Search Results (1,442)

Digital technologies are widely viewed as important tools for enhancing corporate environmental performance. However, there is growing recognition that their environmental impacts are not uniformly positive and may even generate unintended negative consequences. Drawing on institutional theory and impression management theory, we argue that big data analytics (BDA) provides firms with powerful capabilities to strategically manage environmental impressions in response to external pressures. Using panel data of Chinese listed firms from 2012 to 2023, we provide empirical evidence that BDA significantly promotes corporate greenwashing. Specifically, BDA facilitates greenwashing through the reinforcement of three core dimensions of impression management: self-serving bias, symbolic management, and accounting rhetoric. Moreover, by distinguishing between different types of external pressures, our results show that constraint-based non-market pressures weaken the relationship between BDA and greenwashing, whereas opportunity-based market pressures strengthen it. Our study enriches the digitalization and corporate environmental performance literature by revealing the dark side of digital technologies and offering a more nuanced understanding of how specific technologies shape corporate environmental misconduct. Full article

Since their recognition as human pathogens in the 1970s, Campylobacter spp. have posed persistent challenges to microbiologists due to their fastidious growth requirements and environmental sensitivity. The continuous refinement of selective and differential culture media has been crucial for improving their detection, isolation, and characterization in both clinical and food microbiology. This comprehensive review provides a chronological overview of the evolution of Campylobacter culture media, highlighting the scientific milestones that shaped current cultivation practices—from early blood- and charcoal-based formulations to modern selective, chromogenic, and systems permitting incubation under less stringent atmospheric conditions. Emphasis is placed on the rationale behind medium composition, the transition from empirical experimentation to standardized formulations, and the integration of molecular and metabolic insights into media design. The evolution of Campylobacter growth media mirrors the broader trajectory of microbiology itself, moving from artisanal experimentation toward precision-driven innovation. Ongoing advancements in culture technology, including sustainable and data-guided formulations, will continue to enhance global surveillance, food safety, and pathogen ecology research. Full article

Yak daily behaviors, including feeding, standing, lying down, and walking, are closely related to their health status, making accurate behavior recognition essential for intelligent monitoring and management in yak husbandry. However, real-world grazing environments present significant challenges due to complex backgrounds, occlusions, small or distant targets, and high visual similarity between behavior categories. To address these issues, we propose a problem-driven, multi-scale behavior recognition framework based on an enhanced YOLOv11n architecture specifically designed for outdoor yak monitoring. A dedicated real-world dataset is constructed to capture four fundamental behaviors under diverse natural conditions. Based on this dataset, we develop the DPAP-YOLOv11n model, which incorporates Dynamic Convolution for adaptive feature modulation and Pinwheel-shaped Convolution (PConv) for fine-grained spatial representation. Additionally, a YOLOv7-Aux auxiliary training head is introduced to strengthen intermediate feature learning, and a Focal-PIoU loss function is adopted to improve robustness against hard or ambiguous samples. Experimental results show that DPAP-YOLOv11n outperforms the baseline YOLOv11n, achieving gains of 2.4% in mAP@50 and 2.8% in mAP@50–95. These findings demonstrate the practical potential of the proposed approach for high-precision, real-time yak behavior recognition in complex field environments. Full article

Developing an engineering identity is critical for supporting students’ engineering career pathways. Yet, migratory adolescents are often not afforded engineering experiences to support that identity formation. Early experiences in math and science often serve as gateways to engineering careers; examining students’ attitudes and beliefs in these subjects is essential to understanding identity formation. This study took an exploratory approach to examine how migratory adolescents’ math and science attitudes and beliefs, specifically their interest, recognition, and performance beliefs, contributed to developing an aspirational engineering identity. Mediation analysis was used to explore how math and science interest, recognition, and performance beliefs shaped the engineering identity formation of 227 migratory adolescents. Results show that math and science interest served as both a direct pathway to engineering identity and as the essential mediator linking performance beliefs and recognition to engineering identity development. Performance beliefs and recognition operated as interchangeable predictor variables but supported engineering identity through their influence on students’ interest in math and science. Multiple pathways emerged for fostering an engineering identity among migratory adolescents, rather than a singular path. These findings highlight the importance of cultivating math and science interest as a key mechanism for supporting engineering aspirations and informing future educational interventions for this underrepresented group. Full article

