Search Results (390)

Background/Objectives: The recent advances in pulmonary delivery have shifted the paradigm to the development of inhalable drug-loaded polysaccharide particles that can be positioned at the respiratory interface while reducing the systemic exposure. Quercetin has broad anticancer activity but remains difficult to translate because of its poor aqueous solubility, limited bioavailability and rapid metabolic loss. Here, we report quercetin-loaded chitosan/Salvia hispanica polysaccharide nanoparticles as a natural polyelectrolyte nanocarrier for pulmonary delivery in lung cancer. Methods: Central composite design (CCD) and artificial neural network (ANN) models were used for mapping the factors to responses. Results: The models displayed a higher predictive accuracy and optimization reliability for identifying the optimized formulation. The optimized nanoparticles showed a quasi-spherical morphology (mean hydrodynamic diameter = 331 ± 14.34 nm) with cationic ζ-potential of +36.3 ± 2.56 mV and polydispersity index of 0.15. The optimized formulation showed acceptable powder-flow characteristics, an encapsulation efficiency of 73.8 ± 0.73%, drug loading of 14.20 ± 0.22%, and biphasic release with sustained quercetin release over 48 h. In A549 and H460 cell lines, nanoencapsulation increased the antiproliferative effect of quercetin relative to the free compound, yielding lower IC₅₀ values after 48 h of exposure. The nanoparticles showed greater suppression of wound closure, increased reactive oxygen species fluorescence and clear cellular uptake. Blank nanoparticles produced only limited effects. Conclusions: These results indicate that CCD/ANN-guided chitosan/Salvia hispanica polysaccharide nanoparticles provide a promising nanodelivery tool for quercetin delivery and enhanced in vitro activity in lung cancer cells, while the observed aerosol performance advocates further investigation of their pulmonary delivery potential. Full article

Promoting cycling is an essential component of sustainable urban transportation, particularly in developing countries where bicycle use remains limited. This study proposes a two-step machine-learning framework to identify the predictors associated with bicycle use and cycling popularity in Tehran, Iran. A questionnaire survey was conducted among 1027 respondents, of whom 971 valid responses were retained for analysis. In the first step, the entire sample, including cyclists and non-cyclists, was analyzed using Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) models to predict bicycle use. In the second step, the analysis focused exclusively on existing cyclists. A Cycling Popularity Index (CPI) was developed and classified into three levels, and ten machine-learning algorithms were evaluated. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was incorporated into model development. The results showed that Random Forest achieved the highest predictive performance in the first step, with bicycle-sharing availability, access to private cars, traffic congestion, occupation, proximity to public transportation, and social norms identified as the strongest predictors of bicycle use. In the second step, KNN-SMOTE achieved the best overall predictive performance, while Permutation Feature Importance identified occupation, age, social norms, perceived cycling safety, and physical condition as the most influential predictors of cycling popularity. The proposed framework demonstrates the value of combining multiple machine-learning algorithms with class-balancing techniques for analyzing imbalanced transportation datasets and provides a transferable methodology for similar studies in developing cities. Full article

Construction delays remain a persistent challenge in Australian construction projects, contributing to cost escalation, disrupted work sequences, contractual claims, and reduced confidence in project delivery. Although delay causes have been widely investigated, existing studies often provide broad factor lists and prioritise risks using single-dimension or inconsistent scoring approaches. This limits guidance on which delay risks should receive priority attention when project teams face constrained time, cost, and management resources. This study addresses this limitation by quantifying and prioritising 22 validated delay risk factors in Australian construction projects. Probability of occurrence and schedule impact were evaluated as separate judgement dimensions before being integrated into an overall measure of risk criticality. Data were collected from 48 experienced Australian construction professionals. A dual-dimension Fuzzy Best–Worst Method was applied to derive separate ratio-scale weights for probability of occurrence and schedule impact, with dimension-specific consistency screening used to improve judgement reliability. The resulting weights were integrated using a probability–impact formulation and mapped onto a 5 × 5 Probability–Impact Matrix through quantile-based discretisation. A 10,000-iteration Monte Carlo robustness analysis was subsequently conducted to assess the stability of the resulting rankings under alternative expert-selection and weighting scenarios. The results indicate that the delay risks perceived by the participating professionals as having the highest combined probability and schedule impact are predominantly governance-, approval-, and coordination-related, particularly owner late decisions, change-approval delays, owner requirement changes, cost-estimation deficiencies, design-approval delays, and inadequate planning. The Monte Carlo analysis further indicated that the principal risk rankings remained relatively stable under variations in expert aggregation. Overall, the integrated FBWM–PIM framework provides a structured and practically interpretable approach for eliciting and prioritising expert perceptions of construction delay risk and translating them into an actionable classification tool for allocating limited risk management resources. Full article

