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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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16 pages, 469 KiB

Open AccessArticle

An Adaptation of the Quality–Loyalty Model to Study Green Consumer Loyalty

by Thi Hoang Ha Tran and Tuan Le-Anh

Sustainability 2025, 17(15), 7144; https://doi.org/10.3390/su17157144 (registering DOI) - 6 Aug 2025

Abstract

This research proposes an adaptation of the quality–loyalty model in which affective commitment is integrated as a key factor in the proposed framework. The study presented a comprehensive framework encompassing 11 hypotheses formulated from an extensive literature review. Empirical data collected from 679 environmentally conscious consumers predominantly residing in Vietnam’s three principal urban centers were employed to evaluate these hypotheses. The assessment was executed utilizing the partial least squares structural equation modeling technique. The results of this research authenticate the appropriateness of the integrated model in studying green consumption, verify the critical role of affective commitment in the newly introduced model, and identify the high impact of affective commitment on green loyalty intention and green purchase behavior. This research also shows that other factors of the quality–loyalty model have significant influences on affective commitment and green loyalty intention. Moreover, this study signifies the crucial role of green perceived quality in fostering affective commitment and green loyalty intention. Green perceived quality was identified as a key factor influencing green loyalty intention and played a crucial role in encouraging customers to purchase environmentally friendly products. Full article

(This article belongs to the Section Psychology of Sustainability and Sustainable Development)

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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

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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21 pages, 1748 KiB

Open AccessArticle

Between Text and Form: Expanded Textuality in Contemporary Architecture

by Manuel Iglesias-Vázquez

Humanities 2025, 14(8), 163; https://doi.org/10.3390/h14080163 (registering DOI) - 6 Aug 2025

Abstract

This article explores the concept of textuality as embedded within contemporary architecture, understood as the capacity of buildings to generate meanings, narratives, and interpretations that transcend their physical and functional dimensions. An interdisciplinary approach is adopted, integrating architectural theory, semiotics, hermeneutics, and cultural studies, positioning architecture as a form of symbolic production deeply intertwined with current social and technological contexts. The primary aim is to demonstrate how certain paradigmatic buildings operate as open texts that engage in dialogue with their users, urban surroundings, and cultural frameworks. The methodology combines theoretical analysis with an in-depth study of three emblematic cases: the Guggenheim Museum in Bilbao, the Centre Pompidou in Paris, and the Seattle Public Library. The findings reveal that these buildings articulate multiple layers of meaning, fostering rich and participatory interpretive experiences that influence both the perception and construction of public space. The study concludes that contemporary architecture functions as a narrative and symbolic device that actively contributes to the shaping of collective imaginaries. The article also identifies the study’s limitations and proposes future research directions concerning architectural textuality within the context of emerging digital technologies. Full article

(This article belongs to the Special Issue Beyond and in the Margins of the Text and Textualities)

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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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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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18 pages, 19896 KiB

Open AccessArticle

A Novel Polysilicon-Fill-Strengthened Etch-Through 3D Trench Electrode Detector: Fabrication Methods and Electrical Property Simulations

by Xuran Zhu, Zheng Li, Zhiyu Liu, Tao Long, Jun Zhao, Xinqing Li, Manwen Liu and Meishan Wang

Micromachines 2025, 16(8), 912; https://doi.org/10.3390/mi16080912 (registering DOI) - 6 Aug 2025

Abstract

Three-dimensional trench electrode silicon detectors play an important role in particle physics research, nuclear radiation detection, and other fields. A novel polysilicon-fill-strengthened etch-through 3D trench electrode detector is proposed to address the shortcomings of traditional 3D trench electrode silicon detectors; for example, the distribution of non-uniform electric fields, asymmetric electric potential, and dead zone. The physical properties of the detector have been extensively and systematically studied. This study simulated the electric field, potential, electron concentration distribution, complete depletion voltage, leakage current, capacitance, transient current induced by incident particles, and weighting field distribution of the detector. It also systematically studied and analyzed the electrical characteristics of the detector. Compared to traditional 3D trench electrode silicon detectors, this new detector adopts a manufacturing process of double-side etching technology and double-side filling technology, which results in a more sensitive detector volume and higher electric field uniformity. In addition, the size of the detector unit is 120 µm × 120 µm × 340 µm; the structure has a small fully depleted voltage, reaching a fully depleted state at around 1.4 V, with a saturation leakage current of approximately

4.8 \times 10^{- 10} A

, and a geometric capacitance of about 99 fF. Full article

(This article belongs to the Special Issue Photonic and Optoelectronic Devices and Systems, Third Edition)

28 pages, 13851 KiB

Open AccessArticle

A Spatially Aware Machine Learning Method for Locating Electric Vehicle Charging Stations

by Yanyan Huang, Hangyi Ren, Xudong Jia, Xianyu Yu, Dong Xie, You Zou, Daoyuan Chen and Yi Yang

World Electr. Veh. J. 2025, 16(8), 445; https://doi.org/10.3390/wevj16080445 (registering DOI) - 6 Aug 2025

Abstract

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)

