Fit Framework Research Articles

Investigating factors influencing AI customer service adoption: an integrated model of stimulus–organism–response (SOR) and task-technology fit (TTF) theory

PurposeArtificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.Design/methodology/approachThe study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.FindingsThe PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.Practical implicationsThe study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.Originality/valueThe originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

A first determination of the strong coupling αS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _S$$\\end{document} at approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO order in a global PDF fit

We present the first determination of the value of the strong coupling via a simultaneous global fit of the proton parton distribution functions (PDFs) at approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO (aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO) order in QCD. This makes use of the MSHT global PDF fitting framework, and in particular the recent theoretical advances t l;;mhat allow a PDF fit to now be performed at this order. The value of the strong coupling is found to be αS(MZ2)(aN3LO)=0.1170±0.0016\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha _{S}(M_Z^2)(\\mathrm{aN^3LO}) = 0.1170 \\pm 0.0016$$\\end{document}. This is in excellent agreement with the NNLO value of αS(MZ2)(NNLO)=0.1171±0.0014\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\alpha _{S}(M_Z^2)(\ extrm{NNLO}) = 0.1171 \\pm 0.0014$$\\end{document}, indicating that good perturbative convergence has been found. The resulting uncertainties, calculated using the MSHT dynamic tolerance procedure, are somewhat larger, but more accurate, at aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO, due to the missing higher order theoretical uncertainties that are included at this order, but not at NNLO. We in addition present a detailed breakdown of the individual dataset sensitivity to the value of the strong coupling, with special focus on the impact of fitting dijet rather than inclusive jet data. This choice is found to have a non-negligible impact, but with overall good consistency found, especially at aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{N}^3$$\\end{document}LO.

Noise-robust modal parameter identification and damage assessment for aero-structures

PurposeGround vibration testing is critical for aircraft design and certification. Fast relaxed vector fitting (FRVF) and Loewner framework (LF), recently extended to modal parameter extraction in mechanical systems to address the computational challenges of time and frequency domain techniques, are applied for damage detection on aeronautically relevant structures.Design/methodology/approachFRVF and LF are applied to numerical datasets to assess noise robustness and performance for damage detection. Computational efficiency is also evaluated. In addition, they are applied to a novel damage detection benchmark of a high aspect ratio wing, comparing their performance with the state-of-the-art method N4SID.FindingsFRVF and LF detect structural changes effectively; LF exhibits better noise robustness, while FRVF is more computationally efficient.Practical implicationsLF is recommended for noisy measurements.Originality/valueTo the best of the authors’ knowledge, this is the first study in which the LF and FRVF are applied for the extraction of the modal parameters in aeronautically relevant structures. In addition, a novel damage detection benchmark of a high-aspect-ratio wing is introduced.

Exploring the impact of housing insecurity on the health and wellbeing of children and young people in the United Kingdom: a qualitative systematic review

BackgroundHousing insecurity can be understood as experiencing or being at risk of multiple house moves that are not through choice and related to poverty. Many aspects of housing have all been shown to impact children/young people’s health and wellbeing. However, the pathways linking housing and childhood health and wellbeing are complex and poorly understood.MethodsWe undertook a systematic review synthesising qualitative data on the perspectives of children/young people and those close to them, from the United Kingdom (UK). We searched databases, reference lists, and UK grey literature. We extracted and tabulated key data from the included papers, and appraised study quality. We used best fit framework synthesis combined with thematic synthesis, and generated diagrams to illustrate hypothesised causal pathways.ResultsWe included 59 studies and identified four populations: those experiencing housing insecurity in general (40 papers); associated with domestic violence (nine papers); associated with migration status (13 papers); and due to demolition-related forced relocation (two papers). Housing insecurity took many forms and resulted from several interrelated situations, including eviction or a forced move, temporary accommodation, exposure to problematic behaviour, overcrowded/poor-condition/unsuitable property, and making multiple moves. Impacts included school-related, psychological, financial and family wellbeing impacts, daily long-distance travel, and poor living conditions, all of which could further exacerbate housing insecurity. People perceived that these experiences led to mental and physical health problems, tiredness and delayed development. The impact of housing insecurity was lessened by friendship and support, staying at the same school, having hope for the future, and parenting practices. The negative impacts of housing insecurity on child/adolescent health and wellbeing may be compounded by specific life circumstances, such as escaping domestic violence, migration status, or demolition-related relocation.ConclusionHousing insecurity has a profound impact on children and young people. Policies should focus on reducing housing insecurity among families, particularly in relation to reducing eviction; improving, and reducing the need for, temporary accommodation; minimum requirements for property condition; and support to reduce multiple and long-distance moves. Those working with children/young people and families experiencing housing insecurity should prioritise giving them optimal choice and control over situations that affect them.

