Search Results (1,422)

Fucoidan is a sulfated polysaccharide found in brown seaweed. Due to its reported biological activities, including antiviral, antibacterial and anti-inflammatory activities, it has garnered significant attention for potential biomedical applications. However, the direct relationship between fucoidan extracts’ chemical structures and bioactivities is unclear, making it extremely challenging to predict whether an extract will possess a given bioactivity. This relationship is further complicated by a lack of uniformity in the recent literature in terms of the assessment and reporting of extract properties, yield and chemical composition (e.g., sulfate, fucose, uronic acid and monosaccharide contents). These inconsistencies pose significant challenges when directly comparing extraction techniques across studies. This review collected data on extract contents and properties from a selection of available studies. Where information was unavailable directly, efforts were made to extrapolate data. This approach enabled a comprehensive examination of the correlation between extraction techniques and the characteristics of the resulting extracts. A holistic framework is presented for the selection of fucoidan extraction methods, outlining key heuristics to consider when capturing the broader context of a seaweed bioprocess. Future work should focus on developing knowledge within these heuristic categories, such as the creation of technoeconomic models of each extraction process. This framework should allow for a robust extraction selection process that integrates process scale, cost and constraints into decision making. Key quality attributes for biologically active fucoidan are proposed, and areas for future research are identified, such as studies for specific bioactivities aimed at elucidating fucoidan’s mechanism of action. This review also sets out future work required to standardize the reporting of fucoidan extract data. Standardization could positively enhance the quality and depth of data on fucoidan extracts, enabling the relationships between physical, chemical and bioactive properties to be identified. Recommendations on best practices for the production of high-quality fucoidan with desirable yield, characteristics and bioactivity are highlighted. Full article

This paper examines the role of commons-based activist ethnography in reshaping the dynamics between researchers and underprivileged mobile youth (UMY), focusing on the opportunities and ethical challenges of this approach. Conducted in youth centers in Thessaloniki, Greece, the study explores how trust, affect, and collaborative knowledge creation supported agency among UMY while confronting systemic barriers and power asymmetries. Using qualitative ethnographic methods, the research investigates the role of convivial spaces and the ambiance of the commons in creating meaningful connections and emotional resilience, enabling the participants to articulate aspirations and reclaim agency. The dual role of the researcher as an advocate and observer raised questions about representation and power dynamics. This paper contributes to discussions on activist ethnography by offering insights into its possibilities and limitations in advancing transformative research with marginalized populations. Full article

The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control and the creation of new trading products. Accurate load forecasting relies on high-quality historical load data, with complete load data serving as the cornerstone for both forecasting and transactions in electricity spot markets. However, historical load data at the distribution network or user level often suffers from anomalies and missing values. Data-driven methods have been widely adopted for anomaly detection due to their independence from prior expert knowledge and precise physical models. Nevertheless, single network architectures struggle to adapt to the diverse load characteristics of distribution networks or users, hindering the effective capture of anomaly patterns. This paper proposes a PLS-VAE-BiLSTM-based method for anomaly identification and correction in load data by combining the strengths of Variational Autoencoders (VAE) and Bidirectional Long Short-Term Memory Networks (BiLSTM). This method begins with data preprocessing, including normalization and preliminary missing value imputation based on Partial Least Squares (PLS). Subsequently, a hybrid VAE-BiLSTM model is constructed and trained on a loaded dataset incorporating influencing factors to learn the relationships between different data features. Anomalies are identified and corrected by calculating the deviation between the model’s reconstructed values and the actual values. Finally, validation on both public and private datasets demonstrates that the PLS-VAE-BiLSTM model achieves average performance metrics of 98.44% precision, 94% recall rate, and 96.05% F1 score. Compared with VAE-LSTM, PSO-PFCM, and WTRR models, the proposed method exhibits superior overall anomaly detection performance. Full article

As environmental pollution intensifies, China has begun to implement green credit policies to reduce credit allocation to highly polluting enterprises. This research examines the influence of green credit on the cash flow of heavily polluting enterprises, based on the implementation of the “Green Credit Guidelines”. The policy creates a quasi-natural experimental setting by giving businesses access to an exogenous occurrence. Consequently, this paper uses data from 494 A-share listed companies in China over a fifteen-year period from 2007 to 2021 and employs a Difference-in-Differences (DID) model to assess the net effect of the policy, positing that green credit scheme will prevent highly polluting businesses from making money. The empirical findings show that the green credit policy significantly reduces the cash flow of businesses that emit a lot of pollutants, especially when it comes to operational cash flow. Heterogeneity analysis reveals that the cash flow of high-emission regions and non-state-owned heavily polluting enterprises is affected even more significantly. Previous research has often overlooked cash flow as a metric; however, cash flow is a critical indicator of an enterprise’s operational status. From this angle, this study adds to our knowledge of how green credit schemes affect highly polluting businesses. Additionally, it contributes to the ongoing discussion regarding the relationship between financial constraints and cash flow. China’s government ought to keep encouraging the creation of green credit regulations, enhance supervision of state-owned heavily polluting enterprises, and pay attention to low-emission regions by establishing dynamic regulatory indicators to promote ecological civilization construction and the transformation and upgrading of lagging industries. Full article

