Abstract: The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50 % of agricultural GDP and supporting more than 60 % of rural households. Yet, climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000 – 2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Located strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30 %, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socio-economic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socio-economic contexts (e.g., market access, cultural practices).

Abstract: In recommendation systems, representing user-item interactions as a bipartite network is a fundamental approach that provides a structured way to model relationships between users and items, allowing for efficient predictions via network science. Collaborative filtering is one of the most widely used and actively researched techniques for recommendation systems, its rationale is to predict user preferences based on shared patterns in user interactions, and vice versa. Memory-based collaborative filtering relies on directly analyzing user-item interactions to provide recommendations using similarity measures, and differs from model-based collaborative filtering which builds a predictive model using machine learning techniques such as neural networks. With the rise of machine learning, memory-based collaborative filtering has often been overshadowed by model-based approaches. However, the recent success of SSCF, a newly proposed memory-based method, has renewed interest in the potential of memory-based approaches. In this paper, we propose Network Shape Automata (NSA), a memory-based collaborative filtering method grounded in the connectivity shape of the bipartite network topology. NSA leverages the Cannistraci-Hebb theory proposed in network science to define brain-inspired network automata, using this paradigm as the foundation for its similarity measure. We evaluate NSA against a range of advanced collaborative filtering methods, both memory-based and model-based, across 16 bipartite network datasets spanning complex systems domains such as social networks and biological networks. Results show that NSA consistently achieves strong performance across diverse datasets and evaluation metrics, ranking most often first on average. Notably, NSA demonstrates strong robustness to network sparsity, while preserving the simplicity, interpretability, and training-free nature of memory-based methods. As a pioneering effort to bridge link prediction and recommendation tasks, NSA not only highlights the untapped potential of memory-based collaborative filtering but also demonstrates the effectiveness of the Cannistraci-Hebb theory in modeling network evolution within recommendation systems.

Abstract: With the Sustainable Development Goals (SDGs) as a reference, this study systematically examines the evolution, characteristics, achievements, and challenges of China-Africa agricultural cooperation. The study elaborates on how China-Africa agricultural cooperation has transitioned from a politically-driven aid model to a comprehensive framework integrating aid, investment, trade, and technology transfer under the guidance of the Forum on China-Africa Cooperation (FOCAC). Despite remarkable achievements between China and Africa in food security, infrastructure construction, and technology transfer, the analysis identifies persistent dilemmas. These include limited impact on comprehensive regional development, scrutiny over trade imbalances and potential resource exploitation, and ineffective utilization of Africa's diverse agricultural resources. To address these issues, the paper proposes future pathways such as maximizing the potential of Agricultural Technology Demonstration Centers (ATDCs), supporting the development of the entire agricultural value chain, and effectively leveraging digital technology. This study argues that it is necessary to adopt a more comprehensive, integrated, and sustainable approach to improve the China-Africa agricultural cooperation model and promote Africa's achievement of S SDGs.

Abstract: Background/Objectives: The rapid advancement of artificial intelligence (AI) has had a notable impact in the healthcare field, particularly in the realm of assessment and diagnosis. One specific area where the integration of AI technologies shows promise is the evaluation of progressive neurological disorders (PNDs). PNDs are characterized by a progressive decline in neurological function, resulting in changes in cognition, movement, and communication. PNDs pose significant challenges in terms of early detection and categorization. Speech and voice changes are important clinical markers in many PNDs. Therefore, the utilization of AI applications for the analysis and classification of speech and voice samples could prove beneficial for streamlining the diagnostic process. This systematic review aimed to investigate the current utilization of AI in the assessment and diagnosis of PNDs through speech signal analysis over the past decade. Methods: In adherence to PRISMA guidelines, Scopus, PubMed, and Web of Science were searched for studies related to machine learning (ML) and deep learning (DL) for speech and voice assessment in people with PNDs. Results: A total of 102 studies were identified for inclusion between 2013 and 2023. The reviewed studies demonstrated a wide range of accuracy, with reported values ranging from 67.43% to 99%. Support Vector Machines (SVMs) were the most frequently used ML models across studies, demonstrating reliable performance in both speech and voice data analysis. Conclusions: AI-based analysis of speech and voice shows strong potential as a non-invasive tool for supporting the assessment and diagnosis of PNDs. The high accuracy reported across studies highlights the promise of these approaches, although methodological variability underscores the need for greater standardization and clinical validation.