Metaverse adoption in the manufacturing industry: impact on social and environmental sustainability performance

PurposeThis study aims to examine the adoption of Metaverse technology in the manufacturing industry and its potential impact on firms’ social and environmental sustainability performance.Design/methodology/approachData were collected from 157 technology-based firms in the Malaysian high-tech manufacturing industry and analyzed using PLS-SEM to investigate the influence of social (i.e. social capital, open/innovative culture and empowerment) and technological factors (i.e. digitalization preparedness, integrability and strategic value) on Metaverse adoption and the moderating roles of digital trust and absorptive capacity.FindingsSocial and technological factors were found to significantly impact Metaverse adoption, with digital trust enhancing the influence of social factors. Absorptive capacity strengthens firms’ abilities to use social factors for adoption. However, digital trust does not significantly moderate the relationship between technological factors and adoption, nor does absorptive capacity impact this relationship. Finally, Metaverse adoption is shown to positively contribute to firms’ social sustainability, improving social well-being and equity, but it does not significantly impact environmental sustainability.Practical implicationsFor practitioners, the study highlights the importance of fostering an organizational culture that supports digital trust and developing absorptive capacity as critical enablers of successful Metaverse adoption. Policy implications include the need for creating supportive policies that encourage digital transformation efforts aligned with sustainability goals.Originality/valueTheoretically, this study integrates the Technology-Organization-Environment (TOE) framework, Human-Organization-Technology fit (HOT-fit) framework and Resource-Based View (RBV) to improve understanding of technology adoption and sustainability performance. From a managerial perspective, it highlights the importance of fostering digital trust and developing absorptive capacity as critical enablers of successful Metaverse adoption. Policy implications include the need for policies supporting digital transformation efforts aligned with sustainability goals.

Bleached Hair as Standard Template to Insight the Performance of Commercial Hair Repair Products

The increasing demand for effective hair care products has highlighted the necessity for rigorous claims substantiation methods, particularly for products that target specific hair types. This is essential because the effectiveness of a product can vary significantly based on the hair’s condition and characteristics. A well-defined bleaching protocol is crucial for creating a standardized method to assess product efficacy, especially for products designed to repair damaged hair. The objective of this study was to create a practical bleaching protocol that mimics real-world consumer experiences, ensuring that hair samples exhibit sufficient damage for testing. This approach allows for a reliable assessment of how well various products can repair hair. The protocol serves as a framework for evaluating hair properties and the specific effects of each product on hair structure. Color, brightness, lightness, morphology, and topography were primarily used to understand the big differences in the hair fiber when treated with two repair benchmark products, K18® and Olaplex®, in relation to the Bleached hair. The devised bleaching protocol proved to be a fitting framework for assessing the properties of hair and the unique characteristics of each tested product within the hair fiber. This protocol offers valuable insights and tools for substantiating consumer claims, with morphological and mechanical methods serving as indispensable tools for recognizing and validating claims related to hair. The addition of K18® and Olaplex® demonstrated an increase in hair brightness (Y) and lightness (L* and a*) in relation to the Bleached samples, which were considered relevant characteristics for consumers. Olaplex®’s water-based nature creates a visible inner sheet, effectively filling empty spaces and improving the disulfide linkage network. This enhancement was corroborated by the increased number of disulfide bonds and evident changes in the FTIR profile. In contrast, K18®, owing to the lipophilic nature of its constituents, resulted in the formation of an external layer above the fiber. The composition of each of the products had a discrete impact on the fiber distribution, which was an outcome relevant to the determination of spreadability by consumers.

Toward a person-environment fit framework for artificial intelligence implementation in the public sector

Using an embedded mixed method design, we compared a nationally representative sample of US adults and a sample of US-based emergency managers (EM) on their attitudes toward artificial intelligence (AI) and their intentions to rely on AI in a set of decision-making scenarios relevant to emergency management. Emergency managers reported significantly less positive attitudes toward AI and were less likely to rely on AI for decisions compared to the nationally representative sample. Our analysis of EMs' open-ended responses explaining their choices to use or not use AI-based solutions reflected specific concerns about implementation rather than wariness toward AI generally. These concerns included the complexity of the potential outcomes in the scenarios, the value they placed on human input and their own extensive experience, procedural concerns, collaborative decision-making, team-building, training, and the ethical implications of decisions, rather than a rejection of AI more generally. Managers' insights integrated with our quantitative findings led to a person-environment fit framework for AI implementation in the public sector. Our findings and framework have implications for how AI systems should be introduced and integrated in emergency managerial contexts and in public sector organizations more generally. Public managers' perceptions and intentions to use AI and organizational oversight processes are at least as important as technology design considerations when public sector organizations are considering the deployment of AI.

