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New fuzzy K-nearest neighbor algorithms for classification performance improvement
No full textIn fuzzy k-nearest neighbor, smooth class boundaries are provided by each instance’s fuzzy degree of membership. However, there are additional costs associated with calculating the memberships due to memory limitations and runtime overhead. Furthermore, in the presence of class imbalance and outliers, the effectiveness and efficiency of the most advanced fuzzy kNNs continue to decline. Thus, new fuzzy kNNs with straightforward designs are developed in this study to substantially lessen the influence of these problems and improve overall performance. The local mean vectors with the single linkage and the cumulative means of neighbors are combined, establishing these models, which are referred to as LMSL-FkNN and CMDW-FkNN, respectively. A comprehensive evaluation study spanning five experimental stages is carried out against six cutting-edge kNN competitors utilizing fifty-four real-world (balanced, imbalanced, noisy, and time series) datasets in order to illustrate the competitiveness of the established models. With CMDW-FkNN comfortably dominating the competition across the vast majority of datasets (specifically UCI, highly-Imbalanced, and Time Series datasets), the results supported by statistical tests, across three assessment metrics-accuracy, F-measure, and ROC-show that both models have significantly more promise than their rivals
From conflict to coexistence: A tourism-oriented ecosystem framework for urban nightlife management
No full textThis study examines the management of urban tourism destinations, introducing an innovative framework to address chrono-urbanistic issues related to night-time entertainment. Focusing on Budapest\u27s party zone as a case example, it draws on data from tourism research conducted between 2010 and 2022 and incorporates strategic management principles. The paper presents the concept of a tourism-centric urban ecosystem, utilizing a modified ecosystem pie model to offer a new perspective for managing nightlife tourism in urban settings. It identifies cultural conflict zones arising from the differing use of urban space during day and night, and suggests that a tourism-focused ecosystem model can help resolve these tensions. This approach supports the restructuring of tourism governance at both citywide and local levels, addressing challenges such as unsustainable tourism and socio-cultural frictions while also unlocking potential for innovation and sectoral growth. The research underscores the crucial role of chrono-urbanism in fostering the long-term sustainability of cities characterized by round-the-clock tourism activity
HCI Education and Education for HCI: Current Opportunities and Challenges
No full textBeyond ongoing challenges in Human-Computer Interaction (HCI) education, such as managing interdisciplinarity and the lack of data-driven support for new pedagogical practices, HCI educators worldwide also need to address the impact of rapidly changing socio-technical landscapes. In particular, there is an urgent need to respond to emerging technologies and address social responsibility in diverse and dynamic contexts. This workshop aims to provide a venue for HCI educators, researchers, and other interested parties to reflect upon and exchange experiences on how new and persistent challenges have been addressed. The recent ACM recommendations for HCI education and the impact of the emerging field of Human-AI Interaction (HAI) in HCI are some of the perspectives proposed to frame discussions and opportunities to be explored by participants
Factors influencing the adoption intent of quantum computing in enterprises: An innovation adoption process perspective
No full textWith recent advancements in quantum computing technology, companies have begun considering replacing or jointly utilizing their existing classical computing resources with quantum computing. Despite initial research on the examination of the adoption considerations for quantum computing, there is a lack of studies investigating the conditions that influence the adoption of quantum computing technology. Therefore, the purpose of this study is to identify and analyze the key factors that drive or hinder companies\u27 intention to adopt quantum computing technology. Building on the theory of innovation diffusion and integrating insights from the adoption of multiple emerging technologies, this research aims to extend the understanding of quantum computing adoption dynamics. Specifically, drawing on the theory of innovation diffusion and existing literature related to the adoption of multiple emerging technologies, this research proposes the following research questions to identify the factors influencing the adoption intention of quantum computing. The study employed a Sequential Explanatory design, first exploring the topic through literature and interviews with 11 quantum computing experts, then quantifying findings with a questionnaire survey with 250 IT decision-makers within Korean companies, analyzed using PLS-SEM and MGA. Research findings revealed that belief in quantum