Study of early Chinese ceramics is crucial for understanding cultural, economic, and technological developments in Chinese history. With the evolving deep learning techniques, one urgent question would be, whether we can identify early Chinese ceramics by a simple 2D image without further domain knowledge. This work collected a highly diverse dataset for ancient Chinese ceramics from 15 dynasties, with 4 representative glaze colors and 15 shape types. We studied the performance of five state-of-the-art neural networks on two identification tasks: ceramic visual feature recognition and early Chinese ceramic dating. A class-imbalance learning strategy is designed to improve the models’ performance on multi-label tasks. To the best of our knowledge, our work is the first to introduce deep learning into early Chinese ceramic recognition on a large scale. Experiments prove that deep learning can recognize visual features like glaze and most shape types with high accuracy, while ceramic dating is feasible for the main dynasties but remains challenging along the overall history. Further quantitative assessment shows that cultural inheritance and artistic continuity can lead to reasonable false dating by classifying ceramics into adjacent dynasties or periods. Moreover, although domain knowledge is required for interpretation, deep learning shows great potential in recognizing even unlabeled time-relevant features, which can help study the inheritance and evolution of early Chinese ceramic development. Full article

This study examines how regional gastronomic identity is expressed through restaurant menus in the Okanagan Valley, designated in October 2025 as Canada’s first UNESCO City of Gastronomy. This article aims to assess the emergence of a regional gastronomic identity and the potential recognition of a signature dish. An exploratory sequential mixed-methods approach was used to collect data from 40 restaurants, where 283 main dishes were selected and analyzed. These data were coded primarily to identify recurring compositional structures and emerging ingredient patterns. Several recurring compositional templates appear across restaurants, structured around shared protein–starch–sauce configurations. Although they remain occasional, their repetition across restaurants points to the early formation of a recognizable gastronomic identity. This identity does not rely on a single signature dish but takes shape through shared dish structures that recur across menus. These patterns contribute to ongoing discussions in gastronomy tourism by showing how regional identity can develop through distributed and processual culinary practices. The study shows how menu analysis provides a valuable lens for understanding the development of such an identity in emerging gastronomic destinations. Full article

In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%, and 83.9%, respectively, with improvements of 3.4, 2.9, and 2.5 percentage points over the original model. Specifically, DySnakeConv increased mAP@50 by 0.6 percentage points, DAttention improved mAP@50 by 0.3 percentage points, and HSFPN contributed to a 0.9 percentage point rise in mAP@50. This patrol system can effectively identify and visualize various pollutants in river and lake areas, such as underwater waste, water quality pollution, illegal swimming and fishing, and the “Four Chaos” issues, providing technical support for intelligent river and lake management. Full article

Osteomyelitis (OM), an inflammatory condition of the bone tissue, is a complex orthopedic condition marked by chronic inflammation, diagnostic uncertainty, and recurrent infections. Despite standard treatments—including surgical debridement, antimicrobial therapy, and bone reconstruction—many patients continue to experience recurrence and treatment failure. Growing molecular evidence indicates that host genetic factors play a crucial role in shaping immune responses and influencing disease progression in OM. This narrative review synthesizes current knowledge from candidate gene single-nucleotide polymorphism (SNP) association studies to illustrate how specific genetic variations contribute to OM pathogenesis, diagnostic refinement, and treatment outcomes. We examined key immunogenetic variants within genes involved in inflammatory signaling, pathogen recognition, and neutrophil regulation. Our synthesis identifies a landscape of pro-inflammatory SNPs, such as IL-1β rs16944 and NLRP3 rs10754558, that are associated with increased susceptibility to chronic or post-traumatic OM, as well as SNPs that are associated with protective effects that may favor infection resolution, such as within the NOS2 and VDR genes. These SNP-driven differences in inflammasome activity, cytokine pathways, and oxidative stress responses highlight emerging opportunities for individualized therapeutic strategies. This review consolidates these variants, providing a genetic framework to analyze host susceptibility and differentiating high risk from protective genetic profiles. Integrating genomic insights into OM management represents a promising shift toward personalized medicine, enhancing diagnostic precision, informing targeted interventions, and improving prognostic assessment. Continued large-scale validation of candidate SNPs and translational genomic models will be essential to support their future clinical application. Full article

