Interaction Approach Research Articles

Survey: Unconventional Categories of Chatbots that make use of Machine Learning Techniques

A growing trend in computer interaction approaches is human-computer interaction, which describes communication between a person and a computer and is known as a chatbot. A conversational agent, often known as a chatbot, is a computer program that attempts to provide responses that appear human during a conversation. Studies on several chatbot applications, are been carried out in this research on the areas of Natural Language Processing (NLP), the Natural Language understanding (NLU), and Intent recognition in recent years. There are several NLU applications in various fields, including Chatbots, search in natural language, voice-driven assistants, web- scale information extraction, legal discovery, and content summarization. Intent classification is one of the biggest challenges in NLP. Some of the chatbots classifies students requests to make them simpler to comprehend. A chatbot is an appliance of software that replicates and processes human communication to offer real-time digital assistance. Various tasks that were previously handled by human agents, like assistance to customers, medical counselling, digital commerce and issues addressing, are now being assigned to chatbots. Due to the appearance of machine learning techniques, the main step in chatbot development was defining the rules that would be used to create responses. Here are some strategies for chatbot applications and the machine-learning techniques that underpin them.

Assessment of Soil–Structure Interaction Approaches in Mechanically Stabilized Earth Retaining Walls: A Review

Mechanically stabilized earth (MSE) walls are recognized for their cost-effectiveness and superior performance as earth-retaining structures. The integration of internally reinforced walls has transformed soil preservation practices, garnering significant attention from the global technical community. The construction method of MSE walls has recently gained widespread popularity, likely due to its cost efficiency and simplicity compared to traditional externally reinforced walls. This paper provides a comprehensive review of MSE walls, including their historical development, aesthetics, benefits, drawbacks, factors influencing lateral displacements and stress responses, and the concept of the MSE wall system. Key approaches for analyzing seismic soil–structure interaction (SSI) issues are emphasized, investigating the dynamic interaction between the structure and soil through various research methodologies. This study incorporates multiple publications, offering an in-depth review of the current state of dynamic SSI studies considering surrounding structures. The findings emphasize the significant sensitivity of the dynamic behavior of mechanically stabilized earth (MSE) walls to soil–structure interaction, highlighting the necessity for continuous research in this area. The paper identifies research gaps and proposes future directions to enhance MSE wall design and application, facilitating further advancements in earth-retaining structures.

Digital transformation of antenatal education: A descriptive exploratory study of women’s experiences of online antenatal education

ProblemResearch on how women experience online antenatal education is currently limited. A more nuanced understanding may assist organisations to tailor future digitalisation that best meets the needs of users. BackgroundCOVID-19 emergency measures forced a rapid implementation of online antenatal education. Women are known to enjoy some aspects of online antenatal education, but still desire social interaction. A marked digital divide is evident for more vulnerable populations. AimTo explore how pregnant women experience an online antenatal education program. MethodsA descriptive exploratory study was undertaken through collection of two concurrent data-sets. Quantitative data was collected from the online Parent Education Feedback Form (n = 38) Based on the six-stage process of Braun & Clarke, reflexive thematic analysis was used to analyse data sourced from semi-structured interviews with women (n = 5) who had undertaken online antenatal education. FindingsFour themes, and eight associated sub-themes, were identified to better understand how women experience online antenatal education. The four primary themes identified were: Experiential Digital Learning; Desired Journey; Contemporary Representation; and Human Connection in the Digital Age. DiscussionWell-designed digital platforms provide opportunities for interaction, content personalisation and self-tailored approaches in online antenatal education. Women require caregivers who hold specialist digital capabilities. Further research is warranted to better understand how digitalisation of antenatal education impacts women disadvantaged by digital exclusion. ConclusionThe digital transformation of antenatal education impacts a vast array of factors in women’s experiences during pregnancy. A specialist skill-set from midwives is needed to champion quality antenatal education in the digital age.