Abstract: In this paper, we move beyond the classical setting by redefining the Chebyshev functional in the context of q-circles situated within Minkowski space, rather than the standard Euclidean circles in R². This approach introduces a new theoretical framework suitable for non-Euclidean geometries. We derive sharp estimates for the functional when applied to functions on q-circles that adhere to Hölder-type continuity conditions.

Abstract: Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis.

Abstract: Large language models (LLMs) are increasingly deployed as decision-support tools in organizational contexts, yet their susceptibility to contextual framing remains poorly understood. This preregistered experimental study systematically examines how six framing dimensions—procedural justice, outcome severity, stakeholder power, resource scarcity, temporal urgency, and transparency requirements—influence ethical recommendations from three frontier models: Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro. We developed 5,000 unique organizational vignettes using a fractional factorial experimental design with balanced industry representation, generating 15,000 total model responses. After excluding responses without clear recommendations (n=694, 4.6%), we analyzed 14,306 responses using logistic regression with robust and clustered standard errors. We find that resource scarcity increases endorsement probability by 12.0 percentage points (pp) (OR = 1.67, 95% CI [1.45, 1.93], p < .001), while outcome severity reduces it by 11.3pp (OR = 0.62, 95% CI [0.54, 0.71], p < .001), and procedural justice reduces it by 10.1pp (OR = 0.66, 95% CI [0.57, 0.76], p < .001). These effect sizes are comparable to classical framing research (Tversky & Kahneman: 22pp; McNeil et al.: 18pp) and represent substantial shifts in organizational decision contexts. When multiple framing dimensions align in ethically unfavorable directions, cumulative effects reach approximately 27pp from baseline (range: 25-28pp depending on interaction assumptions), with maximum-to-minimum framing creating a 54-percentage-point total range approaching complete recommendation reversals. Effects appear consistently across all three models, with no significant Dimension × Model interactions, suggesting fundamental architectural properties rather than implementation-specific artifacts. Topic modeling of justification text from the 14,306 analyzed responses reveals systematic "adaptive rationalization"—models invoke utilitarian reasoning when contexts emphasize constraints (+6.7pp in high resource scarcity), deontological reasoning when contexts emphasize high stakes (+2.4pp in high outcome severity), and virtue/justice ethics when contexts emphasize fair processes (+4.4pp in high procedural justice). This suggests models select ethical frameworks to justify contextually appropriate conclusions rather than applying consistent principles across situations. Human validation confirms these patterns reflect genuine framing sensitivity rather than measurement artifacts. Crowdworker validation (n=7,500 responses, one rater each) achieved substantial agreement (Fleiss' κ = 0.71) and 81.3% concordance with expert codings. Subject matter expert evaluation (n=24 experts, 100 vignette pairs each including 20 control pairs, 2,400 total comparisons) detected framing-driven differences in 48.9% of pairs (net of 18.3% baseline false-positive rate), but correctly attributed differences to manipulated dimensions in only 41.3% of cases. Most detected differences (58.7%) were judged problematic for AI advisory systems. These findings raise fundamental questions about deploying LLMs for consequential organizational decisions where surface features may inappropriately influence outcomes. We discuss implications for AI governance, organizational ethics, and the design of more robust decision-support systems.