Background/Objectives: The present study examined the effect of age on sentence recognition in noise in different noise conditions among adults with normal hearing sensitivity throughout the adult lifespan. Methods: A total of 113 adults aged between 21 and 65 years participated in the study; based on age, they were categorized into five groups. The sentence recognition was assessed in five noise conditions: speech-shaped noise (SSN), amplitude-modulated speech-shaped noise (AM-SSN), two-male-talker babble (2MB), four-male-talker babble (4MB), and four-female-talker babble (4FB). The sentences were presented at a signal-to-noise ratio of −5 dB in all noise conditions. Results: The sentence recognition scores declined with increasing age in all noise conditions. In addition, age had a differential effect on the sentence recognition scores in the AM-SSN and 2MB conditions compared with the SSN, 4MB, and 4FB conditions. In the AM-SSN and 2MB conditions, the scores were significantly different in the fourth decade compared with young adults. In other noises, the scores were significantly different after 30 years compared with younger adults. Further, across noise conditions, greater scores were obtained in the AM-SSN and 2MB conditions, and the lowest scores were obtained in the 4FB condition. Partial Spearman correlations revealed a moderate-to-strong negative correlation between age and sentence recognition scores across noise conditions. Conclusions: The findings of the present study showed that sentence recognition is negatively affected by age. In addition, age has a differential effect on sentence recognition in different noises. Full article

Increasing recognition of food as a sustainable tourism product has led to further interest in how it can impact tourist experiences. This study examined the relationships between key constructs of food tourism experiences by utilizing the stimulus–organism–response (S-O-R) framework. Through an examination of perceived food authenticity, perceived restaurant quality, place attachment, tourist satisfaction, and destination loyalty, this research explored these constructs in a food tourism context. Data from food tourists in China were analyzed using confirmatory factor analysis and structural equation modeling to test seven hypotheses. Results indicated that perceived local food authenticity influenced perceived restaurant quality, place attachment, and satisfaction. Perceived restaurant quality and place attachment also influenced satisfaction, while place attachment and satisfaction influenced destination loyalty. The findings confirmed all tested hypotheses, supporting the construct relationships indicated by the S-O-R framework and demonstrating how external stimuli and internal dynamics shape responses in a food tourism context. The findings underscore that authentic food tourism experiences can positively influence tourist perceptions, satisfaction, and loyalty. This has implications for destination sustainability, as authentic food tourism experiences can help to preserve cultural traditions and provide economic benefits to destination communities. Full article

Background/Objectives: The earlier, more accurate, and more consistent prediction of the brain tumor recognition process requires automated systems to minimize diagnostic delays and human error. The automated system provides a platform for handling large medical images, speeding up clinical decision-making. However, the existing system is facing difficulties due to the high variability in tumor location, size, and shape, which leads to segmentation complexity. In addition, glioma-related tumors infiltrate the brain tissues, making it challenging to identify the exact tumor region. Method: The above-identified research difficulties are overcome by applying the Swin-UNet with cuttlefish-optimized attention-based Graph Neural Networks (SCAG-Net), thereby improving overall brain tumor recognition accuracy. This integrated approach is utilized to address infiltrative gliomas, tumor variability, and feature redundancy issues by improving diagnostic efficiency. Initially, the collected MRI images are processed using the Swin-UNet approach to identify the region, minimizing prediction error robustly. The region’s features are explored utilizing the cuttlefish algorithm, which minimizes redundant features and speeds up classification by improving accuracy. The selected features are further processed using the attention graph network, which handles structural and heterogeneous information across multiple layers, improving classification accuracy compared to existing methods. Results: The efficiency of the system, implemented with the help of public datasets such as BRATS 2018, BRATS 2019, BRATS 2020, and Figshare is ensured by the proposed SCAG-Net approach, which achieves maximum recognition accuracy. The proposed system achieved a Dice coefficient of 0.989, an Intersection over Union of 0.969, and a classification accuracy of 0.992. This performance surpassed the most recent benchmark models by margins of 1.0% to 1.8% and with statistically significant differences (p < 0.05). These findings present a statistically validated, computationally efficient, clinically deployable framework. Conclusions: The effective analysis of MRI complex structures is used in medical applications and clinical analysis. The proposed SCAG-Net framework significantly improves brain tumor recognition by addressing tumor heterogeneity and infiltrative gliomas using MRI images. The proposed approach provides a robust, efficient, and clinically deployable solution for brain tumor recognition from MRI images, supporting accurate and rapid diagnosis while maintaining expert-level performance. Full article

