Interactive Interface Research Articles

A matter of time and space: reflections on the use of chatbots to assist the collection of audio diaries during the COVID-19 pandemic in Brazil

ABSTRACT The use of qualitative audio diaries in research allows people to generate multi-occasion reports of events in their natural, spontaneous contexts in a recorded audio format. Recent technological advances have enabled more rapid, spontaneous means for audio diaries recordings, such as smartphones. This article reports on methodological insights regarding the feasibility and contributions of using chatbots – digital systems that provide an interface for interaction with users that emulate human conversation – to assist the collection of audio diaries. We describe the development of a WhatsApp chatbot for the remote collection and sharing of audio files using a diary format. Methodological considerations are presented, drawing from the results of a remote collection of spoken reports from 19 Brazilian undergraduate students who were experiencing social isolation due to COVID-19 pandemic-related containment measures. Results emphasize the temporal aspect of the data collection process, and indicate that the tool allowed participants to generate accounts depicting changes in their understanding of perceptions and experiences over time. Although leveraging digital technology and conversational-like features of chatbots may promote greater participation and less participant burden, results also suggest that using pre-scripted prompts in the form of open questions may pose a challenge for reflexive engagement with audio diaries.

Zooming in the plastisphere: the ecological interface for phytoplankton-plastic interactions in aquatic ecosystems.

Phytoplankton is an essential resource in aquatic ecosystems, situated at the base of aquatic food webs. Plastic pollution can impact these organisms, potentially affecting the functioning of aquatic ecosystems. The interaction between plastics and phytoplankton is multifaceted: while microplastics can exert toxic effects on phytoplankton, plastics can also act as a substrate for colonisation. By reviewing the existing literature, this study aims to address pivotal questions concerning the intricate interplay among plastics and phytoplankton/phytobenthos and analyse impacts on fundamental ecosystem processes (e.g. primary production, nutrient cycling). This investigation spans both marine and freshwater ecosystems, examining diverse organisational levels from subcellular processes to entire ecosystems. The diverse chemical composition of plastics, along with their variable properties and role in forming the "plastisphere", underscores the complexity of their influences on aquatic environments. Morphological changes, alterations in metabolic processes, defence and stress responses, including homoaggregation and extracellular polysaccharide biosynthesis, represent adaptive strategies employed by phytoplankton to cope with plastic-induced stress. Plastics also serve as potential habitats for harmful algae and invasive species, thereby influencing biodiversity and environmental conditions. Processes affected by phytoplankton-plastic interaction can have cascading effects throughout the aquatic food web via altered bottom-up and top-down processes. This review emphasises that our understanding of how these multiple interactions compare in impact on natural processes is far from complete, and uncertainty persists regarding whether they drive significant alterations in ecological variables. A lack of comprehensive investigation poses a risk of overlooking fundamental aspects in addressing the environmental challenges associated with widespread plastic pollution.

Protein-based 2D Nanoarchitectures Constructed by Heterochiral π-Stacking Dimerization of Helical Foldamers.

In this study, we focus on the designability and controllability of the interaction interface between secondary structures, and discover an important interface interaction between helical secondary structures by non-covalent synthesis along the helical axis. The formation of discrete heterochiral dimers consisting of left-handed helix and right-handed helix not only helps to discover nonclassical supramolecular chirality phenomena, but also enables controllable protein assembly. Highly ordered nanostructures were thus constructed using p-stacking dimerization of helical foldamers to control tetrameric avidin proteins. The designable and modifiable primitives of artificial folded molecules enable the modification of secondary structure interfaces through non-covalent interactions, leading to the generation of unique structures and functions. These findings are of fundamental importance to the understanding of the precise assembly process of helical foldamers and can provide insights to facilitate the rational design of abiotic protein-like tertiary structures and further functionalization.

Human-computer interaction interface design of flight simulator based on situation awareness.

To meet the comprehensive requirements of drivers for Human-Computer Interaction (HCI) during operation and to enhance situation awareness (SA), this paper proposes a design and evaluation method for flight simulator HCI interfaces grounded in SA. Firstly, the widely adopted Endsley's SA model, which comprehensively covers processes relevant to individual drivers was selected as the foundational framework. The study concentrates on flight, cruise, and landing scenarios within the simulator, analyzing and restructuring the information architecture of the HCI interface. Secondly, a self-assessment scale was developed based on the three-level model of information processing for SA to measure drivers' SA cognitive levels. The core scenarios of the simulation training were analyzed to establish a design-making process. Thirdly, invite participants were invited to engage in simulated driving sessions on the flight simulator, where their SA cognitive levels were assessed. An analysis of detail features was performed on samples with high SA cognitive levels. Subsequently, drivers' requirements for the control layout features of the HCI interface design were gathered through surveys. The design detail features of the HCI interface associated with high SA cognitive levels were identified and synthesized into four distinct dimensions to guide the detailed design process. Finally, participants engaged in simulated driving using the finalized interface design, and their SA cognitive levels were reassessed. The results demonstrated that the HCI interface design derived from this method significantly improved SA cognitive levels compared to the original design.

