Multidisciplinary Digital Publishing Institute (Switzerland)
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A Grammar of Speculation: Learning Speculative Design with Generative AI in Biodesign Education
No full textThis study examines how undergraduate design students imagined and critiqued biotechnological futures through speculative work with generative AI in a semester-long biodesign course. Using inductive qualitative coding and visual discourse analyses, we traced how students’ prompts, images, and reflections reveal an evolving grammar of speculation. Students shifted from crisis description to design-oriented possibility and socio-political reasoning about ecological, cultural, and ethical implications. Generative AI supported this shift by offering visual feedback that enabled students to recognize assumptions and critically examine speculative designs. Through repeated cycles of prompting and refinement, students advanced biodesign prototypes and developed a nuanced understanding of AI’s affordances and limits. Extending constructionism learning theories into speculative design with generative AI, this study examines how learners externalize discursive and imaginative thought through prompt-crafting. These findings articulate a grammar of speculation, showing how generative AI mediates critical AI literacy as a discursive and constructionist learning process
Preparation Strategy of Hydrogel Loaded with Natural Products and Its Research Progress in Skin Repair
No full textHydrogels are three-dimensional hydrophilic network structures with one or more polymers cross-linked, with excellent biocompatibility, drug-carrying function, and biodegradability. Meanwhile, skin wound repair includes hemostasis and coagulation, an inflammation stage, a proliferation stage, and a remodeling stage. Therefore, hydrogels loaded with natural products are widely used in repairing skin wounds through various mechanisms such as hemostasis, antibacterial activity, anti-inflammatory activity, angiogenesis promotion, skin regeneration, and skin repair monitoring. In addition, this study provides the cross-linking mechanism (physical cross-linking and chemical cross-linking) and construction mode (self-assembly and physical parcels) of the loaded natural product hydrogel. In general, the purpose of this paper is to comprehensively understand the mechanism and preparation strategy of hydrogels loaded with natural products for skin repair and provide theoretical reference for future skin repair research
Integrating a Convolutional Neural Network and MultiHead Attention with Long Short-Term Memory for Real-Time Control During Drying: A Case Study of Yuba (Tofu Skin)
No full textAchieving comprehensive improvements in the drying rate (DR) and the quality after drying of agricultural products is a major goal in the field of drying. To further shorten the drying time while improving product quality, this study introduced a Convolutional Neural Network (CNN) and MultiHead Attention (MHA) to enhance the prediction accuracy of the Long Short-Term Memory (LSTM) network regarding the properties of dried samples. These properties included DR, shrinkage rate (SR), and total color difference (ΔE). The CNN-LSTM-MHA network was proposed, developing a novel hot-air drying (HAD) scenario utilizing an intelligent temperature control system based on the real dynamics of material properties. The results of drying experiments with temperature-sensitive yuba showed that the CNN-LSTM-MHA network’s predictive accuracy was better than that of other networks, as evidenced by its coefficient of determination (R2: 0.9855–0.9999), root mean square error (RMSE: 0.0001–0.0099), and mean absolute error (MAE: 0.0001–0.0120). Comparative analysis with fixed-temperature drying indicated that CNN-LSTM-MHA-controlled drying significantly reduced drying time and enhanced the SR, color, rehydration ratio (RR), texture, protein content, fat content, and microstructure of yuba. Overall, the findings highlight the potential of CNN-LSTM-MHA-based intelligent drying as a viable strategy for yuba stick processing, providing insights for other food drying applications
Risk Assessment of Chemical Mixtures in Foods: A Comprehensive Methodological and Regulatory Review
No full textOver the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making
A Novel UHPC-NC Composite Column Frame Structure: Design and Seismic Performance Investigation
