Search Results (1,812)

This study explores the practice of “soundtalking”, a novel method of participant-led sound practice, across the Dee Estuary in the UK. Over the course of twelve months, the Our Dee Estuary Project facilitated monthly meetings where participants engaged in sound workshops, in-depth discussions, and sound-making activities, with the aim of fostering a deeper connection with both their local and sonic environments. This longitudinal practice-based research study created an environment of sonic learning and listening development, documenting how participants’ interactions and narratives both shape and are shaped by the estuarial environment, its soundscape, and their sense of place. Participant-led conversations formed the basis of the methodology, providing rich qualitative data on how individuals perceive, interpret, and interact with their surroundings and the impact that the soundscape has on the individual. The regular and unstructured discussions revealed the intrinsic value of soundscapes in participants’ lives, emphasising themes of memory, reflection, place attachment, environmental awareness, and well-being. The collaborative nature of the project allowed for the co-creation of a film and a radio soundscape, both of which serve as significant outputs, encapsulating the auditory and emotional essence of the estuary. The study’s initial findings indicate that “soundtalking” as a practice not only enhances participants’ auditory perception but also fosters a sense of community and belonging. The regularity of monthly meetings facilitated the development of a shared acoustic vocabulary and experience among participants, which in turn enriched their collective and individual experiences of the estuary. Soundtalking is proposed as an additional tool in the study of soundscapes to complement and extend more commonly implemented methods, such as soundwalking and soundsitting. Soundtalking demonstrates the efficacy of longitudinal, participant-led approaches in capturing the dynamic and lived experiences of soundscapes and their associated environments, over methods that only create fleeting short-term engagements with the soundscape. In conclusion, the Our Dee Estuary Project demonstrates the transformative potential of soundtalking in deepening our understanding of human–environment interactions and, in addition, has shown that there are both health and well-being aspects that arise from the practice. Beyond this, the project has output a film and a radio sound piece, which not only document but also celebrate the intricate and evolving relationship between the participants and the estuarine soundscape, offering valuable insights for future soundscape research and community engagement initiatives. Full article

The high-elevation ecosystems of the Tibetan Plateau provide crucial ecosystem services including watershed protection and water provision for downstream human and wildlife communities. Thus, understanding the relationship between soil properties and vegetation under different management regimes is important as a warming climate alters these systems. This study assessed vegetation cover, quantified the distribution of soil nutrients, and examined the relationships among soil chemical properties and plant cover in the high-elevation shrublands (3300 to 3700 m) in the Qilian Mountains on the northeastern Tibetan Plateau of China. These vegetation surveys and soil sample collections were conducted on 15 shrubland plots at different soil depths and soil chemical properties were investigated at each elevation. The content of soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), and available potassium (AK) fluctuated along the elevational gradient, while soil pH was close to neutral (pH 7.4). At our sites, SOM and TN contents generally increased with elevation, and AK was positively correlated with Salix plant cover. Using PCA, we determined that PC1 captured 43% of the total variance, and SOM and TN were the top contributing features. As climate in the region warms and precipitation becomes more variable, understanding the current soil–vegetation equilibria and how vegetation may migrate in future years is important to predicting changes in this region, especially at high elevations. From a managerial perspective, our goal was to provide additional information for restoring and managing subalpine and alpine shrubland vegetation in the Qilian Mountains to ensure the future sustainable use of these systems. Full article

Sinibotia species, investigated for morphology and species divergence owing to comparable body patterns and frequent sympatric occurrences, show high morphological similarity and close phylogenetic relationships, which challenge their accurate distinguishing via conventional morphological methods. Hence, multivariate morphometric (MM) and geometric morphometric (GM) analyses were used to assess the morphological differences between Sinibotia species (S. superciliaris, S. reevesae, S. robusta, S. pulchra, and S. zebra) habiting the Tuo River (Zizhong County) and Li and Lipu Rivers (Pingle County) based on 40 morphological traits and 34 landmarks. The morphological traits of S. robusta contrasted with those of S. pulchra and S. zebra, whereas S. superciliaris and S. reevesae showed similar morphologies, consistent with the cluster results. MM analysis using discriminant function analysis along with GM methods such as canonical variate analysis and relative distortion analysis enabled the differentiation between the Sinibotia species. Morphological variations were primarily reflected in snout length, nasal snout distance, head depth, body depth, caudal fin length, and dorsal fin length. MM effectively quantified linear size differences, whereas GM better captured and visualized complex variations in overall shape. The combined morphological evidence presented in this study contributes significantly to the identification of species, phylogenetic relationships, and ecological adaptations of Sinibotia species, thereby strengthening the theoretical rationale for the conservation and sustainable utilization of this genus. Full article