Based on resource orchestration theory, this paper examines the influence of data-intelligence empowerment on enterprise knowledge recombinant capabilities empirically, using A-share-listed high-technology enterprises from 2012 to 2021 as research examples. Research shows that data-intelligence empowerment can promote both enterprise knowledge recombinant reuse capability and knowledge recombinant creation capability. Furthermore, knowledge depth diversification acts as a mediator between data-intelligence empowerment and knowledge recombinant reuse capability, and knowledge breadth diversification plays a mediating role between data-intelligence empowerment and knowledge recombinant creation capability. The heterogeneity analysis demonstrates that the data-intelligence empowerment has a more promotive impact on knowledge recombinant reuse capability in non-state-owned enterprises and eastern region enterprises, and there is a promotion effect on the knowledge recombinant creation capability across enterprises with different ownership structures and regions. This study provides theoretical guidance as well as practical references for enterprises to take advantage of digital intelligence technology to empower themselves, match internal and external resources and capabilities, and ultimately accomplish sustainable recombinant innovation. Full article

The efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R² of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environmental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance. Full article

The socio-political climate in the UK, compounded by government austerity measures, has intensified the cost-of-living crisis, significantly impacting the affordability of the food supply chain. This has led to an increasing disconnect from healthy dietary practices and has contributed to declines in mental and physical wellbeing. This paper examines the role of edible city (EC) initiatives in addressing these challenges by enhancing food security, fostering social resilience and supporting community wellbeing. The aim of this research is to understand the tangible links between EC mechanisms and how they align with local, cultural and geographical perspectives. Focusing on the City of Lincoln, UK, this study aims to develop a contextualised EC framework that is culturally and geographically relevant and aligns with global goals for sustainable urban development as outlined in the United Nation’s Sustainable Development Goal 11 (SDG11), promoting inclusive, environmentally safe and economically viable communities. Utilising a mixed methods approach, this research combines qualitative interviews and a critical cross-case analysis of four community growing groups in the UK, alongside quantitative socio-demographic data analysis. The findings reveal that successful EC initiatives adapt to their environment and socio-cultural conditions and highlight the importance of EC diversification for long-term sustainability. Four core themes emerged across the case studies: (1) social prescribing, (2) social capital (3) knowledge sharing and (4) social value. These findings inform the creation of a novel flexible EC framework, Lincoln, providing actionable insights and policy recommendations to support sustainable urban regeneration and enhance community resilience. This research contributes transferable knowledge to inform EC strategies across diverse global urban contexts, promoting a pathway toward resilient and socially cohesive urban environments. Full article

The LBS2ITS project, titled “Curricula Enrichment Delivered through the Application of Location-Based Services to Intelligent Transport Systems”, is a collaborative initiative funded by the Erasmus+ program of the European Union. The primary objectives of the project were twofold: to develop new curricula and modernize existing programs at four universities in Sri Lanka. This effort was driven by the need to align educational offerings with the rapidly evolving fields of Location-Based Services (LBSs) and Intelligent Transport Systems (ITSs). A key feature of the LBS2ITS project is its interdisciplinary approach, which draws on expertise from a range of academic disciplines. The project has successfully developed curricula that integrate diverse fields such as geomatics, cartography, transport engineering, urban planning, environmental engineering, and computer science. By blending these perspectives, the curricula provide students with a holistic understanding of LBSs and ITSs, preparing them to address complex, real-world challenges that span multiple sectors. In this paper, the curriculum development and modernization process is detailed, with a particular focus on the two key phases: teacher training and curriculum development. The teacher training phase was crucial in equipping educators with the skills and knowledge necessary to deliver the new and updated courses. This phase also provided an opportunity for teachers to familiarize themselves with the latest trends and technologies in LBSs and ITSs, ensuring that they could effectively convey this information to students. The development phase focused on the creation of the curriculum itself, ensuring that it met both academic standards and industry needs. The curriculum was designed to be flexible and responsive to emerging technologies and methodologies, allowing for continuous improvement and adaptation. Additionally, the paper delves into the theoretical frameworks underpinning the methodologies employed in the project. These include Problem-Based Learning (PBL) and Problem-Based e-Learning (PBeL), both of which encourage active student engagement and foster critical thinking by having students tackle real-world problems. The emphasis on PBL ensures that students not only acquire theoretical knowledge but also develop practical problem-solving skills applicable to their future careers in LBSs and ITSs. Furthermore, the project incorporated rigorous quality assurance (QA) mechanisms to ensure that the teaching methods and curriculum content met high standards. This included regular feedback loops, stakeholder involvement, and iterative refinement of course materials based on evaluations from both students and industry experts. These QA measures are essential for maintaining the relevance, effectiveness, and sustainability of the curricula over time. In summary, the LBS2ITS project represents a significant effort to enrich and modernize university curricula in Sri Lanka by integrating cutting-edge technologies and interdisciplinary approaches. Through a combination of innovative teaching methodologies, comprehensive teacher training, and robust quality assurance practices, the project aims to equip students with the skills and knowledge needed to excel in the fields of LBSs and ITSs. Full article