Development of the ECLIPSE model of meaningful outcome domains following lower limb amputation and prosthetic rehabilitation, through systematic review and best fit framework synthesis.

Little is known about which outcome domains characterise meaningful recovery following prosthetic rehabilitation and should be measured. Our previous qualitative work developed a conceptual model of outcome domains which are meaningful to patients. This qualitative synthesis aims to develop that model by exploring views and experiences of recovery captured in the limb loss literature, and use these to produce a second iteration of the model describing outcome domains of importance following prosthetic rehabilitation from the patient's perspective. Systematic searches were conducted using CINAHL, Psychinfo and Web of Science from 2011 to early 2023. Studies with a qualitative design focusing on views and experiences of lower limb prosthetic users were eligible for inclusion. Quality was assessed using the CASP tool. 'Best Fit' framework synthesis was used to synthesise the evidence and develop the conceptual model. 40 studies were included, describing the experiences of 539 participants. Data supported the pre-existing conceptual model and led to development of four of the five domains. The newly named ECLIPSE model describes meaningful outcome domains as 1) Being able to participate in important activities and roles, 2) Participating in the way I want to, 3) My prosthesis works for me, 4) If I am in pain, I can manage it, and 5) I am able to accept my new normal. Studies came from 15 countries showing good coverage of high-income settings. Few participants from low-and-middle-income countries were included, it is unclear if the ECLIPSE model describes outcome domains of importance in these settings. This synthesis provides a rigorous foundation for understanding outcome domains of importance following lower limb prosthetic rehabilitation from the patient's perspective. The ECLIPSE model is an accessible representation of recovery which could direct rehabilitation programmes, as well as inform the evaluation of prosthetic care through the selection of outcome measures.

Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning

An innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

Topological derivative based sensitivity analysis for three-dimensional discrete variable topology optimization

This study introduces a novel Topological Derivative-based Sensitivity Analysis (TDSA) methodology for three-dimensional (3D) discrete variable topology optimization. Recently, the authors pointed out that the discrete variable sensitivity can be related to the topological derivative, and thus can be rationally approximated by the specially customized topological derivative for plane stress problems. However, the 3D discrete variable sensitivity requires the 3D topological derivative with element-shaped (e.g., unit cube) domain perturbation, whose analytical solution is not available in the literature. This paper proposes a unified parameter fitting framework to calculate the 3D topological derivative with arbitrary shape 3D domain perturbation. The formula for spherical hole perturbation obtained through this parameter fitting framework is identical to the well-known analytical topological derivative formula. Further, the 3D discrete variable sensitivity can be easily obtained through element-shaped domain perturbation. With the recently proposed Sequential Approximate Integer Programming (SAIP), numerical examples demonstrate that TDSA is precise not only for the self-adjoint 3D minimum compliance problems but also for the non-adjoint 3D compliant mechanism design problems. In sum, numerical examples have been conducted by feeding the derived sensitivity to the SAIP optimization algorithms, and the correctness of the sensitivity information has been well demonstrated. This research further solidifies the foundation of rational discrete variable sensitivity analysis in 3D topology optimization.

Cultural values and the P-O fit: comparative NLP analysis of German online job advertisements

PurposeWithin the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.Design/methodology/approachThe study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.FindingsThis study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.Originality/valueThis study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.

The impact of LHC jet and ZpT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_T$$\\end{document} data at up to approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO order in the MSHT global PDF fit

We present an analysis of two key sets of data constraining the high x gluon at up to approximate N3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO in QCD within the MSHT global PDF fitting framework. We begin with LHC 7 and 8 TeV inclusive jet and dijet production at both NNLO and aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO. This makes use of the formalism established in the previous global MSHT20aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO PDF fit, but now considers the role of dijet production for the first time at this order. We present a detailed comparison of the fit quality and PDF impact for both cases, and consider the role that electroweak corrections, and the scale choice for inclusive jet production has. Some mild tension between these data sets in the impact on the high x gluon is seen at NNLO, but this is largely eliminated at aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO. While a good fit quality to the dijet data is achieved at both orders, the fit quality to the inclusive jet data is relatively poor. We examine the impact of including full colour corrections in a global PDF fit for the first time, finding this to be relatively mild. We also revisit the fit to the ATLAS 8 TeV ZpT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_T$$\\end{document} data, considering the role that the pT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p_T$$\\end{document} cuts, data selection and different aspects of the aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO treatment have on the fit quality and PDF impact. We observe that in all cases the aN3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${}^3$$\\end{document}LO fit quality is consistently improved relative to the NNLO, indicating a clear preference for higher order theory for these data.

