Search Results (1,589)

This study maps the intellectual structure and thematic evolution of buy now, pay later (BNPL) research published between 2010 and 2025, with particular attention to how impulsive buying and post-purchase regret are positioned within the broader BNPL knowledge domain. Drawing on an integrated bibliometric science mapping and focused behavioral synthesis approach, the study first mapped a broad Scopus dataset of BNPL-related digital consumer credit and deferred payment research published between 2010 and 2025. This dataset was used for performance analysis and VOSviewer-based science mapping. A second, narrower PRISMA-guided screening process was then applied to identify empirical studies that directly examined BNPL-related behavioral and psychological outcomes, resulting in 13 studies retained for focused qualitative synthesis. The bibliometric findings show that BNPL scholarship expanded sharply after 2020, with research concentrated in marketing, consumer behavior, fintech, and digital commerce outlets. The science mapping results reveal a fragmented field structured around digital finance adoption, impulsive consumption, consumer vulnerability, and emerging ethical and regulatory concerns. The systematic synthesis further indicates that BNPL-related mechanisms, including installment framing, urgency cues, perceived affordability, and reduced payment salience, are consistently associated with impulsive buying tendencies. However, post-purchase regret remains underexamined and is rarely modeled as a distinct emotional outcome. By integrating bibliometric evidence with behavioral synthesis, this study clarifies how BNPL research has developed, where conceptual fragmentation remains, and why future studies should connect digital payment design, cognitive distortions, impulsive purchasing, and post-purchase emotional consequences within more comprehensive theoretical models. The study contributes by offering a structured research agenda for advancing responsible BNPL scholarship, consumer protection, and future digital finance research. Full article

Interior built environments influence user behavior through more than deliberate rational evaluation. They shape attention, movement, affective comfort, perceived safety, wayfinding, and well-being through bounded cognition, affective appraisal, heuristics, embodied perception, and automatic approach–avoidance processes. The research gap addressed in this review concerns the fact that prior work on interior environments, wayfinding, indoor environmental quality, neuroarchitecture, atmospherics, and behavioral decision-making remains fragmented across separate studies, and existing reviews rarely explain how these mechanisms can be organized into a design-usable framework for interior built environments. This narrative review synthesizes foundational and recent literature across building design, environmental psychology, neuroarchitecture, virtual reality, indoor environmental quality, wayfinding, and behavioral decision-making to clarify how decision mechanisms translate into interior design variables such as lighting, color, spatial organization, materiality, form, sensory atmosphere, environmental legibility, thermal comfort, and controllability. The review distinguishes bounded rationality, heuristics and biases, dual-process accounts, affective and atmospheric processing, prospect–refuge dynamics, mere exposure, and room-effect research rather than treating them as a single “non-rational” category. It proposes an integrative framework in which interior cues are processed through perceptual and affective appraisal; moderated by individual, cultural, contextual, temporal, and ethical factors; and expressed through behavioral outcomes such as navigation, approach or withdrawal, dwell time, perceived quality, usability, stress regulation, and well-being. The paper contributes to human-centered building design by formalizing a mechanism-based account of how interior environments can support behavior without reducing users to passive recipients of environmental manipulation. It concludes with practical implications for design briefing, post-occupancy evaluation, VR-based testing, healthcare and workplace audits, safety-critical settings, and future longitudinal validation. Full article

