Shibaura Institute of Technology

ABSTRACT This study aims to reduce waste derived from extractant-impregnated adsorbents by assessing the reusability of spent adsorbents generated during extraction chromatography for the separation of minor actinides (MA(III)) from high-level liquid waste (HLLW). Regeneration was investigated via (i) stripping of the extractant with organic solvents and (ii) re-impregnation of the support. n-dodecane and 1-octanol were examined as candidate solvents. In addition to evaluating stripping performance and the adsorption/elution performance of re-impregnated adsorbents, vacuum distillation was applied to the post-stripping solutions to separate and recover the extractant and solvent. The results demonstrate that the extractant can be stripped from the adsorbent by solvent treatment and that the adsorption and elution performance can be restored by re-impregnation. Furthermore, efficient separation of solvent and extractant from the stripping solution was achieved when n-dodecane was used.

This article presents the Urashima Tarō Mirror (UTM), an interactive installation that allows users to visually experience self-transformation associated with aging through AI-generated portraits. UTM generates a series of portraits ranging from childhood to old age based on the user’s facial photograph, and the displayed image changes—appearing younger or older—depending on the user’s distance from the mirror. Through this mirror-like interface, users can experience in a short moment the physical changes that would normally take years to occur. This experience evokes awareness of one’s own transformation and aging, offering an experience that encourages reflection on identity and self-acceptance.

This study proposes a methodology to support the planning of smart and efficient harvesting techniques within the agricultural sector of developing countries. Even where labour costs are low in developing nations, repeated inefficient harvesting operations result in wasted time, effort, and money. While digital technology has advanced and various applications have been developed, these remain underutilised in developing countries due to cost constraints. Consequently, there is a need to develop methodologies that harness these applications to enhance harvesting efficiency. Therefore, Smart Optimal Collection Planning Method was developed in this research. This method determines the location coordinates of trees from aerial photographs freely available in many countries, including developing nations. It then uses visual or internet of things (IoT) technology to ascertain the number of fruits per mango tree, subsequently devising an optimal collection route that minimises harvesting costs. The proposed method was applied to a mango plantation in Cambodia to investigate optimal collection routes based on the number of harvesters. The results revealed that, compared to existing methods, the proposed approach achieves greater reductions in collection around 47 % time/cost when fewer harvesters are employed. This research is significant because the proposed methodology enables orchard farmers to calculate appropriate labour costs and implement efficient operations. Incorporating this into international cooperation frameworks is expected to contribute to sustainable agricultural support and food security.

Image quality assessment (IQA) plays a fundamental role in image processing and has undergone various developments. As IQA methods are specialized for image coding, SSIMULACRA2 exists, and these are utilized in recent image coding standards: JPEG XL, Jpegli, and AVIF. However, the new IQA of SSIMULACRA2 is computationally intensive, and even in reference implementations, the computation time can exceed that of the encoding process itself. In this paper, we propose a fast implementation of SSIMULACRA2. We achieve this by replacing convolution with separable FIR filters, skipping the computation of the low-contributed features, and separating the evaluation process for input and degraded images. The experimental results show that SSIMULACRA2 achieves a speedup of up to x82.4 compared to the original, with almost no degradation in correlation as measured by subjective evaluation.

Titanium dioxide nanoparticles (TiO2 NPs) are used in agriculture, cosmetics, energy, and environmental applications, necessitating advanced methods to evaluate their effects on biological systems such as plant growth. This study demonstrates the use of biospeckle optical coherence tomography (bOCT), a novel and non-invasive technique, to rapidly assess the size and concentration-dependent impacts of TiO2 NPs as well as the synergistic effects of TiO2 NPs and polyethylene microplastics (PEMPs) on lentil (Lens culinaris) seeds. The primary objective was to validate bOCT as a rapid and non-invasive tool for assessing NPs-induced biological responses in plants. Seeds were treated with TiO2 particles (<5 µm,<100 nm, 21 nm) at concentrations of 0, 25, 100, and 200 mg/L. The sizes were selected based on commercially available TiO2 NPs grades in industrial and agricultural applications. For synergy experiments, TiO2 NPs (21 nm) at 25 and 100 mg/L were combined with PEMPs (744–4990 nm) at concentration-based ratios of 1:1 and 1:2. A swept-source OCT system (central wavelength: 1.3 µm; bandwidth: 125 nm; sweep frequency: 20 kHz), acquired OCT structural images at 12.5 frames per second and biospeckle images were calculated as the ratio of the standard deviation to the mean of 100 OCT structural images over an 8-second interval, at 0, 5, 10, and 20 hours (h) post-exposure. Smaller TiO2 NPs (<100 nm) enhanced internal activity at lower concentrations (25 mg/L), while larger particles (<5 µm) exhibited similar effects at higher concentrations (200 mg/L). These observations were qualitatively consistent with conventional physiological measurements, including germination rates, growth parameters, and antioxidative enzyme activities recorded over 7 days. The co-application of TiO2 NPs and PEMPs at a 1:1 ratio alleviated the reduction in internal activity caused by PEMPs alone, while the 1:2 ratio led to a significant decrease in biospeckle contrast, indicating suppressed internal seed activity. bOCT successfully detected early biological responses of TiO2 NPs within 20 h, demonstrating its efficiency compared to the conventional methods. The ability of bOCT to monitor dynamic internal changes highlights its potential as a rapid tool for assessing NPs as well as their synergistic effects with polyethylene microplastics on plants.

