Search Results (79)

This study examines community-based wellness tourism (CBWT) in Ubud, Bali, focusing on ownership structures, community participation, and the role of local traditions. Using a qualitative design, the data were collected through semi-structured interviews with wellness stakeholders and field observations of spas and yoga centers. The findings reveal that spas are predominantly locally owned and staffed, ensuring value retention and skill development, while flagship yoga and retreat centers are dominated by non-local actors, creating risks of economic leakage and weaker cultural stewardship. Community involvement is strong in operations but limited in planning and governance, highlighting a policy–practice gap. Integrating Balinese traditions, such as Usada Bali and Melukat, could enhance authenticity but requires careful protection against commodification. The findings reveal that locally owned spas contribute to SDG 1 (No Poverty) and SDG 8 (Decent Work and Economic Growth) through local value retention, employment creation, and skill development, while non-local dominance of yoga and retreat centers risks economic leakage and weakened cultural guardianship. The study also identifies gaps in governance and planning, underscoring the need for inclusive participation and capacity building to align with SDG 11 (Sustainable Cities and Communities). Integrating Balinese traditions, such as Usada Bali and Melukat, highlights the opportunities for safeguarding cultural heritage, provided that protocols against commodification are enforced. To address these challenges, the study proposes a strategic framework emphasizing governance reform through a quadruple-helix model, shared-equity ownership, standardized human capital development, and protocol-based cultural guardianship. Despite the limitations of this being a single-case, cross-sectional study, the findings contribute to wellness tourism research by shifting attention from visitor demands to governance and equity. The study offers practical strategies for institutionalizing CBWT in Ubud while providing a transferable model for destinations seeking to balance growth with tradition. Full article

Schizopygopsis younghusbandi is an endemic and ecologically important fish species on the Tibetan Plateau. However, its dietary carbohydrate requirement remains unexplored, limiting the development of cost-effective and physiological-friendly artificial feed. This study investigated the effects of different dietary carbohydrate levels on the growth performance, antioxidant capacity, and hepatointestinal morphology of S.younghusbandi. Six experimental diets were formulated with graded carbohydrate levels of 9% (C9), 12% (C12), 15% (C15), 18% (C18), 21% (C21), and 24% (C24). A total of 720 fish (initial weight 37.49 ± 0.25 g) were randomly allocated to six groups in quadruplicate (30 fish per replicate) and reared in tanks (0.6 m × 0.5 m × 0.4 m) for 8 weeks. Results demonstrated that the diet in the C12 group significantly improved weight gain rate (WGR), specific growth rate (SGR), and feed conversion ratio (FCR) (p < 0.05). Regression fitting analysis on growth performance indicated that the optimal carbohydrate level ranged from 10.42% to 10.49%. Additionally, the C12 group exhibited enhanced total superoxide dismutase (T-SOD) activities and reduced malondialdehyde (MDA) content in the liver, along with reduced interleukin-1β (IL-1β) levels in the serum (p < 0.05). Histological analysis revealed superior hepatointestinal integrity in the C12 group, characterized by lower hepatic lipid droplet accumulation, reduced vacuolation, decreased hepatosomatic index (HSI) (p < 0.05), as well as higher intestinal villus height and muscle thickness (p < 0.05). In conclusion, the C12 group optimally enhanced the growth, antioxidant response, and hepatointestinal health of S. younghusbandi, indicating that the suitable dietary carbohydrate level for this species is 12%. Full article

In this work, we propose a novel quantum-informed epistemic framework that extends the classical notion of probability by integrating plausibility, credibility, and possibility as distinct yet complementary measures of uncertainty. This enriched quadruple (P, Pl, Cr, Ps) enables a deeper characterization of quantum systems and decision-making processes under partial, noisy, or ambiguous information. Our formalism generalizes the Born rule within a multi-valued logic structure, linking Positive Operator-Valued Measures (POVMs) with data-driven plausibility estimators, agent-based credibility priors, and fuzzy-theoretic possibility functions. We develop a hybrid classical–quantum inference engine that computes a vectorial aggregation of the quadruples, enhancing robustness and semantic expressivity in contexts where classical probability fails to capture non-Kolmogorovian phenomena such as entanglement, contextuality, or decoherence. The approach is validated through three real-world application domains—quantum cybersecurity, quantum AI, and financial computing—where the proposed model outperforms standard probabilistic reasoning in terms of accuracy, resilience to noise, interpretability, and decision stability. Comparative analysis against QBism, Dempster–Shafer, and fuzzy quantum logic further demonstrates the uniqueness of architecture in both operational semantics and practical outcomes. This contribution lays the groundwork for a new theory of epistemic quantum computing capable of modelling and acting under uncertainty beyond traditional paradigms. Full article