Construction project delays remain a persistent challenge in the construction industry, yet existing studies have generally examined delay risk prioritisation and organisational risk management maturity as separate research domains. This study addresses this gap by investigating how organisational risk management maturity influences the evaluation and prioritisation of construction delay risks in the Australian construction context. A structured survey instrument based on the Generic Risk Maturity Model was used to assess maturity across five dimensions: management commitment and leadership, organisational risk culture, risk identification, risk analysis, and standardised risk management processes. Expert judgements were used to derive impact severity, probability of occurrence, and composite criticality scores for 22 construction delay risk factors. Extreme Gradient Boosting (XGBoost) models with SHapley Additive exPlanations (SHAP) were then applied to explain how maturity dimensions influence risk evaluations. The results show that maturity is associated with changes in both the level and pattern of delay risk prioritisation. Owner’s late decisions remained the top-ranked composite risk across all maturity levels, while other risks exhibited maturity-related shifts, with the composite risk weight of poor project cost estimation declining from 0.079 at maturity Level 1 to 0.041 at maturity Levels 3–4, and rainfall and weather conditions becoming more prominent at higher maturity levels. The proposed XGBoost–SHAP framework provides a transparent, data-driven diagnostic tool for linking organisational capability with delay risk prioritisation and supporting more consistent and evidence-based delay risk management. The results further indicate that weaker organisational risk culture and lower process standardisation are associated with greater perceived exposure to planning-, coordination-, and decision-related delay risks. Within the sampled Australian construction organisations, the XGBoost–SHAP framework provides a transparent analytical approach for examining associations between organisational capability and delay risk prioritisation. Full article

The migratory shad, Hilsa (Tenualosa ilisha) is an iconic species of profound economic and cultural value across the Indian sub-continent due to its delicious taste and significant contributions to gross domestic product (GDP). Lack of fundamental genomic data regarding sex determination, impedes development of optimized breeding techniques and target conservation goals. In this study, a next-generation sequencing (NGS)-based genotyping technique was applied to identify sex-linked markers, modes of sex determination, putative sex-determining genes and the population genomic structure of Hilsa. Genotyping of 94 Hilsa individuals (46 males and 48 females) collected from four distinct locations of Bangladesh (three different river systems and Bay of Bengal as a marine site) revealed 31,696 single-nucleotide polymorphisms (SNPs) and 12,754 presence/absence (PA) loci. Among these SNPs and PA, we identified 20 SNPs that were heterozygous in females but homozygous in males and 4 PA loci which were only present in females. Therefore, this study conclusively identifies a female heterogametic (ZZ/ZW) sex determination system in Hilsa. Comparative BLAST analysis using sex-linked loci against Hilsa genomes resulted in the identification of five candidate genes potentially involved in sex-determination pathways. Moreover, population genetic analysis revealed low spatial genetic differentiation among the four sampling sites but notable divergence between males and females (minimum 1.8–2.8% variation in principal coordinate analysis). For most of the sampling sites, higher observed heterozygosity (Ho) compared to expected heterozygosity (He) is the indicative of a robust population status with minimal evidence of inbreeding. Our study provides a baseline for further improving the management and conservation of the wild populations of the species. Full article

