Search Results (107,382)

Sustainable agritourism has been raised as a vital ally for rural development, green preservation, and experiential tourism enrichment. However, guests’ behavioral intentions in the agritourism context are regularly shaped not only by sustainability concerns but also by nostalgic ties to rural life and traditional farming practices. This study explored how pro-environmental behavior (PEB) and intrinsic motivation can influence visitors’ revisit and recommendation intentions in agritourism settings, while testing the moderating effects of personal nostalgia. Based on Self-Determination Theory (SDT) and the PEB literature, this study assumes that visitors who are internally driven by learning, enjoyment, and personal achievement, as well as those who exhibit environmentally accountable orientations, are more likely to develop favorable revisit intentions toward agritourism places. Data was collected from 420 visitors to agritourism sites using a self-administered questionnaire and tested using PLS-SEM. The results revealed that both intrinsic motivation and PEB have significant positive impacts on revisit and recommendation intentions. Furthermore, personal nostalgia can intensify these relationships. The study can contribute to the sustainable tourism and agritourism literature by emphasizing the joint roles of internal motivation, PEB, and emotional bond in reshaping visitors’ revisit intention and positive word of mouth. Full article

The global and national skills shortages, shifting employee work attitudes post-COVID pandemic, and the presence of a multigenerational workforce with diverse needs and preferences have sparked interest in employee retention. Traditional one-size-fits-all retention strategies are becoming less effective, and contemporary organisations are focusing on tailored retention strategies. The effectiveness of the tailored retention strategy does not only rely on its design but also on how it is perceived and experienced by employees. However, few studies have explored employees’ perceptions of their organisation’s employee retention strategy in the South African context. Hence, the objective of this study is to explore professional engineers’ perceptions of their organisation’s employee retention strategy and how these perceptions influence their intention to stay or leave the organisation. A qualitative research approach underpinned by the constructivism paradigm was employed in this study. A single case study was adopted, and data were collected through semi-structured interviews with 12 professional engineers working at a manufacturing organisation participating in the study. Thematic analysis was used to analyse the data. The findings indicated that the professional engineers were unaware of, and did not fully understand, their organisation’s employee retention strategy, and they felt that their organisation was not adequately implementing a robust, dynamic one, which resulted in high turnover. They indicated that the retention strategy seemed to lack provisions for career growth opportunities and formal mentorship programs and failed to embrace technological advancement, which influenced engineers to leave the organisation. They perceived that their organisation provided competitive compensation, onboarding, and offboarding, as well as training and development, though implementation gaps existed. This study suggests that organisations should develop a robust, dynamic employee retention strategy and widely communicate it to their workforce. A robust, well-communicated employee retention strategy is likely to positively influence employee perceptions and enhance the organisation’s employer brand, thereby facilitating retention. Full article

This paper investigates the impact of a regional innovation-driven platform (Qinchuangyuan Innovation-driven Platform) on entrepreneurial confidence, particularly in technology-based small and medium-sized enterprises (TSMEs) during their start-up period. By analyzing data collected from 132 TSMEs, this study explores how regional innovation-driven platforms influence entrepreneurial confidence. The main findings are as follows: First, the results of ordinary least squares (OLS) regression reveal that the innovation-driven platform significantly improves entrepreneurial confidence, and the results of propensity score matching (PSM) remain still positive. Second, we conduct instrumental variable (IV) estimation as supplementary robustness evidence for potential endogeneity concerns, using whether an enterprise participates in market expansion activities and whether an enterprise uses government support services as two instrumental variables. Third, the innovation-driven platform is mediated by entrepreneurial satisfaction with the business environment and entrepreneurial satisfaction with the government, thereby enhancing entrepreneurial confidence. This paper provides a new perspective for assessing business development through entrepreneurial confidence rather than traditional performance metrics and provides a valuable reference for the development and optimization of innovation-driven platforms in similar regional contexts, especially in supporting sustained entrepreneurial activity, technology transformation, and regional economic resilience. Full article