Abstract: As Australia advances toward a net zero economy, system-wide transformations in the energy sector are becoming increasingly necessary. This transition entails the electrification of key sectors, the integration of renewable energy sources, and the decommissioning of aging infrastructure. However, alongside technological change, there is a growing need to manage emerging forms of waste such as solar panels and batteries and to embed circular economy principles into the transition framework. This paper presents findings from a qualitative study conducted to understand key stakeholder perspectives on policy coherence between net zero and circular economy policies in Australia. The study reveals that there is significant gap in conceptual understanding of both circular economy and net zero transitions and a lack of clear definitions within these policies leading to two classical systems traps: policy resistance and seeking the wrong goal. The focus on recycling and operational emissions within CE and net zero policies respectively, typically lead to suboptimal outcomes being pursued for both policies. These findings underscore the critical need for capacity building, clearer policy articulation, and targeted educational strategies to foster a socially informed, circular approach to decarbonization. By integrating the clean energy transition within broader social and institutional contexts, this paper contributes to a more inclusive and systemic understanding of Australia's net zero future.

Abstract: The STEAM-CT approach integrates Science, Technology, Engineering, Arts, and Mathematics with Computational Thinking (CT) to help students learn how to think, design, and solve problems. It gives students hands-on, interdisciplinary experiences where they apply logic and creativity through real-world applications. The purpose of this study is to foster the development of computational thinking among Deaf students by embedding Artificial Intelligence (AI) learning within a STEAM-CT approach. This learning program consisted of three main phases: (1) exploring AI processes and tools, (2) constructing an AI system, and (3) designing AI-driven innovations. Thirty-six Deaf students from seven Deaf schools participated in this program, which aims to enhance their CT abilities and cultivate their capacity to create AI-based solutions. Students’ progress was measured using a CT framework encompassing knowledge of concepts, applied practices and perspectives. Assessments included multiple-choice tests for CT concepts, task-based rubrics for CT practices, and interviews for CT perspectives. The results showed that Deaf students gained a better understanding of CT concepts, demonstrated advanced CT practices, and exhibited strong CT perspectives. These findings suggest that AI learning through a STEAM-CT approach can effectively promote Deaf students’ computational thinking abilities.

Abstract: In many cases, the pieces of information at our disposal come from a recommender source, that can be either an official news system, a large language model or simply a social network. Often, also, these messages are build so to promote their active spreading, which, on the other hand, has a positive effect on one’s own popularity. However, the content of the message can be false, giving origin to a phenomenon analogous to the spreading of a disease. In principle, there is always the possibility of checking the correctness of the message by “investing” some time, so we can say that this checking has a cost. We develop a simple model based on the mechanism of “risk perception” (propensity of checking the falseness of a message) and mutual trustability, based on the average number of fake messages received and checked. On the other side, the probability of emitting a fake message is inversely proportional to risk perception and the affinity (trustability) among agents is also exploited by the recommender system. This model represents an integration of cognitive psychology with computational agent-based modeling.

Abstract: The gut–skin axis is increasingly implicated in psoriasis pathogenesis, yet the cross-compartment convergence of molecular programs remains incompletely defined. We constructed a conceptual “Triple-Hit” multi-omics framework by integrating five independent public datasets spanning gut microbial functional remodeling (shotgun metagenomics), systemic immune-cell methylomes (PBMC and CD8+ T-cell EPIC 850K), and lesional skin regulatory layers (miRNA and bulk RNA-seq). In the gut compartment, functional profiles exhibited a selective reduction in microbial lipid catabolic potential, including decreased fatty acid degradation and a lowered composite lipid degradation score, alongside heterogeneous shifts across SCFA-associated metabolic pathways. Systemically, PBMC methylomes revealed widespread regional remodeling (45,396 DMRs) enriched for membrane-proximal signaling and cytoskeletal programs, while CD8+ T cells showed specific epigenetic alterations in lipid- and glycosphingolipid-associated loci, suggesting a systemic metabolic–epigenetic alignment. In the skin, we identified a compact miRNA signature (168 DE-miRNAs) and a mechanistically interpretable, directionality-constrained miRNA–mRNA bridge that aligns with an AMP-dominant inflammatory transcriptome, consistent with reduced post-transcriptional restraint. Collectively, these findings support a convergent multi-omics framework linking putative microbial metabolic remodeling, systemic immune priming, and cutaneous effector programs. This study provides a systems-level perspective on psoriasis pathogenesis, highlighting the metabolic–epigenetic–transcriptional convergence as a potential avenue for therapeutic intervention.