C. albicans small GTPase Ras1 belonging to the cAMP-Protein Kinase A (PKA) signaling pathway is a well-established master regulator of hyphal development, taking its environmental cues from N-acetylglucosamine (GlcNAc) as a carbon source. Iron is also known to induce filamentation in C. albicans. However, the influence of iron availability on Ras1-cAMP-PKA signaling in response to GlcNAc-induced filamentation has never been studied. In this study, we investigated the role of Ras1 in hyphal induction under varying iron conditions, using both in vitro systems and an in vivo model of mucosal colonization in Caenorhabditis elegans. Surprisingly, upon GlcNAc exposure, ∆/∆ras1 cells formed true hyphae exclusively under high-iron conditions, whereas its parent strain (CAI4-Ura+) showed hyphal formation irrespective of environmental iron levels. Further analysis revealed that this GlcNAc-mediated hyphal formation under high iron in ∆/∆ras1 cells was independent of cAMP levels but required the downstream effectors Efg1 and Tpk2. A similar iron-dependent pattern of hyphal formation in Δ/Δras1 cells was also observed in vivo in C. elegans. Transcriptomic analysis indicated that high iron induced robust expression of hypha-associated genes in ∆/∆ras1, accompanied by downregulation of BCY1, a negative regulator of PKA. Overexpression of BCY1 in ∆/∆ras1 cells completely blocked the iron-dependent hyphal formation, highlighting a previously unrecognized Ras1-independent, iron-responsive mechanism controlling PKA-mediated filamentation. Collectively, our findings reveal that increased environmental iron availability can bypass Ras1 to regulate hyphal development by limiting Bcy1 levels to allow PKA activation. This provides insights into how C. albicans can exploit iron replete host niches for enhanced pathogenicity, eliminating the need for key modulators such as Ras1. Full article

Bone defects from trauma, tumour resection, infection, and degenerative disease remain a major clinical burden, and autografts face limitations of supply and donor-site morbidity. Three-dimensional (3D) printing offers a route to patient-specific, architecturally defined bone scaffolds, while biopolymers from natural sources provide biodegradability, biocompatibility, and extracellular matrix-mimicking cues consistent with sustainable, green biomaterials science. This review synthesises recent progress in 3D printing of biopolymer-based scaffolds for bone tissue engineering. We first examine the principal feedstocks—alginate, gelatin and gelatin methacryloyl, collagen, chitosan, silk fibroin, cellulose, and microbial polyesters—and their preparation, crosslinking chemistry, and printability. We then compare extrusion, light-based, and indirect printing technologies and the process–property relationships governing resolution, mechanical competence, and cell viability. Composite and functionalisation strategies, including biopolymer–bioceramic hybrids and controlled delivery of growth factors and antimicrobial agents, are analysed as routes to osteoinduction, vascularisation, and infection control. Finally, we evaluate translational performance in preclinical models and outline central challenges of vascularisation, mechanical–degradation matching, scalability, and regulatory standardisation. Biopolymer 3D printing is positioned as a ve rsatile, sustainable platform whose clinical maturation depends on integrated material, structural, and biological design. Full article

Soil microbial carbon use efficiency (CUE) is a pivotal determinant of soil carbon sequestration, yet how forest quality gradients regulate CUE through the interplay of mineral-microbial interactions in subtropical conifer ecosystems remains poorly understood. To address this, we examined the CUE response and its drivers across a forest quality gradient (high-quality to poor-quality stands) in subtropical coniferous forests in China. Soil mineral composition (including soil texture and the contents of Fe₂O₃, CaO, and MgO), physicochemical properties, microbial community diversity, and CUE were quantified. The results showed that CUE decreased by 2.7%, from 0.533 in high-quality stands to 0.519 in low-quality stands. Concurrently, soil organic carbon (SOC), nutrient availability, and microbial diversity exhibited consistent declining trends along the forest quality gradient. The CUE showed a significant positive correlation with SOC (r > 0.90, p < 0.001). Structural equation modeling and random forest revealed that microbial diversity was the most dominant correlated factor of CUE (the total effects on CUE = 0.932), followed by SOC. However, soil minerals indirectly influenced CUE via SOC. These findings highlight microbial diversity as the dominant observed correlate of CUE across forest quality gradients. This study not only deepens the understanding of the microbial mechanisms underlying soil carbon dynamics in subtropical forests but also provides key scientific basis for ecological restoration of poor-quality forests and nature-based climate solutions. Full article

