Search Results (2,360)

This study develops and validates the Cultural Heritage Awareness Scale (CHAS), a multidimensional measurement instrument designed to assess individuals’ awareness of cultural heritage within the context of sustainable heritage management. The study addresses the need to move beyond cognitively oriented awareness models by conceptualising cultural heritage awareness as an integrated construct encompassing ethical responsibility, functional engagement, and governance-oriented conservation. The scale was developed using a quantitative scale development design, informed by expert-generated items and psychometric validation procedures applied to university student samples. Factor analyses confirmed a stable three-dimensional structure with satisfactory model fit and strong internal consistency, indicating that the proposed model reliably captures distinct yet interrelated dimensions of heritage awareness. The findings demonstrate that cultural heritage awareness extends beyond recognition and appreciation to include ethical accountability, engagement with use, and participation in governance-related processes. By integrating ethical, functional, and governance dimensions within a single validated instrument, the CHAS offers an original contribution to heritage awareness measurement. The scale provides a practical tool for assessing heritage awareness in educational, planning, and policy-related contexts, particularly in relation to participatory and sustainability-oriented heritage governance. While the scale shows robust performance within a university-based sample, further research is recommended to examine its applicability across more diverse socio-cultural contexts. Full article

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) and Obesity Hypoventilation Syndrome (OHS) are core components of the obesity-related respiratory disease spectrum, and their comorbidity has become a major challenge in the global public health field. This review systematically summarizes the epidemiological characteristics, pathophysiological mechanisms, diagnostic criteria, diagnostic technologies and treatment strategies of OSAHS-OHS comorbidity, with a focus on the cutting-edge progress of digital therapeutics and metabolic intervention, as well as the historical evolution and current status of clinical management. We also conduct an in-depth analysis of the unresolved controversies and practical challenges in the current clinical management of this comorbidity. OSAHS-OHS comorbid patients have a significantly higher risk of cardiovascular complications than those with a single disease, and chronic intermittent hypoxia (CIH) forms a vicious cycle with obesity through multiple pathophysiological pathways. The combination of multi-dimensional assessment tools and portable monitoring devices has improved the screening efficiency of OSAHS-OHS comorbidity, and the selection of respiratory support therapies such as continuous positive airway pressure (CPAP) and non-invasive ventilation (NIV) depends on patient phenotypes. Digital therapeutics and novel metabolic intervention drugs have shown promising clinical value in the management of this comorbidity. The multidisciplinary collaboration model is the key to improving the prognosis of comorbid patients, while current clinical management is still faced with challenges such as policy lag, ethical controversies and uneven resource allocation. Future research should focus on individualized therapeutic targets, the integration of digital technologies and the optimization of health policies to achieve precise and efficient management of OSAHS-OHS comorbidity. Full article

Cultural tourism (CT) has emerged as a critical driver of destination competitiveness; however, stakeholders struggle to balance heritage preservation, sustainable growth, and visitor management. Current decision making often lacks the practical information required to assess the multi-dimensional impacts of CT and to align strategies with sustainability goals. This paper presents a user-centred digital decision support system (DSS) developed under the European project IMPACTOUR. The methodological contribution is a procedure that uncovers links among strategies, actions, and performance indicators, conditioned on destination characteristics, by leveraging hierarchical multi-criteria analysis to weight sustainability domains. Co-designed with stakeholders, it integrates social and technological components and uses triangulated data to prioritise strategies and evaluate impacts. The visual interface offers a smart dashboard that supports strategic decision making and displays related key performance indicators, enabling stakeholders to monitor outcomes against predefined sustainability objectives. Pilot implementations in several European regions demonstrate the tool’s efficacy in fostering data-driven planning to achieve a balanced approach between tourism and liveability. While the system is scalable, its current limits include regional specificity and data availability. Future work will incorporate AI-driven predictive analytics and adapt the DSS for application in non-European contexts, providing a replicable framework for advancing sustainable tourism policies in culturally rich destinations. Full article