The Impact of Organizational Culture on Non-Financial Performance: A Mediation Effect of Sustainability Balanced Scorecard Measurements

Purpose—This research aims to illuminate the non-financial performance level in the hotel industry in East Java cities in Indonesia. That is, it aims to analyze the impact of organizational culture on non-financial performance by investigating the effectiveness of using Sustainability Balanced Scorecard (SBSC) measurements in assessing the relation between organizational culture and non-financial performance. Design/methodology/approach — This exploratory research examines a sample of 65 hotels operating in different targeted markets. In order to have a better understanding and provide a clear discussion related to these relationships in sufficient depth, the theoretical framework of this research has been designed and developed based on a wide review of relevant research, theories, and findings. The developed research framework adopts the mixed form of the contingency of fit (selection and mediating-based interaction approaches) to investigate the contingency relationship. The survey questionnaire is distributed using an offline approach. Therefore, the collected data is analyzed by using the Partial Least Square Path Modelling (PLS-PM) method along with taking advantage of SmartPLS 3.0 Software. Findings — The findings indicated that organizational culture (Clan, Adhocracy, Market, and Hierarchy) has a positive and significant influence on the effective implementations and usage of Sustainability Balanced Scorecard Measurements, namely, financial, internal business process, customer, learning and growth, environmental, and community. These five perspectives of SBSC have a mediation-interaction relationship between the impact of organizational culture and the organizational non-financial performance levels. Therefore, SBSC measurements significantly and positively influence non-financial performance (product/ quality service level, customer satisfaction level, employee loyalty level, and social sustainability level). Practical implications—This research contributes empirically to the existing literature related to management accounting practices, and more particularly, evaluation and performance practices, by providing findings from developing countries and focusing on the SBSC’ measurements’ usage at hotel businesses operating in three cities in East Java. Originality/value — The multiple evaluations and performance measurements of BSCS proved to be innovative business measurements to enhance the decision-making process and improve a business organization's overall value and performance. Thus, this research paper adopts the contingency model to determine the main internal contingency factor that explains the extent of SBSC measurements’ usage and the effective influence of SBSC measurements on non-financial performance levels. Keywords — Organizational Culture, Non-Financial Performance, Sustainability Balanced Scorecard’s Measurements, Hotel Industry, East Java, Indonesia. Paper type — Exploratory Research

Assessing the Effectiveness of Interaction Approach in Learning about Rivers among Grade IX Students in Geography

This study investigated the effectiveness of the interaction approach (IA) in learning about rivers among the ninth grade students of a middle secondary school in western Bhutan. The study adopted the mixed-method explanatory sequential design. The quantitative data were gathered through a quasi-experiment, involving pre-test and post-test on Geography achievement tests (GAT) for both the control and experimental groups (EG) and survey questionnaires were administered to gather the perception of students from the EG. Additionally, structured interviews were employed to gather qualitative data from an equal representation of genders within the EG.
 The GAT scores consisted of two sections viz. the CG (N = 34) and the EG (N = 34). In this study, a targeted sample selection was made with a sample size of 68 students. The analysis of the independent t-test to compare between CG and EG showed a statistically significant difference (P < .001) in post-test for both CG and EG. Similarly, a paired-sample t-test was conducted for comparative analysis. There was a statistically significant difference (P < .001) between the pre-test and post-test results of the CG (P = 0.05) in the students’ learning achievements with the post-test. Likewise, the EG shows a statistically significant difference (P < .001) between the test scores in the pre-test and post-test.
 The results of the study confirmed that using the IA improved students’ GAT scores compared to the conventional teaching method. The descriptive analysis of the study also indicated that students have positive perceptions towards the use of IA in learning of river in Geography. In addition, the study also revealed higher perceptions of the themes of self-confidence, motivation and collaboration when using IA, which was further supported by student respondents’ positive perceptions of the benefits of IA. However, when using IA, the large class size pose a challenge as perceived by students. In conclusion, this study recommends Geography teachers to use IA as an intervention approach to stimulate students to learn about rivers effectively. These predictors, however, need further work to validate its reliability.