Multilevel active bridge converters are potential candidates for many modern high-power DC applications due to their ability to integrate multiple sources while minimizing weight and volume. Therefore, this paper deals with an analytical, simulation-based, and experimentally verified investigation of their circulating current behavior, power factor performance, and power loss characteristics. A high-frequency link analysis framework is developed to characterize voltage, current, and power transfer waveforms, providing insight into reactive power generation and its impact on overall efficiency. By introducing a modulation-based control approach, the proposed converters significantly reduce circulating currents and enhance the power factor, particularly under varying phase-shift conditions. Compared to quadruple active bridge topologies, the discussed multilevel architectures offer reduced transformer complexity and improved power quality, making them suitable for demanding applications such as electric vehicles and aerospace systems. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

This cross-country study examines how higher education institutions collaborate with government, industry, and civil society to promote the European Green Deal and Just Transition initiatives. Framed within the quadruple helix (QH) model, the research investigates emerging partnerships and the integration of green policies across six European countries: Bulgaria, Cyprus, France, Greece, Serbia, and Spain. Special emphasis is placed on the strategic role of universities in advancing the environmental, social, and economic dimensions of sustainability through their initiatives. Drawing on 30 semi-structured interviews with key stakeholders, including local public officials, academics, entrepreneurs, students, and unemployed youth, the study uncovers a growing alignment between academic initiatives and national sustainability agendas. While the extent of policy integration and collaboration varies, the findings underscore the importance of universities in shaping environmental awareness, fostering green innovation, and advancing multi-actor partnerships. The study contributes to the theoretical discourse on the QH model by applying it to the field of green transition policy and offers practical recommendations for enhancing the role of universities in sustainability-oriented governance and education. Full article

Monitoring optically-active water quality (OAWQ) parameters faces key challenges, primarily due to limited in situ measurements and the restricted availability of high-resolution multispectral remote sensing imagery. While deep learning has shown promise for OAWQ estimation, existing approaches such as GeoTile2Vec, which relies on geographic proximity, and SimCLR, a domain-agnostic contrastive learning method, fail to capture land cover-driven water quality patterns, limiting their generalizability. To address this, we present deep meta-connectivity representation (DMCR), which integrates multispectral remote sensing imagery with limited in situ measurements to estimate OAWQ parameters. Our approach constructs meta-feature vectors from land cover images to represent the water quality characteristics of each multispectral remote sensing image tile. We introduce the meta-connectivity concept to quantify the OAWQ similarity between different tiles. Building on this concept, we design a contrastive self-supervised learning framework that uses sets of quadruple tiles extracted from Sentinel-2 imagery based on their meta-connectivity to learn DMCR vectors. After the core neural network is trained, we apply a random forest model to estimate parameters such as chlorophyll-a (Chl-a) and turbidity using matched in situ measurements and DMCR vectors across time and space. We evaluate DMCR on Lake Erie and Lake Ontario, generating a series of Chl-a and turbidity distribution maps. Performance is assessed using the $R^2$ and $RMSE$ metrics. Results show that meta-connectivity more effectively captures water quality similarities between tiles than widely utilized geographic proximity approaches such as those used in GeoTile2Vec. Furthermore, DMCR outperforms baseline models such as SimCLR with randomly cropped tiles. The resulting distribution maps align well with known factors influencing Chl-a and turbidity levels, confirming the method’s reliability. Overall, DMCR demonstrates strong potential for large-scale OAWQ estimation and contributes to improved monitoring of inland water bodies with limited in situ measurements through meta-connectivity-informed deep learning. The temporal-spatial water quality maps can support large-scale inland water monitoring, early warning of harmful algal blooms. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