Monopolar radiofrequency (MRF) is a well-established modality for non-invasive facial rejuvenation; however, its clinical utility is frequently constrained by patient discomfort and inconsistent thermal delivery. This study evaluated the efficacy, safety, and mechanistic profile of a novel MRF system incorporating continuous water cooling (RF-CWC) designed to optimize thermal distribution and enhance patient tolerance. In a prospective, single-arm clinical trial involving 22 female participants, a single RF-CWC treatment utilizing region-specific static and sliding delivery modes yielded statistically significant improvements in jawline lifting, alongside a volumetric increase in the midface and a concomitant volumetric reduction in the lower face (p < 0.001) over an 8-week follow-up period, with no adverse events reported. To elucidate the underlying cellular mechanisms, the system was further evaluated using an ultraviolet B (UVB)-induced ex vivo human skin model and an in vivo porcine model. Histological, immunohistochemical, and ELISA analyses revealed that RF-CWC effectively mitigated UVB-induced dermal degradation ex vivo by significantly up-regulating elastin, insulin-like growth factor, and hyaluronic acid, while down-regulating matrix metalloproteinase-1, interleukin-1α, and heat shock protein 72 (p < 0.05). Furthermore, the in vivo model demonstrated time-dependent increases in collagen types I and III and elastin without thermal tissue damage, with the sliding mode and higher shot counts correlating with enhanced extracellular matrix (ECM) remodeling. Comparative analyses demonstrated that RF-CWC achieved superior ECM restoration and reduced inflammatory cell infiltration relative to traditional cryogen spray-cooled RF systems. Taken together, these findings suggest that the RF-CWC system may promote robust ECM remodeling and significant facial neocollagenesis while minimizing inflammatory responses, potentially presenting an optimized, highly effective, and patient-friendly advancement in MRF technology. Full article

Movie genre classification is a significant challenge in narrative analysis, as traditional methods often fail to capture complex structural relationships within movie stories. This study introduces the Intra-Cluster Weighted Movie Network (ICWMN), a novel framework designed to improve classification by using intra-movie relationships through Graph Neural Networks (GNNs). We constructed a large-scale dataset of 1631 movie character networks using an automated pipeline comprising web scraping, regular expressions, and fine-tuned BERT models for entity recognition. To address the computational limitations of fully connected models, we partition ICWMN into clusters and establish edges only between the k-most similar nodes using the K-Nearest Neighbor algorithm and various distance measures, such as the Laplacian and NetLSD. XGBoost is applied to optimize high-dimensional node feature vectors. Experimental results demonstrate outstanding performance, with the Graph Attention Network (GAT) emerging as the top-performing architecture, resulting in classification accuracies that peak at $95.00\%$ on our 1631-movie dataset and an exceptional $97.30\%$ on the 773-movie Moviegalaxies dataset. These findings confirm that prioritizing spectral properties and cluster-based network topologies significantly improve the precision and stability of genre classification compared to state-of-the-art methods. Full article

Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. Full article

Small, dispersed, and vegetated creeping landslides in hilly areas of eastern China hinder traditional remote sensing and detection. To address this, this study takes Yixian County (Anhui Province) as a representative area. Based on high-resolution GF-2 satellite images, three deep learning models embedded with the Squeeze-and-Excitation (SE) attention mechanism (ResNet18-SE, VGG13-SE, UNet-SE) were developed and compared with the original UNet model. Combined with field investigation, landslide mapping and accuracy assessment were conducted to evaluate the feature extraction capabilities of four models. The results indicate that the UNet-SE model achieved optimal performance (Precision: 0.911, Recall: 0.685, F1-score: 0.782, Kappa: 0.730, IoU: 0.643). Its F1-score exceeds ResNet18-SE, VGG13-SE, and the original UNet by 8%, 3%, and 5%, respectively, proving superior regional adaptability and generalization performance. Additional verification on creeping landslides in Kecun Town (Yixian County) and post-earthquake landslides in Lushan County (Sichuan Province) further confirms the reliability of the UNet-SE model. Furthermore, Frequency Ratio (FR), Random Forest (RF), and SHapley Additive exPlanations (SHAP) were adopted to reveal the correlation between landslide occurrence and seven geological-environmental factors. Landslides are most susceptible to develop at elevations of 400–500 m, NDVI values of 0.4–0.5, slopes below 10°, east and northeast aspects, 300–500 m away from rivers, 500–1000 m away from faults, and areas covered by soft sedimentary lithology. Distance from faults, distance from rivers, and elevation are identified as the three favorable conditional factors. In conclusion, the proposed landslide detection framework can provide reliable spatial data and technical references for regional geological hazard prevention, ecological conservation and sustainable development in hilly areas. Full article

Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) is an emerging intervention that provides a minimally invasive approach to drainage of the gallbladder, showing promising results in treating acute cholecystitis (AC) and malignant biliary obstruction (MBO). This review summarizes the current applications of EUS-GBD and compares its clinical effectiveness with traditional methods such as percutaneous transhepatic gallbladder drainage (PT-GBD) and endoscopic transpapillary gallbladder drainage (ET-GBD). Available evidence suggests that EUS-GBD may offer potential advantages in terms of success rates and complication profiles, particularly in patients who are not candidates for surgery or those at high surgical risk. The method is effective in reducing inflammation, alleviating symptoms from obstruction, and improving patient quality of life. This article also discusses the technical evolution of EUS-GBD, its indications, complications, and its comparative advantages over other drainage techniques. These observations suggest that EUS-GBD may represent a valuable addition to the therapeutic armamentarium for selected high-risk patients. Full article

This article re-examines the formation of early Dilun Buddhism in Luoyang era Northern Wei China by foregrounding the role of the Mahāparinirvāṇa Sūtra (Ch. Da banniepan jing). While previous scholarship has emphasized Yogācāra-related texts such as the Daśabhūmika-śāstra (Ch. Shidi jing lun), this study argues that the Da banniepan jing constituted an important component of the intellectual and exegetical environment within which early Dilun thought emerged. Drawing on prosopographical evidence, this article examines the recurrent presence of the Da banniepan jing among early Dilun-associated figures and situates it within the broader intellectual environment of early Dilun Buddhism. It further proposes that Nirvāṇa-related doctrinal concepts may have provided one important conceptual framework through which the graded bodhisattva path was interpreted: the concept of universal Buddha-nature renders progressive cultivation intelligible as the gradual actualization of an inherent potential. The study also situates this doctrinal convergence within the broader hermeneutical culture of Northern Buddhism, characterized by text-centered exegesis, and traces several layers of interaction among court patronage, monastic scholarship, and lay devotion in which Nirvāṇa-related learning remained visible. On this basis, the formation of early Dilun Buddhism is associated with a Northern scholastic culture in which Nirvāṇa-related learning constituted an important intellectual layer. Full article

The growing diffusion of dealcoholized wines calls for a deeper understanding of how information and sensory evaluation jointly shape consumer acceptance, particularly in traditional wine markets. This study investigates the effects of different combinations of information and tasting on sensory evaluation and willingness-to-pay for a partially dealcoholized red wine. A between-subjects experiment was conducted during a scientific festival in Apulia (Italy), where participants were assigned to three conditions (INFO-SENS, SENS, INFO) and evaluated a non-commercialized Apulian Primitivo (1.5% ABV), produced through low-temperature vacuum evaporation and not pasteurized. Sensory attributes (visual, olfactory, gustatory, overall) were rated on 9-point hedonic scales, and WTP for a 125 mL glass was elicited using a payment card. The results show that the information-only group (INFO) reported the highest WTP, compared to the tasting-only group (SENS). Information exposure increased visual and olfactory evaluations, but not gustatory ratings. Prior knowledge of NoLo wines was associated with negative expectations, though this effect was attenuated by information. Overall, within this experimental setting, information emerged as a key driver of perceived value outweighing sensory liking, although the two remained positively correlated. These findings highlight the importance of transparent communication in fostering acceptance and repositioning dealcoholized wine as a credible category within traditional markets. Full article

Despite the proliferation of digital integration initiatives, many Small and Medium-sized Enterprises (SMEs) remain trapped in a persistent “Conversion Gap,” where digital adoption fails to manifest as tangible financial performance. Grounded in Resource Conversion Theory, this study anatomizes the structural bottlenecks of this process through a multi-stage Causal AI architecture. Utilizing time-lagged data from 649 SMEs to control for endogeneity, I integrate Gaussian Mixture Modeling (GMM), Tiered Grand-DAG algorithms, and Necessary Condition Analysis (NCA) to decode the non-linear trajectories of value realization. The findings identify a “Low Integration” cohort (34.2%) that fails to translate digital usage into realized outcomes due to a severe deficit in Absorptive Capacity (ACAP). Crucially, NCA diagnostics reveal that ‘perceived usefulness’ serves merely as a necessary baseline condition, whereas ‘user satisfaction’ functions as the primary catalyst for value conversion. Furthermore, multi-group analysis (MGA) confirms that for the most vulnerable SMEs, the causal pathway to revenue is structurally severed (β = 0.000), rendering traditional, linear training interventions ineffective. I propose a fundamental shift toward data-driven, targeted interventions to address these specific structural barriers and facilitate sustainable digital value creation in the SME ecosystem. Full article