No full textExisting studies have demonstrated that insufficient horizontal deformation capacity of columns under high axial compression ratios constitutes a key factor leading to seismic damage in ordinary concrete frame structures. This paper proposes a novel framed structure incorporating composite columns by combining ultra-high performance concrete (UHPC), which exhibits excellent mechanical properties, with normal concrete (NC). The design concept maintains the overall mechanical performance of the composite column frame structure while significantly reducing the lateral stiffness when the composite columns are configured in a “split-column form.” For instance, the lateral stiffness of ZH-5 in the “split-column form” is only one-tenth of that of ZT-1 in its initial state, leading to a substantial enhancement in horizontal deformation capacity. This design approach maintains the overall mechanical performance of the composite column frame structure while significantly enhancing its horizontal deformation capacity by reducing lateral stiffness through the “split-column” configuration. Using the ABAQUS finite element software 2021, a finite element model of a multi-story frame column structure was developed. Research findings indicate that the frame structure utilizing UHPC-NC composite columns exhibits reduced tensile damage, lower peak and plastic displacements, and a relatively smaller inter-story drift angle. Specifically, the plastic drift angle of the UHPC-NC composite column frame structure from the first to the fourth story is 5% to 31% smaller than that of the conventional reinforced concrete column frame structure. The novel UHPC-NC composite column frame structure demonstrates superior seismic performance
Decision Tree-Based Pilot Workload Prediction Through Optimized HRV Features Selection
No full textThis research explores the use of physiological signals derived from heart activity to assess mental effort during flight-related tasks. Data were collected through wearable sensors during simulations with varying cognitive demands. Specific indicators related to heart rate variability (HRV) were extracted and tested in different combinations to identify those most relevant for distinguishing levels of mental workload (WL). A Random Forest (RF) ensemble method is applied to classify two conditions, and its performance is examined under various settings, including model complexity and data partitioning strategies. Results showed that certain feature pairs significantly enhanced classification accuracy. The best features settings obtained from the RF are then used to train the other two decision trees-based classifiers, namely the AdaBoost and the XGBoost. Moreover, the decision trees models output is compared with predictions from a Kriging spatial interpolation technique, showing superior results in terms of reliability and consistency. This study highlights the potential of using heart-based physiological data and advanced classification techniques for developing intelligent support systems in aviation
Genome-Wide Identification and Expression Analysis of LBD Gene Family in Neolamarckia cadamba
No full textLateral Organ Boundaries Domain (LBD) proteins are plant-specific transcription factors characterized by a typical N-terminal LOB domain and are critical for plant growth, development, and stress response. Currently, LBD genes have been investigated in various plant species, but they have yet to be identified in Neolamarckia cadamba, known as a ‘miracle tree’ for its fast growth and acknowledged for its potential medicinal value in tropical and subtropical areas of Asia. In this study, a total of 65 NcLBD members were identified in N. cadamba by whole-genome bioinformatics analysis. Phylogenetic analysis revealed their classification into two clades with seven distinct groups, and their uneven distribution across 18 chromosomes, along with 6 tandem repeats and 58 segmental duplications. Furthermore, enrichment analysis of transcription factor binding motifs within NcLBD promoters identified the MYB-related and WRKY families exhibited the most significant enrichment in the NcLBD promoter. Protein interaction network analysis revealed potential interactions among NcLBD proteins, as well as their interactions with various transcription factors. RNA-seq and qRT-PCR analyses of NcLBDs transcript levels showed distinct expression patterns both across various tissues and under different hormone and abiotic stress conditions. Specifically, NcLBD3, NcLBD37, and NcLBD47 were highly expressed in vascular cells and induced by abiotic stress, including cold, drought, and salt, suggesting their significant role in the processes. In summary, our genome-wide analysis comprehensively identified and characterized LBD gene family in N. cadamba, laying a solid foundation for further elucidating the biological functions of NcLBD genes
Identification of Immune&Driver Molecular Subtypes Optimizes Immunotherapy Strategies for Gastric Cancer
