in deterministic codes and/or user expertise with such codes. These factors ultimately complicate the objective interpretation of qualification results. A criterion-based method for qualifying the strategy for operating nuclear reactors at increased nominal power levels has been developed. This method is based on upgrading the thermophysical properties of nuclear fuel and the design and technical parameters of fuel element components. It eliminates the influence of variations in deterministic codes and/or user expertise with such codes and considers safety assurance conditions under the maximum allowable temperatures for fuel cladding and nuclear fuel.

Fuzzy-Based Virtual Reality System for Cultural Heritage: Enhancing User Interaction and Experience Through Contextual Assistive Messaging

The article highlights the need for enhanced user interaction and personalization in virtual reality (VR) systems for cultural heritage. As cultural sites become accessible through VR, delivering meaningful and tailored user experiences is crucial for effective learning and engagement. Traditional VR systems often fail to address varying levels of user expertise, limiting the accessibility of cultural content. To solve this, the study introduces SculptMate, a fuzzy logic-based VR system that estimates user proficiency in computers and VR, delivering context-sensitive assistive messages. In a study with 64 participants, SculptMate was compared to a standard VR system. Results demonstrated significantly higher engagement and comprehension of cultural artifacts with SculptMate, offering a personalized, immersive experience. The study emphasizes the potential of integrating fuzzy logic into VR to improve educational outcomes and user satisfaction. Future enhancements include dynamic machine learning, multisensory components, and platform optimization for broader accessibility and better user experiences.

Characterizing Computer Aided Design Performance: What can we learn from expert users?

Students who participate in engineering technical extracurricular teams such as Mini Baja and Formula Electric tend to have excellent Computer Aided Design (CAD) skills that often exceed that of their curricular peers with similar levels of education. This study characterized the differences in CAD performance between student CAD “experts” and their more novice peers. It was initially hypothesized that the baseline spatial ability between groups will be similar, but that specific differences in the quality of the technical drawings would exist between the groups. Three groups of students were studied: “Senior” students (Year 3 or more and on an extracurricular technical team, n=12), “Novice” (Year 2, not on a team, n=13) and “Junior” (Year 2 and on a team, n=7). Participants completed the same CAD task and a spatial test. The task output, technical drawings, were rated against a rubric by an expert assessor blinded to group membership. The scores were analyzed in MATLAB with a Mann-Whitney U-test. Junior and Senior students scored higher on a spatial visualization test compared to Novices, but only the Novice-Junior comparison met the criteria for statistical significance. There were no significant differences on any technical drawing criteria between Juniors and Seniors. Seniors’ assembly drawings were significantly better than Novices on the criteria relating to parts list and labelling. This contributed to a statistically significant difference in the overall assembly drawing score between the Novice and Senior groups, even though there was no significant differences in total overall score (part design, part drawing, assembly drawing summed together) between any groups or any significant correlations with spatial ability (measured by PSVT-R). Differences relating to expertise level (group) were primarily related to the more complicated assembly drawing task.

DEVELOPING ENGLISH MATERIALS WITH ISLAMIC CONTENT THROUGH TECHNOLOGICAL, PEDAGOGICAL AND CONTENT KNOWLEDGE APPROACH

The integration of technology in English teaching materials is essential to meet the growing demands of modern learners including those who learn in Islamic educational contexts. This study aimed to (1) identify the English learning needs related to Technological, Pedagogical, and Content Knowledge (TPACK); (2) develop an English language textbook based on the TPACK approach; and (3) evaluate its suitability for use in Islamic higher education. Employing the Research and Development (R&D) framework by Gall et al. (2003), the study was conducted in seventh phases: research and data collection, planning, preliminary product development, preliminary field testing, product revision, main field testing, and final product revision. The participants were 77 students from three Islamic higher education institutions in Lampung, Consist of 20 students from IAI Agus Salim, 30 students from IAIN Metro, and 27 students from IAI Ma’arif Metro.The subject in this research are expert jugment and user. Data were collected through questionnaires and interviews. The data were analyzed using both qualitative and quantitative techniques. Expert and user evaluations of the developed textbook, Basic English, demonstrated its good quality and effectiveness for use in Islamic higher education, with minor revisions recommended for optimization. The findings imply that the integration of the TPACK framework into textbook development can enhance the quality of English language learning in Islamic higher education. It is concluded that the textbook is suitable for use as an English Material in the learning process.