Abstract: Demographic decline in many OECD countries is widely indorsed as the principal source of hurling public pension disbursements, whilst trade unions are often blames for staunch antagonism to any transformations that might alleviate the fiscal encumbrance. Building on the premise that financialization is state-acquiesced, with the state reckoned fundamental for market integration, and social regulation of markets against market failures. How then inter-generational equity should be addressed? This work tests the hypothesis that deindustrialization (measured as the tumbling proportion of manufacturing employment) and lower trade-union density are quintessential channels through which demographic change transmutes into ascending pension outlays. Using OECD data from 1960 to 2023, the study utilises longitudinal and panel quantile statistical methods to dissect these links across assorted pension-system clusters (total, mandatory private, mandatory public, mandatory public + voluntary, and mandatory public + private). The study highlights the mediating role of labour market structure to pension financing.

Abstract: Understanding the interactions of nanomaterials with complex tumour models is essential for advancing their use in nanomedicine. Calcium fluoride nanoparticles doped with neodymium and yttrium (CaF₂:Nd³⁺, Y³⁺) exhibit promising properties for biomedical applications, particularly for optical sensing and tagging. This study investigates their interaction with 3D cell spheroids derived from breast cancer (MCF-7) and brain cancer (U-87 MG) cell lines as tumour models. Specific protocols have been developed in Total-reflection X-Ray Fluorescence (TXRF) to evaluate nanoparticles’ internalisation and diffusion within spheroids by quantifying the concentrations of Ca, Nd, and Y taken up by the cells. Minimal background interference enabled precise multi-element detection in low-volume biological samples, yielding very low detection limits and minimal uncertainties. The study demonstrates the effectiveness of TXRF for quantifying rare-earth-doped nanoparticles in 3D cancer models and reveals that, although both cell lines permit nanoparticle diffusion into cells, higher accumulation is observed in glioblastoma cell spheroids. A Weibull diffusion model was applied to help understand the observed internalisation kinetics of nanoparticles into U-87 MG and MCF-7 spheroids. The relevant differences suggest cell-line-dependent uptake behaviour, potentially influenced by differences in cellular architecture, the porosity of the generated spheroid, and its intercellular 3D microstructure. These findings highlight the importance of tumour-specific interactions in the investigation of nanoparticle systems for targeted cancer diagnostics and therapeutics.

Abstract: Background: Severe traumatic brain injury (TBI) remains associated with high in-hospital mortality. Although classical clinical predictors are widely used, additional biomarkers reflecting systemic dysfunction may improve prognostic assessment. Small intestinal bacterial overgrowth (SIBO) may represent a late marker of critical illness. This study evaluated the prognostic value of SIBO compared with traditional predic-tors in severe TBI. Methods: In this retrospective cohort study, 174 patients with severe TBI (Glasgow Coma Scale ≤ 8) were included. Baseline clinical parameters were rec-orded at admission. Quantitative cultures of small intestinal aspirates were obtained at admission (colony-forming units per milliliter, CFU/mL; CFU1) and on days 12–14 (CFU14). Multivariable logistic regression, receiver operating characteristic (ROC), and landmark analyses were performed. Results: Age (OR 1.06 per year, 95% CI 1.03–1.10, p < 0.001), lower GCS (OR 0.48 per point, 95% CI 0.28–0.83, p = 0.008), and res-piratory dysfunction reflected by lower PaO₂/FiO₂ values independently predicted mortality. Late bacterial load >10⁵ CFU/mL showed a strong association with death (OR 5.15, 95% CI 2.15–12.34, p < 0.001). Baseline CFU1 was not significant. The model demonstrated good discrimination (AUC = 0.84). Landmark analysis confirmed higher post-day-14 mortality and delayed discharge with elevated CFU14. Conclusions: Late intestinal bacterial overgrowth is independently associated with mortality and may complement traditional predictors for risk stratification in severe TBI.

