Smooth Interaction Research Articles

SiargaoStayFinder: Discover Homestays and Accommodations in Siargao Island

The rapid growth of tourism on Siargao Island has heightened the demand for efficient and accessible accommodations. However, local homestay providers predominantly rely on manual booking processes, which are often time-consuming, lack transparency, and limit both visibility and revenue potential. This study introduces SiargaoStayFinder, a digital platform designed to centralize accommodation options and streamline the booking experience for tourists while enhancing operational efficiency for providers. Using a mixed-methods approach, this research gathered quantitative data from tourists and qualitative insights from homestay providers to assess key needs. Findings indicate a strong demand for features like real-time availability, secure payment options, and user reviews. The system was developed through a structured process that included requirements gathering, system design, implementation, testing, and deployment. The final platform integrates a user-friendly frontend (HTML, CSS, JavaScript) with a secure backend (PHP, MySQL), ensuring smooth and efficient data interactions. By bridging the gap between tourists and local businesses, SiargaoStayFinder not only improves the booking experience but also contributes to sustainable economic growth on the island, providing a model for similar tourism-focused destinations

Human-machine interaction with intelligent machine learning algorithms for smart classrooms

ABSTRACT The modern technology of learning and teaching in education has moved on to smart classes. The introduction of artificial intelligence (AI) in the education sector has essentially taken over traditional classrooms and transformed how education leads to admiration. Various data types, such as videos, images, sound, text, and biometric data like eye-tracking potentially captured through smart classrooms, pose new issues and challenges for artificial intelligence algorithms. Hence, this paper suggests the Machine Learning Assisted Intelligent Human-Machine Interaction (MLIHMI) model for the smart classroom monitoring system. The real-time interactive dimension signifies the capability to support the Smart classroom’s teaching and human-computer interactions, including smooth interaction, convenient operation, and interactive monitoring. This paper used ML and HMI models to introduce a new effective database for online learning and smart classroom environments utilising the learners’ body postures, facial expressions, and hand gestures. The numerical findings show that the suggested system achieves less error rate of 10.1% and enhances the accuracy rate of 96.5%, with the highest classification ratio of 95.6%, prediction rate of 93.6%, the detection rate of 97.3%, and engagement rate of 81.3% in student gesture and postures recognition and the identification of facial expressions with the high performance of 93.3% when compared to other existing models.

Cybernic robot hand-arm that realizes cooperative work as a new hand-arm for people with a single upper-limb dysfunction.

A robot hand-arm that can perform various tasks with the unaffected arm could ease the daily lives of patients with a single upper-limb dysfunction. A smooth interaction between robot and patient is desirable since their other arm functions normally. If the robot can move in response to the user's intentions and cooperate with the unaffected arm, even without detailed operation, it can effectively assist with daily tasks. This study aims to propose and develop a cybernic robot hand-arm with the following features: 1) input of user intention via bioelectrical signals from the paralyzed arm, the unaffected arm's motion, and voice; 2) autonomous control of support movements; 3) a control system that integrates voluntary and autonomous control by combining 1) and 2) to thus allow smooth work support in cooperation with the unaffected arm, reflecting intention as a part of the body; and 4) a learning function to provide work support across various tasks in daily use. We confirmed the feasibility and usefulness of the proposed system through a pilot study involving three patients. The system learned to support new tasks by working with the user through an operating function that does not require the involvement of the unaffected arm. The system divides the support actions into movement phases and learns the phase-shift conditions from the sensor information about the user's intention. After learning, the system autonomously performs learned support actions through voluntary phase shifts based on input about the user's intention via bioelectrical signals, the unaffected arm's motion, and by voice, enabling smooth collaborative movement with the unaffected arm. Experiments with patients demonstrated that the system could learn and provide smooth work support in cooperation with the unaffected arm to successfully complete tasks they find difficult. Additionally, the questionnaire subjectively confirmed that cooperative work according to the user's intention was achieved and that work time was within a feasible range for daily life. Furthermore, it was observed that participants who used bioelectrical signals from their paralyzed arm perceived the system as part of their body. We thus confirmed the feasibility and usefulness of various cooperative task supports using the proposed method.