Globally, Trichogramma Westwood (Hymenoptera: Trichogrammatidae) is known as the most effective biological control agent due to its ability to parasitize insect pest eggs. However, identifying an appropriate host is vital for Trichogramma to prosper. Therefore, this study delves into the complex role of semiochemicals in shaping the host-seeking behavior of Trichogramma parasitoids, with a particular focus on their responses to both plant-derived and host-derived cues. The mechanism of semiochemical reception in Trichogramma wasps relies on a highly specialized, sensitive olfactory and gustatory system to locate host eggs and mates. Semiochemicals, which mediate ecological interactions, have been identified as pivotal in influencing the parasitic efficiency of Trichogramma species. Trichogramma’s host-seeking behavior is influenced not solely by ovipositional cues but also by the intrinsic physical attributes of Lepidopteran hosts, such as the scales on the wings and abdomen, which emit semiochemicals capable of eliciting positive chemotactic responses, thereby guiding parasitoids toward optimal sites for oviposition. Furthermore, the interplay between insect-derived and plant-derived chemical cues exhibits a synergistic effect, collectively enhancing the chemotactic attraction of Trichogramma, thereby fine-tuning its host-seeking behavior with greater precision and specificity. This study further underscores Trichogramma’s innate behavioral ability to discriminate between host eggs of varying developmental stages, facilitating the precise identification and selection of the most suitable host for parasitization. Age and experience both make Trichogramma more selective of hosts, but younger parasitoids may take a broader approach to host selection due to their greater life expectancy. Furthermore, the removal of these cues affects their host localization and learning abilities. Associative learning enables Trichogramma to exhibit flexible behaviors, providing them with a selective advantage; allows them to explore various hosts; and reduces environmental uncertainty. Plant structure, host density, and host age are the key factors that significantly influence the foraging and parasitism of Trichogramma. The searching speed of this parasitoid is significantly influenced by temperature. Heat stress increases VOC emissions in plants such as potato via stomatal opening, reducing herbivore attraction and enhancing parasitoid recruitment. Furthermore, air pollution, including CO₂, O₃, and NOx, impairs parasitoid efficiency by disrupting volatile-mediated host location and reducing biological control performance. Trichogramma wasps are generally effective biological control agents, but their success depends on the species used, target pest, crop, release density, and field conditions. Overall, species such as T. ostriniae, T. japonicum, and T. leucaniae show the strongest performance in several crops by increasing parasitism, reducing pest damage, and improving yield. This study highlights the successful integration of semiochemical cues in pest management programs and the effective utilization of Trichogramma in conjunction with entomopathogenic bacteria to control Lepidopteran pests. This approach contributes to the development of more effective pest management strategies, thereby promoting agricultural sustainability. Full article

The growing use of artificial intelligence (AI) in social media marketing has accelerated the emergence of AI-generated virtual influencers. While these influencers offer brands advantages such as scalability and message control, they also raise concerns regarding manipulation and ethical persuasion. Grounded in the Persuasion Knowledge Model (PKM), this study examines how different AI disclosure conditions influence perceived manipulation, perceived ethics, and purchase intention in AI influencer marketing. A three-condition between-subjects experimental design was employed to compare a human influencer, a disclosed AI influencer, and an undisclosed AI influencer using identical Instagram stimuli. Data were collected from 762 Generation Z female consumers in Türkiye. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that both disclosed and undisclosed AI influencer conditions significantly increased perceived manipulation. Perceived manipulation negatively affected perceived ethics, whereas perceived ethics positively influenced purchase intention. In addition, AI literacy positively affected perceived manipulation and perceived ethics while negatively affecting purchase intention. The findings further demonstrated that disclosure conditions indirectly influenced purchase intention through sequential cognitive and ethical evaluation processes. The study contributes to the AI influencer and digital persuasion literature by demonstrating that disclosure cues shape consumer responses through interconnected psychological mechanisms. Full article