Designing an accessible, financially viable healthcare system is a key challenge for society. The value-based healthcare (VBHC) strategic model aims to simultaneously improve the quality of healthcare and the efficiency of health systems. The aim of this research was to describe the perceptions of different stakeholders in the Portuguese health industry about the creation of value and the understanding of VBHC as a competitive advantage. A qualitative study was conducted using the inductive method of Braun and Clarke, designed according to the COREQ criteria. Based on the results of the literature review, a semi-structured script for an interview was created, consisting of eight questions. The initial interview script was based on a thorough narrative literature review and tested with two professionals with practical experience in VBHC. The final version of the semi-structured interview guide consisted of eight open-ended questions. The questions were designed to elicit in-depth, reflective responses, and their neutrality was reviewed to avoid leading language that might introduce bias. As the interviews progressed, minor iterative changes were made to include participant-suggested additions, always maintaining alignment with the research objectives. This iterative process was essential to capture the nuanced perspectives of stakeholders and conformed to COREQ standards for qualitative research. A total of 15 stakeholders in VBHC were interviewed. The interviews were transcribed and coded, and 605 codes were created, divided into subthemes and themes. VBHC implementation faces several challenges, requiring a collaborative effort by the stakeholders involved, to achieve a comprehensive vision of value and appropriate multi-stakeholder alignment. The implementation of VBHC can confer a sustainable competitive advantage, and its adoption as a strategic model will be inevitable in the future. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

Against the dual backdrop of iterative AI advancement and deepening green development imperatives, AI-driven industrial intelligence (INT) has emerged as a pivotal force in driving sustainable economic growth. While the existing literature has explored the correlation between INT and green total factor productivity (GTFP), significant gaps remain in the design of multidimensional variables, analysis of environmental regulation (ER), and capture of dynamic effects. From the perspective of ER, this study utilizes provincial panel data from China (2012–2023) to construct an 11-indicator evaluation system for INT development and employs the EBM super-efficiency model to measure GTFP. Furthermore, a two-way fixed effects model combined with a moderated mediation model is established to systematically elucidate the intrinsic linkage mechanism between INT and GTFP. The key findings are as follows: First, INT has a significant positive impact on GTFP. Second, green innovation and spatio-economic synergy are crucial pathways through which INT empowers GTFP. Third, ER exhibits a substitutive effect within both the direct and indirect impacts of INT on GTFP, where intensified ER significantly attenuates INT’s positive impacts. Fourth, the enhancement effect of INT on GTFP remains statistically significant with a one-year lag, and the substitution effect of ER persists. This study provides an in-depth analysis of the mechanisms of INT-driven green economic transformation, offering valuable insights for governments to implement differentiated environmental governance strategies tailored to local conditions. Full article

Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose DAR-MDE, a novel framework that combines an autoencoder backbone with a Multi-Scale Feature Aggregation (MSFA) module and a Refining Attention Network (RAN). The MSFA module enables the model to capture geometric details across multiple resolutions, while the RAN enhances depth predictions by attending to structurally important regions guided by depth-feature similarity. We also introduce a multi-scale loss based on curvilinear saliency to improve edge-aware supervision and depth continuity. The proposed model achieves robust and accurate depth estimation across varying object scales, cluttered scenes, and weak-texture regions. We evaluated DAR-MDE on the NYU Depth v2, SUN RGB-D, and Make3D datasets, demonstrating competitive accuracy and real-time inference speeds (19 ms per image) without relying on auxiliary sensors. Our method achieves a $\delta$ < 1.25 accuracy of 87.25% and a relative error of 0.113 on NYU Depth v2, outperforming several recent state-of-the-art models. Our approach highlights the potential of lightweight RGB-only depth estimation models for real-world deployment in robotics and scene understanding. Full article

As the next generation of time–space infrastructure, low-earth-orbit navigation augmentation (LEO-NA) technology has become a hot research topic, since it can overcome the vulnerabilities and limitations of global navigation satellite systems (GNSSs). Meanwhile, a LEO-NA signal can serve as a better cooperative illuminator to build more powerful passive radar (PR). This paper proposes and investigates a new and promising PR system, LEO-NA signal-based PR (LNAS-PR), which utilizes LEO-NA signals as the illuminator and utilizes an unmanned aerial vehicle (UAV) to carry the receiver. Taking advantage of higher landing power and global coverage, LNAS-PR can be used to detect maritime targets with benefits of low cost and high efficiency. However, new technical challenges of information capture and processing need to be dealt with. Therefore, this paper presents the system model, signal model, and performance analyses within a maritime monitoring scenario, providing a foundation for future in-depth research. Full article