A nationwide questionnaire survey was conducted in Japanese junior high schools using a random sampling method in January 2022. The purpose was to investigate the relationship between various physical elements of classrooms and teachers’ evaluation on classroom size. The collected data underwent multi-level analysis and decision tree analysis. The results revealed that class sizes had the most significant influence on teachers’ evaluation, followed by the availability of storage for students’ belongings. This suggests that, to have better learning environment, consideration should be given to reducing class size and/or improving storage facilities where appropriate.

Maintaining medication adherence remains a major clinical challenge, as higher daily dosing frequencies are often associated with decreased adherence. Although once-daily regimens are generally preferred, specific patient preferences regarding dosing frequency are unclear, despite implications for improving patient satisfaction and optimizing pharmacotherapy. Here, we evaluated the frequency at which patients begin to perceive dosing as excessive. A web-based questionnaire survey using the personal health record infrastructure of electronic medication notebooks was administered between July 20 and 26, 2023. Eligible participants were aged ≥20 years and had received oral tablets or capsules within 90 d of the survey. The questionnaire consisted of 6 items, including whether participants felt that the daily medication frequency was excessive. A multivariate logistic regression analysis was performed to identify the frequency beyond which patients perceive dosing as excessive, adjusting for confounders, such as medication formulation, number of medications, and patient characteristics. Of 1478 respondents, 1236 were included in the analysis. In total, 28.9% of participants reported that their medication frequency felt excessive. In the multivariate logistic regression analysis, twice-daily or more frequent dosing was significantly associated with the perception of excessiveness, using once-daily dosing as the reference. Twice-daily dosing is the frequency beyond which patients are significantly more likely to perceive medication as excessive. Our findings emphasize the importance of simplifying dosing regimens.

DNA and RNA, by focusing on their unique molecular properties, have transcended their role as carriers of genetic information in life and pioneered new application fields such as molecular robotics and molecular computing. However, as these technologies advance, the limitations inherent in natural nucleic acids and their ecosystems are increasingly becoming apparent as barriers to further application. To overcome these constraints, efforts to create artificial nucleic acids using chemical synthesis are underway and are now reaching a new stage of development. This paper proposes a concept of ultimate nucleic acid, "Omega Nucleic Acids (ΩNA)," as a thought experiment. We discuss the specifications required for this molecule, its implementable functions and approaches, and the construction of an ecosystem centered around ΩNA. By working backward from the characteristics of known natural and artificial nucleic acids, while envisioning next-generation artificial systems and applications in extreme environments, we aim to explore new approaches to nucleic acid chemistry and provide guidelines for constructing innovative artificial molecular systems.

Existing three-dimensional (3D) anomaly detection approaches typically rely on reconstruction, external memory banks, or fixed-radius clustering and often fail to generalize to noisy, irregularly sampled industrial scans or to capture the full diversity of real defects. We present Vote3D-AD, a single-pass framework that trains only on defect-free data and addresses these gaps with two principal contributions. First, we introduce Varied Defect Synthesis (VDS), a saliency-guided pseudo-anomaly generator that produces diverse, physically plausible defects (bulges, dents, holes, cracks, and surface roughness) together with sensor-level degradations to narrow the synthetic-to-real gap. Second, we develop a vote-and-cluster architecture in which local geometric representations predict learned, scale-aware votes that encode both spatial and boundary cues, and a differentiable soft-assignment clustering module aggregates these votes into coherent anomaly regions without relying on fixed-radius grouping or external memory structures. We evaluated our method on the synthetic Anomaly-ShapeNet benchmark and a new industrial dataset using three metrics: point-level Area Under the Receiver Operating Characteristic curve (AUROC), object-level Area Under the Precision-Recall curve (AUPR), and F1-Score. On average across both benchmarks, our method improves point-level AUROC by 6.7%, AUPR by 10.1% and F1 by 11.2%, and improves object-level AUROC by 5.3%, AUPR by 3.8% and F1 by 5.4% over the strongest baseline, while maintaining inference speeds above 9 frames per second (FPS).