Study on visual matching cognition of automobile interior and exterior.

This work aims to study the visual matching cognition of people with different professional backgrounds for automobile interiors and exteriors, using gasoline-powered automobiles and electric automobiles as the research objects. First, three classes of automobiles, B-class, C-class, and sport-type class automobiles, of different brands, with large differences in styling and styles, were taken as the experimental sample sources, and high-definition interior and exterior pictures of the two power types of automobiles were randomly selected as the experimental samples. The participants were then divided into expert and general user groups according to their professional background. The two groups of participants were asked to conduct a total of 3 groups of visual matching cognition experiments on the interior and exterior of gasoline-powered automobiles, electric automobiles, and automobiles mixed with the same number of gasoline-powered automobiles and electric automobiles. The statistical analysis of the data of the above three groups of experiments revealed that there was no significant difference between the two groups of participants in terms of the visual matching cognition of the interior and exterior of the gasoline-powered automobiles and electric automobiles. When the two types of automobiles were mixed for the experiment, the participants still showed visual matching cognition for the interior and exterior, but there were obvious differences in cognitive abilities between the two groups. Research has shown that both general users and experts have the ability to perform visual matching of automobile interiors and exteriors, however, the cognitive ability of general users is weaker than that of experts. The visual matching cognition of the automobile interior and exterior is not affected by the power type or the automobile type.

Microwave-Energy-Based Device for the Treatment of Cellulite and Localized Adiposity: Recommendations of the "Onda Coolwaves" International Advisory Board.

The body contour market has grown rapidly in recent years, due to persistent requests for noninvasive treatments for localized fat adiposities, cellulite, and skin laxity. A variety of different methods are now available to improve body shaping. This review aims to provide an exhaustive compendium of the main recommendations for the optimal use of an innovative device delivering microwaves (MWs) for unwanted fat and cellulite reduction (Onda Coolwaves, DEKA, Florence, Italy), resulting from the experiences of the most expert international users. The availability of this new technology has led to an increasing number of treated patients and clinical studies. However, what is still missing, to the best of our knowledge, is an evaluation of the long-term efficacy and safety of this method. Based on the most recent data available, this compendium focuses on the ideal parameters, patient selection, and treatment methodology for providing safe and effective treatment protocols. Future research findings may suggest changes to the conclusions or recommendations in this report.

Utilization of Website and Interface to Help Raise the Brand Identity of Micro, Small and Medium Enterprises Owners

In today's modern era, a website serves as a key aspect of a brand's identity, with ownership often signifying credibility for a business. However, many individuals still struggle to market their brand through a website, often due to complex website designs or limited accessibility. With numerous businesses emerging every year, a brand's identity and uniqueness are essential in standing out from competitors. To address this challenge, a solution has been developed, which is a website builder company that enhances brand identity. This research aims to explore the importance of websites for MSMEs (Micro, Small, and Medium Enterprises) and identify how they can be designed to be effective, focusing on simple interfaces and clear communication of essential information. The data was gathered through quantitative interviews with expert, extreme, and non-expert users. The findings suggest that owning a website is crucial for MSMEs, significantly influencing consumer behavior and that the design should be well-organized, visually appealing, and engaging.

Musical Experience and Speech Processing: The Case of Whistled Words.

In this paper, we explore the effect of musical expertise on whistled word perception by naive listeners. In whistled words of nontonal languages, vowels are transposed to relatively stable pitches, while consonants are translated into pitch movements or interruptions. Previous behavioral studies have demonstrated that naive listeners can categorize isolated consonants, vowels, and words well over chance. Here, we take an interest in the effect of musical experience on words while focusing on specific phonemes within the context of the word. We consider the role of phoneme position and type and compare the way in which these whistled consonants and vowels contribute to word recognition. Musical experience shows a significant and increasing advantage according to the musical level achieved, which, when further specified according to vowels and consonants, shows stronger advantages for vowels over consonants for all participants with musical experience, and advantages for high-level musicians over nonmusicians for both consonants and vowels. By specifying high-level musician skill according to one's musical instrument expertise (piano, violin, flute, or singing), and comparing these instrument groups to expert users of whistled speech, we observe instrument-specific profiles in the answer patterns. The differentiation of such profiles underlines a resounding advantage for expert whistlers, as well as the role of instrument specificity when considering skills transferred from music to speech. These profiles also highlight differences in phoneme correspondence rates due to the context of the word, especially impacting "acute" consonants (/s/ and /t/), and highlighting the robustness of /i/ and /o/.