Multi-UAV missions for remote sensing and environmental monitoring under extreme conditions require task allocation and path optimisation to efficiently distribute goals across vehicles. These methods must also be executed reliably inside an autonomous robotics framework. Several methods for the multiple travelling salesman problem (mTSP) show robust offline routeing efficiency. However, system-level validation under realistic operational conditions including waypoint management and inter-UAV separation remains limited. This research transforms the previously proposed Distance Efficient Clustering Kmeans Genetic Algorithm (DECK_GA) from an offline model into a deployment-focused multi-UAV remote sensing framework implemented in ROS2, Aerostack2, and Gazebo. A uniform waypoint management interface integrates planning, Rviz visualisation, and autonomous execution. The system combines Dynamic Centroid Kmeans (DCKmeans) for spatially coherent waypoint allocation with a Distance Efficient Genetic Algorithm (DEGA) for individual UAV route optimisation. The evaluation is conducted in a high-fidelity Antarctic environment where waypoints represent survey desired objectives in moss regions, and altitude is managed using terrain-referenced control involving two to five UAVs and 30 to 120 waypoints. The framework was evaluated against two baselines under identical mission configurations, with 10 trial runs for each: a Traditional GA Divide & Conquer planner and a Classical Kmeans DEGA planner, which utilises the same route optimisation method and differentiates the outcomes of the allocation stage. DECK_GA showed reduced mean planned and executed distances compared to the Traditional GA Divide & Conquer baseline across all configurations, achieving planned distance reductions ranging from 15.99% to 75.36%. Additionally, it produced shorter path than Classical Kmeans DEGA in 14 out of 16 configurations. The average minimum inter-UAV separation was greater than the Traditional GA Divide & Conquer baseline in 15 of the 16 configurations and higher than Classical Kmeans DEGA in 14 of the 16, which demonstrates that the DCKmeans allocation improves spatial separation. This research focuses on the framework for planning to execution instead of the introduction of a new optimisation method, as DECK_GA was proposed in previous research and is now incorporated and tested within an autonomy framework. This evaluation is simulation only. Real world flying, hardware in the loop testing, wind, communication latency, and location error prediction tend to be future developments. Full article

Red mud is a waste byproduct of alumina production. Its release into the environment poses risks, highlighting the need for strategies to limit pollution. Using red mud as a filler in polymer-matrix composites can reduce the leaching of heavy metals and metalloids. We fabricated composites with high red mud content (up to 60 wt.%) using two particle fractions (<125 μm and <500 μm). The study examined how filler concentration and particle size affected the composites’ microstructure and mechanical properties. Results showed that composites with smaller particles had better encapsulation and enhanced structural qualities, such as reduced porosity and fewer cracks. Among four filler mass loadings (20, 30, 40, and 60 wt.%), composites with 40 and 60 wt.% red mud exhibited greater epoxy penetration into agglomerates and partial deagglomeration, resulting in small, uniformly dispersed red mud particles within the matrix. Calorimetry analysis demonstrated that increasing the red mud concentration slows the curing process: for composites with 20 wt.% red mud, the curing time is approximately 10 h, whereas for composites with 60 wt.%, approximately 35 h. We performed a thorough environmental safety evaluation of high-loaded red mud–epoxy composites in accordance with the standard AS 4439.3:2019. The tests showed that epoxy resin significantly reduces the levels of potentially hazardous elements, such as Na and Al, in the leachates, demonstrating the safety of the composites. Composites with 40 wt.% red mud (particle size < 125 μm) showed the most effective epoxy impregnation into red mud agglomerates and demonstrated the best encapsulation behaviour, releasing the least amount of metals compared to red mud during both 20 h and 4-week, long-term leaching tests. Full article

Iron is an essential element for almost all living beings. Ferroportin is the only known cellular iron exporter and is responsible for maintaining iron homeostasis. The hepcidin-ferroportin axis is central to iron regulation. Dysregulation of ferroportin is thus associated with iron disorders. Understanding how ferroportin is regulated will provide greater insight into iron metabolism and potential therapies for iron-related disease. This review synthesizes current knowledge on ferroportin biology with a particular focus on its regulatory modulators and their therapeutic potential and provides an updated perspective on the molecular pathogenesis and clinical spectrum of ferroportin disease. Elucidating these mechanisms will be essential for the development of targeted interventions to correct iron dysregulation in diverse human diseases. Full article

