Search Results (136,081)

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

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

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 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

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

Sperm count, motility, and morphology are semen parameters that directly affect male fertility. The presence of cytokines in seminal plasma negatively or positively influences these parameters. Interleukins and prostaglandins are proinflammatory cytokines present in human seminal plasma and play crucial roles in fertilization, in general and after intracytoplasmic sperm injection (ICSI) procedures. This study aimed to investigate the possible influence of interleukins IL-17 and IL-18, and prostaglandins PGE2 and PGF2α on male infertility. Semen samples were collected from 58 males who underwent the ICSI procedure. An enzyme-linked immunosorbent assay (ELISA) was used to determine the levels of IL-17, IL-18, PGE2, and PGF2α, and these concentrations were then correlated with semen parameters and the rate of fertilization. Furthermore, the chromatin integrity of the sperm was evaluated with an Acridine Orange (AO) assay. The results showed an inversely proportional relationship between the AO binding intensity and fertilization rate (r = −0.394; p ≤ 0.002). Furthermore, a negative correlation was observed between the IL-18 concentration and positive AO (p ≤ 0.021). Moreover, the IL-18 concentration was positively correlated with the fertilization rate (p ≤ 0.05). In contrast, IL-17 did not significantly correlate with any semen parameters or with the fertilization rate. Seminal PGE2 levels were significantly correlated with embryo cleavage at 72 h (p ≤ 0.05). To conclude, this study revealed that denaturation of sperm nuclear deoxyribonucleic acid (DNA) contributes to low fertilization rates. In addition, this study proposed a potential role for IL-18 in fertilization. PGE2 likely influences embryo development, but further studies are needed to examine the impact of seminal PGE2 on the oocyte to fully elucidate its contribution to this complex biological process. Full article

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

Crimean–Congo hemorrhagic fever (CCHF) is a zoonotic viral disease endemic to parts of Africa, Asia and southeastern Europe. Bulgaria is one of the few European countries with the consistent annual reporting of human CCHF cases. This study provides a descriptive overview of 24 confirmed CCHF cases in Bulgaria between 2015 and 2024. Laboratory confirmation was performed by an enzyme-linked immunosorbent assay (ELISA) and/or real-time reverse transcriptase polymerase chain reaction (RT-qPCR) testing. Common findings included fever, fatigue, gastrointestinal symptoms, thrombocytopenia, leukopenia, liver dysfunction and coagulopathy. Two fatal cases were recorded. Two samples collected in 2016 and 2024 were subjected to whole-genome sequencing. Phylogenetic analysis showed that both strains clustered within the Turkish branch of the Europe 1 genotype and shared high genetic similarity with previous Bulgarian strains, as well as strains from neighboring countries. These findings suggest the long-term persistence of a genetically stable viral lineage in the region. Continuous molecular and clinical surveillance is necessary to monitor the evolution and public health impact of CCHFV in endemic areas. Full article

Pediatric asthma is a multifactorial and heterogeneous disease determined by the dynamic interplay of genetic susceptibility, environmental exposures, and immune dysregulation. Recent advances have highlighted the pivotal role of epigenetic mechanisms, in particular, DNA methylation, histone modifications, and non-coding RNAs, in the regulation of inflammatory pathways contributing to asthma phenotypes and endotypes. This review examines the role of respiratory viruses such as respiratory syncytial virus (RSV), rhinovirus (RV), and other bacterial and fungal infections that are mediators of infection-induced epithelial inflammation that drive epithelial homeostatic imbalance and induce persistent epigenetic alterations. These alterations lead to immune dysregulation, remodeling of the airways, and resistance to corticosteroids. A focused analysis of T2-high and T2-low asthma endotypes highlights unique epigenetic landscapes directing cytokines and cellular recruitment and thereby supports phenotype-specific aspects of disease pathogenesis. Additionally, this review also considers the role of miRNAs in the control of post-transcriptional networks that are pivotal in asthma exacerbation and the severity of the disease. We discuss novel and emerging epigenetic therapies, such as DNA methyltransferase inhibitors, histone deacetylase inhibitors, miRNA-based treatments, and immunomodulatory probiotics, that are in preclinical or early clinical development and may support precision medicine in asthma. Collectively, the current findings highlight the translational relevance of including pathogen-related biomarkers and epigenomic data for stratifying pediatric asthma patients and for the personalization of therapeutic regimens. Epigenetic dysregulation has emerged as a novel and potentially transformative approach for mitigating chronic inflammation and long-term morbidity in children with asthma. Full article