Web-GIS Application for Hydrogeological Risk Prevention: The Case Study of Cervo Valley

Natural disasters have increasingly threatened human life, infrastructure, and ecosystems, exacerbated by climate change, urbanization, and deforestation. Effective disaster risk management is crucial to mitigate these impacts. Traditionally, Geographic Information Systems (GISs) have provided spatial data analysis capabilities, but the advent of Web-GIS applications has revolutionized this field. Web-GIS platforms enable real-time data access and facilitate enhanced stakeholder collaboration. This paper details the development of a Web-GIS application tailored for hydrogeological risk management in Cervo Valley, part of the NODES—Nord Ovest Digitale e Sostenibile project under Italy’s National Recovery and Resilience Plan (NRRP). The application integrates both static and dynamic geospatial data to create an interactive interface for evaluating and planning responses to hydrogeological hazards, specifically floods, landslides, and debris flow cones. By utilizing advanced Web-GIS capabilities, the project aims to refine the risk management practices and decision-making processes, thereby bolstering territorial resilience and addressing contemporary spatial challenges with enhanced precision and efficiency.

Image modeling algorithm for environment design based on augmented and virtual reality technologies

Abstract With the continuous development of computer vision technology, the image modeling of environmental design has also received more and more attention. This study proposed an image modeling algorithm for environment design based on augmented reality (AR) and virtual reality (VR) technologies. On the system architecture, the environment design image modeling algorithm based on AR and VR technologies has provided users with an immersive 3D simulation space environment. It also has provided users with a good interactive interface through AR technology, enabling users to interact with computers in a realistic virtual three-dimensional environment and interactive operation. This study verified the algorithm from three aspects: time, authenticity, and interactivity. When facing object A, the modeling time values of the traditional environmental design image modeling method and the environmental design image modeling method proposed in this study were 12.14 and 8.72 s, respectively. The modeling authenticity reached 87.51 and 98.57%, respectively, and the interactivity reached 79.14 and 96.45%, respectively. Some columns of data have proved that the environmental design image modeling method proposed in this study was superior to the traditional environmental design image modeling method. In addition, this study also verified the system model from three aspects: stability, running smoothness, and performance loss. When the number of image modeling in the environment design was 2, the stability, running smoothness, and performance loss of the system were 99.37, 99.25 and 0.75%, respectively. The experimental data again proved that the algorithm system model proposed in this study was feasible and worthy of further application.

AgDataBox-Map - A web application for creating thematic maps and management zones: users' evaluation

Agriculture is confronted with the imperative to enhance output while simultaneously ensuring increased profitability and minimizing ecological repercussions. Consequently, digital technologies have emerged as essential instruments for the acquisition of data, the analysis of information, and the facilitation of decision-making. The digital platform AgDataBox (ADB, https://adb.md.utfpr.edu.br/map) provides no-cost computational resources for agricultural professionals, scholars, and service entities, with a focus on Precision Agriculture (PA) by integrating data, software, protocols, and methodologies to facilitate agricultural progress through the utilization of open-access technologies. The purpose of this study was to examine how users assess the ADB-Map. The AgDataBox Map (ADB-Map) application works with spatial data to create thematic maps and management zones to subsidize PA and digital agriculture. The ADB-Map functionalities are divided into different layers, composed of (i) a back-end, which contains algorithms and rules of business operation, and (ii) a front-end, which is the interface of interaction with the user. By applying the evaluation methods suggested by NBR ISO/IEC 14598-6 across all areas, the ADB-Map achieved scores above 85% (the standard mandates a minimum of 70%), and an overall score of 92% was achieved. Hence, we believe users widely accept the software.

