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The Uncanny Valley hypothesis implies that people perceive a subjective border between human and robot faces. The robot–human border refers to the level of human-like features that distinguishes humans from robots. However, whether people’s perceived anthropomorphism and robot–human borders are consistent across different robot occupations remains to be explored. This study examined the robot–human border by analyzing the human photo proportion represented by the point of subjective equality in three image classification tasks. Stimulus images were generated by morphing a robot face photo and one each of four human photos in systematically changed proportions. Participants classified these morphed images in three different robot occupational conditions to explore the effect of changing robot jobs on the robot–human border. The results indicated that robot occupation and participant age and gender influenced people’s perceived anthropomorphism of robots. These can be explained by the implicit link between robot job and appearance, especially in a stereotyped context. The study suggests that giving an expected appearance to a robot may reproduce and strengthen a stereotype that associates a certain appearance with a certain job.

Over-the-counter medication electronic health records (OTC-EHRs) play a significant role in users’ self-medication practices. In this study, we consider the potential advantages of wearable smart devices in health management, along with the information processing capabilities of artificial intelligence (AI), and we propose a conceptual design for an AI-assisted wearable OTC-EHR system. Our objective was to systematically explore the relationship between eHealth literacy, users’ attitudes, and willingness to use the proposed system, as well as to discuss AI interventions. Internet users from China participated in an online survey examining eHealth literacy, subjective attitudes, and motivation to use this conceptual design. Descriptive statistical, correlation, difference, and regression analyses were conducted on 372 valid responses to test the research hypotheses. The results showed that the wearable-device-based OTC-EHR system with AI assistance was accepted by most responders and positively associated with eHealth literacy, which was, in turn, associated with decision-making preferences. This study suggests that AI may be perceived as an auxiliary tool for medication-related decision-making and is associated with the degree of eHealth literacy. Individuals with higher eHealth literacy are more likely to make autonomous decisions, whereas those with lower literacy will potentially rely more on AI support and professional guidance.

Since the enactment of the revised Pharmaceutical Affairs Act in Japan in 2009, self-medication practices have increased in the country. However, studies report that consumers pay little attention to the medication facts and risks indicated on the packages of over-the-counter (OTC) medicines, which could be a potential risk. Since the COVID-19 pandemic, the digital transformation of purchasing OTC medicines has progressed. As an appropriate design for the digital transformation is likely to improve consumers' literacy and them obtaining medical information, this study systematically examines Japanese consumers' attitudes toward the digital transformation of OTC medicine purchase behavior and its correlation to eHealth literacy, exploring an appropriate digital experience design in purchasing OTC medicine. Participants from the Greater Tokyo Area of Japan participated in an online survey. Consumers' current behavior and preferences in accessing OTC medicine, receiving medication guidance, and obtaining medical information were examined. eHealth literacy was assessed using the J-eHEALS. Descriptive statistics, text mining, and thematic analysis were conducted to answer research questions. Over 89% of the respondents who had experience in purchasing OTC medicines preferred local pharmacies or stores rather than online purchasing,  < 0.001. Obtaining medicine guidance in pharmacies or stores was the main preference over other approaches,  < 0.001. Furthermore, most of the participants accepted selecting medicine on shelves and digital screens in-store. However, they were accustomed to using smartphones to obtain additional information at the pharmacy or drug store,  < 0.001; this behavior was positively correlated with eHealth literacy,  < 0.001. Japanese consumers are seeking a combination of conventional and digital behaviors for purchasing OTC medicine rather than opting for a particular method. Most consumers prefer purchasing and receiving instructions in-store while searching for additional decision-making information online. eHealth literacy is positively associated with digital behaviors of OTC medicine information acquisition but less associated with medicine purchases and selections. The hybrid digital experience design may enhance the OTC medicine purchase experience and reduce potential risks by providing appropriate information.

Designing an over-the-counter medication electronic health record (OTC-EHR) may help improve OTC usage. An online survey for the conceptual OTC-EHR design examined participant characteristics, attitudes towards obtaining user-shared OTC medication information, health-related application usage, and the inclination to share anonymized health information. Descriptive statistics, tests to assess statistical significance, and text mining were used to analyze the results. The findings revealed that Japanese consumers, particularly those with high eHealth literacy and women, possess relatively positive attitudes towards obtaining user-shared OTC medication information than those with low eHealth literacy (t (280.71) = −4.11, p < 0.001) and men (t (262.26) = −2.78, p = 0.006), respectively. Most consumers own smartphones but do not use health-related applications. A minority held positive attitudes about sharing anonymized health information. The perceived helpfulness of OTC-EHR was positively associated with the usage of health-related applications (χ2 (4) = 18.35, p = 0.001) and attitude towards sharing anonymized health information (χ2 (3) = 19.78, p < 0.001). The study findings contribute towards OTC-EHR’s design to enhance consumers’ self-medication and reduce risks, while the psychological barriers to sharing anonymized health information should be improved by increasing the OTC-EHR’s penetration rate and engaging in appropriate information design.

With the development of smart technology and aging societies, the living and housing environments for older people are undergoing transformation. Designers must understand the changing capabilities, lifestyles, preferences, and inspirations of older people for their future homes, in which the kitchen is seen as the heart. To gain a deeper understanding of the requirements of older people in promoting healthier lifestyles and inclusive daily practices, the authors identified five key factors of kitchen design through a literature review, developing an initial model. Subsequently, a focus group was conducted in the UK to explore the perspectives and expectations of older people, where metaphors for future kitchens were collected, and further insights were used to refine the model. The refined model for a future-inclusive kitchen encompasses six dimensions: Environment/space, Technology/interaction, Emotion/affect, Health and safety, Human factors and well-being, and Sustainability. Through using metaphors, this study offers a multidimensional lens to investigate the future user experience of inclusive kitchens. The significance of this study lies in the originality of combining a literature review, and user study with design metaphors. A future-proof inclusive kitchen design model is proposed to provide guidance for future design directions of age-friendly environments.