While the Ankom RF system facilitates efficient high-throughput in vitro fermentation studies, its high cost and limited flexibility constrain its broader applicability. To address these limitations, we developed and validated a low-cost, modular gas monitoring system (FerME), assembled from commercially available components. To evaluate its performance and reproducibility relative to the Ankom RF system (Ankom Technology, Macedon, NY, USA), in vitro rumen fermentation experiments were conducted under strictly controlled and identical conditions. Whole rumen contents were collected approximately 2 h post-feeding from individual mid- or late-lactation dairy cows and immediately transported to the laboratory. Each fermenter received 50 mL of processed rumen fluid, 100 mL of anaerobically prepared artificial saliva buffer, and 1.2 g of the donor cow’s diet. Bottles were sealed with the respective system’s pressure sensors, flushed with CO₂, and incubated in a 50 L water bath maintained at 39 °C. FerME (New Taipei City, Taiwan) and Ankom RF fermenters were placed side-by-side to ensure uniform thermal conditions. To assess the effect of filter bag use, an additional trial employed Ankom F57 filter bags (Ankom Technology, Macedon, NY, USA; 25 μm pore size). Trial 1 revealed no significant differences in cumulative gas production, volatile fatty acids (VFAs), NH₃-N, or pH between systems (p > 0.05). However, the use of filter bags reduced gas output and increased propionate concentrations (p < 0.05). Trial 2, which employed filter bags in both systems, confirmed comparable results, with the FerME system demonstrating improved precision (CV: 4.8% vs. 13.2%). Gas composition (CH₄ + CO₂: 76–82%) and fermentation parameters remained consistent across systems (p > 0.05). Importantly, with 12 pressure sensors, the total cost of FerME was about half that of the Ankom RF system. Collectively, these findings demonstrate that FerME is a reliable, low-cost alternative for real-time rumen fermentation monitoring and could be suitable for studies in animal nutrition, methane mitigation, and related applications. Full article

With the acceleration of urbanization in China, water resources have become a key factor restricting regional sustainable development. Current research primarily examines the temporal or spatial variations in the water resources ecological footprint (WREF), with limited emphasis on the integration of both spatial and temporal scales. In this study, we collected the data and information from the 2005–2022 Statistical Yearbook and Water Resources Bulletin of the Yangtze River Delta Urban Agglomeration (YRDUA), and calculated evaluation indicators: WREF, water resources ecological carrying capacity (WRECC), water resources ecological pressure (WREP), and water resources ecological surplus and deficit (WRESD). We primarily analyzed the temporal and spatial variation in the per capita WREF and used the method of Geodetector to explore factors driving its temporal and spatial variation in the YRDUA. The results showed that: (1) From 2005 to 2022, the per capita WREF (total water, agricultural water, and industrial water) of the YRDUA generally showed fluctuating declining trends, while the per capita WREF of domestic water and ecological water showed obvious growth. (2) The per capita WREF and the per capita WRECC were in the order of Jiangsu Province > Anhui Province > Shanghai City > Zhejiang Province. The spatial distribution of the per capita WREF was similar to those of the per capita WRECC, and most areas effectively consume water resources. (3) The explanatory power of the interaction between factors was greater than that of a single factor, indicating that the spatiotemporal variation in the per capita WREF of the YRDUA was affected by the combination of multiple factors and that there were regional differences in the major factors in the case of secondary metropolitan areas. (4) The per capita WREF of YRDUA was affected by natural resources, and the impact of the ecological condition on the per capita WREF increased gradually over time. The impact factors of secondary metropolitan areas also clearly changed over time. Our results showed that the ecological situation of per capita water resources in the YRDUA is generally good, with obvious spatial and temporal differences. Full article