No full textImmunotherapy has become a promising treatment for gastric cancer. However, its effectiveness varies significantly across subtypes because of heterogeneous immune microenvironments and genomic alterations. Here, we established Immune&Driver molecular subtypes CS1 and CS2 by systematically integrating multi-omics data for immune-related and driver genes. CS1 was linked to a better prognosis, while CS2 represented a poorer prognostic phenotype. CS1 displayed enhanced genomic instability, marked by higher mutation frequency and chromosomal alterations. In contrast, CS2 exhibited higher immune activity, with a higher density of immune cell infiltration and increased expression of chemokines and immune checkpoint genes. Among FDA-approved anti-cancer agents included in a pan-cancer drug sensitivity prediction framework, CS1 was predicted to be more sensitive to conventional chemotherapeutic agents, whereas CS2 was predicted to be more responsive to immune-related agents. In melanoma datasets, a CS2-like transcriptomic pattern was associated with improved response to anti-PD-1 therapy, with the combination of anti-PD-1 and anti-CTLA-4 showing more favorable response patterns compared to anti-PD-1 monotherapy. Additionally, we developed an immunotherapy response prediction model using PCA-based logistic regression according to the transcriptional expression of CS biomarkers. The model was trained in melanoma immunotherapy cohorts and validated across independent melanoma datasets, and it further achieved a higher AUC in an external gastric cancer cohort treated with anti-PD-1 therapy. Collectively, this study highlights immune and genomic heterogeneity in gastric cancer and provides a hypothesis-generating framework for exploring immunotherapy response
School Mental Health Interventions for Adolescents: A Meta-Analysis of Effectiveness and Relevant Moderators
No full text(1) Background: School-based mental health interventions represent a promising approach to address the substantial treatment gap affecting adolescents, with only 20% of youth with diagnosable mental health conditions receiving adequate care. (2) Methods: This meta-analysis synthesized evidence from 18 randomized controlled trials to examine the effectiveness of school-based mental health interventions and potential moderators of outcomes. (3) Results: Using Hedges’ g as the effect size index and a random-effects model, the analysis revealed a statistically significant overall effect size of 0.068 (95% CI [0.019, 0.117], p = 0.006), indicating small but reliable improvements in adolescent academic, social, emotional, behavioral, and mental health outcomes. Heterogeneity across studies was minimal (I2 = 15%), suggesting consistent effects across diverse intervention types and contexts. Meta-regression analyses examining eight potential moderators including intervention focus, grade level, provider type, delivery format, duration, study design, geographic location, and theoretical foundation did not reveal statistically significant moderation effects, likely due to limited statistical power. However, descriptive patterns suggested that targeted interventions, small-group formats, and interventions delivered by mental health professionals may produce larger effects than universal programs, classroom-based approaches, and teacher-delivered interventions. (4) Conclusions: These findings support continued investment in school-based mental health programming while highlighting the need for specialized focus to optimize outcomes for all adolescents
Artificial Neural Network-Based Conveying Object Measurement Automation System Using Distance Sensor
No full textMeasuring technology is used in various ways in the logistics industry for defect inspection and loading optimization. Recently, in the context of the fourth industrial revolution, research has focused on measurement automation combining AI, IoT technologies, and measuring equipment. The 3D scanner used for field logistics measurements offers high performance and can handle large volumes quickly; however, its high unit price limits adoption across all lines. Entry-level sensors are challenging to use due to measurement reliability issues: their performance varies with changes in object location, shape, and logistics environment. To bridge this gap, this study proposes a systematic framework for geometry measurement that enables reliable length and width estimation using only a single entry-level distance sensor. We design and build a conveyor-belt-based data acquisition setup that emulates realistic logistics transfer scenarios and systematically varies transfer conditions to capture representative measurement disturbances. Based on the collected data, we perform robust feature extraction tailored to noisy, condition-dependent signals and train an artificial neural network to map sensor observations to geometric dimensions. We then verified the model’s performance in measuring object length and width using test data. The experimental results show that the proposed method provides reliable measurement results even under varying transfer conditions