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Virtual AI-driven streamers have been gradually used in live commerce, and they may affect consumer impulsive purchase intention. Drawing on the stimulus–organism–response (S-O-R) model, this research examined consumer impulsive purchase intention in virtual AI streaming. Based on survey data from 411 predominantly young and educated virtual AI streaming users recruited through snowball sampling, we found that perceived responsiveness, perceived likeability, perceived expertise, and perceived anthropomorphism of virtual AI streamers are associated with trust and flow experience, both of which predict consumers’ impulsive purchase intentions. The fsQCA identified two paths that lead to impulsive purchase intention. The results imply that live streaming platforms need to engender consumers’ trust and flow experience in order to increase their impulsive purchase intention. Full article

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102 pages, 29310 KiB

Open AccessArticle

“We Begin in Water, and We Return to Water”: Track Rock Tradition Petroglyphs of Northern Georgia and Western North Carolina

by Johannes H. Loubser

Arts 2025, 14(4), 89; https://doi.org/10.3390/arts14040089 (registering DOI) - 6 Aug 2025

Abstract

Petroglyph motifs from 23 sites and 37 panels in northern Georgia and western North Carolina foothills and mountains are analyzed within their archaeological, ethnographic, and landscape contexts. The Track Rock Tradition comprises 10 chronologically sequenced marking categories: (1) Cupules/Meanders/Open Circles; (2) Soapstone Extraction cars; (3) Vulva Shapes; (4) Figures; (5) Feet/Hands/Tracks; (6) Nested Circles; (7) Cross-in-Circles; (8) Spirals; (9) Straight Lines; and (10) Thin Incised Lines. Dating spans approximately 3800 years. Early cupules and meanders predate 3000 years ago, truncated by Late Archaic soapstone extraction. Woodland period (3000–1050 years ago) motifs include vulva shapes, figures, feet, tracks, and hands. Early Mississippian concentric circles date to 1050–600 years ago, while Middle Mississippian cross-in-circles span 600–350 years ago. Late Mississippian spirals (350–200 years ago) and post-contact metal tool incisions represent the most recent phases. The Track Rock Tradition differs from western Trapp and eastern Hagood Mill traditions. Given the spatial overlap with Iroquoian-speaking Cherokee territory, motifs are interpreted through Cherokee beliefs, supplemented by related Muskogean Creek ethnography. In Cherokee cosmology, the matrilocal Thunderers hierarchy includes the Female Sun/Male Moon, Selu (Corn Mother)/Kanati (Lucky Hunter), Medicine Woman/Judaculla (Master of Game), and Little People families. Ritual practitioners served as intermediaries between physical and spirit realms through purification, fasting, body scratching, and rock pecking. Meanders represent trails, rivers, and lightning. Cupules and lines emphasize the turtle appearance of certain rocks. Vulva shapes relate to fertility, while tracks connect to life-giving abilities. Concentric circles denote townhouses; cross-in-circles and spirals represent central fires. The tradition shows continuity in core beliefs despite shifting emphases from hunting (Woodland) to corn cultivation (Mississippian), with petroglyphs serving as necessary waypoints for spiritual supplicants. Full article

(This article belongs to the Special Issue Advances in Rock Art Studies)

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30 pages, 5262 KiB

Open AccessArticle

Alternative Hydraulic Modeling Method Based on Recurrent Neural Networks: From HEC-RAS to AI

by Andrei Mihai Rugină

Hydrology 2025, 12(8), 207; https://doi.org/10.3390/hydrology12080207 (registering DOI) - 6 Aug 2025

Abstract

The present study explores the application of RNNs for the prediction and propagation of flood waves along a section of the Bârsa River, Romania, as a fast alternative to classical hydraulic models, aiming to identify new ways to alert the population. Five neural architectures were analyzed as follows: S-RNN, LSTM, GRU, Bi-LSTM, and Bi-GRU. The input data for the neural networks were derived from 2D hydraulic simulations conducted using HEC-RAS software, which provided the necessary training data for the models. It should be mentioned that the input data for the hydraulic model are synthetic hydrographs, derived from the statistical processing of recorded floods. Performance evaluation was based on standard metrics such as NSE,