Wetlands characterized by the presence of rare and endangered reed plant communities are seriously threatened by hydrological changes and pollution caused by human activity, e.g., drainage, river regulation, and conversion to agricultural land. Despite numerous studies of wetland communities, the “volume of ecological niche” based on Ellenberg indices, i.e., the ecological preferences of vascular plant species, has rarely been analyzed at the level of entire plant communities. Properly defined indicators of microclimatic and habitat factors (ranges of environmental conditions), appropriate for individual rush and sedge communities (specific communities), are very important for the sustainable management of ecosystems and potential restoration processes in renaturation activities. Therefore, a comprehensive floristic and habitat assessment of wetland communities of the Phragmitetea class was conducted in a Natura 2000 site in southeastern Poland (name and number of the Natura 2000 site—Wolica Valley PLH060058), located within the East European Lowland. The communities were analyzed in the context of the variability of individual Ellenberg indices and designated ecological hypervolumes. These were typical rush communities occurring in wet and fertile soils with a neutral or alkaline pH. The microclimatic conditions were typical for these habitats. The studied communities differ in terms of the variability of Ellenberg ecological indices. Some of them are characterized by low ecological niches, while others are characterized by larger ones. The volume of determined multidimensional hypervolumes allowed us to distinguish two communities (Phragmitetum australis and Caricetum rostratae) to have greater generality compared to the others. They can occur in a greater variety of environmental conditions than other communities that require more specific conditions. Other phytocenoses with low hypervolume values (hypervolumes more than 10 times smaller than mentioned before) were distinguished by high habitat specialization. In turn, the analysis of the overlapping of hypervolumes allowed us to group communities into four clusters with similar ranges of Ellenberg indices’ values: (1) Caricetum distichae and Caricetum gracilis; (2) Glycerietum maximae, Iridetum pseudoacori, Caricetum appropinquatae, and Phalaridetum arundinaceae; (3) Phragmitetum australis and Caricetum rostratae; and (4) Caricetum acutiformis, Caricetum vesicariae, and Caricetum elatae. Full article

Ecolabelling has emerged as a key instrument to communicate environmental performance to consumers, particularly in the agri-food sector where resource use and ecological pressures are highly interlinked. Conventional Life Cycle Assessment (LCA)-based ecolabels often suffer from methodological discretion, lack of territorial specificity, and limited consumer trust. This study investigates how the Water–Energy–Food–Ecosystem (WEFE) Nexus could be integrated into LCA-based ecolabelling, with a specific focus on pasta production as a representative case in the food industry. Indicators were collected from recent literature on LCA and Nexus applications, selected for simplicity and clear attribution to one WEFE dimension, and then evaluated by experts from COST Action CA20138 (NexusNet) through a two round Delphi protocol. The process yielded 23 indicators distributed across the four dimensions, which were subsequently compared with six Environmental Product Declarations to assess data availability and compatibility. The results suggest that many indicators can be computed with standard LCA inventories, while the Nexus perspective adds value by capturing multidimensional impacts and regional resource pressures. Further refinement and empirical testing are expected to enhance the framework’s applicability, but the findings already indicate that incorporating WEFE-based indicators into pasta ecolabelling could represent a promising pathway to improve analytical depth and consumer relevance, aligning circular economy principles with corporate assessment practices. Full article

For mine ventilation-on-demand (VOD) scenarios, conventional joint optimisation of airflow augmentation and energy saving in mine ventilation systems is often constrained in practical engineering applications by shrinkage of the feasible region, limited adjustable resistance margins, and strongly multi-modal objective functions. These factors tend to result in low solution efficiency, pronounced sensitivity to initial values and insufficient solution robustness. In response to these challenges, a two-layer intelligent evolutionary optimisation framework, termed ES–Hybrid JADE with Competitive Niching, is developed in this study. In the outer layer, four classes of evolutionary algorithms—CMAES, DE, ES, and GA—are comparatively assessed over 50 repeated test runs, with a combined ranking based on convergence speed and solution quality adopted as the evaluation metric. ES, with a rank_mean of 2.0, is ultimately selected as the global hyper-parameter self-adaptive regulator. In the inner layer, four algorithms—COBYLA, JADE, PSO and TPE—are compared. The results indicate that JADE achieves the best overall performance in terms of terminal objective value, multi-dimensional performance trade-offs and robustness across random seeds. Furthermore, all four inner-layer algorithms attain feasible solutions with a success rate of 1.0 under the prescribed constraints, thereby ensuring that the entire optimisation process remains within the feasible domain. The proposed framework is applied to an exhaust-type dual-fan ventilation system in a coal mine in Shaanxi Province as an engineering case study. By integrating GA-based automatic ventilation network drawing (longest-path/connected-path) with roadway sensitivity analysis and maximum resistance increment assessment, two solution schemes—direct optimisation and composite optimisation—are constructed and compared. The results show that, within the airflow augmentation interval [0.40, 0.55], the two schemes are essentially equivalent in terms of the optimal augmentation effect, whereas the computation time of the composite optimisation scheme is reduced significantly from approximately 29 min to about 13 s, and a set of multi-modal elite solutions can be provided to support dispatch and decision-making. Under global constraints, a maximum achievable airflow increment of approximately 0.66 m³·s⁻¹ is obtained for branch 10, and optimal dual-branch and triple-branch cooperative augmentation combinations, together with the corresponding power projections, are further derived. To the best of our knowledge, prior VOD airflow-augmentation studies have not combined feasibility-region contraction (via sensitivity- and resistance-margin gating) with a two-layer ES-tuned JADE optimiser equipped with Competitive Niching to output multiple feasible optima. This work provides new insight that the constrained airflow-augmentation problem is intrinsically multimodal, and that retaining multiple basins of attraction yields dispatch-ready elite solutions while achieving orders-of-magnitude runtime reduction through prediction-based constraints. The study demonstrates that the proposed two-layer intelligent evolutionary framework combines fast convergence with high solution stability under strict feasibility constraints, and can be employed as an engineering algorithmic core for energy-efficiency co-ordination in mine VOD control. Full article