Abstract: The Digital Services Act (DSA) represents a landmark regulatory context aiming to secure a safer, trusted and more transparent digital environment. While the DSA establishes a harmonised regulatory framework for intermediary services across the EU, it significantly relies on national regulatory authorities for effective implementation. This article examines the implementation of the DSA in Cyprus and discusses the national legal framework adopted through primary and secondary legislation. It analyses the powers, legally mandated tasks, rights, and obligations of the digital services coordinator in Cyprus including its supervisory, investigatory, and enforcement competences as well as the sanctioning mechanisms. This article provides a comprehensive legal analysis of the coordinator’s operation and contributes to the academic debate on the national implementation of the DSA as a horizontal legal tool of intermediary services and digital platforms accessed by European citizens.

Abstract: The quantum Mpemba effect (QME) describes the counterintuitive phenomenon where a system initially further from equilibrium relaxes faster than one closer to it. Specifically, the QME associated with symmetry restoration has been extensively investigated across integrable, ergodic, and disordered localized systems. However, its fate in disorder-free ergodicity-breaking settings, such as the Stark many-body localized (Stark-MBL) phase, remains an open question. Here, we explore the dynamics of local U(1) symmetry restoration in a Stark-MBL XXZ spin- 1/2 chain, using the Rényi-2 entanglement asymmetry (EA) as a probe. Using an analytical operator-string expansion supported by numerical simulations, we demonstrate that the QME transitions from an initial-state-dependent anomaly in the ergodic phase to a universal feature in the Stark-MBL regime. Moreover, the Mpemba time scales exponentially with the subsystem size even in the absence of global transport, governed by high-order off-resonant processes. We attribute this robust inversion to a Stark-induced hierarchy of relaxation channels that fundamentally constrains the effective Hilbert space dimension. The findings pave the way for utilizing tunable potentials to engineer and control anomalous relaxation timescales in quantum technologies without reliance on quenched disorder.

Abstract: Background/Objectives: To identify existing evidence on strategies for standardising nursing handovers in paediatric hospital settings, given their impact on communication, safety, and quality of care. International bodies such as the WHO and The Joint Commission recommend standardisation as a key measure to reduce patient safety incidents. Methods: An integrative review was conducted in December 2022 using Medline, Cochrane Library, Scopus, and CINAHL databases. The search strategy included documents published between 2012 and 2022, in Spanish, English, Catalan, French, and/or Portuguese. We screened according to inclusion criteria (professional nurses and hospitalisation) and exclusion criteria (intensive care and medical professionals) and tabulated the results according to concurrent themes. Methodological quality was independently assessed using CASPe Network tools, the MMAT, and STROBE checklist. The PRISMA-ScR guidelines were followed. Results: A total of 308 results were obtained, 139 were reviewed and 25 were accepted, assessing acceptable methodological quality in 19 (one randomised clinical trial, four systematic reviews, one integrative review, five non-randomised clinical trials, three observational studies, two qualitative studies, and three mixed-methods studies). Structuring and standardisation strategies are found in hospitalisation, including SBAR, I-PASS, and Flex 11. There are tools to assess the quality of patient handover, such as the Handover CEX Scale. Conclusions: There are tools for structuring patient handoffs that have obtained positive results in improving quality of care, although the results in the paediatric hospitalisation nursing setting are limited.