Predicting Protein Corona Formation on Carbon Nanotubes

Engineered nanoparticles are poised to transform sensing, imaging, and delivery in biological systems. In particular, single-walled carbon nanotubes (SWCNTs) are uniquely suited for biological sensing and imaging due to their tissue-transparent and photostable near-infrared fluorescence. SWCNTs have been functionalized with various biomolecules, including synthetic peptides and proteins, to construct fluorescence-based nanosensors. Optimizing these -nanoparticle interactions is key in enhancing nanotechnology function. Yet, functionalized SWCNTs and other nanotechnologies more broadly suffer from as-of-yet unpredictable interactions with the biological environments in which they are applied: when nanoparticles are introduced into biological systems, endogenous proteins rapidly bind and often lead to decreased ability of the nanoparticles to perform their intended functions. As such, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where this unpredictable protein corona formation often prevents effective implementation.In this work, we develop a classifier that identifies proteins binding to nanotubes based solely on the protein’s amino acid sequence. We build and validate our classifier using mass spectrometry-based quantitative proteomic data that characterizes proteins bound to SWCNT-based nanosensors. Distribution changes among protein properties allow us to determine protein features associated with increased likelihood of SWCNT binding, including high content of solvent-exposed glycines and non-secondary structure-associated amino acids. Understanding these key protein features that drive protein-nanotube binding informs the design of physics-based modeling approaches of such interfacial nano-bio interactions. Lastly, we apply the classifier to rapidly identify candidate proteins with high binding affinity to SWCNTs and experimentally validate adsorption of these proteins with a kinetic exchange assay to evaluate the predictive power of our model. In sum, this classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.

Toward a multimodal and continuous approach of infant-adult interactions

AbstractAdult-infant early dyadic interactions have been extensively explored by developmental psychologists. Around the age of 2 months, infants already demonstrate complex, delicate and very sensitive behaviors that seem to express their ability to interact and share emotions with their caregivers. This paper presents 3 pilot studies of parent-infant dyadic interaction in various set-ups. The first two present longitudinal data collected on two infants aged between 1 and 6 months and their mothers. We analyzed the development of coordination between them, at the motor and at the vocal level. The 3rd pilot study aims to explore interpersonal coordination in both vocal behavior and motor activity for one infant and his mother at 2, 4 and 6 months. These pilot studies however leave a number of questions open concerning developmental changes and infants’ progressive mastery of interaction. We identify areas worth examining and try to tease out specific issues that may help develop new methodological pathways for the study of early naturalistic social interaction. We assume that a continuous, rather than discrete, approach would better capture the changes taking place in the various communicative modalities, while also displaying each dyad’s specificity and the narrative dimension of social engagement between infants and caregivers.

(Invited) On-the-Fly Non-Adiabatic Dynamics Simulations of Single-Walled Carbon Nanotubes with Covalent Defects

Single-walled carbon nanotubes (SWCNTs) with covalent surface defects have been explored recently due to their promise for use in single-photon telecommunication emission and in spintronic applications. The dynamical evolution of excitons (the primary electronic excitations) in these systems has only been loosely explored using atomistic modeling due to the size-limitations of these large systems. We present computational modeling of non-radiative relaxation on a variety of SWCNT chiralities with single-defect functionalization schemes. Our modeling uses a trajectory surface hopping algorithm accounting for excitonic effects with a configuration interaction approach. We find a strong chirality and defect-composition dependence on the population relaxation (50 – 500 fs) between the nanotube band-gap excitation and the defect-associated, single-photon-emitting state, giving insight into the dynamic trapping nature of these localized excitonic states. Engineering fast population decay into the quasi-two-level sub-system with weak coupling to higher-energy states increases the effectiveness and controllability of these quantum light emitters.