The intensifying pressures of urbanization and climate change on coastal zones necessitate a holistic understanding of the interplay between human activity and ecological integrity for sustainable development. However, prevailing methods for assessing coastal vibrancy often overlook direct measures of human presence and fail to quantitatively capture its complex relationship with ecological vulnerability. To address these gaps, this study develops a novel multi-dimensional assessment framework for Coastal Landscape Vibrancy (CLV) and empirically examines its interaction with ecological vulnerability factors in Beihai, China. Moving beyond built-environment-centric approaches, our framework integrates the ‘Crowd’ dimension, directly quantified using Baidu Heat Index data, with the ‘Place’ dimension, characterized by urban features, natural attributes, and visual experience. Principal Component Analysis (PCA) was employed to objectively weight these indicators and construct a composite CLV index. We then applied multiple linear regression to analyze the influence of ecological factors constructed based on the Sensitivity-Resilience-Pressure (SRP) model. The results revealed that vibrancy was highly concentrated in urban cores and exhibited significant spatiotemporal variations. Regression analysis revealed that while ecological quality factors like green coverage (β = 0.236, p < 0.001) positively influenced vibrancy, anthropogenic stressors such as slope (β = −0.457, p < 0.001) and the impervious surface index (β = −0.092, p < 0.001) had significant negative impacts, highlighting a critical trade-off between human activity and ecological conditions. The findings provide a quantitative, evidence-based foundation for spatial planning, demonstrating that sustainable coastal vibrancy is achieved through a balanced integration of human activity and ecological conservation, rather than through unchecked development. This framework offers critical insights for formulating strategies that simultaneously enhance ecological resilience and optimize human service facilities. Full article

Global extreme poverty has fallen dramatically over the past two centuries, yet hundreds of millions remain impoverished, underscoring the need for scalable monitoring tools. In Mexico, poverty metrics are available only sporadically in terms of time and space (e.g., every 5 years at the municipal level), making it difficult for decision-makers to access reliable, up-to-date, and sufficiently detailed information, highlighting the need for higher-resolution, timely methods. To address this problem, we propose a two-stage approach that combines socioeconomic and Earth Observations-based data. Initially, a machine learning model maps census variables to official poverty indicators belonging to a multidimensional model, yielding fine-scale poverty estimates. A census-based model trained with eXtreme Gradient Boosting (XGBoost) achieved a determination coefficient ($R^2$) of approximately $0.842$, indicating strong agreement with official poverty figures and providing high-resolution proxies. Afterward, we use features based on remote observations to predict these poverty estimates at a 469 m grid scale. In this case, advanced foundation models outperformed other machine learning (ML) approaches, achieving an $R^2$ of $0.683$. While foundation models enable more accurate, fine-scale poverty mapping and could accelerate poverty assessments, their use comes at a heavy price in terms of carbon emissions. Full article