superiority, quantum advantage, continuous budget allocation, and regulatory support significantly and positively influenced quantum computing adoption, whereas organizational resistance had the most substantial negative impact. Furthermore, firm size and industry significantly moderate these adoption intentions. Interestingly, companies expressed a desire to adopt quantum computing despite uncertainties. Theoretically, this study contributes to innovation diffusion theory by contextualizing its application to the adoption of a highly complex and nascent technology such as quantum computing. Practically, the findings offer actionable implications for policymakers and business leaders by illuminating the key drivers and barriers that must be addressed to promote quantum computing adoption
Removal of a quaternary ammonium compound by electrocoagulation: Mechanistic analysis and multi-response optimization using response surface methodology and machine learning
No full textThe widespread use of quaternary ammonium compounds (QACs), intensified by the COVID-19 pandemic, has led to their increasing presence in aquatic environments, thereby demanding effective treatment strategies for shock loads from industrial discharges. This study presents a hybrid modeling and multi-response optimization (MRO) framework to optimize the electrocoagulation (EC) process for removing cetyltrimethylammonium bromide (CTAB) as a model QAC. A central composite design was employed for efficient experimentation and data collection regarding the effects of four variables on CTAB removal efficiency, energy consumption, and electrode consumption. While the response surface methodology (RSM) model yielded the best prediction for energy demand, a machine learning (ML)-based linear regression (LR) model showed superior accuracy for CTAB removal and electrode consumption. Optimization with Pyomo (compared with RSM, PyTorch, and Scikit-learn) proved most effective, yielding a Pareto-optimal solution (100 mg L-1 CTAB, 1.5 A, 2.5 g L-1 Na2SO4, and 15 min) that achieved a 94.95 % removal with energy and electrode consumptions of 26.33 kWh kg-1 and 12.28 kg Fe kg-1 CTAB removed, respectively. This optimum reduced the treatment cost by 38.5 % (to 5.59 USD per kg of CTAB removed) compared with the RSM-derived solution. Mechanistic studies involving kinetics, sludge characterization, and density functional theory (DFT) calculations revealed that CTAB removal proceeds through the electrostatic adsorption of CTA+ onto in-situ generated Fe–Al hydroxide flocs via electrostatic interaction, followed by sweep flocculation. DFT analysis further localized the reactive site on the quaternary ammonium head. The developed ML–MRO framework provides a scalable strategy for designing intelligent, cost-effective electrochemical systems to mitigate emerging contaminants
Gendered experiences of spirituality at work: Implications for constructive employee voice behavior
No full textThis research enhances our understanding of how workplace spirituality influences the voice behaviors of male and female employees by exploring the underlying mechanisms of psychological safety and psychological empowerment. Specifically, we propose the moderation of gender in this theoretical framework. To analyze our theoretical model, we conducted two independent studies. Study 1 involved 331 employees in the manufacturing sector, while Study 2 replicated the findings with 255 employees in the service (banking) sector. By using structural equation modeling in Mplus, we combined the results from both studies and found that workplace spirituality directly and indirectly influences the voice behaviors of both male and female employees, albeit with some variations. However, the direct effect of spirituality on female voice behavior was stronger compared to male employees. Additionally, the results from moderated mediation analysis in Mplus revealed specific indirect effects. The indirect effect of spirituality on female voice behavior primarily occurs through psychological safety, whereas for male voice behavior, it operates via psychological empowerment
Diving into recession: the collective knowledge of online users as an early warning system for recessionary expectations
No full textAs concerns about economic downturns manifest in online discussions, we investigate whether sentiment extracted from social media can serve as an early warning signal for recessionary pressures. Using a dataset of Twitter (X) posts related to economic prospects, we apply a range of sentiment analysis techniques, including a lexicon and rule-based method (VADER) and deep learning approaches (GPT and BERT). We assess the relationship between online sentiment and key recession indicators, such as the yield curve and GDPNow forecasts, using a combination of econometric and machine learning methods. In addition, we perform a comparative evaluation of sentiment classification techniques, incorporating both traditional models and deep learning architectures. Our results confirm that Twitter discussions precede changes in recessionary indicators and can thus provide forward-looking insights into economic sentiment. Furthermore, the comparative analysis reveals variations in sentiment detection across different methodologies, emphasizing the importance of selecting appropriate approaches in economic forecasting