Augmented reality borobudur temple: Development of the aves booklet on the avadana relief story

The issue identified in the aves sub-material for the class The media has not provided a thorough explanation of the structure and morphology of birds. The objective of this study is to create aves booklet media using augmented reality technology and evaluate its appropriateness through expert evaluations and user feedback. The study methodology employed is exploratory descriptive research, which is subsequently followed by development research utilizing the ADDIE model. Utilizing validation evaluation instruments for data analysis. The review of the augmented reality-based Aves booklet by media experts yielded a validity and feasibility score of 91.6%, while material specialists rated it at 80%. Meanwhile, Biology teachers rated the user reaction to augmented reality-based aves booklets as 92.5, indicating a very excellent evaluation. Similarly, students' user responses received a rating of 83.25%, indicating a good assessment.

The Rewardable Persuasive Model: A Mobile Exergame Conceptual Design Model that Facilitates Youths to Exercise through Mobile Gaming

This study seeks to assess current design models to propose a conceptual design model for a mobile exergame that promotes physical activity among youths. Encouraging youths to engage in physical activity rather than remaining sedentary is essential due to the numerous health benefits associated with regular exercise. Exercise is a subset of physical activity structured for fitness. Exergames have the potential to inspire youths to be active by combining exercise and gaming engagingly and enjoyably. The proliferation of mobile gaming has further broadened the availability and accessibility of exergames. In this study, we conducted four stages of information gathering to assess and propose a mobile exergame conceptual design model: a literature review, a user survey, expert reviews, and in-depth user interviews. Based on the study's findings, we identified appropriate components and their rationale by adapting an existing design model to our conceptual design model for a mobile exergame. This conceptual design model is called the Rewardable Persuasive Model (RPM). This model aims to help youths achieve their weekly physical activity targets using an engaging and functional mobile application (app). The app incentivizes exercise by integrating it as a key element for unlocking gameplay. With the introduction of these components, an exergame can be designed to engage youths and facilitate physical activity effectively. In a future study, youths will use an app to assess the RPM's effectiveness over time. This assessment will ascertain its appropriateness for facilitating exercise during inactivity.

SingletSeeker: an unsupervised clustering approach for automated singlet discrimination in cytometry.

Flow cytometry is a high-throughput, high-dimensional technique that generates large sets of single-cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single-cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time-consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high-density clusters of events using a density-based clustering algorithm, and then classifies the clusters based on their properties. Multiplets are discarded in the first step, while singlets are distinguished from debris in the second step. The algorithm can use different strategies on imaging feature selection-based user's preferences and imaging features available. In addition, the relative importance of singlets precision vs. sensitivity can be further tweaked via a density coefficient adjustment. Twenty-two datasets from various sites and of various cell types acquired on the BD FACSDiscover™ S8 Cell Sorter with CellView™ Image Technology were used to develop and validate the algorithm across multiple imaging feature sets. A consistent singlets precision >97% with a solid >88% sensitivity has been demonstrated with a LightLoss feature set and the default density coefficient. This work yields a high-precision, high-sensitivity algorithm capable of objective and automated singlet discrimination across multiple cell types using various imaging-derived parameters. A free FlowJo™ Software plugin implementation is available for simple and reproducible singlet discrimination for use at the beginning of any user's workflow.

A Large Language Model-based Approach for Personalized Search Results Re-ranking in Professional Domains

Search result personalization and re-ranking in professional domains present significant challenges due to the complexity of domain-specific terminology and varying user expertise levels. This paper proposes a novel framework integrating Large Language Models (LLMs) with personalized search re-ranking for professional domains. The framework incorporates four key components: LLM-based user profile construction, professional domain knowledge encoding, cross-encoder re-ranking, and dynamic weight allocation. The user profile construction module utilizes historical interactions and professional behaviors to generate comprehensive user representations, while the domain knowledge encoding module captures specialized terminology and relationships. A cross-encoder architecture performs deep semantic matching between queries and documents, with results optimized through a dynamic weight allocation strategy. Experimental evaluation on three professional datasets (MedSearch, LegalDoc, and TechQuery) demonstrates significant improvements over existing methods, achieving 15.2% higher nDCG@10 and 12.8% better MRR compared to traditional ranking approaches. The framework maintains stable performance under varying query loads while effectively handling domain-specific terminology and user expertise variations. Ablation studies reveal the substantial impact of each component, with the LLM-based user profile construction and domain knowledge encoding contributing 7.5% and 6.3% improvements respectively. The proposed approach establishes new benchmarks for professional search systems while providing insights into effective integration of LLMs in domain-specific information retrieval.