Application Research of Power Measurement Data Exchange Based on Uninterruptible Power Supply

With the maturity of the smart meter embedded system integration mode, the power consumption information can be fed back to the management center by the intelligent terminal through the power Internet of Things. The smart terminal adopts the narrowband data communication mode. However, the narrowband channel interval is hindered by interference signals, resulting in serious data packet loss. Therefore, a stable and efficient acquisition scheme is needed to ensure its smooth interaction. Based on the application of smart meter data communication, we analyze the power consumption optimization and carrier transmission optimization of the power Internet of Things and propose a data analysis method based on self-learning and external characteristics, which is used to calculate the offline execution strategy of smart meters when they encounter factors such as power outage or intrusion. A production task-driving rule is designed. The external characteristics of deep learning data of the power Internet of Things are collected. The accuracy of data classification is judged according to the parallel Internet of Things of a convolutional neural network and a long-term and short-term memory deep neural network. When dealing with high-dimensional original data, the data network is graded to reduce the work of feature selection and the difficulty of training. Moreover, we improve the autonomous execution ability of an embedded terminal device in an offline state. We also strengthen the ability of abnormal data detection and judgment learning to meet the requirements of efficient and stable operation of the power grid. Simulation results verify that compared with the current mainstream schemes, the performance of the proposed scheme is improved by more than 17%. Besides, the fault tolerance rate is enhanced by 29%, and the power consumption after optimization is reduced by 26.3% and 42.8%. These effects highlight the role of data processing in embedded systems. It solves the problem of abnormal data and energy loss affecting the accuracy of error estimation of electric energy meters, opens up a new way for data acquisition and processing of large-scale smart meters, and further improves the service level of information management of power systems.

Hand Gesture Recognition for Interactive On-Screen Drawing with Real-Time Subtitling for Teaching

This research presents the development of an innovative educational tool leveraging hand gesture recognition for interactive on-screen drawing with real-time subtitling. Built on OpenCV and Python, the platform enables educators to draw and manipulate digital content through intuitive hand gestures, enhancing classroom engagement. Additionally, real-time subtitle generation offers accessibility for hearing-impaired students by converting drawn content into text notes. This system addresses key challenges in traditional digital drawing tools, such as limited interaction methods and accessibility, aiming to deliver a more inclusive learning environment. Preliminary results indicate high recognition accuracy, smooth interaction, and potential for broader educational applications. Future improvements will involve expanding the gesture set and refining accuracy for complex gestures. Keywords: Hand Gesture Recognition, Interactive Learning, Real-Time Subtitling, Educational Technology, OpenCV

Neural Mechanisms of Social Interaction Perception: Observing Interpersonal Synchrony Modulates Action Observation Network Activation and Is Spared in Autism.

How the temporal dynamics of social interactions are perceived arguably plays an important role in how one engages in social interactions and how difficulties in establishing smooth social interactions may occur. One aspect of temporal dynamics in social interactions is the mutual coordination of individuals' behaviors during social interaction, otherwise known as behavioral interpersonal synchrony (IPS). Behavioral IPS has been studied increasingly in various contexts, such as a feature of the social interaction difficulties inherent to autism. To fully understand the temporal dynamics of social interactions, or reductions thereof in autism, the neural basis of IPS perception needs to be established. Thus, the current study's aim was twofold: to establish the basic neuro-perceptual processing of IPS in social interactions for typical observers and to test whether it might differ for autistic individuals. In a task-based fMRI paradigm, participants viewed short, silent video vignettes of humans during social interactions featuring a variation of behavioral IPS. The results show that observing behavioral IPS modulates the Action Observation Network (AON). Interestingly, autistic participants showed similar neural activation patterns as non-autistic participants which were modulated by the behavioral IPS they observed in the videos, suggesting that the perception oftemporal dynamics of social interactions is spared and may not underly reduced behavioral IPS often observed in autism. Nevertheless, a general difference in processing social interactions was found in autistic observers, characterized by decreased neural activation in the right middle frontal gyrus, angular gyrus, and superior temporal areas. These findings demonstrate that although the autistic and non-autistic groups indeed differed in the neural processing of social interaction perception, the temporal dynamics of these social interactions were not the reason for these differences in social interaction perception in autism. Hence, spared recruitment of the AON for processing temporal dynamics of social interactions in autism does not account for the widely reported attenuation of IPS in autism and for the widely reported and presently observed differences in social interaction perception in autism.

Generalized Additive Models

Generalized additive models are generalized linear models in which the linear predictor includes a sum of smooth functions of covariates, where the shape of the functions is to be estimated. They have also been generalized beyond the original generalized linear model setting to distributions outside the exponential family and to situations in which multiple parameters of the response distribution may depend on sums of smooth functions of covariates. The widely used computational and inferential framework in which the smooth terms are represented as latent Gaussian processes, splines, or Gaussian random effects is reviewed, paying particular attention to the case in which computational and theoretical tractability is obtained by prior rank reduction of the model terms. An empirical Bayes approach is taken, and its relatively good frequentist performance discussed, along with some more overtly frequentist approaches to model selection. Estimation of the degree of smoothness of component functions via cross validation or marginal likelihood is covered, alongside the computational strategies required in practice, including when data and models are reasonably large. It is briefly shown how the framework extends easily to location-scale modeling, and, with more effort, to techniques such as quantile regression. Also covered are the main classes of smooths of multiple covariates that may be included in models: isotropic splines and tensor product smooth interaction terms.

