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Microplastic contamination in green mussels (Perna viridis Linnaeus, 1858) from traditional seafood markets in Jakarta, Indonesia, and an evaluation of potential hazards
No full textMarine organisms, especially mussels, can efficiently take up microplastics (MPs) through their filtration processes. This study evaluated the characteristics of MPs in green mussels (Perna viridis) sold at traditional seafood markets in Jakarta, Indonesia. The polymers of MPs were examined using Fourier Transform Infrared spectroscopy, while the chemical components of MPs in green mussels were analyzed using Gas Chromatography-Mass Spectrometry. The MPs identified in green mussels sold at traditional seafood markets in Jakarta are predominantly of fiber type, display a black coloration, and measure <100 μm in size. The density of these microplastics in green mussels is uniform across all traditional markets. The concentration of microplastics in green mussels correlates positively with the length of the green mussel shell. The average annual consumption of mussel products by people in Jakarta was 11,170 items/year/person. Green mussels from Jakarta exhibited a significantly elevated polymer hazard index (III), indicating the presence of polymers categorized as high risk. Certain plasticizers (phthalates) and specific additive chemicals (phenol, butylated hydroxytoluene, and hexadecanamide) were also present in green mussels sourced from traditional markets in Jakarta. These substances are toxic and have a negative impact on both aquatic life and humans
Efficient Time-Stepping Methods for Isogeometric Analysis of Nonlinear Heat Conduction in Composites
No full textIn this paper, we propose a class of high-order time integration schemes combined with high-order IsoGeometric Analysis (IGA) in three space dimensions. The combined methods offer robust solutions of nonlinear heat diffusion in three-dimensional composites that pose numerical challenges. This tailored strategy significantly enhances computational efficiency, especially crucial when addressing nonlinear heat transfer in three-dimensional enclosures. Leveraging precise geometry representation and seamless high-order element continuity of the IGA, this method effectively exploits these advantages. It emphasizes the vital synergy between high-order spatial discretization and an equivalent high-order time integration scheme. This study also highlights the risks of overlooking this pairing, which can lead to a degradation of the overall high-order accuracy and increased computational demands due to the complexity of high-order nonuniform rational B-splines. Numerical examples, such as applications involving a furnace wall segment and a rail wheel heat transfer, are used to validate the efficiency and accuracy of the combined approach. Consistently surpassing the conventional methods in both aspects, the proposed method notably excels in providing precise solutions for steep heat gradients even on coarse meshes. Consequently, this approach constitutes a substantial advancement in the field of transient heat transfer analysis within composite domains
Dynamics of street views and socio-economic conditions in profiling illegal dumping ‘black spots’: An LLM-enabled study in Hong Kong
No full textIllegal dumping remains a persistent urban problem. Previous research has established that a neighborhood’s socioeconomic status and certain urban features, observed from a bird’s-eye view, influence dumping behavior. However, environmental criminologists contend that granular, eye-level street views offer more immediate and relevant environmental cues for potential offenders. This study aims to develop an explanatory model to profile illegal dumping ’black spots’ in urban areas by employing street view analytics. The innovative aspect of this approach lies in leveraging emerging large language models (LLMs) to extract street-level cues, which are then combined with census-based socioeconomic indicators using a spatially adaptive Geographic Random Forest. The model achieved a predictive accuracy of R2 = 0.7574 and an RMSE of 0.9368 on the held-out test set. Local feature analysis revealed that compact hotspot clusters with visible waste or dense vegetation significantly increase illegal dumping risk. Compared to traditional computer vision methods, LLMs proved more efficient in extracting meaningful features without manual annotation or specialized training. These findings demonstrate that integrating scalable, LLM-derived environmental cues with spatial machine learning enables more targeted and effective interventions for urban waste management
Analysing the effects of integrating local tanks in water distribution networks on water age
No full textExtended water retention in a distribution network increases water age, heightening vulnerability to microbial growth and quality issues. Designers must ensure the system allows for frequent water flow without complete stagnation. Private tanks play a crucial role in regulating system flow and influencing water age. This investigation aimed to assess how private tank retention time and orifice sizes affect water age within a schematic of an existing network in Dubai, UAE. The results indicated that longer retention times facilitated greater flow, thereby reducing water age due to increased filling times. Similarly, smaller orifice diameters contributed to this effect. Additionally, the presence of float valves was found to hinder water flow, leading to higher water age. Ultimately, the study identified a more effective approach than float valves for minimizing water age, demonstrating the importance of optimizing tank design and flow regulation in water distribution systems to enhance water quality
Africanus III. pfb-imaging–A flexible radio interferometric imaging suite
No full textThe popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces pfb-imaging: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances
AV-SLAF:A Scenario-Layered Framework for Safety Analysis of Autonomous Vehicles Based on STPA and CTA