Big data analysis of Spanish wine consumers reviews

Purpose Wine is a complicated and difficult product to know, which makes it extremely difficult for people with little knowledge to choose the wine they want. This study aims to analyze whether the vocabulary used in reviews on wine written by experts and amateurs on the specialized website is useful for those consumers who wish to search for information on this website to choose a wine. Design/methodology/approach The analysis combines text mining, Natural Language Processing and the Biterm Topic model applied to 25,847 reviews, evaluating a total of 13,263 Spanish wines made by 17 selected users of a specialized wine website. Findings The results show that wine consumers and users of the specialized wine website who write reviews can be divided into expert users and amateur users. Both experts and amateurs use a specific vocabulary related to the wines they review. Unlike amateurs, experts have a broader and more precise vocabulary, and greater consistency in the use of words with the aspects of the wine. For the revised wines, they address fewer and more specific aspects of wine (such as vintages), but they do so with more depth and rigor. Originality/value The originality and value of this research work lie in addressing two aspects that have hardly been analyzed: the reviews of experienced consumers and amateur consumers, and the textual information referring to the Spanish language, which distinguishes this analysis from other similar analyses carried out on the English language.

The Chemical Probes Portal - 2024: update on this public resource to support best-practice selection and use of small molecules in biomedical research.

The Chemical Probes Portal (www.chemicalprobes.org) is a free, public resource, based on expert-reviews, that supports the assessment, selectionand use of small-molecule compounds that qualify as chemical probes. These high-quality reagents are essential for exploring the function of individual proteins in complex biological systems, such as cells and organisms, and for validating proteins as potential therapeutic targets. The use of reliable chemical probes accelerates protein annotation in basic biological studies and informs drug discovery. However, the use of low-quality compounds has historically led to erroneous conclusions in biomedical research, and experience shows that failure to follow best practice continues, an issue which the Portal aims to address. Here, we describe the latest updates to the Chemical Probes Portal in both content and functionality. The number of chemical probes and human protein targets covered has increased significantly, with improvements in the processes for obtaining expert reviews and user engagement. Moreover, new functionalities and enhanced tools have been introduced to better support biological researchers in selecting and using the best chemical probes for their studies.

Live or Let Die: MILSTD 1472 Type Usability Checklists in an Agile Development World

Traditional Human System Integration (HSI) processes are at risk of being eliminated given the industry driven velocity and continuous delivery methods of Agile and Development to Operations Engineering. One such process at risk is usability standard checklists like MIL-STD 1472. The DoD Design Criteria Standard—Human Engineering, or MIL-STD 1472 is a military standard that provides guidelines for the ergonomic design of military systems and equipment. It is primarily focused on ensuring that military systems are designed to optimize human performance, safety, and comfort. However, it can be time-consuming to use given that it contains over 4,400 compliance items in its entirety. In an Agile DevOps environment, the typical delivery of products is 2 weeks with some software products boasting deployments every 11 min. Incorporation of usability and rigorous human factors checklists in the design of products using a DevOps environment is a challenge given the shorter timelines and smaller increments between product development and deployment combined with the expertise and hours needed to execute the checklists. The need for regression testing, the act of re-checking items to make sure new features have not created unforeseen usability issues, exacerbates the problem. Software quality testing for each release demands automatic methods to reduce the efforts of recursive testing. Industry standard methods in agile engineering utilize heuristics for quick turn low-cost usability inspections. This paper describes the derivation of a set of usability heuristics based on MIL-STD 1472 which can be used to identify potential human factors issues more quickly, with less cost, and reduced amount of expertise required. The paper also gives an example of a case study which incorporated automatic checking to support usability experts keeping pace with the high velocity pipelines of software development in an agile DevOps environment.