Temperature Dependence of Photoluminescence Intensity and Spin Contrast in Nitrogen-Vacancy Centers.

We report on measurements of the photoluminescence properties of single nitrogen-vacancy centers in diamond at temperatures between 4K and 300K. We observe a strong reduction of the PL intensity and spin contrast between ca. 10K and 100K that recovers to high levels below and above. Further, we find a rich dependence on magnetic bias field and crystal strain. We develop a comprehensive model based on spin mixing and orbital hopping in the electronic excited state that quantitatively explains the observations. Beyond a more complete understanding of the excited-state dynamics, our work provides a novel approach for probing electron-phonon interactions and a predictive tool for optimizing experimental conditions for quantum applications.

Electrostatically triggered autonomous self-healable and mechanically robust hydrogel in harsh environments for wearable electronics

The expeditious growth of self-healable multifunctional electronics poses a challenge on power devices to acquire fast autonomous self-healing of solids with efficient electrical recovery. However, realizing this goal remains elusive due to the sluggish dynamics of bonded ions. Herein, we overcome this limitation by introducing hydrogen bonding cluster and non-bonding electrostatic centers in solids to accelerate ion dynamics via more delocalization centers that can switch healing reformations at various physical states. As such, the matrix can retain fast autonomous self-healing characteristics in dry, aqueous, and freezing states via diffusion-less self-healing mechanism. The self-healable hydrogel renders a triboelectric nanogenerator on contact with skin with a power density of 11.1 W m–2 at a matching impedance as low as 9 MΩ, retaining 7.28 W m–2 in aqueous and 7.04 W m–2 freezing states, which outperforms all the reported self-healable induction devices. This diffusion-less approach of intrinsic switchable interaction is promising for robotics, sportswear, all weather prosthetics, cryogenics, and harsh weather power back-up applications.

Increased interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects

Hand preference is one of the behavioral expressions of lateralization in the brain. Previous fMRI studies showed the activation in several regions including the motor cortex and the cerebellum during single-hand movement. However, functional connectivity related to hand preference has not been investigated. Here, we used the generalized psychophysiological interaction (gPPI) approach to investigate the alteration of functional connectivity during single-hand movement from the resting state in right-hand subjects. The functional connectivity in interhemispheric motor-related regions including the supplementary motor area, the precentral gyrus, and the cerebellum was significantly increased during non-dominant hand movement, while functional connectivity was not increased during dominant hand movement. The general linear model (GLM) showed activation in contralateral supplementary motor area, contralateral precentral gyrus, and ipsilateral cerebellum during right- or left-hand movement. These results indicate that a combination of GLM and gPPI analysis can detect the lateralization of hand preference more clearly.

Administrative Unit Proliferation Through Spatial Interaction Approach: Case Study of Lembang City Region

Administrative unit proliferation is a decision that needs to be taken based on several considerations based on research. The proposed Lembang City region is an area that will develop into a city in the next few years. For this reason, this research attempted to evaluate the proposed area’s ability to become an administrative city using various spatial aspects. The analytical approaches used in this study were: spatial interaction analysis, which helps to determine the regional economic center; thematic overview analysis, implementing village SDGs through a spatial analysis approach; and SWOT analysis to identify Lembang City region’s strengths, weaknesses, opportunities, and threats in qualitative terms. The study results show that the proposed Lembang City area has excellent potential as a city region. Through a spatial analysis approach supported by geographic information system tools, this research was conducted as a consideration in deciding to expand Lembang Regency may provide significant assistance to the government in its work related to this decision.