This paper explores how migrants’ communities shape and contest their urban spaces, focusing on Aachen. The primary objective of this research was to capture the cultural urban change in migrants’ settings. It further contributes to building an insight into the various urban typologies strengthened by migrants’ settings, which could have implications on the urban fabric of the city, thus adding value to socially inclusive development. Qualitative approaches in identifying a street taxonomy were adopted in this paper. An in-depth analysis encompassing narrations behind urban layouts, behavioral patterns, and typological distinctions was made to highlight the differences within the setting. Notably, a pivotal aspect of this study is the endeavor to underscore the constructive, yet frequently underappreciated contributions made by migrants to the dynamics and development of urban areas. Therefore, more attention shall be given to the urban layouts contributing to more socially cohesive communities in and around such settings. In doing so, this research makes a substantial contribution to the broader discourse surrounding urban resilience and the fluid planning strategies adopted by cities in response to evolving demographic landscapes. Full article

Conventional geostress methods of measurement cannot reveal an accurate geostress field distribution in an engineering area, limited by both cost and prevailing geological conditions. This study introduces an improved LSTM–Attention neural network for in situ stress field inversion. By integrating long short-term memory (LSTM) networks—which capture temporal dependencies in sequential data with attention mechanisms that emphasize critical features, the proposed method addresses inherent non-linearity and discontinuity challenges in deep subsurface stress field inversion. The integrated LSTM and multi-head attention architecture extracts temporal features and weights critical information within ground stress field data. Through iterative refinement via optimizers and loss functions, this framework successfully inverts stress boundary conditions while mitigating overfitting risks. The inversion of the stress field around a hydropower station indicates that the proposed method allows accurate inversion of distribution of the geostress field; the inversion values of the maximum principal stress, intermediate principal stress, and minimum principal stress conform to those measured. This study provides a new method for accurately and reliably inverting the stress field for deep engineering geological surveys and rock mass engineering design, which has significant scientific value and engineering application prospects. The rockburst risk of chambers is evaluated according to the stress field, which shows that locations with a burial depth of 274.3 m are at moderate to weak risk of rockburst. Full article

Empirical evidence highlights the critical role of effective school leadership in driving school improvement, enhancing teacher performance, and improving student outcomes. In Omani schools, where adaptive strategies are increasingly essential, the collaborative roles of principals and teachers are pivotal in achieving meaningful educational change. This qualitative study explores how principal–teacher interactions influence teacher leadership development and contribute to overall school improvement. Using a multiple-case study design across eight schools in the Muscat Governorate, in-depth interviews with eight principals and focus group discussions with twelve teachers were conducted to capture diverse perspectives across these settings. The data were analyzed using thematic analysis to identify key leadership dynamics and their implications for teacher leadership and school improvement. Findings indicate that principals who act as change agents, offer targeted support, and cultivate a collaborative culture empower teachers to take on leadership roles. Key themes include fostering professional growth, building trust, and addressing systemic challenges. These interactions enhance school culture, classroom practices, and student outcomes, ultimately contributing to sustainable school improvement. The study underscores the importance of collaborative leadership practices and calls for strategies that optimize these dynamics to advance educational outcomes. Future research should explore the broader applicability of these findings across diverse educational contexts to inform policy and practice. Full article

The behavior of gas emissions at the heading face of the coal mine is a key indicator of potentially harmful gas disaster risk, necessitating in-depth study via analytical and statistical methods. However, conventional prediction and evaluation methods depend on long-interval statistical data, which are too coarse for and lack the immediacy required for real-time applications. Based on the physical laws of gas storage and flow, a refined computational model has been developed to compute dynamic gas emission rates that vary with geology and excavating process. Furthermore, by comparing the computed outputs with actual monitoring data, it becomes possible to assess whether abnormal gas emissions are occurring. Methodologically, this model first applies the finite difference method to compute the dynamic gas flux and the dynamic residual gas content. It then determines the exposure duration of each segment of the roadway wall at any given moment, as well as the mass of newly dislodged coal. The total gas emission rate at a specific sensor location is obtained by aggregating the contributions from all of the exposed wall and the freshly dislodged coal. Owing to some simplifications, the model’s applicability is currently restricted to single, medium-thick coal seams. The model was preliminarily implemented in Python (3.13.2) and validated against a case study of an active heading face. The results demonstrate a strong concordance between model predictions and field measurements. The model notably captures the significant variance in emission rates resulting from different mining activities, the characteristic emission surges from dislodged coal and newly exposed coal walls, and the influence of sensor placement on monitoring outcomes. Full article