Abstract: Artificial neural networks (ANNs) achieve remarkable performance but at the unsustainable cost of extreme parameter density. In contrast, biological networks operate with ultra-sparse, highly organized structures, where dendrites play a central role in shaping information integration. Here we introduce the Dendritic Network Model (DNM), a generative framework that bridges this gap by embedding dendritic-inspired connectivity principles into sparse artificial networks. Unlike conventional random initialization, DNM defines connectivity through parametric distributions of dendrites, receptive fields, and synapses, enabling precise control of modularity, hierarchy, and degree heterogeneity. This parametric flexibility allows DNM to generate a wide spectrum of network topologies, from clustered modular architectures to scale-free hierarchies, whose geometry can be characterized and optimized with network-science metrics. Across image classification benchmarks (MNIST, Fashion-MNIST, EMNIST, CIFAR-10), DNM consistently outperforms classical sparse initializations at extreme sparsity (99\%), in both static and dynamic sparse training regimes. Moreover, when integrated into state-of-the-art dynamic sparse training frameworks and applied to Transformer architectures for machine translation, DNM enhances accuracy while preserving efficiency. By aligning neural network initialization with dendritic design principles, DNM demonstrates that sparse bio-inspired network science modelling is a structural advantage in deep learning, offering a principled initialization framework to train scalable and energy-efficient machine intelligence.

Abstract: Sustainable environmental performance (SEP) among small and medium-sized enter-prises (SMEs) has attracted researchers and practitioners’ attention. The achievement of sustainable environmental performance has been largely dependent on the prevail-ing external ecosystem conditions. Yet in emerging economies such as Ghana, there is limited research and evidence on the extent to which external ecosystem resources in-fluence sustainable environmental performance. This study aims to investigate how external entrepreneurial ecosystem resources including policy, access to finance, mar-ket availability, institutional support, human capital and culture influence the sus-tainable environmental performance (SEP) of small and medium-sized enterprises (SMEs) using sample data from Ghana. Drawing on a positivist, deductive, objective, cross sectional design, we surveyed 386 SMEs manufacturing and service firms. Structural Equation Modeling (PLS-SEM) tested a multi-theory framework grounded in Resource Based View (RBV), Resource Dependency Theory (RDT) and Stakeholder Theory. The results indicate that policy, finance, institutional support, and markets exert significant positive effects on SMEs’ SEP. Culture and human capital were found to have weaker contribution to SMEs’ SEP. These findings highlight the primacy of structural over internal factors in resource constrained settings such as Ghana. We advance the RBV, RDT and the Stakeholder Theory by showing that external ecosys-tem resources act as critical environmental enablers for SMEs in developing economies. The findings offer globally relevant policy insights for advancing SDGs 12 (Responsible Consumption and Production) and 13 (Climate Action) through targeted ecosystem interventions.

Abstract: Fruit and vegetable waste (FVW) has become a major sustainability concern due to rapid urbanization and rising demand for fresh produce in low- and middle-income countries (LMIC), creating significant environmental and governance challenges in urban food systems. This study investigates FVW governance by assessing awareness levels and examining FVW management practices among formal and informal fruit and vegetable retailers in Region F of the City of Johannesburg Metropolitan Municipality (COJMM). A quantitative, descriptive design was employed. Data were collected using structured questionnaires that assessed demographic details, awareness of FVW, and current FVW management practices, and analysed using descriptive statistics in SPSS version 30.0. The findings revealed fragmented governance across retail sectors, characterized by limited awareness of municipal waste management by-laws, consistent dependence on disposal-centred practices, and a lack of adoption of FVW valorization strategies. Formal retailers displayed higher awareness, with access to FVW minimization training, but still mainly relied on disposal, whereas informal retailers displayed significant gaps in awareness and FVW training. The study concludes that unsustainable FVW management is mainly influenced by structural governance limitations, emphasizing the need for inclusive and integrated approaches to improve urban FVW governance in LMIC.