R^{2}

MSE, and RMSE. The results indicate that all studied networks performed well, with NSE and

R^{2}

values close to 1, thus validating their capacity to reproduce complex hydrological dynamics. Overall, all models yielded satisfactory results, making them useful tools particularly the GRU and Bi-GRU architectures, which showed the most balanced behavior, delivering low errors and high stability in predicting peak discharge, water level, and flood wave volume. The GRU and Bi-GRU networks yielded the best performance, with RMSE values below 1.45, MAE under 0.3, and volume errors typically under 3%. On the other hand, LSTM architecture exhibited the most significant instability and errors, especially in estimating the flood wave volume, often having errors exceeding 9% in some sections. The study concludes by identifying several limitations, including the heavy reliance on synthetic data and its local applicability, while also proposing solutions for future analyses, such as the integration of real-world data and the expansion of the methodology to diverse river basins thus providing greater significance to RNN models. The final conclusions highlight that RNNs are powerful tools in flood risk management, contributing to the development of fast and efficient early warning systems for extreme hydrological and meteorological events. Full article

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18 pages, 2279 KiB

Open AccessArticle

MvAl-MFP: A Multi-Label Classification Method on the Functions of Peptides with Multi-View Active Learning

by Yuxuan Peng, Jicong Duan, Yuanyuan Dan and Hualong Yu

Curr. Issues Mol. Biol. 2025, 47(8), 628; https://doi.org/10.3390/cimb47080628 (registering DOI) - 6 Aug 2025

Abstract

The rapid expansion of peptide libraries and the increasing functional diversity of peptides have highlighted the significance of predicting the multifunctional properties of peptides in bioinformatics research. Although supervised learning methods have made advancements, they typically necessitate substantial amounts of labeled data for yielding accurate prediction. This study presents MvAl-MFP, a multi-label active learning approach that incorporates multiple feature views of peptides. This method takes advantage of the natural properties of multi-view representation for amino acid sequences, meets the requirement of the query-by-committee (QBC) active learning paradigm, and further significantly diminishes the requirement for labeled samples while training high-performing models. First, MvAl-MFP generates nine distinct feature views for a few labeled peptide amino acid sequences by considering various peptide characteristics, including amino acid composition, physicochemical properties, evolutionary information, etc. Then, on each independent view, a multi-label classifier is trained based on the labeled samples. Next, a QBC strategy based on the average entropy of predictions across all trained classifiers is adopted to select a specific number of most valuable unlabeled samples to submit them to human experts for labeling by wet-lab experiments. Finally, the aforementioned procedure is iteratively conducted with a constantly expanding labeled set and updating classifiers until it meets the default stopping criterion. The experiments are conducted on a dataset of multifunctional therapeutic peptides annotated with eight functional labels, including anti-bacterial properties, anti-inflammatory properties, anti-cancer properties, etc. The results clearly demonstrate the superiority of the proposed MvAl-MFP method, as it can rapidly improve prediction performance while only labeling a small number of samples. It provides an effective tool for more precise multifunctional peptide prediction while lowering the cost of wet-lab experiments. Full article

(This article belongs to the Special Issue Challenges and Advances in Bioinformatics and Computational Biology)

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25 pages, 3691 KiB

Open AccessArticle

Research on Motion Control Method of Wheel-Legged Robot in Unstructured Terrain Based on Improved Central Pattern Generator (CPG) and Biological Reflex Mechanism

by Jian Gao, Ruilin Fan, Hongtao Yang, Haonan Pang and Hangzhou Tian

Appl. Sci. 2025, 15(15), 8715; https://doi.org/10.3390/app15158715 (registering DOI) - 6 Aug 2025