Single-pixel imaging enables two-dimensional image capture through a single-pixel detector, yet extending this to three-dimensional or higher-dimensional information capture in single-pixel optical imaging systems has remained a challenging problem. In this study, we present a single-pixel camera system for three-dimensional (3D) imaging based on compressed sensing (CS) with continuous wave (CW) pseudo-random volume structured illumination. An estimated image, which incorporates both spatial and depth information of a 3D scene, is reconstructed using an L1-norm minimization reconstruction algorithm. This algorithm employs prior knowledge of non-overlapping objects as a constraint in the target space, resulting in improved noise performance in both numerical simulations and physical experiments. Our simulations and experiments demonstrate the feasibility of the proposed 3D CS framework. This approach achieves compressive sensing in a 3D information capture system with a measurement ratio of 19.53%. Additionally, we show that our CS 3D capturing system can accurately reconstruct the color of a target using color filter modulation. Full article

Background/Objectives: Transfer RNA-derived fragments (tRFs) are small non-coding RNAs increasingly implicated in gene regulation and disease, yet their target specificity and disease relevance remain poorly understood. This is an exploratory study that investigates the phenomenon of identical tRF sequences reported in distinct disease contexts and evaluates the consistency between experimental findings and predictions from both target-based and abundance-based tRF databases. Methods: Five tRFs with identical sequences across at least two peer-reviewed disease studies were selected from a recent systematic review. Their validated targets and disease associations were extracted from the literature. Motifs and predicted targets were cross-referenced using three target-oriented databases: tatDB, tRFTar, and tsRFun. In parallel, the abundance enrichment of cancer-associated tRFs was assessed in OncotRF and MINTbase using TCGA-based abundance data. Results: Among the five tRFs, only LeuAAG-001-N-3p-68-85 showed complete alignment between experimental data and both tatDB and tRFTar predictions. Most of the other four displayed at least partial overlaps in motif/binding regions with some of validated targets. tRF abundance data from MINTbase and OncotRF showed inconsistent enrichment, with only AlaAGC-002-N-3p-58-75 exhibiting concordance with its experimentally validated cancer type. Most functionally relevant tRFs were not strongly represented in abundance-only databases. Conclusions: Given the limited number of tRFs analyzed, this study serves primarily as a pilot analysis designed to generate hypotheses and guide future in-depth research, rather than offering comprehensive conclusions. We did, however, illustrate how the analysis of tRFs can benefit from utilizing currently available databases. Target-based databases more closely reflected experimental evidence for mechanistic details when a tRF or a motif match is found. Yet all database types are incomplete, including the abundance-focused tools, which often fail to capture disease-specific regulatory roles of tRFs. These findings underscore the importance of using integrated data sources for tRF annotation. As a pilot analysis, the study provides insights into how identical tRF sequences might function differently across disease contexts, highlighting areas for further investigation while pointing out the limitations of relying on expression data alone to infer functional relevance. Full article

The geometry and quality of a weld seam are critical factors in laser beam welding, influencing mechanical performance and structural integrity. Dynamically modulated laser beams provide a precise means of tailoring energy input in high-power laser welding processes. This study investigates the influence of beam shape and modulated frequency on weld seam geometry, penetration depth, and capillary behaviour using a coherent beam combining (CBC) laser system from Civan Lasers. Three beam intensity distributions—single point, line–point–line (LPL), and boomerang—were applied across a modulation frequency range of 1, 10, and 100 kHz during the welding of duplex and austenitic stainless steels. High-speed imaging captured real-time capillary dynamics, and the data were analysed to assess capillary stability, measure capillary diameter, and determine the capillary front angle as a function of frequency and beam shape. Transverse cross-sections of the welds were prepared to evaluate seam geometry and microstructure. The results show that beam shape significantly affects energy distribution and weld profile, while modulation frequency critically influences capillary behaviour and penetration characteristics. These findings highlight the critical role of dynamic beam shaping and frequency modulation in optimizing laser welding processes for material-specific performance, offering a versatile platform for advancing precision manufacturing using CBC technology. Full article