We developed a dedicated computational framework by coupling the lattice-Boltzmann-method (LBM) modeling and the particle-swarm-optimization (PSO) algorithm to search optimal strategies of magnetic nanoparticle (MNP) injection for hyperthermia-based cancer treatment. Two simplified tumor models were considered: a circular model representing geometrically regular tumors and an elliptic model representing geometrically irregular tumors, both sharing the same area. The temperature distribution in the tumor and its surrounding healthy tissue was predicted by solving the Pennes’ bio-heat transfer equation (PBHTE). Both single- and multi-site injection strategies were explored. The results suggest that the multi-site injection strategies generally work well, while the single-site injection strategy fails even on the simplest circular tumor model. The more the injection sites, the better the performance. In particular, when the number of injection sites reaches eight, all temperature requirements can be nearly 100% satisfied in both tumor models. Whether or not including the minimum dose requirement in the objective function only affects the optimization results by less than 2%. The thermal dose was also assessed by considering both temperature and heat exposure time. It was found that the optimal multi-site injection strategies perform reasonably well for both tumor models. Although the setting is only two dimensional and the optimization is on very simplified tumor models, the framework adopted in this present study works well and can provide useful insights into magnetic hyperthermia treatment.

Phthalic acid esters (PAEs) are widely used as plastic additives to increase the flexibility and durability of plastics. Constantly leaching out from plastics, PAEs are ubiquitously found in the environment. As PAEs exhibit biological activities such as being endocrine disruptive, the quest for efficient degradation strategies continues. Here, we report a bienzymatic degradation system for PAEs to phthalic acid (PA) using a cascade comprising two hydrolases, EstJ6 and P8219. The reaction conditions were optimized with respect to concentrations of both enzymes, temperature, and initial pH. Finally, the substrate scope of the new cascade was investigated, revealing that particularly PAEs with relatively small alcohols were degraded to more than 90 %. This present study provides a potential doable biocatalytic strategy for the complete hydrolysis of PAEs.

Anaerobic digestion (AD) of source-diverted blackwater (toilet flush) at ambient room temperature presents challenges for fast hydrolysis of particulate matters. This study investigated the effect of different micro-aeration dosages for blackwater AD. Sequencing batch reactors were operated at ambient room temperature (22 ± 1°C) with micro-aeration (0, 5, 10, 50, and 150 mg O₂ g⁻¹ COD_feed per cycle) and gradually reduced hydraulic retention times from 5 d to 2 d. The methanogenesis efficiencies were greater at low oxygen dosages (i.e., 0, 5, 10) while the volatile fatty acids (VFAs) accumulated more at high oxygen dosages (i.e., 50, 150). Microbial communities were significantly different under different oxygen dosages (p<0.05), with segregation of microbial ecological niches in low and high oxygen dosage communities. The low-oxygen-dosage niche (0, 5, and 10 mg g⁻¹ COD_feed) was inhabited by fermenting and syntrophic bacteria (e.g., Cytophaga, Syntrophomonas) and methanogens (e.g., Methanobacterium, Methanolinea, Methanosaeta). The high-oxygen-dosage niche (50 and 150 mg g⁻¹ COD_feed) had significantly (p<0.05) more facultative anaerobic bacteria (Ignavibacteriales and Cloacamonales), and aerobic bacteria (Rhodocyclales). Moreover, blackwater can be a source of antimicrobial resistance genes (ARGs), which are affected by different oxygen dosages. The ARG variation correlated with the microbial community composition (p<0.05). Low-oxygen-dosage communities contained a higher prevalence of mobile gene elements (intI1 and korB) and tetM, ermB, sul1, sul2, and blaCTX-M than the high-oxygen-dosage communities, indicating that oxygen dosage influenced the prevalence of populations carrying ARGs. These findings suggest that application of micro-aeration to AD can be used to control ARG profiles.

Power transformers being among the power system key components, are very important to protect. Various approaches have so far been proposed for this topic. Artificial intelligence (AI) has provided a concrete basis for numerous power transformer differential protection methodologies. However, the capabilities of AI-based methods have never been put into comparison. This paper provides a comparative analysis of various AI-based approaches for differential protection of power transformers. The performances of AI-based differential protection schemes are investigated in the presence of current transformer (CT) saturation condition, series capacitors compensation, and superconductor fault current limiter (SFCL), which possibly deteriorate the capability of differential protections to correctly tell inrush currents and internal faults apart. The PSCAD/EMTDC simulation tool is applied in producing the necessary test dataset for performance evaluation of the suggested novel strategy. The attained outcomes from the assessment of various methods have been analyzed to introduce the most superior AI-based differential protection scheme.

Car-oriented transport infrastructure developments have had detrimental impacts on the public realm in terms of poor walkability and fractured leftover urban spaces. To build integrated transport infrastructure and public space systems with considering non-motorized travelers’ behavior, we present an integrated methodology incorporating an agent-based simulation model, serious games, and co-design which provides opportunities to involve citizens into the urban design process. In this paper, we show this process for a case study in London Hackney Wick. Qualitative data collected from collaborative experiments, cognitive and human needs mapping, interviews and conversations offer insights into people’s engagement with their environment and the public expectations. In parallel, an Agent-Based Model (ABM) informed by the gathered data is used to visualize local activities for the residents and to predict travel demand and spaces occupancy patterns of various designs. The prediction results indicate that a holistic design strategy is needed for planning attractive and pedestrian-friendly transport-public space systems. Lessons learned also lead to a proposal to improve the model with more realistic human behavior and activity schedules. The coupling of ABM–Game–Design is a valuable tool for engaging the audience and providing both qualitative and quantitative supports to decision-making.