Abstract

With the development of inspection robot control technology, wheel-legged robots are increasingly used in complex underground space inspection. To address low stability during obstacle crossing in unstructured terrains, a motion control strategy integrating an improved CPG algorithm and a biological reflex mechanism is proposed. It introduces an adaptive coupling matrix, augmented with the Lyapunov function, and vestibular/stumbling reflex models for real-time motion feedback. Simulink–Adams virtual prototypes and single-wheeled leg experiments (on the left front leg) were used to verify the system. Results show that the robot’s turning oscillation was ≤±0.00593 m, the 10° tilt maintained a stable center of mass at 10.2° with roll angle fluctuations ≤±5°, gully-crossing fluctuations ≤±0.01 m, and pitch recovery ≤2 s. The experiments aligned with the simulations, proving that the strategy effectively suppresses vertical vibrations, ensuring stable and high-precision inspection. Full article

14 pages, 1554 KiB

Open AccessArticle

Cytokinin Potentials on In Vitro Shoot Proliferation and Subsequent Rooting of Agave sisalana Perr. Syn

by Mayada K. Seliem, Neama Abdalla and Mohammed E. El-Mahrouk

Horticulturae 2025, 11(8), 929; https://doi.org/10.3390/horticulturae11080929 (registering DOI) - 6 Aug 2025

Abstract

Agave species are plants with great economic value and multiple possibilities of use as ornamentals, medicinal plants, and fibers, as well as being significant sources of bioethanol. However, their long life cycles hinder their conventional breeding. Therefore, biotechnology tools are the most effective means for clonal propagation and genetic improvement. In vitro micropropagation of A. sisalana via axillary shoot proliferation from bulbil explants was attained using Murashige and Skoog medium (MS) supplemented with cytokinins (CKs), such as 6-benzyladenine (BA), kinetin (KIN), or thidiazuron (TDZ). The optimum significant shoot proliferation (14.67 shoots/explant) was achieved on 1.0 mg L⁻¹ TDZ. The carry-over effect of CKs on subsequent rooting could be detected. Control and KIN treatments could enhance the rooting of shoots on shoot proliferation media. The regenerated plantlets were acclimatized directly with 100% survival. To mitigate this carry-over effect, that causes hindering further root growth and development, and promote healthy growth of roots, subculturing shoots onto a CK-free medium is a recommended practice. The shoots induced on all BA treatments, and TDZ at 0.5 and 1.0 mg L⁻¹ could be rooted after two subcultures on CK-free medium, then they were acclimatized with 100% survival. However, the higher concentrations of TDZ inhibited in vitro rooting even after two subcultures on CK-free medium, and the acclimatization percentage was reduced by increasing the TDZ concentration recorded from 10 to 0%. Full article

(This article belongs to the Special Issue In Vitro Propagation and Biotechnology of Horticultural Plants, 2nd Edition)

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21 pages, 559 KiB

Open AccessReview

Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges

by Simeon Ogunbunmi, Yu Chen, Qi Zhao, Deeraj Nagothu, Sixiao Wei, Genshe Chen and Erik Blasch

Future Internet 2025, 17(8), 357; https://doi.org/10.3390/fi17080357 (registering DOI) - 6 Aug 2025

Abstract

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

(This article belongs to the Special Issue Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks)

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17 pages, 3538 KiB

Open AccessArticle

Enhanced Nanoparticle Collection Using an Electrostatic Precipitator Integrated with a Wire Screen

by Raíssa Gabrielle Silva Araújo Andrade and Vádila Giovana Guerra

Powders 2025, 4(3), 23; https://doi.org/10.3390/powders4030023 (registering DOI) - 6 Aug 2025