Sensorless Human–Robot Interaction: Real‐Time Estimation of Co‐Grasped Object Mass and Human Wrench for Compliant Interaction

Human–robot physical interaction in shared object manipulation can fully leverage the strengths of both humans and collaborative robots, achieving better interaction outcomes. However, in typical physical interactions, the inertia parameters of the object are either assumed to be known or ignored, simplifying the interaction to be directly between the human and the collaborative robot. This simplification can lead to inaccuracies in the robot's understanding of human intent due to deviations in object dynamics. This article presents a method that eliminates the need for a force sensor on the human side during human–robot collaboration. Using an extended Kalman filter, the method estimates object parameters online and applies a disturbance observer to determine human force. This approach reduces human effort and improves tracking performance compared to traditional methods. It also avoids the hassle of installing and removing sensors in complex scenarios. By estimating object mass and human force in real time, the robot can adjust its behavior to ensure safer and smoother interactions. Furthermore, the system can handle various objects without requiring specific programming for each case.

Evaluating Mixed Reality Technology for Enhancing Art Pedagogy

The lack of interest among students in studying art, particularly the traditional Indonesian art form of batik, poses a significant challenge for educational institutions. Despite its cultural significance, the education sector lacks effective strategies to introduce and enhance students' interest in batik within the art curriculum. Several consequences can arise if the education sector fails to implement strategic measures to address this issue promptly. This could lead to a gradual erosion of cultural heritage and a loss of artistic traditions passed down through generations, and students may miss out on valuable opportunities for self-expression and cultural exploration. This study addresses this issue by leveraging mixed reality and gamification in a batik creation application. This innovative approach not only enhances the pedagogy of art education but also aims to revive cultural interest. The study employs Software Testing and PIECES to evaluate user experiences, emphasizing user comfort and smooth interactions. By assessing the application with tools like Unity Profiler and Hololens 2 performance testing, the study ensures an optimal user experience, contributing to the broader goal of preserving Indonesia's cultural heritage through innovative and accessible educational solutions. The results fall within the range of 4.04 to 4.24, categorizing user satisfaction as "satisfied" and the application running at an optimal 60 frames per second (FPS). This implies that users responded positively to the application, indicating that implementing mixed reality technology in batik learning provides a satisfying experience.

Enhanced Impedance Control of Cable-Driven Unmanned Aerial Manipulators Using Fractional-Order Nonsingular Terminal Sliding Mode Control with Disturbance Observer Integration

The article presents a novel control strategy for cable-driven aerial manipulators (UAMs) aimed at enhancing impedance control during contact operations in complex environments. A fractional-order nonsingular terminal sliding mode control (FONTSMC) integrated with a disturbance observer (DOB) is proposed to improve the robustness and precision of the UAM under lumped disturbances. This developed approach utilizes the flexibility of fractional calculus, the finite-time stability of nonsingular terminal sliding mode, and the real-time disturbance estimation capabilities of the DOB to ensure smooth and compliant contact interactions. The effectiveness of the proposed control strategy is validated through comprehensive simulation studies, which demonstrate significant improvements in control performance, stability, and disturbance rejection when compared to traditional methods. The results indicate that the FONTSMC-DOB framework is highly suitable for complex aerial manipulation tasks, offering both theoretical and practical insights into the design of advanced control systems for UAMs.

Multi-language Psychological Health Chatbot

Diagnosing and treating mental and psychological health problems has long been a challenge for people and psychologists. This is due to several reasons, one of which is society's view, where people can be treated as abnormal. Therefore, with the development of technology, especially artificial intelligence techniques, researchers began to work on finding different ways for people with psychological problems to ensure their privacy and personal information. In this paper, the design and implementation of a chatbot was reviewed for helping people who suffer from some psychological problems, and to take advice or suggest ways to recover based on a database of books related to this topic. A modified artificial intelligence algorithm was used; the chatbot integrates natural language processing (NLP) algorithms to facilitate smooth and intuitive interactions. Taking into account the inability of some Arabic-speaking users to write or understand the answers in English, as the approved sources are mostly written in English. So the feature of translating questions was added and then replying also in the language that the user understands. After the implementation and testing of the chatbot by a group of people, the accuracy of the results was satisfactory. The accuracy rate is up to 85% and the accuracy of the results depends on the accuracy of the questions.

Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments.

Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called "conflict-blocking", where agents' conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.

Vitamin C and suicidal ideation: A cross-sectional and Mendelian randomization study

BackgroundStudies have shown that a history of suicide attempts is associated with low levels of antioxidant vitamins. However, the specific relationship between vitamin C and suicidal ideation remains poorly understood. The purpose of this study is to investigate the effect of vitamin C on the development of suicidal ideation and to explore further the causal relationship using Mendelian randomization (MR). MethodsUsing the 2005–2018 National Health and Nutrition Examination Survey (NHANES) dataset, a comprehensive, cross-sectional, stratified survey of 28,623 participants aged 20 years and older was conducted. Suicidal ideation was assessed using item 9 of the Patient Health Questionnaire-9 (PHQ-9) item. The relationship between vitamin C and suicidal ideation was revealed by weighted multiple linear regression, smooth curve fitting, hierarchical analysis, and interaction test. To determine causality, MR was performed on vitamin C and suicidal ideation. ResultsIn cross-sectional studies, all models showed a negative association between vitamin C and suicidal ideation, with a 37 % reduction in the risk of suicidal ideation for each unit increase in vitamin C in the highest quartile in the fully adjusted model. The results of the subgroup analysis showed that the association between vitamin C and suicidal ideation was stable across gender, age, education, smoking status, and marital status. In the MR study, our bivariate model showed no significant causal relationship between vitamin C and the development of suicidal ideation. ConclusionsDietary vitamin C intake was negatively associated with the development of suicidal ideation. However, genetic evidence does not support causation of these associations.

High recovery speed optical humidity sensing enabled with GQD for non-contact finger distance detection

Finger distance perception plays a crucial role in enabling humanoid intelligent robotic systems to achieve smooth interaction with humans. Here, a rapidly response optical humidity sensor enabled with graphene quantum dots (GQD) for non-contact finger distance detection was proposed. The GQD was prepared through the functionalization of graphene oxide (GO) with hydrophilic groups. This modification enhances the responsiveness of GO to atmospheric humidity and promotes the deposition of conformal films of functional materials on the surfaces of optical fiber. Utilizing a light-driven coating method, the GQD was deposited onto a tilted fiber Bragg grating (TFBG) to fabricate humidity sensor. When water molecules were absorbed by GQD, the dielectric constant of GQD film was changed, causing an alteration in the intensities of the TFBG’s cladding modes. The proposed sensor shows a sensitivity of 0.032 dB/%RH with the equilibrium response and recovery time of 3.66 s and 0.92 s, respectively. The equilibrium recovery time is the fastest as far as we know. Furthermore, the sensor is capable of precisely identifying the action of a finger, such as approaching, remaining, and departing with a detection range of 0–10 mm.

A Blockchain and PKI-Based Secure Vehicle-to-Vehicle Energy-Trading Protocol

With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.

You Reap What You Sow: Customer Courtesy and Employees' Prosocial Service Behavior.

Smooth and effective interactions between customers and customer-contact employees are important for building seamless service delivery. The present study examined the influence of customer courtesy on customer-contact employees' prosocial service behavior. Additionally, the mediating role of organization-based self-esteem and the moderating role of focus of attention at work are investigated. Data were collected from 401 customer-contact employees from the South Korean hospitality industry. The results demonstrated that customer courtesy positively influenced prosocial service behavior, and organization-based self-esteem mediated this relationship. Furthermore, employees' focus of attention at work moderated the relationship between customer courtesy and organization-based self-esteem, such that the relationship was stronger for employees with a high focus of attention. Similarly, the focus of attention also moderated the relationship between organization-based self-esteem and prosocial service behavior. The findings have important theoretical and practical implications by demonstrating the role of external resources to promote prosocial service behavior.