Two-Branch Feature Interaction Fusion Method Based on Generative Adversarial Network

This study proposes a fusion method of infrared and visible images based on feature interaction. Existing fusion methods can be classified into two categories based on a single-branch network and a two-branch network. Generative adversarial networks are widely used in single-branch-based fusion methods, which ignore the difference in feature extraction caused by different input images. Most two-branch-based fusion methods use convolutional neural networks, which do not take into account the inverse promotion of fusion results and lack the interaction between different input features. To remedy the shortcomings of these fusion methods and better utilize the feature from source images, this study proposes a two-branch feature interactions method based on a generative adversarial network for visible and infrared image fusion. In the generator part, a two-branch feature interaction approach was designed to extract features from different inputs and realize feature interaction through the network connection of different branches. In the discriminator part, a double-classification discriminator was used for visible images and infrared images. Extensive comparison experiments with state-of-the-art methods have demonstrated the advantages of this proposed generative adversarial network based on two-branch feature interaction, which can enhance the texture details of objects in fusion results and reduce the interference of noise information from source inputs. In addition, the above advantages were also confirmed in generalization experiments of object detection.

Prediction of the phase difference between large-scale velocity and Reynolds stress fluctuations in wall turbulence

A resolvent-based model was used to predict the phase-difference profile between velocity and stress coherent motions measured in a high Reynolds number channel flow as a proxy for predicting large- and small-scale turbulent interactions. The resolvent model is based on the transfer-function approach for scale interactions in wall turbulence proposed in Jacobiet al.(J. Fluid Mech., vol 914, 2021, pp. 1–27), but incorporates a quasi-empirical weighting scheme to construct composite mode shapes that represent the realistic dispersion of convection velocities associated with the large scales of turbulence. The weighting scheme was derived from the observed similarity between the spectral region where the resolvent operator is low rank and the streamwise spectral energy density of wall-bounded turbulence, and was found to be superior to both single-convection velocity models and models based on linearly weighted modes, when compared with cross-spectral phase calculations from a channel flow computation. The ability to predict the phase relationship between large-scale coherent motions and their associated stress fluctuations allows for refining and extending resolvent-based models of turbulence to describe small-scale features of wall-bounded flows.

Digital Alternative Communication for Individuals with Amyotrophic Lateral Sclerosis: What We Have.

Amyotrophic Lateral Sclerosis is a disease that compromises the motor system and the functional abilities of the person in an irreversible way, causing the progressive loss of the ability to communicate. Tools based on Augmentative and Alternative Communication are essential for promoting autonomy and improving communication, life quality, and survival. This Systematic Literature Review aimed to provide evidence on eye-image-based Human-Computer Interaction approaches for the Augmentative and Alternative Communication of people with Amyotrophic Lateral Sclerosis. The Systematic Literature Review was conducted and guided following a protocol consisting of search questions, inclusion and exclusion criteria, and quality assessment, to select primary studies published between 2010 and 2021 in six repositories: Science Direct, Web of Science, Springer, IEEE Xplore, ACM Digital Library, and PubMed. After the screening, 25 primary studies were evaluated. These studies showcased four low-cost, non-invasive Human-Computer Interaction strategies employed for Augmentative and Alternative Communication in people with Amyotrophic Lateral Sclerosis. The strategies included Eye-Gaze, which featured in 36% of the studies; Eye-Blink and Eye-Tracking, each accounting for 28% of the approaches; and the Hybrid strategy, employed in 8% of the studies. For these approaches, several computational techniques were identified. For a better understanding, a workflow containing the development phases and the respective methods used by each strategy was generated. The results indicate the possibility and feasibility of developing Human-Computer Interaction resources based on eye images for Augmentative and Alternative Communication in a control group. The absence of experimental testing in people with Amyotrophic Lateral Sclerosis reiterates the challenges related to the scalability, efficiency, and usability of these technologies for people with the disease. Although challenges still exist, the findings represent important advances in the fields of health sciences and technology, promoting a promising future with possibilities for better life quality.

