This paper introduces a "lifecycle perspective" on social robot design and human-robot interaction, and explores the practices of maintenance, repair, and letting go of social robots. Drawing on interviews with robot owners and representatives of robot development and repair companies, we argue that these previously disregarded aspects of everyday use provide a context for negotiating the social value and meaning of interactions with robots. We discuss owner concerns about robot obsolescence, as well as company support for long term human-robot interaction through repair, reuse, and giving owners closure in letting go of robots they can no longer use. Our work expands the purview of HRI study and design beyond the common focus on initial design and adoption and to perceptions and practices that can foster more enduring relationships with social robots, support sustainability in robot design, and address owners’ emotional attachment to robots.

The sources of a person’s ikigai—their sense of meaning and purpose in life—often change as they age. Reflecting on past and new sources of ikigai may help people renew their sense of meaning as their life circumstances shift. Building on insights from an initial Wizard-of-Oz robot prototype [1], we describe the design of an autonomous robot that uses a semi-structured conversation format to help older adults reflect on what gives their life meaning and purpose. The robot uses both pre-determined (scripted) and Large Language Model (LLM) generated questions to personalize conversations with older adults around themes of social interaction, planning, accomplishments, goal setting, and the recent past. We evaluated the autonomous robot with 19 older adult participants in a lab setting and at two eldercare facilities. Analysis of the older adults’ conversations with the robot and their responses to an evaluative survey allowed us to identify several design considerations for an autonomous robot that can support ikigai reflection. Interweaving simple yet detailed predetermined questions with LLM-generated follow-up questions yielded enjoyable, in-depth conversations with older adults. We also recognized the need for the robot to be able to offer relevant suggestions when participants cannot recall events and people they find meaningful. These findings aim to further refine the design of an interactive robot that can support users in their exploration of life’s purpose.

Using socially assistive robots (SARs) as specialized companions for those living with depression to manage symptoms provides a unique opportunity for exploration of robotic systems as comfort objects. Moreover, the robotic components allow for specialized behavioral responses to particular stimuli, as preferred by the user. We have conducted semi-structured interviews with 10 participants about the zoomorphic robot’s Therabot™ desired behaviors and focus groups with five additional participants regarding the preferred sensors within the Therabot™ system. In this paper, using the data from interviews and focus groups, we explore SAR input and output for depression management. While participants overall expected the robot to respond in much similar ways as a well-trained service animal, they expressed interest in the robot understanding unique information about the environment and the user, such as when the user might need interaction.

Past research with participants in the United States showed that, in competitive group tasks, they have more positive attitudes and behaviors toward robots on their team over humans in another team. Here we present a study in which two Japanese students and two robots, placed in a randomly assigned group, compete with another student-and-robot team in a digital game. We explored participants’ moral behavior towards the robots, measured through their assignment of loud noise blasts to human and robot participants, and their perceptions of and attitudes towards the robots. We then compared this data to that which was collected within the United States. Results indicated that participants in Japan favored their ingroup humans and robots over outgroup agents and differentiated ingroup members more than outgroup members, as within the US. Japanese participants also anthropomorphized robots more than US participants and treated them more positively than US participants.

Under UNICEF’s Policy guidance on AI for children, child-centered AI should always ‘ensure inclusion of and for children.’ To spotlight youth visions for robots, we led co-design workshops with children between 5-14 years old. Youth designs were expressive, customized, relatable, and approachable. Based on 54 drawings and descriptions of the social robot Haru, we suggest that future child-centered robots should 1) be expressive across verbal and non-verbal channels of communication, 2) allow for customization to give children more agency when interacting with the robot, 3) adapt to children’s style and hobbies to make them feel seen, and 4) aesthetically keep proportions of robot faces consistent and cartoon-like to make robots more approachable.

