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Operationalising Blue Carbon: Australia\u27s Framework as a Model for Global Nature-Based Climate Mitigation
No full textBlue carbon ecosystems offer a critical solution to nature-based climate change mitigation. These habitats not only sequester significant amounts of carbon per hectare but also support biodiversity and increase climate resilience for coastal communities. Yet, despite the widespread recognition of these benefits in global climate discussions, most existing carbon sequestration and crediting frameworks remain focused on terrestrial ecosystems and fail to account for the greater variability in marine and costal habitats. This policy gap creates a significant barrier in the capacity for costal carbon sequestration projects to be scaled globally. Australia has addressed this implementation gap at a national level by developing an accredited framework for restoring coastal ecosystems. This model sets out requirements for project oversight, transparency in monitoring, and maintaining market trust. By integrating emission reductions, biodiversity, disaster resilience, and community benefits in a single, scalable process, the Australian blue carbon framework provides nations with a tested pathway to move blue carbon restoration from ambition to measurable, impactful climate action. This policy paper therefore recommends that COP30 formally endorse Australia’s blue carbon framework. More specifically, UNFCCC should reference Australia’s blue carbon protocols as a model for high-integrity credits in Article 6, to support the development of regional and global blue carbon projects
Reengineering Resilience: Bio-Resilience Bonds for Financing Microbial Infrastructure and Climate Equity
No full textThis policy proposal introduces Bio-Resilience Bonds (BRBs), a performance-based financial instrument designed to monetise microbial ecosystem services as measurable climate infrastructure. Microbial ecosystems are crucial for climate resilience, yet they are often overlooked in mainstream adaptation f inance frameworks. Their ability to regulate carbon and nitrogen cycles, reduce methane emissions and enhance soil and water stability (Delgado-Baquerizo et al., 2016) makes them essential assets for climate mitigation and adaptation. With global adaptation needs exceeding £2.7 trillion (UNEP, 2024), this oversight indicates a systemic failure to recognise biology as a form of infrastructure. BRBs transform microbial outputs into localised key performance indicators (KPIs) (Bodkhe et al., 2025), which are validated through biosensor networks, blockchain-enabled ledgers and third-party monitoring protocols (Rinken & Kivirand, 2019). This decentralised verification system enhances operational credibility, allowing sovereign and sub sovereign entities to incorporate living systems into their adaptation planning. Case studies have demonstrated the feasibility of BRBs across soil, aquatic and reef ecosystems. Additionally, by incorporating Indigenous governance, protecting microbial intellectual property and establishing revenue-sharing frameworks (Riley & Moran, 2010), BRBs address long-standing equity and stewardship gaps in climate finance. This advocates for a strategic rethinking of adaptation infrastructure, one that prioritises biological measurability, inclusive resilience and scalable innovation
Strengthening Climate Empowerment in Pharmacy Education and Practice: A Literature Review
No full textPharmacists are increasingly acknowledged as essential contributors to climate resilience as climate change poses ever-increasing challenges to global health. Within the framework of the United Nations Framework Convention on Climate Change (UNFCCC), Action for Climate Empowerment (ACE) provides a framework that is focused on capacity building, public engagement, and education. The application of ACE principles to pharmacy practice and education to advance sustainability is examined in this article. It looks at national projects that support ACE objectives like Kerala\u27s pharmacy-led waste management campaign and the Greener-NHS programme. The review finds gaps and opportunities for incorporating climate action into pharmacy through an examination of financial models, technological developments, and policy support. It ends with tactical suggestions to strengthen pharmacists\u27 contributions to climate adaptation and enable them to spearhead sustainable health shifts. In addition to case studies from the UK and India, this review draws on secondary literature analysis and findings from a Knowledge, Attitude and Practice (KAP) study of pharmacy students
Chambers of Change: Students’ Unions as Civic Accelerators of ACE
No full textThe transformative potential to incorporate students’ unions as an institutional gateway for the implementation of Action for Climate Empowerment (ACE) in higher education systems is discussed in this paper. Working on the outlines of the Glasgow Work Programme and prominent ACE frameworks as a basis of support, it asserts that student governments are structurally aligned with ACE goals, providing them procedural legitimacy, direct access to young people, and the capacity to put all six ACE pillars into practice: education, training, public awareness, public participation, access to information, and international cooperation. The study applies case studies from the University of Florida, American University, and Harvard where student-led climate legislation showed peer-driven climate literacy, governance capability, and policy leverage. While, examining South African universities\u27 data demonstrated that students’ unions can influence sustainability reforms through institutional channels. Students\u27 unions have a deeply rooted penetration to huge masses of young people yet are scarcely integrated into national ACE plans. With their financing, institutional partnerships, and their role in reporting ACE, the paper suggests that they should be made explicitly visible in policy designs. Therefore, the aim of this work is to propose a strategy for incorporating student governance in ACE policy designs
Learning platforms: a future in environmental education or a motionless present?