Microbial Contamination and Antibiotic Resistance in Fresh Produce and Agro-Ecosystems in South Asia—A Systematic Review

Fresh produce prone to microbial contamination is a potential reservoir for antimicrobial-resistant bacteria (ARB) and antimicrobial resistance genes (ARGs), posing challenges to food safety and public health. This systematic review aims to comprehensively assess the prevalence of bacterial pathogens and the incidence of ARB/ARGs in fresh produce and agro-ecosystems across South Asia. Twenty-two relevant studies published between 2012 and 2022 from three major scientific databases and the grey literature were identified. The results revealed a wide occurrence of microbial contamination in various types of fresh produce across South Asia, with a predominance of E. coli (16/22), Salmonella spp. (13/22), Staphylococcus spp. (5/22), and Klebsiella spp. (4/22). The agro-ecosystem serves as a complex interface for microbial interactions; studies have reported the prevalence of E. coli (1/4), Salmonella spp. (1/4) and Listeria monocytogenes (1/4) in farm environment samples. A concerning prevalence of ARB has been reported, with resistance to multiple classes of antibiotics. The presence of ARGs in fresh produce underscores the potential for gene transfer and the emergence of resistant pathogens. To conclude, our review provides insights into the requirements of enhanced surveillance, collaborative efforts, implementation of good agricultural practices, and public awareness for food safety and safeguarding public health in the region.

Visualisation Design Ideation with AI: A New Framework, Vocabulary, and Tool

This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, and experimenting with different methods to visually represent data or information. Sometimes, designers feel incapable of sketching, and the ideation process can be quite lengthy. In such cases, generative AI can provide assistance. However, even with AI, it can be difficult to know which vocabulary to use and how to strategically approach the design process. Our strategy prompts imaginative and structured narratives for generative AI use, facilitating the generation and refinement of visualisation design ideas. We aim to inspire fresh and innovative ideas, encouraging creativity and exploring unconventional concepts. VisAlchemy is a five-step framework: a methodical approach to defining, exploring, and refining prompts to enhance the generative AI process. The framework blends design elements and aesthetics with context and application. In addition, we present a vocabulary set of 300 words, underpinned from a corpus of visualisation design and art papers, along with a demonstration tool called VisAlchemy. The interactive interface of the VisAlchemy tool allows users to adhere to the framework and generate innovative visualisation design concepts. It is built using the SDXL Turbo language model. Finally, we demonstrate its use through case studies and examples and show the transformative power of the framework to create inspired and exciting design ideas through refinement, re-ordering, weighting of words and word rephrasing.

Admissions in the age of AI: detecting AI-generated application materials in higher education

Recent advances in Artificial Intelligence (AI), such as the development of large language models like ChatGPT, have blurred the boundaries between human and AI-generated text. This has led to a pressing need for tools that can determine whether text has been created or revised using AI. A general and universally effective detection model would be extremely useful, but appears to be beyond the reach of current technology and detection methods. The research described in this study adopts a domain and task specific approach and shows that specialized detection models can attain high accuracy. The study focuses on the higher education graduate admissions process, with the specific goal of identifying AI-generated and AI-revised Letters of Recommendation (LORs) and Statements of Intent (SOIs). Detecting such application materials is essential to ensure that applicants are evaluated on their true merits and abilities, and to foster an equitable and trustworthy admissions process. Our research is based on 3755 LORs and 1973 SOIs extracted from the application records of Fordham University’s Master’s programs in Computer Science and Data Science. To facilitate the construction and evaluation of detection models, we generated AI counterparts for each LOR and SOI using the GPT-3.5 Turbo API. The prompts for AI-generation text were derived from the admission data of the respective applicants, and the AI-revised LORs and SOIs were generated directly from the human-authored versions. We also utilize an open-access GPT-wiki-intro dataset to further validate our hypothesis regarding the feasibility of constructing domain-specific AI content detectors. Our experiments yield promising results in developing classifiers tailored to a specific domain when provided with sufficient training samples. Additionally, we present a comparative analysis of the word frequency and statistical characteristics of the text, which provides convincing evidence that ChatGPT employs distinctive vocabulary and paragraph structure compared to human-authored text. The code for this study is available on GitHub, and the models can be executed on user-provided data via an interactive web interface.

Employing stable isotopes to reveal temporal trajectories of water travelling through the soil–plant-atmosphere continuum