The rapid and accurate detection of SARS-CoV-2 biomarkers remains a critical requirement for effective outbreak control and decentralized diagnostics. Although RT-PCR is the current gold standard, its reliance on centralized laboratories and long processing times limits its applicability in point-of-care settings. In this context, optical biosensing platforms based on surface plasmon resonance (SPR) offer attractive features, including label-free, real-time, and quantitative detection. This study explores the use of synthetic receptors for the highly sensitive detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Specifically, soft molecularly imprinted polymer nanoparticles (nanoMIPs) were employed as synthetic receptors and integrated into a high-sensitivity, portable plasmonic platform based on a D-shaped plastic optical fiber (POF) SPR sensor. The nanoMIPs were selectively imprinted against the RBD, characterized by Dynamic Light Scattering (DLS), Isothermal Titration Calorimetry (ITC), and Scanning Electron Microscopy (SEM) to confirm nanoMIPs size, binding properties, and surface morphology. Next, the nanoMIPs were immobilized onto a gold-coated sensing surface, enabling enhanced specificity, affinity, and signal amplification compared to conventional biological recognition elements. The resulting RBD-SPR-nanoMIPs sensor demonstrated promising analytical performance, exhibiting high selectivity against potentially interfering proteins and an anticipated sensitivity suitable for RBD detection at femtomolar concentrations. The inherent stability of nanoMIPs suggests the potential for reusable SPR sensing platforms, paving the way for next-generation synthetic receptor-based plasmonic biosensors. Full article

Steel defects, stemming from issues like raw material imperfections and processing inconsistencies, present substantial challenges for the material’s effective use and subsequent manufacturing. Consequently, the real-time, accurate, and rapid detection of these defects is paramount in production, playing a vital role in cost reduction, efficiency enhancement, and resource conservation. To address these needs, this paper proposes a deep deep-learning-based image recognition method for defect detection using YOLOv7 (You Only Look Once), designated YOLOv7-SGS. This approach introduces a novel architecture, the YOLOv7-SGS network, which builds upon the standard YOLOv7. The enhancements include integrating a Shape-IoU model into the core backbone, innovatively incorporating an SGE attention mechanism, and refining the convolution algorithm with GSConv to boost model performance. The resulting YOLOv7-SGS model achieves an absolute 6% improvement in mAP@0.5 compared to the baseline model. Moreover, it attains a detection speed of 32 FPS, showcasing significant advantages and offering valuable insights for future research and practical applications. Full article

Background/Objectives: Shared decision-making is a central principle in pediatric practice, yet its implementation becomes challenging in the context of alternative family configurations. Stepparents have substantial caregiving roles, but Belgian legislation does not include them in medical information or decision-making authority, creating a gap between legal frameworks and clinical realities. The objective of this study was to explore pediatricians’ perspectives on the involvement of stepparents in medical information sharing and decision-making for minors, and to identify factors influencing whether and how stepparents are included. Methods: A qualitative study was conducted using six semi-structured focus group interviews with 30 pediatricians from six hospitals across Flanders, Belgium. Participants were purposively sampled based on clinical experience. The interviews explored experiences with consent, confidentiality, and stepparent involvement in pediatric care. Data were audio-recorded, transcribed verbatim, and analyzed using constant comparative analysis to identify overarching themes. Results: Three overarching themes emerged. First, the medical context strongly shaped decisions: medical information and minor decision-making were frequently shared, while major decision-making often involved consultation with the legal guardian. Second, relational dynamics, including the quality of the stepparent–child relationship, co-parenting conflict, and physicians’ intuitive assessments, influenced the extent to which stepparents were involved. Third, vulnerability was a recurring theme across all actors: physicians felt legally exposed, children risked fragmented care, legal guardians feared loss of control, and stepparents lacked recognition despite significant caregiving roles. Conclusions: This study shows the importance of a better alignment between clinical practice and legal reality. Aligning legal frameworks with contemporary family patterns may support more consistent, child-centered decision-making in pediatric practice. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article