It is projected that the world population will quadruple over the next century, and to meet future food demands, agricultural production will need to increase by 70%. Therefore, there has been a transition from traditional farming methods to autonomous modern agriculture. One such modern technique is aeroponic farming, in which plants are grown without soil under controlled and hygienic conditions. In aeroponic farming, plants are significantly less affected by climatic conditions, infectious diseases, and biotic and abiotic stresses, such as pest infestations. Additionally, this method can reduce water, nutrient, and pesticide usage by 98%, 60%, and 100%, respectively, while increasing the yield by 45–75% compared to traditional farming. In this study, a three-dimensional industrial design of an innovative aeroponic plant growth chamber was presented for use by individuals, researchers, and professional growers. The proposed chamber design is modular and open to further innovation. Unlike existing chambers, it includes load cells that enable real-time monitoring of the fresh weight of the plant. Furthermore, cameras were integrated into the chamber to track plant growth and changes over time and weight. Additionally, RGB power LEDs were placed on the inner ceiling of the chamber to provide an optimal lighting intensity and spectrum based on the cultivated plant species. A customizable chamber design was introduced, allowing users to determine the growing tray and nutrient nozzles according to the type and quantity of plants. Finally, system models were developed for temperature control of the chamber. Temperature control was implemented using a proportional-integral-derivative controller optimized with particle swarm optimization, radial movement optimization, differential evolution, and mayfly optimization algorithms for the gain parameters. The simulation results indicate that the temperatures of the growing and feeding chambers in the cabinet reached a steady state within 260 s, with an offset error of no more than 0.5 °C. This result demonstrates the accuracy of the derived model and the effectiveness of the optimized controllers. Full article

India’s revised nationally determined contribution at COP26 set a net-zero target for 2070, but the role of carbon dioxide removal (CDR) in achieving this goal remains unclear. This study quantifies the contribution of land-based CDR—bioenergy carbon capture and storage, biochar, and afforestation—in achieving India’s net-zero goal. Additionally, a stylised scenario explores an accelerated net-zero target by 2050 in India`s climate target. The global emission target is modelled to follow India’s climate ambition in both stylised scenarios. The results show that the ambitious 2050 net-zero pathway requires 56 GtCO₂ of cumulative novel CDR across the century, compared to 47 GtCO₂ under the 2070 scenario, with both requiring around 1 GtCO₂/year at net-zero. A higher ambitious pathway leads to increased economic costs, with a mid-century carbon price of USD 938, compared to USD 174 in the 2070 scenario. Without novel CDR methods, the cost of achieving net zero by 2050 quadruple. The accelerated 2050 net-zero pathway also intensifies land and water trade-offs, reducing land for crop production while increasing water demand for electricity and biomass. Despite these challenges, it limits end-of-century warming to 1.46 °C, compared to 1.79 °C under the 2070 scenario. These findings highlight the importance of clearly defined climate targets, scalable CDR strategies, and integrated resource management to balance climate ambition with sustainable development. Full article

Background/Objectives: The corneal epithelium plays a vital role in maintaining corneal transparency and ocular surface integrity. Chronic topical use of antiglaucoma medications may induce epithelial changes, especially with the concurrent use of multiple agents. This study aimed to evaluate the association between the number and class of antiglaucoma medications and central corneal epithelial thickness (CET), measured using a spectral-domain optical coherence tomography (SD-OCT) device. Methods: This cross-sectional study included 456 eyes from 242 adults (median age 72 years), grouped by the number of antiglaucoma agents used (0–4 medications). All pharmacologically treated participants had received the same regimen for ≥6 months. CET was measured using SD-OCT (SOLIX, Optovue). Generalized estimating equations (GEEs) accounted for inter-eye correlation. Two models were constructed: one evaluating specific medication effects and another assessing CET reduction per additional drug used. Age and sex were included as covariates. Results: CET progressively decreased with the number of medications, ranging from 53 µm in controls to 48 µm with quadruple therapy. Multivariable GEE analysis confirmed a cumulative thinning effect, with each additional medication associated with further CET reduction (β = −2.83 to −9.17 µm, p < 0.001). Latanoprost exerted the most pronounced single-drug effect (β = −3.01 µm, p < 0.001). Age was a modest negative predictor, while sex showed no significant effect. Conclusions: The cumulative number and specific class of antiglaucoma medications have a significant impact on corneal epithelial thickness. These results emphasize the need for vigilant ocular surface evaluation in patients on multi-drug regimens and propose CET as a surrogate marker for the burden of topical therapy. Full article