The spatiotemporal patterns of water travelling through the soil–plant-atmosphere continuum (SPAC) have received much attention due to the frequency of extreme weather and water scarcity. However, the interconnections and transboundary transport of specific compartments within the hydrologic cycle are poorly understood. We portrayed the propagation paths of water isotope signals by analyzing isotopes of precipitation, soil water, and xylem water. The isotope sine curves analysis method was improved to quantify water transfer efficiency, mean transmission time from precipitation to soil (MTT1), mean transmission time from soil to trees (MTT2), mean residence time within soil (MRTSW), and mean residence time within tree xylems (MRTXY). The temporal trajectory of water traveling through SPAC was depicted, and its influencing factors and intrinsic mechanisms were explored. Our results showed that (1) water isotopes were blocked when crossing water pools (precipitation, soil water, and xylem water) and the transfer efficiency was progressively weaker (85.5 %→13.5 %). (2) The MTT1 was significantly and positively correlated with soil porosity (Spearman correlation coefficient, 0.551). Its spatiotemporal pattern (0.8–15.9d) indicated that the transport and mixing of soil water involved two modes: displacement flow and bypass flow. The MTT2 (−13.2d–4.3d) of Platycadus orientalis was in general smaller than that of Quercus variabilis (−4.7d–11.5d). (3) The MRT (35.2d–343.8d) was influenced by a combination of soil physicochemical properties, root distribution, and tree morphological traits. Our study shows that connectivity between pools is better reflected with transfer efficiency. Comparing MTT and MRT of P. orientalis and Q. variabilis, tree hydrodynamic processes were quantified, and P. orientalis was more sensitive to seasonal moisture variability. Additionally, our study improved the understanding of the “black box” within the hydrological interface of plant-soil interactions.

Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks

Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and Tcell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.

ChatGPT and me: First-time and experienced users’ perceptions of ChatGPT’s communicative ability as a dialogue partner

Chatbots like ChatGPT have the potential to produce more natural conversational user interface interactions. Yet, we currently know little about perceptions of ChatGPT as a dialogue partner, and if interaction changes these. Through an online, two-stage, mixed methods study conducted in July 2023, in which first-time and experienced users living in the UK or Ireland engaged in tasks with ChatGPT, we show that interaction improves attitudes towards the system for first-time users, while these attitudes are already positive and stable in experienced users. We further show that first-time users’ perceptions of ChatGPT’s communicative ability (competence, human-likeness, and flexibility) are more dynamic than those of experienced users, although the experienced users’ perceptions also peak post-interaction. When reflecting on their interaction experience with ChatGPT, both groups were positive with little mention of limitations. We discuss the implications of these findings for user perceptions of ChatGPT as a dialogue partner, and highlight the potential risks of uncritical adoption of such technology.

Spcolor: Semantic prior guided exemplar-based image colorization

Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods directly search for correspondence over the entire reference image, and this type of global matching is prone to mismatch. Intuitively, a reasonable correspondence should be established between objects which are semantically similar. Motivated by this, we introduce the idea of semantic prior and propose SPColor, a semantic prior guided exemplar-based image colorization framework. Several novel components are systematically designed in SPColor, including a semantic prior guided correspondence network (SPC), a category reduction algorithm (CRA), and a similarity masked perceptual loss (SMP loss). Different from previous methods, SPColor establishes the correspondence between the pixels in the same semantic class locally. In this way, improper correspondence between different semantic classes is explicitly excluded, and the mismatch is obviously alleviated. In addition, SPColor supports region-level class assignments before SPC in the pipeline. With this feature, a category manipulation process (CMP) is proposed as an interactive interface to control colorization, which can also produce more varied colorization results and improve the flexibility of reference selection. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively on public dataset. Our code is available at https://github.com/viector/spcolor.

Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.

Prediabetes and diabetes are both abnormal states of glucose metabolism (AGM) that can lead to severe complications. Early detection of AGM is crucial for timely intervention and treatment. However, fasting blood glucose (FBG) as a mass population screening method may fail to identify some individuals who are actually AGM but with normoglycemia. This study aimed to develop and validate machine learning (ML) models to identify AGM among individuals with normoglycemia using routine health check-up indicators. According to the American Diabetes Association (ADA) criteria, participants with normoglycemia (FBG≤5.6mmol/L) were collected from 2019 to 2023, and then divided into AGM and Normalgroups using glycosylated haemoglobin (HbA1c) 5.7% as the threshold. Data from 2019 to 2022 were divided into training and internal validation sets at a 7:3 ratio, while data from 2023 were used as the external validation set. Seven ML algorithms-including logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting machine, multilayer perceptron (MLP), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost)-were used to build models for identifying AGM in normoglycemia population. Model performance was evaluated using the area under the receiver operating characteristic curve (auROC) and the precision-recall curve (auPR). The feature contributions to the optimal model was visualised using the SHapley Additive exPlanations (SHAP). Finally, an intuitive and user-friendly interactive interface was developed. A total of 59,259 participants were finally enroled in this study, and then divided into the training set of 32,810, the internal validation set of 14,060, and the external validation set of 12,389. The Catboost model outperformed the others with auROC of 0.806 and 0.794 for the internal and external validation set, respectively. Age was the most important feature influencing the performance of the CatBoost model, followed by fasting blood glucose, red blood cells, haemoglobin, body mass index, and triglyceride-glucose. A well-performed ML model to identify AGM in the normoglycemia population was built, offering significant potential for early intervention and treatment of AGM that would otherwise have been missed.