No full textEnsuring safety in autonomous vehicles (AVs) requires addressing hazards beyond functional failures, especially those arising from Performance Limitations (PLs) and Triggering Conditions (TCs) under varying Operational Design Domains (ODDs). This paper proposes AV-SLAF, a scenario-layered safety analysis framework integrating System-Theoretic Process Analysis (STPA) with Cause Tree Analysis (CTA) and internal algorithm modeling. By incorporating layered ODD scenarios into the control structure and modeling internal logic of AV modules, AV-SLAF systematically identifies PLs and TCs critical for Safety of the Intended Functionality (SOTIF). Unlike traditional methods focusing solely on structural-level interactions, the proposed framework bridges external scenario modeling with internal algorithms, enabling a more complete view of hazard propagation. A case study on autonomous port vehicles demonstrates the framework’s applicability, yielding a structured set of 84 PL-TC pairs and a partial cause tree for the Planning and Control module. The resulting causal structure reveals dependencies among algorithmic components and their safety-relevant conditions. The proposed framework enhances the traceability and completeness of safety analysis for complex AV applications.</p
Amplification of Nonlinear Response of Floating Photovoltaics by Coastal Topography:Experimental and Numerical Study
No full textNearshore coastal regions have become popular for floating photovoltaics (FPV) installations. During propagation over seabed topography towards nearshore FPV systems, waves undergo intricate transformations by shoaling, reflection and refraction, potentially influencing hydrodynamic responses of these emerging marine renewable energy structures in ways that are not well understood. Therefore, wave flume experiments and multiscale fully coupled time-domain fluid-structure interaction (FSI) simulations are performed to examine the topography effect on the nonlinear responses of nearshore FPV systems at a field site in the East China Sea. Experimental results reveal that near-resonant wave interactions in coastal regions drive significant energy transfer among different wave frequencies, amplifying the nonlinear dynamic responses of FPV systems by channeling energy toward their natural modes. As a result, second-order heave and pitch responses are amplified by up to 117.87% and 136.38% compared to the case without topography, which in turn lead to an increase in mooring tension. Moreover, the topography-induced amplification of nonlinear wave harmonics enhances the surge mean drift of FPV. This enhancement exhibits a negative correlation with the relative FPV length with respect to the wavelength. Comparisons between experiments and fully coupled simulations for irregular waves indicate that neglecting topography causes the FPV dynamic response model to produce inaccurate estimations of heave/pitch motions, while FSI simulations forced by high-fidelity local wave fields predicted by the fully nonlinear Boussinesq wave model are capable of capturing the observed topographic effect. These findings provide the theoretical basis for design consideration of the safe, cost-effective deployment of efficient FPV systems in coastal waters
Africanus III. pfb-imaging–A flexible radio interferometric imaging suite
No full textThe popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to increased computational complexity, absence of mature implementations, and the need for astronomers to tune obscure algorithmic parameters. This work introduces pfb-imaging: a flexible library that implements the scaffolding required to develop and accelerate general radio interferometric imaging algorithms. We demonstrate how the framework can be used to implement a sparsity-based image reconstruction technique known as (unconstrained) SARA in a way that scales with image size rather than data volume and features interpretable algorithmic parameters. The implementation is validated on terabyte-sized data from the MeerKAT telescope, using both a single compute node and Amazon Web Services computing instances
The missing puzzle piece: contextual insights for enhanced pharmaceutical supply chain forecasting
No full textAccurate forecasting in pharmaceutical supply chains is essential for ensuring medicine availability, particularly in low-resource settings. However, many existing approaches rely solely on historical consumption data, provide only point forecasts and fail to account for the operational context and uncertainty inherent in these environments. In this study, we collaborated with experts at the Ethiopian Pharmaceutical Supply Service to identify key contextual factors, such as stock replenishment cycles, fiscal inventory counts and seasonal disease outbreaks and integrated them into forecasting models. Using five years of monthly distribution data (December 2017 to July 2022) for 33 essential medicines, we evaluated a range of forecasting methods, including statistical, machine learning and foundational models. We assessed point and probabilistic forecast accuracy using standard evaluation metrics. Our findings show that incorporating contextual variables significantly improves forecast performance, especially for classical time series models. We recommend investing in the routine collection of contextual indicators and adopting transparent, low complexity forecasting methods that can be sustained in practice. To support reproducibility and wider use, we provide all data, code and the full manuscript as an open, executable Quarto project developed in R and Python
Strategies enhancing the implementation of design for adaptability in the Ghanaian construction industry: An exploratory and confirmatory factor analyses
No full textDespite the benefits design for adaptability (DfA) contributes to achieving sustainability and circularity in the construction industry, studies have demonstrated that construction professionals are yet to realize its full potential. This study examines the strategies that can enhance the practice of DfA among design professionals in the Ghanaian construction industry (GCI). A quantitative approach was used to achieve the aim of the study by soliciting the views of 236 design professionals in the GCI through structured questionnaires. Data gathered were analyzed via descriptive and inferential statistics. The findings revealed six key categories of strategies (i.e., management strategies, economic strategies, governmental regulations and policy strategies, design strategies, technological strategies and social strategies) to enhance the implementation of DfA practices in the GCI. This study highlights the theoretical and practical implications of DfA implementation, offering actionable insights for construction stakeholders to foster sustainability and resilience in the built environment. It contributes to academic discourse by categorizing strategies and proposing an implementation framework relevant to developing economies like Ghana
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