Request Strategies of EFL University Learners at Jambi University

Making appropriate requests is a crucial aspect of pragmatic competence in language learning. This research aimed to investigate common request strategies used by EFL University Learners at Jambi University when making requests at the ages of 19, 20, and 21. This research design was an intrinsic case study to gain an in-depth knowledge of the phenomenon within its specific context. Data were collected through a written discourse completion test (DCT) from six participants, two from each age group. They were analyzed using discourse analysis, specifically focusing on the request strategies proposed by Blum-Kulka and Olshtain (1984), Blum-Kulka (1987), and Trosborg (1995). The findings reveal that most participants employed conventionally indirect (hearer-based) strategies in the form of ability, willingness, and permission (25 times, 83.3% frequency of semantic formulas) and conventionally indirect request (speaker-based) strategies in the form of desires/needs (3 times, 10% frequency of semantic formulas), followed by direct request strategies in the form of performatives (2 times, 6.6% frequency of semantic formulas). The research concluded that conventionally indirect request strategies (hearer-based) in the form of ability, willingness, and permission are mostly used in making requests among the studied age groups. It also found that age influenced how social power as a social variable maintained smooth social interactions in making requests. Those different age groups exhibited diversities in the use of directness level in making requests based on the age gap between the speaker and the hearer. Additionally, the research indicates how the focus shifts to the hearer's wants as the speaker casts requests in various direct forms. This suggests that people tend to make requests subtly, especially within academic settings. These findings have important implications for language teaching and pragmatics instruction, potentially influencing approaches to improving learner awareness of appropriate request strategies in academic and social contexts.

Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hypertension in American adults: a NHANES cross-sectional study.

The relationship between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and hypertension remains uncertain, warranting further investigation. This study aims to elucidate the association between NHHR and hypertension. A comprehensive cross-sectional stratified survey involving 30,602 participants aged 20years and older was conducted using the National Health and Nutrition Examination Survey (NHANES) dataset from 2001 to 2018. NHHR was calculated as [total cholesterol (TC) - high-density lipoprotein cholesterol (HDL-C)]/HDL-C. The relationship between NHHR and hypertension was examined using weighted multiple linear regression, smooth curve fitting, hierarchical analysis, and interaction testing. The mean age of participants was 49.82 ± 17.64 years, with 15,266 women included. The average NHHR was 2.94 ± 0.56. A positive correlation between NHHR and hypertension was observed. Stratification of NHHR into quartiles, in the fully adjusted Model 3, revealed that individuals in the highest NHHR quartile had a 60% increased risk of hypertension for each unit increase in NHHR compared to those in the lowest quartile. Interaction tests indicated that the relationship between NHHR and hypertension remained consistent across subgroups, except for gender, age, education, and smoking status, which influenced this association. Analysis of NHANES data from 2001 to 2018 demonstrated a consistent positive association between NHHR and hypertension. NHHR may provide potential assistance in hypertension prevention and diagnosis.

Investigation of the impacts of the deployment of autonomous vehicles on first responders

Purpose The purpose of this study is to address the significant impact AVs will have on public services and the ability of first responders to conduct their jobs safely and effectively. Autonomous vehicles (AVs) are expected to drastically change the transportation industry, and it is vital that first responders be equipped to integrate them into their occupational responsibilities. Design/methodology/approach A systematic literature review was conducted, and following a multistep exclusion process, 161 articles were selected for detailed review. The impacts of AVs on first responders were identified, classified and categorized into lists of challenges and opportunities. Based on the findings of the literature review, a SWOT (strengths, weaknesses, opportunities and threats) analysis was conducted, and stakeholder management strategies were designed. Findings Through the examination of the impacts of AVs on first responders, 17 identified challenges and opportunities were classified into the following categories: AV-related emergency response and training, perceptions and acceptance of AVs, technology development and laws and regulations. The study revealed that the optimal benefits of AVs would require stakeholders to focus more on how they interact with first responders; thus, 14 stakeholder management strategies were identified. First responders, AV manufacturers, legislators and future research paths will all benefit from this study, as it can facilitate smooth interactions between AVs and first responders. Originality/value A range of studies have been published on the safety of AVs and the public’s perceptions of this new technology; however, the integration of AVs and their interactions with first responders has been neglected. The goal of this study was to fill that research gap by providing a thorough synthesis of autonomous driving systems in the context of their interactions with first responders.

Voice-Assisted News App using Natural Language Processing

In recent times, the advancements in Artificial Intelligence (AI) and natural language processing (NLP) have enabled the development of novel voice-assisted apps. This study introduces a cutting-edge Voice-Assisted News App that utilizes the capabilities of Alan M, a prominent AI platform that revolutionizes the way people access news. This research endeavor attempts to utilize Alan Al's capabilities to offer a personalized news experience and enhance clarity through voice interactions. Users of the Voice-Assisted News software can effortlessly request and select their preferred news topics, and the computer will generate the most relevant news updates. Through integration with diverse news sources and the application of sophisticated algorithms, the program generates an individualized news stream for every user, ensuring they receive the latest and most relevant information. Users can efficiently access story audio descriptions. The app's speech interface allows users to navigate across different news categories. The NLP algorithms developed by Alan AI offer precise understanding of user demands by ensuring smooth interaction and an authentic conversational experience