Abstract

Electrostatic precipitators (ESPs) are widely applied to reduce particle concentrations. However, the performance of ESPs is impaired in the nanosized diameter range due to the difficulty in electrically charging these particles. The present work evaluated the inclusion of a wire screen, perpendicular to the airflow, as an additional collecting electrode of a single-stage wire-plate ESP containing two collecting plates and a single discharge wire. ESP performance was evaluated in terms of voltage, air velocity and electrode positioning in relation to the beginning of the collecting plate (inlet spacings of 1.5, 10 and 23 cm). When compared to theoretical prediction, the penetration results presented a decay for larger particles not predicted by the diffusion battery model. It was observed that the inclusion of the wire screen increased the removal of ultrafine particles and that the overall collection efficiencies increased up to 70% in the operating conditions evaluated. Moreover, the central positioning of the electrodes (inlet spacing of 10 cm) achieved the highest collection efficiencies at high voltages, but the final positioning (inlet spacing of 23 cm) presented a better performance at higher air velocities. Therefore, the wire screen can be an alternative to enhance nanoparticle collection. Full article

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30 pages, 5684 KiB

Open AccessArticle

Exploring Relationships Between Qualitative Student Evaluation Comments and Quantitative Instructor Ratings: A Structural Topic Modeling Framework

by Nina Zipser, Dmitry Kurochkin, Kwok Wah Yu and Lisa A. Mincieli

Educ. Sci. 2025, 15(8), 1011; https://doi.org/10.3390/educsci15081011 (registering DOI) - 6 Aug 2025

Abstract

This study demonstrates how Structural Topic Modeling (STM) can be used to analyze qualitative student comments in conjunction with quantitative student evaluation of teaching (SET) scores, providing a scalable framework for interpreting student evaluations of teaching. Drawing on 286,203 open-ended comments collected over fourteen years at a large U.S. research university, we identify eleven latent topics that characterize how students describe instructional experiences. Unlike traditional topic modeling methods, STM allows us to examine how topic prevalence varies with course and instructor attributes, including instructor gender, course discipline, enrollment size, and numeric SET scores. To illustrate the utility of the model, we show that topic prevalence aligns with SET ratings in expected ways and that students associate specific teaching attributes with instructor gender, though the effects are relatively small. Importantly, the direction and strength of topic–SET correlations are consistent across male and female instructors, suggesting shared student perceptions of effective teaching practices. Our findings underscore the potential of STM to contextualize qualitative feedback, support fairer teaching evaluations, inform institutional decision-making, and examine the relationship between qualitative student comments and numeric SET ratings. Full article

(This article belongs to the Special Issue Recent Advances in Measuring Teaching Quality)

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27 pages, 10748 KiB

Open AccessArticle

Rolling Bearing Fault Diagnosis Based on Fractional Constant Q Non-Stationary Gabor Transform and VMamba-Conv

by Fengyun Xie, Chengjie Song, Yang Wang, Minghua Song, Shengtong Zhou and Yuanwei Xie

Fractal Fract. 2025, 9(8), 515; https://doi.org/10.3390/fractalfract9080515 (registering DOI) - 6 Aug 2025

Abstract

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

(This article belongs to the Special Issue Implementations and Applications of Algorithms Based on Fractional Calculus to Engineering Problems)

43 pages, 8518 KiB

Open AccessReview

Cutting-Edge Sensor Technologies for Exosome Detection: Reviewing Role of Antibodies and Aptamers

by Sumedha Nitin Prabhu and Guozhen Liu

Biosensors 2025, 15(8), 511; https://doi.org/10.3390/bios15080511 (registering DOI) - 6 Aug 2025

Abstract

Exosomes are membranous vesicles that play a crucial role as intercellular messengers. Cells secrete exosomes, which can be found in a variety of bodily fluids such as amniotic fluid, semen, breast milk, tears, saliva, urine, blood, bile, ascites, and cerebrospinal fluid. Exosomes have a distinct bilipid protein structure and can be as small as 30–150 nm in diameter. They may transport and exchange multiple cellular messenger cargoes across cells and are used as a non-invasive biomarker for various illnesses. Due to their unique features, exosomes are recognized as the most effective biomarkers for cancer and other disease detection. We give a review of the most current applications of exosomes derived from various sources in the prognosis and diagnosis of multiple diseases. This review also briefly examines the significance of exosomes and their applications in biomedical research, including the use of aptamers and antibody–antigen functionalized biosensors. Full article