Sustainable innovation is important in Albania, a small transition economy facing pressures from digitalization, the green transition, and increased competition. Yet the country’s innovation system is still developing, and academia–business linkages remain weak. This article investigates how academia–business (A2B) collaboration contributes to firms’ sustainable innovation, addressing the lack of quantitative evidence from a country in the Western Balkans context. Building on innovation systems and resource-based perspectives, A2B cooperation is conceptualized as a multidimensional latent construct, capturing types of collaboration, key actors, and organizational drivers. Using survey data from 298 firms operating in Albania, collected in 2025, the study applies Covariance-Based Structural Equation Modeling (CB-SEM) to test whether the intensity and quality of A2B cooperation are linked to sustainable innovation outcomes. The findings indicate that collaboration is still limited and inconsistent, dominated by student internships and sporadic joint projects. However, the CB-SEM results confirm that more intensive and better-structured cooperation is strongly associated with higher levels of sustainable innovation. The study offers one of the first CB-SEM-based quantitative assessments of A2B collaboration and sustainable innovation in Albania and provides policy implications for strengthening innovation-oriented partnerships in transition economies. Full article

The task of identifying high-value projects from vast investment portfolios presents a major challenge in the construction industry, particularly within the energy sector, where decision-making carries high financial and operational stakes. This complexity is driven by both the volume and heterogeneity of project documentation, as well as the multidimensional criteria used to assess project value. Despite this, research gaps remain: large language models (LLMs) as pretrained transformer encoder models are underutilized in construction project selection, especially in domains where investment precision is paramount. Existing methodologies have largely focused on multi-criteria decision-making (MCDM) frameworks, often neglecting the potential of LLMs to automate and enhance early-phase project evaluation. However, deploying LLMs for such tasks introduces high computational demands, particularly in privacy-sensitive, enterprise-level environments. This study investigates the application of the robustly optimized BERT model (RoBERTa) for identifying high-value energy infrastructure projects. Our dual objective is to (1) leverage RoBERTa’s pre-trained language architecture to extract key information from unstructured investment texts and (2) evaluate its effectiveness in enhancing project selection accuracy. We benchmark RoBERTa against several leading LLMs: BERT, DistilBERT (a distilled variant), ALBERT (a lightweight version), and XLNet (a generalized autoregressive model). All models achieved over 98% accuracy, validating their utility in this domain. RoBERTa outperformed its counterparts with an accuracy of 99.6%. DistilBERT was fastest (1025.17 s), while RoBERTa took 2060.29 s. XLNet was slowest at 4145.49 s. In conclusion, RoBERTa can be the preferred option when maximum accuracy is required, while DistilBERT can be a viable alternative under computational or resource constraints. Full article

Urban metro systems are highly sensitive to seismic disturbances, and the ability of metro stations to manage emergencies effectively has become an increasingly important component of urban resilience. This study develops a resilience-oriented evaluation framework that conceptualizes emergency management as a sequential managerial process encompassing preparedness, response, and recovery. A multi-dimensional indicator system was constructed based on the four resilience capacities—absorptive, maintaining, recovery, and adaptive—and operationalized through a multi-stage Data Envelopment Analysis (DEA) model. The framework enables both overall efficiency assessment and stage-specific diagnosis of managerial weaknesses. Methodologically, the study demonstrates how resilience theory can be operationalized into a network efficiency structure suitable for process-level diagnosis rather than aggregate scoring. A case study of a representative metro station demonstrates the applicability of the proposed method. The results reveal that while preparedness practices are relatively mature, notable inefficiencies exist in real-time response and post-event recovery due primarily to managerial factors such as communication reliability, personnel coordination, and restoration planning. Improvement simulations confirm that targeted enhancements in these management processes can substantially increase overall emergency efficiency. The findings highlight that seismic resilience is not solely determined by physical infrastructure but is heavily dependent on managerial effectiveness across the emergency cycle. The proposed framework contributes a process-oriented, data-driven tool for evaluating and improving emergency management performance and offers practical guidance for metro operators seeking to strengthen resilience under earthquake scenarios. Full article

River and lake health assessment has evolved from a purely ecological concept to a multidimensional framework integrating ecosystem integrity and social service functions. Based on a comprehensive dataset of 1412 papers (1991–2024), this study combines bibliometric mapping with a systematic review to track the evolution of biological monitoring and assessment methodologies. Quantitative analysis of keywords reveals that while traditional focuses on heavy metals, fish, and sediments remain dominant, there is a significant shift towards integrated frameworks where biological indicators (e.g., benthic macroinvertebrate integrity and fish retention) are increasingly coupled with social services. We critically review three assessment paradigms: single-factor bio-indicators, biological predictive models such as RIVPACS and AUSRIVAS, and multi-factor comprehensive models. The study identifies critical gaps in ecological connectivity and the management of transboundary lakes under climate change. Consequently, we propose a strategic roadmap leveraging the National Ecological Connectivity Optimization Platform and mandatory “health audits” for transboundary waters to ensure the long-term sustainability of aquatic biodiversity. This review provides a scientific basis for balancing biodiversity conservation with sustainable water resource utilization. Full article