Farmer adoption-based prompt networking and modeling for targeting optimal agro-conservation practices

Despite the availability of robust agricultural models, the successful on-ground implementation of all Best Management Practices (BMPs) remains elusive due to the lack of consideration for farmers' preferences. This study develops a unique network-based optimization technique coupled with Soil and Water Assessment Tool (SWAT) which delineates combined set of cost-effective BMPs at field-scale based on farmer's conservation identity in Ganges River Basin, India. SWAT was calibrated and validated using 10-year monthly discharge and nitrate data, and the selected BMPs were simulated using sequential uncertainty fitting framework. Farmers belonging to the Shahpur subdistrict were found to have the maximum normalized conservation identity of 90%, whereas the northeastern portion of the watershed contributed a maximum nitrate load of 7.5–9.92 kg/acre to the watershed outlet. An optimized combination of BMPs (riparian buffer-conservation tillage) with an efficiency of 0.28 was obtained through the modified network technique that targeted subbasins of the Morna subdistrict using farmer conservation identities. The results provide valuable guidance for watershed modelers, conservation planners, and researchers in pinpointing regions for targeted farmer training, subsidies, and other initiatives aimed at facilitating prompt field-scale BMP adoption. A flexible feature of the approach is its ability to modify the BMP action plans by replacing even technically feasible, cost-effective combinations based on farmers' preferences.

GLS‐PIA: n‐Dimensional Spherical B‐Spline Curve Fitting based on Geodesic Least Square with Adaptive Knot Placement

AbstractDue to the widespread applications of curves on n‐dimensional spheres, fitting curves on n‐dimensional spheres has received increasing attention in recent years. However, due to the non‐Euclidean nature of spheres, curve fitting methods on n‐dimensional spheres often struggle to balance fitting accuracy and curve fairness. In this paper, we propose a new fitting framework, GLS‐PIA, for parameterized point sets on n‐dimensional spheres to address the challenge. Meanwhile, we provide the proof of the method. Firstly, we propose a progressive iterative approximation method based on geodesic least squares which can directly optimize the geodesic least squares loss on the n‐sphere, improving the accuracy of the fitting. Additionally, we use an error allocation method based on contribution coefficients to ensure the fairness of the fitting curve. Secondly, we propose an adaptive knot placement method based on geodesic difference to estimate a more reasonable distribution of control points in the parameter domain, placing more control points in areas with greater detail. This enables B‐spline curves to capture more details with a limited number of control points. Experimental results demonstrate that our framework achieves outstanding performance, especially in handling imbalanced data points. (In this paper, “sphere” refers to n‐sphere (n ≥ 2) unless otherwise specified.)

Bibliometric and Systematic Analysis of Artificial Intelligence Chatbots’ Use for Language Education

This research aimed to comprehensively analyze the use of chatbots in language education and its potential for advancing educational development. Through a bibliometric and systematic approach, the study identified influential authors, references, organizations, and countries in the field of chatbot application in language education using VOSviewer. A total of 26 peer-reviewed publications were selected for a systematic review. The findings of the study indicated that chatbots have a positive impact on language learning, although they are limited in terms of facilitating listening and writing practice. The study extended the Human-Organization-Technology (HOT) fit framework for chatbots’ use for language education and discussed the factors that frustrate learners' use of chatbots for language education from human, organization, and technology dimensions. Furthermore, the author further discussed the suggestions for chatbots’ better application for language education based on the three dimensions.

The Role of Neighbourhood Social and Built Environments – Including Third Places – in Older Adults’ Social Interactions

ABSTRACT Neighbourhood environments shape older adults’ social interactions. This research conceptualises a comprehensive set of perceived and objective measures of neighbourhood social and built environments, including third places, with older adults’ social interactions. In Melbourne, based on the person-environment fit framework, mediation analyses showed that the perceived social environment measures of community spirit, participation in community groups and belonging to suburb, were the strongest predictors of social interactions, followed by a few types of perceived third places. Our findings suggest that policymakers should focus on how the objective environment characteristics are interpreted as levers when planning for changes.