Sustainable paper production from date palm and reed leaves through the valorization of agricultural waste products
No full textThe environmental consequences of wood-based paper production, including greenhouse gas emissions, have accelerated the search for sustainable alternatives. This study investigates the use of reed and date palm fibers as eco-friendly raw materials for paper production, focusing on starch\u27s influence on their thermal, structural, and mechanical properties. Reed fibers exhibited a higher pulp yield (58.2 %) and lower lignin content (7.8 %) compared to date palm fibers (55.9 % yield, 14.1 % lignin), contributing to papers with smoother textures and greater flexibility. The incorporation of starch into both fiber types resulted in notable performance improvements, though the effects were more pronounced in reed papers, as demonstrated by Fourier Transform Infrared and thermogravimetric analyses. Starch enhanced hydrogen bonding within the fiber matrix, leading to stronger fiber interactions and greater network integrity, as evidenced by superior thermal stability and cohesion in reed-based papers. This resulted in enhanced resistance to thermal degradation and increased fiber diameter. While the abrasion resistance, tensile strength, and tensile index of both paper types improved with starch, their crease recovery performance decreased. Reed papers, characterized by finer fibers and stronger hydrogen bonding, displayed superior flexibility and smoothness, making them suitable for applications requiring resilience and adaptability. Conversely, with their higher lignin content and coarser fibers, date palm papers exhibited greater rigidity and strength, suiting products prioritizing durability. Letterpress printing trials proved both paper types suitable for high-quality printing. The work findings highlight reed and date palm fibers, particularly with starch, as viable sustainable alternatives to traditional wood fibers in paper production
Conceptualizing a Model for Transforming Academic Search with Retrieval-Augmented Generation
No full textThis study conceptualizes the transformative potential of RetrievalAugmented Generation (RAG) in academic research, addressing limitations of traditional search methods reliant on keyword matching, Boolean logic, and metadata analysis. RAG combines precise data retrieval with the generative capabilities of advanced AI models, synthesizing dispersed information into coherent outputs. By bridging retrieval accuracy and contextual generation, RAG offers a framework for providing researchers with comprehensive, nuanced, and up-todate insights tailored to their queries. This article explores the integration of tools like ChatGPT and LangChain to outline a RAGbased system, illustrating its potential to enhance academic discovery by harmonizing natural language processing, semantic search, and generative synthesis. It also discusses the challenges of deploying RAG systems—such as data reliability, scalability, and ethical considerations—and proposes strategies to address these obstacles. While untested, this conceptual model presents a pathway for academic libraries to redefine their role in supporting modern research needs, making academic search more intuitive, efficient, and aligned with contemporary workflows. Keywords : Academic Search, Retrieval-Augmented Generation, RAG in Academic Search, Semantic Searc
Numerical simulation and nonlinear dynamics in rotating magnetoconvection: Chaos, Attractors, and Stability Transitions
No full textThis study presents a comprehensive numerical investigation of magnetohydrodynamic (MHD) convection in a conductive fluid subjected to a rotating magnetic field within a rectangular cavity. The model incorporates a convective flow induced by differential heating of opposing vertical walls under adiabatic conditions. The governing equations are derived based on Maxwell\u27s equations and the incompressible Navier–Stokes equations, with the magnetic forcing term, averaged over time under low magnetic Reynolds number conditions. A high-resolution numerical algorithm is employed to analyze the stability and transition to turbulence as the magnetic Taylor number (Ta) and Rayleigh number (Ra) increase. The results are consistent with prior experimental observations of flow destabilization at critical values of Ta. Furthermore, the study investigates the emergence of large-scale, nonstationary structures in the turbulent regime, quantifying the finite-time blow-up of solutions as a function of Pr, Ra, and Ta. Attractor formation in velocity space is examined to distinguish deterministic non-periodic solutions from fully developed turbulence. By computing over 104 parameter points, phase diagrams are constructed to illustrate regions of flow stability, deterministic chaos, and turbulence. These results offer novel insights into the interplay between electromagnetic forcing and convective instability, with potential applications in metallurgy, electrochemistry, and crystal growth processes