No full textIn a global context marked by the climate crisis, environmental education and training have taken a different path, adapting to current technologies. It is in these circumstances that learning platforms (from now on LP’s) emerge, changing the way we learn and perform around climate change. This type of web app can provide remote knowledge, and has taken on a growing role the last few years. However, despite their potential, challenges remain, such as inequality in digital access, poor variety of languages and cultural adaptation, a lack of media outreach, and the absence of assessments of their real impact on communities in emerging countries. Added to this is the fact that most of these platforms operate without governmental support or local educational networks. This research aims to analyze both the advantages and disadvantages of these LPs, their level of access, and their contribution to climate education in countries of the Global South, presenting strategies and recommendations to consolidate a more effective climate learning platform in emerging countries
COP-30 Simulation Report Ignite and Insight: A Global Mutirão
No full textThe first Global Stocktake (GST) certifies that there is a widening gap between our collective ambition and the real-world deliveries. And while the COP29 did set a new climate finance floor of US$300 billion/yr by 2035, the science however showed that this is far below developing country needs, and the current trajectories still overshoot the Paris 1.5 C limit. With the most recent updates, Article 6 market rules are now in full operation. That is inclusive of a mandatory 5% share of proceeds for the Adaptation Fund; it is an underused lever to channel resources to the frontline communities (UNFCCC, 2025). Looking ahead to Belém, the Brazilian Presidency\u27s framing of a Globally Determined Contribution (GDC) , a global NDC that translates GST findings into a shared implementation mandate across six axes and 30 objectives, it offers a practical bridge from dialogue to delivery, with the Global Mutirão inviting bottom-up participation at scale. This report has the aim of synthesizing the latest technical and governance evidence while proposing an implementation toolkit aligned with COP30\u27s agenda: first is an AI GapFinder to surface the policies\u27 and finance\u27s shortfalls across NDCs/BTRs; then Mutirao Multipliers which are community monitoring hubs feeding national inventories. These measures operationalize GST guidance into measurable progress while upholding the justice and CBDR principles
Pattern Recognition of sEMG Signals by CNN-BiLSTM Algorithm for Bio-Robotics Applications
No full textThis paper presents a hybrid CNN-BiLSTM model for surface Electromyography (sEMG) signal classification towards bio-robotic applications. The EMG signals were segmented into four-channels, 50 -sample windows and then passed through a deep convolutional network for spatial feature extraction followed by a bidirectional LSTM layer to address temporal dependencies. Dynamic learning rate adaptation and class weight balancing were employed to train the model in terms of addressing dataset variability. Evaluation on the test set demonstrated an overall testing accuracy of (87 %) with Average False Negative Rate (FNR) 13.5%, and Average False Positive Rate (FPR) 5.0%. The gestures in the data predicted as rest (96 %), extension (95 %), flexion (95 %), ulnar (93 %) and radial deviation of the wrist (82 %), grip (87 %), supination (77 %), and pronation (67 %). The results indicate the promise of the model for reliable real-time control in human-machine interface (HMI) and assistive robotic systems. This is an application that extends beyond signal classification which emphasizes the application of Knowledge Representation and Reasoning (KRR) within AI as communication principles. Human-Robot Interaction (HRI) is improved while broad scope prospects are opened for use in assistive and prosthetic devices. Surface electromyography (sEMG) signals from human muscles contain complex spatiotemporal patterns that are challenging to classify accurately for real-time robotic control applications. The inherent variability, noise, and non-stationary nature of sEMG signals pose significant challenges for reliable gesture recognition in human-machine interfaces
Corchorus olitorius exhibits antiproliferative potential supported by metabolic profiling and integrative biological analyses