Hybrid building construction, in which the steel frame is filled with modular panels made of wood, is a relatively new technical solution. This type of structure allows the integration of large window surfaces. The aim of this study is to indicate the optimal glazing system, taking into account energy consumption, thermal comfort and economic indicators. A house made using new hybrid technology with an area of 152.4 m², located in Bialystok (Northeastern Poland) and in Kiruna (Northern Sweden), was selected as the reference object. Energy simulations of this building were performed with DesignBuilder v. 6.1.8.021 software. Due to the large format of the glazing, the assessment of the thermal environment was performed using the PMV index. An economic analysis aimed at selecting the optimal type of glazing was carried out. It was based on the most commonly used indicators such as LCC, NPV and IRR. The results of this study indicated that the selection of triple-glazed windows in the reference house reduced energy demand by over 22% for Bialystok and about 24% for Kiruna compared to double-glazed windows. Even greater effects can be achieved by using quadruple-glazed windows, as they provide energy savings of 36% and 39%, respectively, for these locations. The results of the analysis performed for a 2% increase in energy prices showed that triple and quadruple windows had a similar LCC value when the discount rate was lower than 2.5% for the Bialystok site. Quadruple-glazed windows were the best option for the Kiruna site when the discount rate was less than 5%. This research study found that, assuming a stable financial situation and a small increase in energy prices, it is recommended to use triple-glazed windows in the climate of Northeastern Poland. In more severe weather conditions, for example those characteristic of the area of Northern Sweden, quadruple-glazed windows are recommended. Full article

High-precision and high-resolution scattering data play a crucial role in remote sensing applications, including ocean environment monitoring, target recognition, and classification. This paper proposes a deep learning-based model aimed at enhancing and reconstructing the spatial resolution of Sentinel-1 backscatter NRCS (Normalized Radar Cross Section) data for China’s offshore seas, including the Bohai Sea, Yellow Sea, East China Sea, Taiwan Strait, and South China Sea. The proposed model innovatively integrates a Self-Attention Feature Fusion based on the Weighted Channel Concatenation (SAFF-WCC) module, combined with the Global Attention Mechanism (GAM) and High-Order Attention (HOA) modules. The feature fusion module effectively regulates the proportion of each feature during the fusion process through weight allocation, significantly enhancing the effectiveness of multi-feature integration. The experimental results show that the model can effectively enhance the fine structural features of marine targets when the resolution is doubled, though the enhancement effect is slightly diminished when the resolution is quadrupled. For high-resolution data reconstruction, the proposed model demonstrates significant advantages over traditional methods under a scale factor of 2 across four key evaluation metrics, including PSNR, SSIM, MS-SSIM, and MAPE. These results indicate that the proposed deep learning-based model is not only well-suited for scattering data from China’s offshore seas but also provides robust support for subsequent research on ocean target recognition, as well as the compression and transmission of SAR data. Full article

Environmental degradation remains an increasingly urgent challenge, leading to focused debates at the Rio+20 conference (2012) on how to operationalise sustainability. This conference’s central theme was the green economy and the role of institutions in driving the transition to a more sustainable model. Today, concepts such as the green economy, circular economy, bioeconomy, and circular bioeconomy (CBE) are integral to institutional efforts towards sustainable development. The CBE has significant potential as a driver of sustainability. This article examines the challenges, opportunities, and governance structures that the Andalusian (Southern Spain) public administration is implementing in the context of the CBE. The findings are based on qualitative methods, with a comprehensive literature review, semi-structured interviews, and workshops with different stakeholders from the quadruple helix model, conducted as part of the ROBIN project and other related projects. The results systematises the main weaknesses and strengths collected during the fieldwork in terms of the tools of governance. The conclusions highlight the need to develop this model and outline the actions needed to develop the CBE further. Full article