Physical-chemical properties of biodiesel–diesohol blends: Ultrasonic and molecular simulation studies

Diesohol (blend) is a considerable alternative for diesel that unites ethanol as well as the diesel. The outcomes from the inferences results were properly obtained in the course of investigational measurement of the physical parameters such as the density, ultrasound velocity, viscosity, and surface tension. Additionally, the calculated factors such as adiabatic compressibility, free length, acoustical impedance, and relative association are taken care of. The combination proportions viz. Diesel (DE)-80% + Biodiesel (BDE) −15% + Ethanol (ET)-5% were revealed to have a similar molecular association to diesel. The MD combined with the minimum-distance distribution functions (gmd(r)) exposed an accumulate of diesel closest to biodiesel at ∼2.50 Å, which recommend a considerable hydrogen bonding interaction mapping and approach between ethanol and the dieselaromatic molecules (polycyclic) and methyl esters. Additionally, the simulated result demonstrates an ester relating platform to promote the ethanol molecules dispersion into the diesel solution.

A RESEARCH AGENDA FOR THE AGILE AND BI INTERSECTION

The purpose of this paper is to identify and analyze Business Intelligence (BI) and Agile methodologies literature to establish its current position in Management Information Systems (MIS) to create robust contexts and propose an agenda for future research. A systematic literature review was employed to analyze literature and synthesize findings to clarify the relationship between these subfields. Using Clusters Analysis, it provided a scheme to track this literature for beginners and experienced researchers. From the definition of Agile, we gave objective trends to intertwine the agile market approach with software development and the BI area. The paper concluded with a discussion of pathways to be followed in different academic traditions aiming to develop future studies of the emerging Agile approach in Management Information Systems, while providing an approach for interactions between Agile and BI, seeking to help researchers and managers who wish to deepen the knowledge about this intersection.

Spectroscopic and theoretical approach of DNA interaction and anticancer studies of bio-pharmaceutically active pyrimidine derived Cu(II) and Zn(II) complexes

New metal(II) complexes (CuL2 and ZnL2) with pyrimidine appended Schiff base ligand (HL) were synthesized and characterized by diverse spectroscopic methods, reveals the proposed structure of metal(II) complexes possess square planar geometry. DNA interaction ability of isolated compounds was studied by UV–Visible, fluorescence, viscometric and electrochemical methods and the results showed that isolated compounds intercalated with calf thymus DNA (CT-DNA). In addition, anticancer activities of HL, CuL2, and ZnL2 have been evaluated by MTT assay, signifying moderate cytotoxic activity on selected cancer cell lines and less toxicity on NHDF normal cell line due to the specific targeting of pyrimidine analogues. Moreover, antioxidant activities of isolated compounds towards diverse free radicals have been studied by spectrophotometric methods. These results showed that CuL2 has better antioxidant ability than HL and ZnL2. Finally, antimicrobial activities of isolated compounds against selected antimicrobial pathogens exposed that CuL2 has better antimicrobial activity on E. coli and C. albicans than other antimicrobial pathogens. The DFT calculations have been done to get the optimized geometry of the ligand and the metal complexes. In order to get a broad understanding of the interactions of these synthesized metal complexes, a detailed molecular docking analysis is taken up.

More simple, efficient and accurate food research promoted by intermolecular interaction approaches: A review

The investigation of intermolecular interactions has become increasingly important in many studies, mainly by combining different analytical approaches to reveal the molecular mechanisms behind specific experimental phenomena. From spectroscopic analysis to sophisticated molecular simulation techniques like molecular docking, molecular dynamics (MD) simulation, and quantum chemical calculations (QCC), the mechanisms of intermolecular interactions are gradually being characterized more clearly and accurately, leading to revolutionary advances. This article aims to review the progression in the main techniques involving intermolecular interactions in food research and the corresponding experimental results. Finally, we discuss the significant impact that cutting-edge molecular simulation technologies may have on the future of conducting deeper exploration. Applications of molecular simulation technology may revolutionize the food research, making it possible to design new future foods with precise nutrition and desired properties.

Dielectric approaches for interactions of protons, positrons, and electrons in cold matter and plasmas

Dielectric approaches for interactions of protons, positrons, and electrons in cold matter and plasmas