During large-scale pavement construction, the preparation of SBS-modified asphalt typically produces large amounts of harmful fumes. The emergence of deodorants can effectively alleviate the problem of smoke emissions during the asphalt manufacturing process. On the basis of ensuring the original road performance, exploring more suitable dosages and types of deodorant is urgently needed. Five commercial deodorants were evaluated using an asphalt smoke collection system, and UV-visible spectrophotometry (UV) was employed to screen the deodorants based on smoke concentration. Gas chromatography–mass spectrometry (GC-MS) was used to quantitatively analyze changes in harmful smoke components before and after adding two deodorants. Subsequently, the mechanisms of action of the two different types of deodorants were analyzed microscopically using fluorescence microscopy. Finally, the performance of bitumen and asphalt mixtures after adding deodorants was evaluated. The results showed that deodorant A (reactive type) and D (adsorption type) exhibited the best smoke suppression effects, with optimal addition rates of 0.6% and 0.5%, respectively. Deodorant A reduced benzene homologues by nearly 50% and esters by approximately 40%, while deodorant D reduced benzene homologues by approximately 70% and esters by approximately 60%, without producing new toxic gases. Both deodorants had a minimal impact on the basic properties of bitumen and the road performance of asphalt mixtures, with all indicators meeting technical specifications. This research provides a theoretical basis for the effective application of deodorants in the future, truly enabling a transition from laboratory research to large-scale engineering applications in the construction of environmentally friendly roads. Full article

Metacognitive monitoring plays a crucial role in self-regulated learning, as accurate monitoring enables effective control, which in turn impacts learning outcomes. Most studies on metacognitive monitoring have focused on learners’ monitoring abilities when they are explicitly prompted to monitor. However, in real-world educational settings, learners are more often prompted to control their learning, such as deciding whether to allocate additional time to a learning target. The primary goal of this study was to investigate whether retrieval is engaged when learners are explicitly prompted to control their learning processes by making study decisions. To address this, three experiments were conducted. In Experiment 1, participants (N = 39) studied 70 Swahili–English word pairs in a learning task. Each trial displayed a word pair for 8 s, followed by a distractor task (a two-digit mental addition) and a study decision intervention (choose “Study Again” or “Next”). After learning, participants provided a global judgment of learning (JOL), estimating their overall recall accuracy. Finally, they completed a cued recall test (Swahili cue). Responses were scored for accuracy and analyzed alongside study decisions, study decision reaction time (RT), and metacognitive judgments. Reaction times (RTs) for study decisions correlated positively with test accuracy, global judgments of learning (JOLs), and judgments of confidence (JOCs), suggesting retrieval likely underlies these decisions. Experiment 2 (N = 74, between-subjects) compared memory performance and intervention response time between single-study, restudy, retrieval (explicit recall prompt), and study decision (study decision prompt) groups to have better control over study time and cognitive processes. Although no significant group differences in test accuracy emerged, the retrieval group took longer to respond than the study decision group. Within-subject analyses revealed similar recall accuracy patterns: participants recalled successfully retrieved or “no restudy” items better than failed-retrieval or “restudy” items, implying shared cognitive processes underlying retrieval and study decision interventions. Experiment 3 (N = 74, within-subject, three learning conditions: single-study, retrieval, and study decision) replicated these findings, with no condition effects on test accuracy but longer RT for retrieval than study decisions. The similar recall accuracy patterns between retrieval and study decision interventions further supported shared cognitive processes underlying both tasks. Self-reports across experiments confirmed retrieval engagement in both retrieval and study decision interventions. Collectively, the results suggest that retrieval likely supports study decisions but may occur less frequently or less deeply than under explicit monitoring prompts. Additionally, this study explored objective, online measures to detect retrieval-based metacognitive monitoring. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

In this paper we aimed to compare seasonality, clinical characteristics, and outcomes of Influenza A and COVID-19 in the context of influenza reemergence and ongoing Omicron circulation. We performed a retrospective comparative analysis at the Teaching Hospital of Infectious Diseases in Cluj-Napoca, Romania. We included adult patients hospitalized with Influenza A or COVID-19 between 1 November 2022 and 31 March 2024. Data were collected on demographics, clinical presentation, complications, and in-hospital mortality. We included 899 COVID-19 and 423 Influenza A patients. The median age was 74 years for COVID-19 and 65 for Influenza A (p < 0.001). The age-adjusted Charlson comorbidity index was higher in COVID-19 patients (5 vs. 3, p < 0.001). Despite this age gap, acute respiratory failure was more common in Influenza A (62.8% vs. 55.7%, p = 0.014), but ventilation rates did not differ significantly. Multivariate models showed Influenza A was associated with increased risk of intensive-care unit (ICU) admission or ventilation, whereas older COVID-19 patients had higher in-hospital mortality (5.67% vs. 3.3%, p = 0.064). Omicron COVID-19 disproportionately affected older patients with comorbidities, contributing to higher in-hospital mortality. However, Influenza A remained a significant driver of respiratory failure and ICU admission, underscoring the importance of preventive measures in high-risk groups. Full article