No full textHerbal nutraceuticals could be employed as alternative or complementary routes for alleviating cancer. Corchorus olitorius (Malvaceae) was employed traditionally in the management of tumors. The study aimed to investigate the antiproliferative activity of C. olitorius leaves. In vitro cytotoxic and anti-angiogenic activities of C. olitorius were estimated. The bioactive fraction was subjected to in vivo study on BALB/ c female mice using Ehrlich Ascites Carcinoma model. UPLC-ESI-MS/MS analysis was done to determine the phytometabolites followed by in silico studies on the major identified compounds. The bioactive fraction possessed potent in vitro activity against A549 cells with IC50 = 7.8 µg/mL and exhibited strong anti-angiogenic activity. The in vivo study revealed the safety of the fraction and confirmed its anticancer activity. The tumor volume in the fraction treated group was reduced by 33.7% compared to the control group. UPLC-ESI-MS/MS analysis led to the identification of 25 compounds belonging to different chemical classes. The in silico pharmacodynamic profile revealed that the compounds exhibited agreeable binding affinities toward EGFR, CDK2 and VEGF-A comparable to the standard drugs. C. olitorius is a promising herbal nutraceutical from which effective chemopreventive and anticancer formulations can be developed following further in depth studies
Exploring urban growth drivers in heritage areas using machine learning: The case of Gharb Sohail, Aswan
No full textHeritage areas are increasingly threatened by rapid urban expansion, driven by population growth and insufficient planning measures. These areas are vital as they preserve cultural identity, social cohesion, and economic vitality, making their preservation essential. This study investigates the drivers of urban expansion in Gharb Sohail, Aswan, a culturally and historically significant heritage area. Employing Geographic Information Systems (GIS) and advanced machine learning models, including the Land Change Modeler (LCM) and Multi-Layer Perceptron (MLP) neural networks, the research identifies key factors shaping urban growth and simulates future expansion scenarios. The findings indicate that urban sprawl within the study area is projected to cover 753.65 feddan by 2062. Proximity to the Nile River, mosques, and tourism infrastructure emerge as the dominant factors influencing urbanization, with Cramer’s V values of 0.66, 0.50, and 0.49, respectively. The study forecasts that by 2062, the urbanized area will expand from 20.15 % to 27.39 % of the total study area, resulting in considerable encroachment on non-urban lands. This significant growth poses a direct threat to the integrity of Gharb Sohail’s heritage areas, highlighting the urgent need for comprehensive urban management strategies. In response, the research advocates for the implementation of targeted urban planning measures, including strict urban growth regulation, the promotion of architectural continuity, and the integration of sustainable development practices in adjacent urban areas. These strategic recommendations offer actionable insights for urban planners and policymakers, providing a robust framework for balancing heritage conservation with the socioeconomic demands of urban growth
Predictive quality analytics for the viscosity of water-based architectural paint manufacturing by using improved supervised machine learning and maximum dissimilarity algorithm
No full textViscosity is a key physical property in the production process of paint. In this research, statistical modeling was used to analyze and predict the viscosity of water-based architectural paint as part of quality control in a coating factory. In this sense, quality technicians constantly seek to improve this indicator. The Viscosity difference is modelled as a function of temperature in the ranges of 19–25 °C and 25–32 °C. Parametric polynomial regression, ANOVA analysis, residual plots, and Box-Cox transformation were used as statistical tools for data analytics and prediction. Model corrections were applied by using Cochrane–Orcutt transformation and assumptions were tested using the Kolmogorov–Smirnov statistics by Lilliefors, Breusch–Pagan, and Durbin–Watson. Improved Maximum Dissimilarity algorithm with the small group filter and representative initial set selection was used for selecting the best representative data to validate the models and three supervised machine learning methods (Random Forest, K-nearest neighbors, and Gradient-boosted trees) were employed through hyperparameter optimization, it was found that Random Forest gave the best performance. Two regression models were obtained: a second-degree polynomial model for samples with a temperature less than 25 °C and a simple linear non-parametric model one for samples at temperature greater than 25 °C. Adjusted coefficients of determination are 0.968 and 0.978, respectively. Finally, using the proposed predictive models could reduce the turnaround time by 48.5%