Recent studies indicate that ferroptosis shows unique advantages in oncotherapy, particularly in reversing multidrug resistance (MDR). Despite current therapeutic advancements, the treatment of high-incidence malignancies with dismal prognoses continues to face challenges, including limited clinical efficacy, significant side effects, and drug resistance. In recent years, Chinese herbal medicine (CHM) has gained increasing attention in anti-tumor therapy. CHM bioactive components are highly effective in inducing tumor cell ferroptosis, inhibiting tumor proliferation and migration, and reversing drug resistance. Additionally, some components can protect normal cells and improve the tumor microenvironment. This review systematically summarizes the regulatory roles of various CHM bioactive components in ferroptosis across common human cancers. We further analyze the underlying molecular mechanisms, focusing on the modulation of key regulatory targets (e.g., GPX4, SLC7A11, and Nrf2) and critical signaling cascades (e.g., PI3K/AKT/mTOR and p53). Furthermore, the differential effects of bioactive compounds from CHM on common tumors were evaluated, alongside their potential in combination therapy. This review provides a theoretical foundation for the development of novel anticancer drugs targeting ferroptosis regulation and offers new perspectives for the clinical application of CHM in oncology. Full article

In the localization of a distributed radar cluster under far-field and short-baseline conditions with a configuration that varies with time, traditional translational maneuvering strategies have limited capability to expand the observation angle. This makes it difficult to effectively improve the observation geometry, and can easily lead to the amplification of localization errors. Targeting this problem, this work proposes a localization method with configuration adjustment based on Fisher information matrix (FIM) prediction. Without requiring prior information of the true target position, the proposed method predicts the FIMs of candidate configurations based on the current target position estimate. It evaluates these configurations by minimizing the localization uncertainty along the worst direction, thereby enabling adaptive adjustment of the cluster configuration. Furthermore, an in-place rotational strategy is introduced to enhance angular diversity, and a Gauss–Newton iterative solution is developed by incorporating temporal prior information to improve the stability of nonlinear localization. Simulation results show that the proposed method can effectively improve the observation geometry under far-field and short-baseline conditions, and reduce abnormal jumps caused by noise. Compared with the traditional translational maneuvering strategy, the proposed method reduces the localization error by more than 70%. Full article

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

PM2.5 and near-surface O₃ compound pollution is a major challenge for further air quality improvement in the Yangtze River Delta (YRD). Despite research on the chemical coupling mechanisms and concentration co-variation between PM2.5 and O₃, the directional linkages of compound pollution events among cities and the network mechanisms underlying their formation remain unclear. Here, we identified PM2.5–O₃ compound pollution events for 41 YRD cities from 2015 to 2024 using city-year-specific P80 dual-threshold criteria. We then constructed annual directed synchronization networks based on event-leading relationships and used temporal exponential random graph models to identify the formation mechanisms of significant leading ties. PM2.5–O₃ compound pollution events in the YRD generally decreased during 2015–2024, with characteristics shifting from high frequency, persistence, and strong intercity linkage in the early stage to lower frequency, weaker intensity, and continued episodic fluctuations. Directed event networks exhibited a clear stage-dependent evolution: network density, total edge weight, reciprocity, and local closure were relatively high during 2015–2018, networks became markedly sparse during 2020–2022, and a partial rebound occurred after 2023. Spatial backbone analysis indicated reorganization of the dominant linkage structure, shifting from the Shanghai–southern Jiangsu–northern Zhejiang coastal core toward the northern Jiangsu, Anhui, and interprovincial corridors. Key node analysis further revealed a clear functional differentiation among cities, with some cities acting as potential leading sources, some as receiving nodes, and several non-traditional core cities serving as cross-regional bridges. Significant leading ties were jointly shaped by reciprocity, local closures, temporal memory, economic development, industrial structure, and digital governance. Therefore, as well as a problem of co-occurrence, PM2.5–O₃ compound pollution in the YRD is a cross-city event-network process characterized by directionality, stage-dependent evolution, and differentiated urban roles. This study provides empirical evidence for dynamic joint prevention and control based on event linkages, urban roles, and cross-city coordination. Full article