(This article belongs to the Special Issue Material-Based Biosensors and Biosensing Strategies)

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10 pages, 616 KiB

Open AccessCommunication

Brief Prompt-Engineering Clinic Substantially Improves AI Literacy and Reduces Technology Anxiety in First-Year Teacher-Education Students: A Pre–Post Pilot Study

by Roberto Carlos Davila-Moran, Juan Manuel Sanchez Soto, Henri Emmanuel Lopez Gomez, Manuel Silva Infantes, Andres Arias Lizares, Lupe Marilu Huanca Rojas and Simon Jose Cama Flores

Educ. Sci. 2025, 15(8), 1010; https://doi.org/10.3390/educsci15081010 (registering DOI) - 6 Aug 2025

Abstract

Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs. Full article

(This article belongs to the Special Issue ChatGPT as Educative and Pedagogical Tool: Perspectives and Prospects)

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28 pages, 9378 KiB

Open AccessArticle

A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method

by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao and Kezhao Li

Remote Sens. 2025, 17(15), 2725; https://doi.org/10.3390/rs17152725 (registering DOI) - 6 Aug 2025

Abstract

Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. The approach distinguishes between building and tree occlusions and adjusts signal weights accordingly to enhance positioning accuracy. First, a fisheye camera captures environmental imagery above the vehicle, which is then processed using deep learning to segment sky, tree, and building regions. Subsequently, satellite projections are mapped onto the segmented sky image to classify signal occlusions. Then, based on the type of obstruction, a dynamic weight optimization model is constructed to adjust the contribution of each satellite in the positioning solution, thereby enhancing the positioning accuracy of vehicle-navigation in urban environments. Finally, we construct a vehicle-mounted navigation system for experimentation. The experimental results demonstrate that the proposed method enhances accuracy by 16% and 10% compared to the existing GNSS/INS/Canny and GNSS/INS/Flood Fill methods, respectively, confirming its effectiveness in complex urban environments. Full article

(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)

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9 pages, 236 KiB

Open AccessArticle

Full Automorphism Group of (m,2)-Graph in Finite Classical Polar Spaces

by Yang Zhang, Shuxia Liu and Liwei Zeng

Axioms 2025, 14(8), 614; https://doi.org/10.3390/axioms14080614 (registering DOI) - 6 Aug 2025

Abstract

Let \( \mathcal{Q} \) be the finite classical polar space of rank \( \nu\geq 1 \) over \( \mathbb{F}_q \), and \( \mathcal{Q}_m \) be the set of all m-dimensional subspaces of \( \mathcal{Q} \). In this paper, we introduce the \( (m,2) \)-graph with \( \mathcal{Q}_m \) as its vertex set, and two vertices \(P,Q\) are adjacent if and only if \( P+Q \) is an \( (m+2) \)-dimensional subspace of \( \mathcal{Q} \). The full automorphism group of \( (m,2)\)-graph is determined. Full article

18 pages, 11604 KiB

Open AccessArticle

A Philosophical Framework for Data-Driven Miscomputations

by Alessandro G. Buda, Chiara Manganini and Giuseppe Primiero

Philosophies 2025, 10(4), 88; https://doi.org/10.3390/philosophies10040088 (registering DOI) - 6 Aug 2025

Abstract

This paper introduces a first approach to miscomputations for data-driven systems. First, we establish an ontology for data-driven learning systems and categorize various computational errors based on the Levels of Abstraction ontology. Next, we consider computational errors which are associated with users’ evaluation [...] Read more.

(This article belongs to the Special Issue Semantics and Computation)

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