22 pages, 481 KiB

Open AccessArticle

Early Childhood Education Quality for Toddlers: Understanding Structural and Process Quality in Chilean Classrooms

by Felipe Godoy, Marigen Narea, Pamela Soto-Ramirez, Camila Ayala and María Jesús López

Educ. Sci. 2025, 15(8), 1009; https://doi.org/10.3390/educsci15081009 (registering DOI) - 6 Aug 2025

Abstract

Despite extensive research on early childhood education (ECE) quality at the preschool level, toddler settings remain comparatively understudied, particularly in Chile and Latin America. Research suggests that quality ECE strengthens child development, while low-quality services can be harmful. ECE quality comprises structural features like ratios and classroom resources, and process features related to interactions within classrooms. This study examines how process and structural quality indicators are related in nurseries serving disadvantaged backgrounds. Data were collected from 51 Chilean urban classrooms serving children aged 12–24 months. Classrooms were evaluated using the Classroom Assessment Scoring System (CLASS) for toddlers, questionnaires, and checklists. Latent Profile Analysis identified process quality patterns, while multinomial regression examined associations with structural quality indicators. The results revealed low-to-moderate process quality across classrooms (M = 4.78 for Emotional and Behavioral Support; M = 2.35 for Engaged Support for Learning), with three distinct quality clusters emerging. Marginally significant differences were found between high- and low-performing clusters regarding classroom space (p = 0.06), number of toys (p = 0.08), and staff educational credentials (p = 0.01–0.07). No significant differences emerged for group sizes or adult-to-child ratios, which are heavily regulated in Chile. These findings underscore the need to strengthen quality assurance mechanisms ensuring all children access quality ECE. Full article

(This article belongs to the Special Issue Early Childhood Teacher-Student Interactions: Global Perspectives in Turbulent Times)

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19 pages, 5470 KiB

Open AccessArticle

Synergy of Fly Ash and Surfactant on Stabilizing CO₂/N₂ Foam for CCUS in Energy Applications

by Jabir Dubaish Raib, Fujian Zhou, Tianbo Liang, Anas A. Ahmed and Shuai Yuan

Energies 2025, 18(15), 4181; https://doi.org/10.3390/en18154181 - 6 Aug 2025

Abstract

The stability of nitrogen gas foam hinders its applicability in petroleum applications. Fly ash nanoparticles and clay improve the N₂ foam stability, and flue gas foams provide a cost-effective solution for carbon capture, utilization, and storage (CCUS). This study examines the stability, volume, and bubble structure of foams formed using two anionic surfactants, sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS), along with the cationic surfactant cetyltrimethylammonium bromide (CTAB), selected for their comparable interfacial tension properties. Analysis of foam stability and volume and bubble structure was conducted under different CO₂/N₂ mixtures, with half-life and initial foam volume serving as the evaluation criteria. The impact of fly ash and clay on SDS-N₂ foam was also evaluated. The results showed that foams created with CTAB, SDBS, and SDS exhibit the greatest stability in pure nitrogen, attributed to low solubility in water and limited gas diffusion. SDS showed the highest foam strength attributable to its comparatively low surface tension. The addition of fly ash and clay significantly improved foam stability by migrating to the gas–liquid interface, creating a protective barrier that reduced drainage. Both nano fly ash and clay improved the half-life of nitrogen foam by 11.25 times and increased the foam volume, with optimal concentrations identified as 5.0 wt% for fly ash and 3.0 wt% for clay. This research emphasizes the importance of fly ash nanoparticles in stabilizing foams, therefore optimizing a foam system for enhanced oil recovery (EOR). Full article

(This article belongs to the Special Issue Subsurface Energy and Environmental Protection 2024)

30 pages, 2190 KiB

Open AccessReview

Systematic Review of the State of Knowledge About Açaí-Do-Amazonas (Euterpe precatoria Mart., Arecaceae)

by Sabrina Yasmin Nunes da Rocha, Maria Julia Ferreira, Charles R. Clement and Ricardo Lopes

Plants 2025, 14(15), 2439; https://doi.org/10.3390/plants14152439 - 6 Aug 2025

Abstract

Euterpe precatoria Mart. is an increasingly important palm for subsistence and income generation in central and western Amazonia with growing demand for its fruit pulp, which is an alternative source of açaí juice for domestic and international markets. This study synthesizes current knowledge on its systematics, ecology, fruit production in natural populations, fruit quality, uses, population management, and related areas, identifying critical research gaps. A systematic literature survey was conducted across databases including Web of Science, Scopus, Scielo, CAPES, and Embrapa. Of 1568 studies referencing Euterpe, 273 focused on E. precatoria, with 90 addressing priority themes. Genetic diversity studies suggest the E. precatoria may represent a complex of species. Its population abundance varies across habitats: the highest variability occurs in terra firme, followed by baixios and várzeas. Várzeas exhibit greater productivity potential, with more bunches per plant and higher fruit weight than baixios; no production data exist for terra firme. Additionally, E. precatoria has higher anthocyanin content than E. oleracea, the primary commercial açaí species. Management of natural populations and cultivation practices are essential for sustainable production; however, studies in these fields are still limited. The information is crucial to inform strategies aiming to promote the sustainable production of the species. Full article

(This article belongs to the Section Plant Systematics, Taxonomy, Nomenclature and Classification)

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