This study examines the digital translation of traditional wooden architectural construction techniques through the application of Building Information Modeling (BIM) methods. The development of a Heritage Building Information Modeling (HBIM) model for these techniques necessitates interdisciplinary integration. Critical steps involve the intelligent incorporation of wooden architectural knowledge into parametric models and the creation of advanced modeling methods capable of translating such models. These aspects are essential for bridging existing gaps and enhancing HBIM applications. By using the Dong drum tower as a case study, this paper presents a parametric model for stilt-style wooden structures, emphasizing the generation rules of construction techniques and the extraction and translation of parameters. A smart automated modeling method was developed and programmed collaboratively using Grasshopper (version 1.0.0007) and Python (version 2.7.12.0). This method facilitates the generation of diverse, customizable drum tower models within 60 s and has successfully created the tallest drum tower model in Guangxi based on actual measurements, validating the method’s reliability and effectiveness. The findings of this study offer digital, automated, and intelligent support for the preservation and transmission of traditional architectural techniques. Full article

The extant literature is rich with references to co-creation in research and knowledge initiatives. However, there is a pressing need for clarity regarding the definition of knowledge co-creation and its application in sustainable development research. To fill these gaps, we conducted a systematic review of 42 articles that met the selection criteria. The findings revealed the disciplinarity in the studies, geographical collaboration patterns, and varying interpretations of knowledge co-creation. This study also identified key engagement methods and stages essential for the knowledge co-creation process in the context of sustainable development. The implications for practice highlight the importance for researchers, and possibly policy-makers, to consider not only the various definitions of knowledge co-creation attempted—often lacking consensus—but also guiding principles. Strengthening knowledge co-creation in transdisciplinary studies, fostering robust global partnerships, ensuring equitable regional representation, facilitating the active participation of diverse stakeholders, and maintaining balanced power dynamics rooted in trust and relationality are crucial for achieving positive sustainable development outcomes. The intersection of research, knowledge co-creation, sustainable development, and the Sustainable Development Goals (SDGs), along with the elements proposed for future consideration, which has not been extensively explored in previous studies, underscores the originality of this study, which carries significant theoretical, methodological, and global policy implications. Full article

Within mammalian cells, diverse endocytic mechanisms, including phagocytosis, pinocytosis, and receptor-mediated endocytosis, serve as gateways exploited by many bacterial pathogens and toxins. Among these, caveolae-mediated endocytosis is characterized by lipid-rich caveolae and dimeric caveolin proteins. Caveolae are specialized microdomains on cell surfaces that impact cell signaling. Caveolin proteins facilitate the creation of caveolae and have three members in vertebrates: caveolin-1, caveolin-2, and caveolin-3. Many bacterial pathogens hijack caveolin machinery to invade host cells. For example, the Gram-positive facultative model intracellular bacterial pathogen Listeria monocytogenes exploits caveolin-mediated endocytosis for efficient cellular entry, translocation across the intestinal barrier, and cell–cell spread. Caveolin facilitates the internalization of group A streptococci by promoting the formation of invaginations in the plasma membrane and avoiding fusion with lysosomes, thereby aiding intracellular survival. Caveolin plays a crucial role in internalizing and modulation of host immune responses by Gram-negative bacterial pathogens, such as Escherichia coli K1, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Salmonella enterica serovar Typhimurium. Here, we summarize how bacterial pathogens manipulate the host’s caveolin system to facilitate bacterial entry and movement within and between host cells, to support intracellular survival, to evade immune responses, and to trigger inflammation. This knowledge enhances the intervention of new therapeutic targets against caveolin in microbial invasion and immune evasion processes. Full article