Accurate and robust detection of leaf diseases is a key enabler for precision agriculture and large-scale crop health monitoring. Despite the strong generalization of modern one-stage detectors (e.g., YOLOv8), two domain-specific challenges remain: (i) weak or blurry lesion boundaries hinder precise localization, and (ii) low color contrast between diseased and healthy tissues forces models to rely on subtle texture patterns rather than salient shapes. To tackle these challenges, we reframe the core agricultural disease detection task as the identification of “asymmetric morphological anomalies” and propose a domain-tailored enhancement framework. First, we introduce an Edge Enhancement Module (EEM) that explicitly strengthens boundary-aware representations. Inspired by the natural symmetry of healthy leaves, our EEM is specifically designed to capture symmetry-breaking boundary discontinuities and localized asymmetric edges caused by disease lesions. Our method enhances edge and texture cues that are indicative of disease lesions, which often exhibit local asymmetries and boundary discontinuities. The EEM includes a Differential Normalized Pooling Block (DNPB) that highlights edge responses through discrepancies between max pooling and average pooling, which also models cross-group edge correlations. Second, the Lightweight Texture-Sensitive Feature Enhancement (LTSFE) mechanism amplifies texture-discriminative channels under low-contrast conditions by leveraging complementary global statistics and efficient channel mixing, all with negligible computational overhead. We evaluated our method on a self-constructed dataset of 106,434 images with 225,640 annotations covering diverse crops. Experiments show that the proposed method achieves state-of-the-art accuracy (81.54% mAP@0.5:0.95) while maintaining real-time inference (142 FPS), consistently outperforming strong baselines. Ablations confirm the effectiveness and complementarity of EEM and LTSFE, demonstrating that domain-specific architectural design, inspired by biological symmetry, can substantially improve agricultural vision systems. Full article

In unmanned aerial vehicle (UAV) scenarios, RGB-T salient object detection faces several challenges, including cross-modal spatial misalignment, redundant multi-scale features, and weak responses of small objects in cluttered backgrounds, which together degrade fusion effectiveness and localization stability in complex environments. To address these issues, we propose a Hierarchical Alignment Fusion and Mapping Network (HAFM-Net), a misalignment-robust fusion framework, for unaligned RGB-T salient object detection. The proposed method does not rely on explicit pixel-level preregistration. Instead, it replaces registration-first preprocessing with implicit feature-domain alignment and misalignment-robust fusion, enabling saliency prediction from unregistered RGB-T inputs. Specifically, we design a hierarchical adjacent-scale interaction mechanism to enhance multi-scale contextual modeling while suppressing cross-scale redundancy. We further develop a Misalignment-Robust Correlation Fusion module to explore cross-modal correlations and enable robust feature interaction under positional variations. In addition, a semantic–spatial complementary enhancement is introduced to promote collaboration between high-level semantic cues and low-level spatial details, thereby improving the representation and boundary localization of small salient objects. Experimental results on the UAV RGB-T 2400 dataset and an additional weakly aligned benchmark demonstrate that HAFM-Net achieves competitive performance and exhibits strong robustness in challenging scenarios, such as blur, illumination variation, small-object cases, and foggy conditions. Full article

Chronic kidney disease (CKD) remains a leading cause of premature mortality and global disease burden, yet the molecular mechanisms underlying its progression are still incompletely understood. Accumulating evidence highlights circadian disruption as an underappreciated driver of CKD that warrants systematic re-examination. The kidney harbors an autonomous circadian oscillator, principally regulated by the CLOCK:BMAL1 transcription factor complex, which coordinates glomerular filtration, tubular electrolyte handling, blood pressure rhythmicity, inflammatory tone, and cellular repair. In CKD, retained uremic toxins, sustained oxidative stress, and persistent NF-κB activation collectively suppress this clock machinery, generating a self-reinforcing cycle of renal injury and circadian dysregulation. CKD is also accompanied by progressive attenuation of nocturnal melatonin secretion, weakening a central hormonal cue for peripheral clock entrainment and cytoprotection. Melatonin acts both as a chronobiotic and as a pleiotropic cytoprotective molecule. Through MT1/MT2 receptors, the nuclear receptor RORα, and receptor-independent antioxidant pathways, it may enhance Nrf2/HO-1 signaling, restrain NF-κB and NLRP3 inflammasome activity, suppress TGF-β1/Smad2/3-mediated fibrogenesis, preserve mitochondrial integrity, and engage SIRT1-linked clock regulation. Current clinical studies suggest that nightly melatonin supplementation can improve sleep quality and selected oxidative or circadian surrogate endpoints in hemodialysis patients; however, whether melatonin slows CKD progression or preserves renal function remains unproven. This review synthesizes the molecular interface between circadian dysregulation and CKD progression and articulates a rationale for adequately powered clinical trials evaluating melatonin as a candidate chronotherapeutic adjunct rather than an established renoprotective therapy. Full article