Estimating the adsorption data and understanding the adsorption behavior and mechanism of organic pollutants on nanoplastics are crucial for assessing their ecological risks. Herein, in silico techniques, i.e., grand canonical Monte Carlo simulations, density functional theory computations, and quantitative structure activity relationship (QSAR) modeling, were integrated to examine the adsorption of 39 representative aliphatic and aromatic compounds and nine emerging pollutants (brominated flame retardants and phosphorus flame retardants) onto 12 different nanoplastics under atmospheric conditions. Three QSAR models were constructed to predict the adsorption equilibrium constant (logK) for polyethylene, polyoxymethylene, and polyvinyl alcohol nanoplastics individually, along with 12 QSAR models for separately estimating adsorption capacities (C_m) on different nanoplastics. Furthermore, a novel multi-dimensional prediction model was developed, enabling simultaneous, high-throughput prediction of adsorption capacities across multiple nanoplastics and pollutants with a single input. These results revealed that van der Waals and electrostatic interactions serve as the primary driving forces for the adsorption. The novel multi-dimensional prediction model facilitates rapid and comprehensive assessment of pollutant–nanoplastic interactions with one-click, and paves the way for improved risk evaluations and advancing predictive environmental research. Full article

In this study, we describe a non-invasive approach to objectively assess fragrance-induced emotions using multiplex salivary biomarker profiling. Traditional self-reports, physiological monitoring, and neuroimaging remain limited by subjectivity, invasiveness, or poor temporal resolution. Saliva offers an advantageous alternative, reflecting rapid neuroendocrine changes linked to emotional states. We combined four key salivary biomarkers, cortisol, alpha-amylase, dehydroepiandrosterone, and oxytocin, to capture multidimensional emotional responses. Two clinical studies (n = 30, n = 63) and one user study (n = 80) exposed volunteers to six fragrances, with saliva collected before and 5 and 20 min after olfactory stimulation. Subjective emotional ratings were also obtained through questionnaires or an implicit approach. Rigorous analytical validation accounted for circadian variation and sample stability. Biomarker patterns revealed fragrance-induced emotional profiles, highlighting subgroups of participants whose biomarker dynamics correlated with particular emotional states. Increased oxytocin and decreased cortisol levels aligned with happiness and relaxation; in comparison, distinct biomarker combinations were associated with confidence or dynamism. Classification and Regression Trees (CART) analysis results demonstrated high sensitivity for detecting these profiles. Validation in an independent cohort using an implicit association test confirmed concordance between molecular profiles and behavioral measures, underscoring the robustness of this method. Our findings establish salivary biomarker profiling as an objective tool for decoding real-time emotional responses. Beyond advancing affective neuroscience, this approach holds translational potential in personalized fragrance design, sensory marketing, and therapeutic applications for stress-related disorders. Full article

Although sustainability marketing is gaining popularity, a comprehensive understanding of how green marketing practices affect consumers’ overall well-being remains lacking. Existing studies focus on firm-level sustainability actions or isolated consumer responses. Their mechanisms linking marketing practices, value creation, and life satisfaction are not sufficiently theorized. To bridge the gap, this study develops an integrative conceptual framework that explains how sustainable value creation mediates the enhancement of consumer life satisfaction through the implementation of green marketing practices. The study employs a two-phase integrative review design. Three core constructs, green marketing practices, sustainable value creation, and consumer life satisfaction, are identified in a synthesis of sustainability marketing, consumer value, and well-being literature. Secondly, the initial framework is systematically rooted and refined by drawing on influential empirical research published in marketing and sustainability journals from 2016 to 2025. Analytically constructed tables organize this synthesis by assessing the dominant empirical patterns related to marketing practices, multidimensional value creation, and pathways to life satisfaction. The research advances sustainability marketing theory by reconceptualizing green marketing as a system of consumption-shaping practices and by positioning sustainable value creation as the central mechanism linking firm actions to consumer life satisfaction. Full article