Building on the idea of religion and science as conceptual maps of intellectual territory, I use a settler colonial analysis as a framework for thinking about decolonizing religion and science in a way that moves away from abstraction and towards action; addressing not just the ideas, but the tools of control—the fences—that impose ideas on the territory itself. Comparing the Wyoming prairie with the epistemological prairie, I describe the maps, fences and other tools and technologies of settler colonialism used to appropriate Indigenous Land and knowledge, eventually turning it into private property. It is in this last step—the creation of private property—that fences are most important, because they are tools of ownership that do not merely restrict access to parts of the prairie (land and knowledge), but restrict movement on the prairie itself. I describe patents and intellectual property as examples of fences on the epistemological prairie. Because they are legally and historically connected to technologies of settler colonial appropriation of land—including terra nullius and land patents—they are an excellent example of the connection between land and epistemological territory, and show what epistemological decolonization can look like in practice. Full article

The COVID-19 pandemic caused unprecedented global disruptions, with the hotel and aviation industries—two critical pillars of tourism—among the hardest hit. This study analyzed 451 hotel-related and 336 aviation-related records from the Web of Science database, applying semantic network analysis to uncover eight clusters of crisis management knowledge: basic functions, crisis response, operational strategies, epidemic prevention and control, crisis perception, innovative services, scope of influence, and internal and external environments. Latent Dirichlet Allocation (LDA) topic modeling identified distinct thematic strategies for each sector. In hotels, these included Digital Innovation Transformation, Monitoring Management Procedures, Emotional Awareness Incentives, and Resilience Mechanism Establishment. In aviation, strategies focused on Green Economic Transformation, Co-creation Value Realization, Passenger Incentive Mechanisms, and Balancing Health Risks. By visualizing co-occurrence relationships and mapping thematic intersections and divergences, this study provides actionable insights into the recovery strategies of these industries. The findings offer robust support for developing targeted management approaches and decision-making frameworks to ensure the sustainable growth of the tourism sector. Full article

With the increasing and intensifying effects of global climate change and the rapid development of higher education, energy and resource consumption at university campuses has been rising drastically. This shift has been worsened by campuses’ expanded role in addressing extreme weather hazards and taking on additional cultural and community functions. This article carries out a comprehensive literature review of the low-carbon measures and resilient behaviors implemented on university campuses based on publications published in two major databases, the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS). Results show that: (1) most existing studies only focus on campus carbon emission reduction from a single perspective, without considering the correlation between carbon emissions in different dimensions on campuses and without analyzing the causes of excessive campus carbon emissions from the perspective of the built environment; (2) current studies have not constructed an assessment system for campus carbon resilience and lack the tools and methods for assessment. After summarizing and analyzing, this study proposes the concept of campus “carbon resilience”, which refers to the ability of campuses to cope with the risks of disasters and uncertainties caused by excessive carbon emissions. The research framework of this study is divided into three parts: connotative characteristics, influencing factors, and optimization strategy. Following this framework, the concept and critical features of campus carbon resilience “carbon minus resilience”, “carbon saving resilience”, “carbon reduction resilience”, and “carbon sequestration resilience” are analyzed and outlined. Next, an integrated impact factor system for campus carbon resilience is proposed. This system incorporates aspects such as land utilization, building operation, landscape creation, and energy regeneration from the perspective of the built environment. Finally, with the core objective of effectively reducing the dynamic range of carbon emissions when dealing with critical disturbances and improving the adaptability and resilience of campuses to cope with excessive carbon emissions, this study proposes an optimization strategy of “setting development goals–establishing an evaluation system–proposing improvement strategies–dynamic feedback and adjustment” to provide ideas and theoretical guidance for responding to university campus carbon risk and planning carbon resilience. Full article

Artificial intelligence and virtual reality technologies have significant potential in traditional architectural education. Historically used separately, their educational impacts are not fully understood. To advance sustainable architecture education, this study incorporates language and illustration tools of artificial intelligence, along with immersive painting and simulation capabilities of virtual reality, into the curriculum of Jiangnan traditional architecture. Through a randomized controlled trial, 60 students were divided into AI, VR, and control groups. Based on the establishment of an adaptive course learning assessment system, this study empirically compares the effects of artificial intelligence methods, virtual reality methods, and traditional teaching methods across four dimensions: architectural knowledge, architectural design, architectural computation, and architectural learning. Independent sample t-tests and one-way analysis of variance are used to validate the differences in the effectiveness of these technological applications. Findings reveal that artificial intelligence notably enhances design and learning outcomes, whereas virtual reality shows pronounced effects in bolstering knowledge acquisition and computational tasks. Artificial intelligence proves particularly suited to conceptualization and narrative-based design tasks, while virtual reality aligns closely with model creation and post-design refinement activities. These findings provide new perspectives for hybridizing artificial intelligence and virtual reality in sustainable architecture education, contributing to sustainable outcomes. Full article