Background/Objectives: Household food insecurity (FI) impacts child health and development. Children have distinct and unique experiences of FI, and child self-report measures are increasingly used. However, there is limited reporting of children’s comprehension and engagement with these measures within local settings. The Child Food Insecurity Experiences Scale (CFIES) is a 10-item child self-report tool that is widely used, despite limited validity evidence. This study aimed to explore the face and content validity of the CFIES in an Australian context. Methods: We used cognitive interviews with 25 children aged 8–12 years to document CFIES comprehension. Data were analyzed using inductive and deductive approaches. Results: Children demonstrated awareness of FI (even when food secure themselves) and displayed sophisticated understanding of its social ramifications. While the overall perception of the tool was generally positive, we uncovered that not all key terms or emotion words were universally understood, potentially impacting validity. Similarly, conditional clauses or question modifiers were sometimes overlooked. In addition, the 12-month response timeframe and the answering categories (“many times”, “one or two times” or “never”) proved challenging for some children. In addition to the item-specific impressions, children provided nuanced ideation across four broader themes, which may impact item interpretation and willingness to answer: perceptions of hunger; micro and macroeconomics affecting households; social identity and comparison; and caregiver protection dynamics. Children indicated the need to consider survey modality and ensure privacy. Conclusions: A global tool that reflects children’s cross-cultural conceptualization of FI represents a pragmatic and feasible way to monitor change in FI across regions and may aid advocacy efforts. However, this study suggests the need for further modifications to the CFIES and the necessity to build rapport with communities and organizations that allow for the interpretation of the data within the contextual considerations. Full article

Globally, marine protected areas (MPAs) have been designated with the aim of protecting marine biodiversity, primarily from extractive activities such as fishing. Most MPAs also include a range of zones with different fishing or other activities allowed in each, generally referred to as partially protected areas (PPAs). How these zones are implemented is often subject to disagreement between the impacted interest groups. Opposition to zoning may, in some cases, be the result of political lobbying or other interventions to stall or influence the process. In this study, we identify the key objectives considered by Australian MPA managers when planning uses in different areas of MPAs as well as their relative importance to decision-making. We also assess their relative importance from the perspective of fisheries managers, who are also responsible for the management of the primary users of the PPAs. We apply the modified analytic hierarchy process using data collected in a national survey of both groups. We find that both place the highest importance on achieving ecological objectives, but the importance of different economic and social objectives varies significantly between the groups. This may result in conflict not over the designation of conservation critical areas but over which activities are allowed in the PPAs. Full article

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures. Full article

The reduction in energy demand in buildings through the adaptation of energy-efficient strategies is attracting significant attention from the research community. In this context green building concepts can contribute towards achieving national sustainable development goals (SDGs) and NetZero targets. Given the substantial energy demand associated with heating and cooling in commercial and residential buildings, enhancing energy efficiency has become essential for achieving sustainable development, particularly amid ongoing global energy challenges. The Envelope Thermal Transfer Value (ETTV) model has been established as a simplified method of calculating building loads; however, its integration with green building elements remains limited, particularly in subtropical climates. Furthermore, the combined effects of living walls, green façades, and green roofs on building energy performance have not been comprehensively investigated. In this study, an extensive experimental investigation was conducted using prototype buildings under controlled conditions to evaluate the thermal performance of green elements. Modified ETTV formulations incorporating green envelope systems have been developed, and the thermodynamic effects of these green elements on the building energy performance have been analysed. The results demonstrate that integrating green elements significantly reduces thermal heat gain and cooling energy demand. Specifically, a combination of a living wall on a west facing wall and a green roof could reduce the thermal heat gain by up to 30%. Full article