The influences of four dimensions of perceived fit on individuals’ utilisation of SPOCs: an extension of the task-technology fit model

ABSTRACT Small Private Online Courses (SPOCs) platform enables individuals to carry out their online and offline learning activities. In order to understand individuals’ utilisation of SPOCs, this study develops a research model to examine the joint influences of four dimensions of perceived fit manifested in perceived technology-task fit (TTF), perceived individual-technology fit (ITF), perceived online-offline task fit (OTF), and perceived online-offline interactivity fit (OIF). A survey is conducted at a famous university in China, and 371 data are collected from students who select courses on the SPOC platform. Structural equation modelling method is used to examine the research model. The empirical results suggest that ITF is the most significant antecedent of individual performance expectancy, followed by OTF, TTF, and OIF. Furthermore, individual performance expectancy positively influences satisfaction and continuance intention in the SPOC platform. Moreover, this study incorporates self-regulation as a moderator to explore behavioural differences between individuals with high and low self-regulation. This study extends the traditional perceived fit framework by introducing OTF and OIF and uncovers the antecedents and boundary conditions of individuals’ utilisation of SPOCs in the emerging research context.

Improving ellipse fitting via multi-scale smoothing and key-point searching

Fast and efficient fitting of accurate ellipses from data points has many applications in pattern recognition, machine vision, and robotics. However, the fitting accuracy may significantly degrade in the existence of outliers, such as the least-squares-based approaches. Despite robust methods attaining more accurate results than the least-squares manner under the contamination of outliers, they typically require the careful tuning of the hyper-parameters for good results. To mitigate the outlier disturbance, in this paper, we propose a conceptually simple yet quite useful preprocessing framework for high-precision ellipse fitting. Firstly, we leverage multi-scale operators to shrink the input image, by which a large number of outliers can be removed, followed by the smoothing of the sub-image to further improve the data quality. Then, we propose a key-point searching method to enhance the fitting precision via the analysis of the discrete pixel data in images. We prove that key-point-based ellipse fitting gives the upper bound of the approximation error generated by other sampled points with the same ellipse. Based on the key-point pairs inside and outside the ellipse, we further calculate their barycentric points and then perform fitting on these points to attain high-precision ellipses. We conduct extensive experiments on synthetic and real-world images to validate the proposed method and compare it with representative state-of-the-art approaches. Quantitative and qualitative results demonstrate that our method has more accurate and robust performance than competitors. Additionally, we employ the proposed method to compared approaches as a preprocessing step. Experiments demonstrate that our method is effective to significantly improve their fitting accuracy. Our source code is freely available at https://github.com/ChengQian09/MSKPF.

A goodness of fit framework for relational event models

Abstract We introduce a novel procedure to assess the goodness of fit in relational event models. Building on existing auxiliary variable approaches developed in network modelling, the procedure involves a comparison between statistics computed on observed relational event sequences and statistics calculated on event sequences simulated from the fitted model. We argue that the internal time structure of the relational mechanisms assumed to generate the observations under the model is an important aspect of the fit of a model to observed relational event sequences. We establish the empirical value of the proposed goodness of fit approach in an analysis of data that we collected on collaborative patient-referral relations among healthcare organizations. The illustrative case study that we develop reveals distinctive features of relational event models that have been ignored or overlooked in received empirical studies.

The Implementation of Ethical AI Within Autonomous Vehicles

The integration of Artificial Intelligence in the rapidly evolving landscape of autonomous vehicles necessitates an in-depth exploration of the associated ethical implications, especially in life-or-death scenarios. This research paper delves deeply into the multifaceted ethical issues associated with the deployment of AI-driven autonomous vehicles and the moral implication regarding this development. A core inquiry of this work is discerning the appropriate entity responsible for critical decision-making when human lives are in the balance. Given the unique context of autonomous driving, where split-second decisions could be a norm rather than an exception, we aim to investigate both the current algorithms' decision-making processes as well as their lack of transparency and question the underlying ethics that shape them. Drawing from real-world incidents involving autonomous vehicles, and juxtaposing them with theoretical predicaments, this study endeavors to present a holistic view of the prevailing ethical perspectives. Building on this foundation, we introduce a fitting ethical decision-making framework, rooted in deontological principles, designed to guide autonomous vehicles through morally complex scenarios on the road. To validate and refine our framework, we employ diverse case studies from the world of autonomous driving. Simultaneously, recognizing the inherent challenges that any ethical framework might encounter, we also discuss potential pitfalls and offer suggestions to enhance its robustness and applicability. As a result, this research underscores the pressing need for meticulously crafted guidelines governing AI within autonomous vehicles, ensuring that safety, individual autonomy, and ethical qualifications to be implemented within the age of driverless transport.