Accurate tooth segmentation plays a crucial role in computer-aided dental diagnosis and treatment planning, particularly in applications such as tooth detection, lesion localization, orthodontic analysis, and implant surgery. However, panoramic dental X-ray images often suffer from tooth adhesion, low contrast, and blurred boundaries, making precise delineation difficult and potentially compromising downstream clinical analysis. To address these challenges, we propose a boundary-aware segmentation framework, termed Boundary-Aware ResU-Net (BA-ResUNet), which is built upon a ResU-Net backbone and enhanced with Coordinate Attention (CA) and explicit boundary modeling mechanisms. Specifically, CA modules are introduced into the encoder to improve spatial representation and positional awareness. In addition, a Boundary Extraction Module (BEM) is designed to capture boundary priors from shallow and deep features, while a Boundary Injection Module (BIM) progressively incorporates these cues into the decoder through foreground enhancement and background suppression. This design enables the network to better preserve inter-tooth gaps and improve boundary delineation. Experiments on the MICCAI STS-2D dental dataset demonstrate that the proposed method achieves superior performance in terms of Dice and IoU compared with representative existing methods. Ablation and qualitative analyses further show that CA and BEM/BIM play synergistic roles in improving regional overlap and boundary localization, particularly in challenging cases involving adhesion, low contrast, and indistinct contours. These results indicate that the proposed framework provides a reliable and effective solution for panoramic tooth segmentation and has promising potential for computer-aided dental applications. Full article

Multiple sclerosis (MS) is a neuro-inflammatory disease characterized by demyelination in the central nervous system (CNS). Although a substantial endogenous capacity for remyelination has been demonstrated, this process is frequently incomplete and exhibits marked intra- and inter-individual heterogeneity. Several factors influence the extent of spontaneous myelin regeneration, including age, sex, disease course, and lesion localization. Oligodendrocytes (OL), derived from oligodendrocyte progenitor cells (OPCs), are the principal myelinating cells of the CNS. The regenerative cascade involves several key stages, including OPC activation, recruitment, differentiation into oligodendrocytes (OL), and myelin deposition. This process is orchestrated in a spatiotemporal manner by a complex interplay of intracellular signaling pathways, genetic determinants, and dynamic microenvironmental cues, which together balance inhibitory and pro-remyelinating influences. Several lines of evidence indicate that chronically demyelinated axons are vulnerable to degeneration, whereas successful remyelination may confer neuroprotection. These observations underscore remyelination as a promising neuroprotective therapeutic target for preventing or slowing disability progression in MS, a condition in which gradual neuroaxonal degeneration is believed to underlie irreversible disability progression. In this review, we aim to bridge the gap between fundamental biological mechanisms of remyelination and their clinical relevance. We examine recent advances in in vivo techniques for assessing remyelination and discuss how these measures correlate with clinical and disability outcomes. In addition, we review recent clinical trials of remyelination-promoting therapies and analyze the challenges that have limited their advancement beyond phase II. Overall, we seek to provide a comprehensive overview of the remyelination process from bench to bedside, highlighting both the obstacles and the therapeutic potential of remyelination strategies in MS. Full article