Background: Familial hemiplegic migraine (FHM) is a rare and severe form of migraine disorder featuring aura symptoms that include hemiplegia during attacks. While pathogenic missense variants in CACNA1A, ATP1A2, and SCN1A can cause FHM or its sporadic form, they explain less than 20% of suspected hemiplegic migraine cases, suggesting the involvement of other genes or genetic variations, potentially including copy number variations (CNVs). PPRT2 gene variants including CNVs have also been implicated in hemiplegic migraine. Methods: Multiplex ligation-dependent probe amplification (MLPA) assays were used to investigate the presence of CNVs in the CACNA1A, SCN1A, ATP1A2, and PRRT2 genes in a cohort of 170 unrelated probands suspected to have FHM who had tested negative for pathogenic missense or small indel variants within these genes. Potential CNVs were subsequently confirmed using quantitative PCR. Results: In 15 patients referred for FHM genetic testing, various CNVs in the target genes were detected by MLPA and subsequently validated by quantitative PCR. CACNA1A exon duplications were identified in six patients and deletions found in two. Two patients had ATP1A2 exon deletions, while one had a duplication. For SCN1A, exon deletions were found in three patients and a duplication in one. PRRT2 exon deletions were detected in five patients, with a single nucleotide polymorphism (SNP) array confirming a deletion spanning PRRT2 and neighbouring loci including 26 genes in one of those. Three patients had CNVs in more than one FHM gene. Conclusions: Our study demonstrates the presence of CNVs in FHM genes in a subset of hemiplegic migraine cases (~9%), suggesting a likely role in the disorder and highlighting the need to explore structural variation in addition to the commonly interrogated genetic mutation points. These findings contribute to further understanding of genetic mechanisms that underlie hemiplegic migraine and may inform improved diagnostic and therapeutic strategies. Full article

Remote sensing plays a vital role in monitoring environmental change in Antarctica, offering non-invasive insights into ice dynamics, biodiversity, and fragile ecosystems. Harsh conditions, limited field access, and logistical challenges result in sparse, noisy, and often unlabelled datasets, posing major obstacles for machine learning (ML) approaches. Data scarcity remains a fundamental challenge for uncrewed aerial vehicle (UAV)-based ecological monitoring. While ML models in other Earth observation domains demonstrate state-of-the-art performance, their applicability in Antarctic and polar regions’ settings is limited. This paper reviews the intersection of ML and UAV-based remote sensing in Antarctica under extreme data constraints. We surveyed recent strategies designed to overcome these limitations, including self-supervised learning, physics-informed modelling, and foundation models. Results highlight a notable gap, as polar environments remain excluded from global datasets and benchmarks due to the extensive data requirements of large-scale models. Opportunities exist where multimodal and multi-scale generalisation can enhance cross-domain adaption to data-scarce use cases. Unlike prior reviews on general remote sensing or task-specific polar studies, this work uniquely underscores the need for Antarctic representation in global ML advances, positioning Antarctica as a frontier testbed for machine learning in extreme, inaccessible, and under-resourced fields. Full article

Neural progenitor cell (NPC) fate decisions are governed by transcriptional and signaling programmes, yet the epigenetic mechanisms stabilising early neuronal versus glial lineage trajectories remain unresolved. Here, DNA methylation landscapes in two widely used human NPC models—ReNcell VM (RVM) and ReNcell CX (RCX)—were examined under several different culture conditions to define regulatory pathways shaping lineage specification. Exploratory analyses revealed that the ReNcell lines exhibited methylation similar to primary glial populations rather than neuronal subtypes, with RCX cells positioned further along a maturation trajectory and RVM cells retaining a multipotent state. RCX cultures displayed hypomethylation of neuronal markers (DCX, ENO2, MAP2), whereas RVM cultures showed consistent GFAP hypomethylation, indicative of glial or early progenitor identity. Signaling pathways regulating lineage commitment were highlighted, including TGFβ, Wnt, and Notch signaling. Within the Notch pathway, RCX cells exhibited higher gene expression of NOTCH2 and JAG ligands, consistent with active lateral induction and a developmentally advanced state. In contrast, RVM cells exhibited higher DLL1 and NOTCH1 expression, supporting lateral inhibition and cellular heterogeneity. Knockdown of syndecan-4 (SDC4) revealed opposing effects on Notch activity. Together, these findings established DNA methylation as a determinant of lineage-specific signaling in human NPCs. Full article