The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealing-based differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems.

Optical silicon (Si)-based materials are highly attractive due to their widespread applications ranging from electronics to biomedicine. It is worth noting that while extensive efforts have been devoted to developing fluorescent Si-based structures, there currently exist no examples of Si-based materials featuring phosphorescence emission, severely limiting Si-based wide-ranging optical applications. To address this critical issue, we herein introduce a kind of Si-based material, in which metal-organic frameworks (MOFs) are in-situ growing on the surface of Si nanoparticles (SiNPs) assisted by microwave irradiation. Of particular significance, the resultant materials, i.e., MOFs-encapsulated SiNPs (MOFs@SiNPs) could exhibit pH-responsive fluorescence, whose maximum emission wavelength is red-shifted from 442 to 592 nm when the pH increases from 2 to 13. More importantly, distinct room-temperature phosphorescence (maximum emission wavelength: 505 nm) could be observed in this system, with long lifetime of 215 ms. Taking advantages of above-mentioned unique optical properties, the MOFs@SiNPs are further employed as high-quality anti-counterfeiting inks for advanced encryption. In comparison to conventional fluorescence anti-counterfeiting techniques (static fluorescence outputs are generally used, thus being easily duplicated and leading to counterfeiting risk), pH-responsive fluorescence and room-temperature phosphorescence of the resultant MOFs@SiNPs-based ink could offer advanced multi-modal security, which is therefore capable of realizing higher-level information security against counterfeiting.

RGBT tracking has drawn great attention recently due to its ability to leverage enhancement and complementary information from the RGB and thermal infrared modalities. Nevertheless, RGBT tracking in real-world scenarios inevitably encounters heavy modality-missing challenges caused by substantial environmental factors (such as device overheating, and frame skipping). Existing methods for RGBT tracking are built upon pre-processed missingness-free datasets and suffer significant performance degradation when applied to noisy datasets with random missing modalities. In this paper, we propose a novel missingness-aware prompting framework (MAP) for modality-missing RGBT tracking. It is a lightweight prompting framework consisting of two-stage prompts focusing on compensating essential information for RGBT tracking stage-by-stage. Specifically, prototypical missingness-aware prompts (pMAP) are explored to compensate for modality-specific but instance-agnostic prototypical missing information. Contextual missingness-aware prompts (cMAP) are further designed to compensate for instance-specific detailed missing information. Extensive experiments on three large-scale datasets demonstrate the effectiveness and superiority of the proposed framework for RGBT tracking with random missing modalities.

Biomedical image segmentation plays a crucial role in aiding diagnosis and treatment planning. However, constructing effective frameworks remains challenging due to the variable size and irregular shape of target structures. U-Net has become a cornerstone in this field, but integrating it with Transformer or Multilayer Perceptron (MLP) models faces limitations such as quadratic computational complexity and insufficient interpretability. To address these challenges, we propose KM-UNet, a novel structure inspired by state-space models (SSMs) ( e.g ., Mamba) and the Kolmogorov-Arnold network (KAN). KM-UNet leverages nonlinear, learnable activation functions, rooted in the Kolmogorov-Arnold representation theorem, to enhance interpretability and efficiency. By combining the strengths of state-space models and Kolmogorov-Arnold networks, KM-UNet achieves a balance between accuracy and computational performance. Experiments on five public datasets demonstrate its superiority. On the BUSI dataset, KM-UNet achieved an Intersection over Union (IoU) of 65.21% and an F1-score (F1) of 78.43%, improving IoU by 1.83% over state-of-the-art methods. It also achieved the highest IoUs on the Glas (87.31%) and CVC (85.22%) datasets and delivered the best overall performance on the ISIC series datasets. These results highlight KM-UNet’s ability to effectively integrate global and local information while maintaining interpretability. With its powerful feature extraction capabilities and computational efficiency, KM-UNet emerges as a versatile and reliable framework for medical image segmentation across diverse biomedical applications. The source code is available at https://github.com/2760613195/KM_UNet .

Thermoactinomyces is known for its resistance to extreme environmental conditions and its ability to digest a wide range of hard-to-degrade compounds. Here, Thermoactinomyces sp. strain CDF isolated from soil was found to completely degrade intact chicken feathers at 55 °C, with the resulting degradation products sufficient to support growth as the primary source of both carbon and nitrogen. Although feathers were not essential for the expression of keratinase, the use of this substrate led to a further 50–300 % increase in enzyme production level under different nutrition conditions, with extracellular keratinolytic activity reaching its highest level (∼400 U/mL) during the late-log phase. Full degradation of feathers required the presence of living cells, which are thought to supply reducing agents necessary for the cleavage of keratin disulfide bonds. Direct contact between the hyphae and substrate may enhance the reducing power and protease concentrations present in the local microenvironment, thereby facilitating keratin degradation. The gene encoding the major keratinolytic protease (protease C2) of strain CDF was cloned, revealing an amino acid sequence identical to that of subtilisin-like E79 protease from Thermoactinomyces sp. E79, albeit with significant differences in the upstream flanking region. Exogenous expression of protease C2 in Escherichia coli resulted in the production of inclusion bodies with proteolytic activity, which could be solubilized to an alkaline solution to produce mature protease C2. Purified protease C2 was able to efficiently hydrolyze α- and β-keratins at 60–80 °C and pH 11.0, representing a promising candidate for enzymatic processing of hard-to-degrade proteins such as keratinous wastes.