A green and cost-effective technique for the preparation of silver nanoparticles (Algae-AgNPs) as a colorimetric sensor for hydrogen peroxide (H2O2) is described. Silver nanoparticles were capped using the green algae (Noctiluca scintillans) extract at an optimum time of 3 h at 80 °C. The pH of the plant extract (pH = 7.0) yields nanoparticles with a mean size of 4.13 nm and a zeta potential of 0.200 ± 0.02 mV and negative polarity, using dynamic light scattering (DLS). High-resolution transmission electron microscopy (HRTEM) analysis showed regular spherical particles with the average size of 4.5 nm. Selected area electron diffraction (SAED) results revealed the polycrystalline nature of the silver nanoparticles. The obtained patterns were indexed as (111), (200), (220), and (311) reflections of the fcc (face centered cubic) silver crystal based on their d-spacing of 2.47, 2.13, 1.49, and 1.27 Å, respectively. The apparent color change from brown to colorless was observed when nanoparticles reacted with H2O2. Linear responses were obtained in three different ranges (nM, µM, and mM). Limits of detection (LOD) of 1.33 ± 0.02 and 1.77 ± 0.02 nM and quantitation limits (LOQ) of 7.31 ± 0.03 and 9.67 ± 0.03 nM were obtained for Abs and ΔAbs calibration curves, respectively. 10% v/v Algae-AgNPs solution inhibited Staphylococcus aureus over Escherichia coli, while a 50% reduction of tumor cell growth of MDA-MB-231 human breast adenocarcinoma was obtained.The economic evaluation of health system interventions is challenging, and methods guidance on how to respond to these challenges is lacking. The REACHOUT consortium developed and evaluated complex interventions for community health program quality improvement in six countries in Africa and Asia. Reflecting on the challenges we faced in conducting an economic evaluation alongside REACHOUT, we developed a Structured Economic Evaluation Process for Complex Health System Interventions (SEEP-CI). The SEEP-CI aims to establish the threshold effect size that would justify investment in a complex intervention, and provide an assessment to a decision-maker of how likely it is that the intervention can achieve this impact. We illustrate how the SEEP-CI could have been applied to REACHOUT to identify outcomes where the intervention might have impact and causal mechanisms, through which that impact might occur, guide data collection by focusing on proximal outcomes most likely to illustrate the effectiveness of the intervention, identify the size of health gain required to justify investment in the intervention, and indicate the assumptions required to accept that such health gains are credible. Further research is required to determine the feasibility and acceptability of the SEEP-CI, and the contexts in which it could be used.We investigated the electrical properties of a composite film loaded with semi-conductive poly(3-hexylthiophene) (P3HT) nanofibers dispersed in poly(styrene-b-butadiene-b-styrene) (SBS). This structure can be regarded as the hybrid of SBS matrix with elastic mechanical properties and P3HT nanofibers with semiconducting properties. The P3HT nanofibers were embedded in the fingerprint pattern of microphase-separated SBS, as observed by scanning force microscopy. Furthermore, the electrical conductivity and field-effect mobility of the composite films were evaluated. The field-effect mobility was estimated to be 6.96 × 10-3 cm2 V-1 s-1, which is consistent with the results of previous studies on P3HT nanofibers dispersed in an amorphous polymer matrix including poly(methyl methacrylate) and polystyrene, and we found that the P3HT nanofiber network was connected in the SBS bulk matrix. The film was stretchable; however, at elongation by two times, the nanofiber network could not follow the elongation of the SBS matrix, and the conductivity decreased drastically. The field-effect transistor of this film was operated by bending deformation with a radius of curvature of 1.75 cm, though we could not obtain an off-state and the device operated in a normally-on state.Vehicle-to-Everything (V2X) communication enhances the capability of autonomous driving through better safety, efficiency, and comfort. In particular, sensor data sharing, known as cooperative perception, is a crucial technique to accommodate vulnerable road users in a cooperative intelligent transport system (ITS). In this paper, we describe a roadside perception unit (RSPU) that combines sensors and roadside units (RSUs) for infrastructure-based cooperative perception. We propose a software called AutoC2X that we designed to realize cooperative perception for RSPUs and vehicles. We also propose the concept of networked RSPUs, which is the inter-connection of RSPUs along a road over a wired network, and helps realize broader cooperative perception. We evaluated the RSPU system and the networked RSPUs through a field test, numerical analysis, and simulation experiments. Field evaluation showed that, even in the worst case, our RSPU system can deliver messages to an autonomous vehicle within 100 ms. The simulation result shows that the proposed priority algorithm achieves a wide perception range with a high delivery ratio and low latency, especially under heavy road traffic conditions.Recently, many studies regarding consumer perception of cell-based meat have been published. However, the opinion of the professionals involved in animal production also seems relevant. In particular, veterinarians and animal scientists may be important players in the new cell-based meat production, acting as proponents or barriers to this major improvement for farm animal welfare. Therefore, our aim is to analyse the knowledge and perspective of Brazilian veterinarians and animal scientists regarding cell-based meat. Veterinarians (76.8%; 209/272) and animal scientists (23.2%; 63/272) responded to an online survey. Logistic regression, latent class and logit models were used to evaluate objective answers, and the Discourse of the Collective Subject method was used to interpret open-ended answers. Specialists who were women (62.5%; 170/272), veterinarians (76.8%; 209/272), vegetarians (7.0%; 19/272) and vegans (1.1%; 3/272) were more supportive of cell-based meat. DFMO Lack of knowledge and the connection with artificiality, the most frequent spontaneous word associated with cell-based meat by all respondents, were the main negative points highlighted.