Research on User Preferences for App Icon Design in Digital Interaction Interface

Research on User Preferences for App Icon Design in Digital Interaction Interface

User interface (UI) on self-service kiosks’ machine in fast-food industry in Nueva Ecija, Philippines: Its’ correlation towards customers’ experiences

This study aims to analyze the impact of human-computer interaction (User Interface) on self-service kiosks in the fast food industry in Nueva Ecija, Philippines, and how it affects customer experiences. The study aimed to outline the demographic characteristics such as age, gender, and education level, as well as how often individuals visited fast food restaurants. Additionally, it evaluated participants' opinions on the user interface of self-service kiosks in fast food establishments, focusing on ease of use, satisfaction, and intention to continue using them. Based on the findings of the research, most of the participants aged 21 to 35 utilized self-service kiosks at fast-food establishments, with 66.9% being female. Most of the participants, accounting for 31.5%, are college undergraduates who visit the fast food restaurant on a weekly basis. Utilizing a self-service kiosk at a fast food establishment is simple and convenient, streamlining the service process. Patrons at the fast-food establishment had a positive experience utilizing the self-service kiosk and plan to keep using it to streamline their ordering process. When faced with the issue, 41.04% of respondents experienced technical and mechanical problems that disrupted their ordering process or timing. Based on the findings, recommendations have been crafted, including a development plan created by the researcher to enhance the storage operation.

ScLTdb: a comprehensive single-cell lineage tracing database.

Single-cell lineage tracing (scLT) is a powerful technique that integrates cellular barcoding with single-cell sequencing technologies. This new approach enables the simultaneous measurement of cell fate and molecular profiles at single-cell resolution, uncovering the gene regulatory program of cell fate determination. However, a comprehensive scLT database is not yet available. Here, we present the single-cell lineage tracing database (scLTdb, https://scltdb.com) containing 109 datasets that are manually curated and analyzed through a standard pipeline. The scLTdb provides interactive analysis modules for visualizing and re-analyzing scLT datasets, especially the comprehensive cell fate analysis and lineage relationship analysis. Importantly, scLTdb also allows users to identify fate-related gene signatures. In conclusion, scLTdb provides an interactive interface of scLT data exploration and analysis, and will facilitate the understanding of cell fate decision and lineage commitment in development and diseases.

Filiform Papillae‐Inspired Wearable Pressure Sensor with High Sensitivity and Wide Detection Range

AbstractFlexible pressure sensor (FPS) has promising applications in fields like health monitoring and human–machine interactions. The achieving of both high sensitivity and wide detection range in FPS remains highly challenging. Here, inspired by the filiform papillae on cat tongue, a FPS (noted as FPSp) with a sensitivity up to 504.5 kPa−1, a detection range from 30 Pa to 350 kPa, a fast response time of 83 ms, and a high stability over 8000 cycles is developed. The papilla‐like structure continuously shifts the location of stress concentration under increasing pressure, which avoids the accumulation of stress at papillae tips, resulting in a high sensitivity and wide detection range. Moreover, FPSp demonstrates capabilities in monitoring human physiological signals and movement status and can serve as the human‐machine interaction interface. The work not only presents a promising wearable pressure sensor but also establishes a design strategy for high‐performance wearable bioelectronics.

Study on the Design of a Non-Contact Interaction System Using Gestures: Framework and Case Study

The interaction patterns are constantly changing as the rapid development in technology. The non-contact interaction system using gestures is becoming important in human-computer interaction nowadays. This paper illustrates a design framework of a non-contact interaction system using gestures, which fully considers the approach to fit non-contact gestures into the system. The corresponding user requirements were investigated by surveys, and the universal design guide of non-contact gestures was summarized by statistical analysis. A case study was illustrated for key points of the developed design framework. The scheme, including interaction architecture, gesture definition, and user interface design, was described. The rapid prototyping was built. Evaluations, including eye-tracking measurements and tests of system usability, were implemented and analyzed by statistical methods to present suggestions of iterative design. The feasibility of the developed framework was verified. By statistical methods, the relationship between the interaction usability and the user’s perception for information was discussed, and the user’s perception for the display of dynamic and static gestures was analyzed. The results provide a clear approach to satisfy user’s preferences and improve the interaction quality for non-contact interaction systems using gestures, and furnish empirical grounding for sustainably devising similar interaction systems.