Cross-material continuous tool wear prediction is difficult because a model must preserve the physical wear scale, not only align high-dimensional sensor features. This limitation is critical in milling, where the target variable is the continuous flank wear width ($\mathrm{VB}$) and material shift can distort the mapping from sensor response to wear magnitude. We address this problem by recasting cross-domain tool wear prediction as monotone wear-scale adaptation. We propose Multi-Physics Monotone Score Transport (MPMST), a monotone score transport framework that constructs a tool-wear-oriented score from sensor-derived candidate cues, transports the target-domain score onto the source-domain wear scale, and then predicts wear through isotonic regression. We also evaluate One-Physics Monotone Score Transport (OPMST), a force-only variant that uses the same score-transport pipeline with a restricted cue family. On Mondragon Unibertsitatea–Tool Condition Monitoring (MU-TCM) with two cross-material transfer tasks, the validation-driven MPMST configuration reduces mean absolute error by approximately 63% relative to Correlation Alignment (CORAL) and by approximately 31% relative to a physics-informed Gaussian process baseline. The results support monotone score construction and score transport as practical mechanisms for continuous tool wear prediction under domain shift, while also showing that MU-TCM is strongly force dominated. Full article

This paper studies geometric scene formalization in vision-based educational sensing environments, where textual conditions and geometric diagram images jointly constitute heterogeneous perceptual inputs. The goal is to convert multimodal sensed information into standardized formal representations for machine understandable educational analysis. Existing methods remain limited by unstable cross modal alignment, inadequate expression of geometric relational constraints, and insufficient verifiability of generated outputs. To overcome these challenges, a unified modeling framework is proposed based on multimodal large models with structure-aware prompting and verification feedback. A geometry-oriented structure prompt injection mechanism is first introduced to encode prior cues of geometric entities, relational patterns, and constraint dependencies, which enhances the intrinsic alignment among textual descriptions, visually sensed diagram regions, and formal symbolic representations. In addition, an external verification feedback strategy is employed to constrain and iteratively refine the initial outputs, thereby improving structural consistency, syntactic correctness, and target proposition accuracy. To support this task, a new vision-based multimodal geometry formalization dataset is further constructed for model training and evaluation. Extensive experiments show that the proposed method can more effectively accomplish the transformation from multimodal sensed educational inputs to executable formal expressions, while also demonstrating stronger robustness and reliability in complex visual conditions. These results indicate that the proposed framework offers a feasible solution for structured scene interpretation, automatic problem analysis, error diagnosis, and intelligent feedback in vision-based educational systems. Full article

Universities increasingly rely on digital self-service channels to manage high volumes of time-sensitive admissions enquiries. AI enabled chatbots represent a prominent solution because they can provide round-the-clock responses, standardize guidance, and potentially reduce uncertainty for applicants. Yet evidence on whether such chatbots meaningfully assist students and improve their satisfaction with admissions support remains limited in many developing higher education contexts. This quantitative study evaluates the perceived effectiveness of AI chatbots used for university admissions in Pakistan, with a focus on student assistance and satisfaction as key outcomes. Using a cross-sectional survey design, data were collected from students who had recently engaged with university admissions information services (e.g., website chat widgets, messaging-based virtual assistants, and admissions enquiry portals) across private universities in Pakistan. Admissions chatbot effectiveness was measured through established information systems and service quality constructs system quality (ease of use, responsiveness, accessibility), information quality (accuracy, clarity, completeness), and service quality and trust cues (assurance, privacy confidence, and appropriateness of conversational support). Student assistance captured the extent to which chatbot interactions helped participants complete admissions related tasks and navigate application procedures. Student satisfaction reflected overall evaluation of the admissions support experience. The results indicate a positive association between perceived chatbot quality and perceived student assistance, and a further positive association between student assistance and student satisfaction with admissions support. The overall pattern suggests that student assistance functions as a key mechanism through which chatbot effectiveness translates into satisfaction. At the same time, respondents highlighted limitations in resolving complex or exception based queries, emphasizing the importance of transparent escalation to human admissions staff. The study contributes context specific evidence from Pakistan and offers an empirically grounded framework that university administrators can use to evaluate and improve admissions chatbots. Practical implications emphasize maintaining accurate knowledge bases, designing clear handoff pathways, and implementing governance practices that strengthen students’ confidence in information reliability and data privacy. Full article