The emergence of bacteria with antibiotic resistance and multiple resistance is characteristic of animal and human pathogens. It is wide known that bee products, which have been used in alternative medicine since ancient times, have antimicrobial potential. Application of bee products for therapeutic purposes is defined as apitherapy. The study aimed to evaluate the antimicrobial activity of commercial chestnut honey, pollen and propolis produced in western Bosnia and Herzegovina (Sanski Most) individually and in five combinations (apimixtures). The antimicrobial properties of samples were investigated using the agar well diffusion method against three Gram-positive bacteria (Bacillus subtilis subsp. spizizenii ATCC 6633, Methicillin-resistant Staphylococcus aureus ATCC 33591, Enterococcus faecalis ATCC 29212); three Gram-negative bacteria (ESBL producing Escherichia coli ATCC 35218, Salmonella enterica subsp. enterica serovar Enteritidis ATCC 13076, Pseudomonas aeruginosa ATCC 9027) and one fungal species (Candida albicans ATCC 10231). Pure bee pollen inhibited the growth of only Gram-negative bacteria, concentrated chestnut honey was active against all Gram-negative and Gram-positivebacteria, while 20% propolis extract and apimixtures A2 (80% honey and 20% propolis) and A3 (60% honey, 20% pollen and 20% propolis extract) inhibited the growth of all tested microorganisms. Chestnut honey andthree apimixtures (A1, A2 and A3) showed the highest antibacterial action against all tested Gram-negative bacteria and MRSA compared to other investigated samples. In this study, examined honeybee products from Bosnia and Herzegovina and their mixtures had significant activity against tested bacteria, including strains with proven resistance to conventional antibiotics, MRSA and ESBL producing E. coli.

Data-driven models that act as surrogates for computationally costly 3D topology optimization techniques are very popular because they help alleviate multiple time-consuming 3D finite element analyses during optimization. In this study, one such 3D CNN-based surrogate model for the topology optimization of Schoen’s gyroid triply periodic minimal surface unit cell is investigated. Gyroid-like unit cells are designed using a voxel algorithm and homogenization-based topology optimization codes in MATLAB. A few such optimization data are used as input–output for supervised learning of the topology-optimization process via the 3D CNN model in Python code. These models could then be used to instantaneously predict the optimized unit cell geometry for any topology parameters. The high accuracy of the model was demonstrated by a low mean square error metric and a high Dice coefficient metric. The model has the major disadvantage of running numerous costly topology optimization runs but has the advantages that the trained model can be reused for different cases of TO and that the methodology of the accelerated design of 3D metamaterials can be extended for designing any complex, computationally costly problems of metamaterials with multi-objective properties or multiscale applications. The main purpose of this paper is to provide the complete associated MATLAB and PYTHON codes for optimizing the topology of any cellular structure and predicting new topologies using deep learning for educational purposes.

Using the strong-field-approximation theory beyond the dipole approximation we investigate above-threshold ionization induced by the monochromatic and bichromatic laser fields. Particular emphasis is on the approach based on the saddle-point method and the quantum-orbit theory which provides an intuitive picture of the underlying process. In particular, we investigate how the solutions of the saddle-point equations and the corresponding quantum orbits and velocities are affected by the nondipole effects. The photoelectron trajectories are two dimensional for linearly polarized field and three dimensional for two-component tailored fields, and the electron motion in the propagation direction appears due to the nondipole corrections. We show that the influence of these corrections is not the same for all contributions of different saddle-point solutions. For a linearly polarized driving field, we focus our attention only on the rescattered electrons. On the other hand, for the tailored driving field, exemplified by the ω–2ω orthogonally polarized two-color field, which is of the current interest in the strong-field community, we devote our attention to both the direct and the rescattered electrons. In this case, we quantitatively investigate the shift which appears in the photoelectron momentum distribution due to the nondipole effects and explain how these corrections affect the quantum orbits and velocities which correspond to the saddle-point solutions. Published by the American Physical Society 2024

Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manual artifact rejection methods based on ICA. This study investigates the influence of different ICA-based artifact rejection strategies on EEG-based auditory attention decoding (AAD) analysis. We employ multiple ICA-based artifact rejection approaches, ranging from manual to automatic versions, and assess their effects on conventional AAD methods. The comparison aims to uncover potential variations in analysis results due to different artifact rejection choices within pipelines, and whether such variations differ across different AAD methods. Although our study finds no large difference in performance of linear AAD models between artifact rejection methods, two exeptions were found. When predicting EEG responses, the manual artifact rejection method appeared to perform better in frontal channel groups. Conversely, when reconstructing speech envelopes from EEG, not using artifact rejection outperformed other approaches.

elevated nocturnal BP clinic BP monitoring alone is inadequate. ABPM should become golden standard to confirm adequate BP control in patients with kidney disease.

The integration of artificial intelligence (AI) and the internet of things (IoT) is bringing revolutionary changes to the hospitality industry, enabling the advancement of sustainable practices. This research, conducted using a quantitative methodology through surveys of hotel managers in the Republic of Serbia, examines the perceived contribution of AI and IoT technologies to operational efficiency and business sustainability. Data analysis using structural equation modeling (SEM) has determined that AI and IoT significantly improve operational efficiency, which positively impacts sustainable practices. The results indicate that the integration of these technologies not only optimizes resource management but also contributes to achieving global sustainability goals, including reducing the carbon footprint and preserving the environment. This study provides empirical evidence of the synergistic effects of AI and IoT on hotel sustainability, offering practical recommendations for managers and proposing an innovative framework for enhancing sustainability. It also highlights the need for future research to focus on the long-term impacts of these technologies and address challenges related to data privacy and implementation costs.

Autophagy is the primary intracellular degradation system, and it plays an important role in many biological and pathological processes. Studies of autophagy involvement in developmental processes are important for understanding various processes. Among them are fibrosis, degenerative diseases, cancer development, and metastasis formation. Diabetic kidney disease is one of the main causes of chronic kidney disease and end-stage renal failure. The aim of this study was to investigate the immunohistochemical expression patterns of LC3B, LAMP2A, and GRP78 during different developmental stages of early-developing human kidneys and in samples from patients with type II diabetes mellitus. During the 7/8th DW, moderate expression of LC3B and LAMP2A and strong expression of GRP78 were found in the mesonephric glomeruli and tubules. In the 9/10th DW, the expression of LC3B and LAMP2A was even more pronounced in the mesonephric tubules. LC3B, LAMP2A, and GRP78 immunoreactivity was also found in the paramesonephric and mesonephric ducts and was stronger in the 9/10th DW compared with the 7/8th DW. In addition, the expression of LC3B, LAMP2A, and GRP78 also appeared in the mesenchyme surrounding the paramesonephric duct in the 9/10th DW. In the 15/16th DW, the expression of LC3B in the glomeruli was weak, that of LAMP2A was moderate, and that of GRP78 was strong. In the tubuli, the expression of LC3B was moderate, while the expression of LAMP2A and GRP78 was strong. The strongest expression of LC3B, LAMP2A, and GRP78 was observed in the renal medullary structures, including developing blood vessels. In postnatal human kidneys, the most extensive LC3B, LAMP2A, and GRP78 expression in the cortex was found in the epithelium of the proximal convoluted tubules, with weak to moderate expression in the glomeruli. The medullary expression of LC3B was weak, but the expression of LAMP2A and GRP78 was the strongest in the medullary tubular structures. Significantly lower expression of LC3B was found in the glomeruli of the diabetic patients in comparison with the nondiabetic patients, but there was no difference in the expression of LC3B in the tubule–interstitial compartment. The expression of LAMP2A was significantly higher in the tubule–interstitial compartments of the diabetic patients in comparison with the nondiabetic patients, while its expression did not differ in the glomeruli. Extensive expression of GRP78 was found in the glomeruli and the tubule–interstitial compartments, but there was no difference in the expression between the two groups of patients. These data give us new information about the expression of LC3B, LAMP2A, and GRP78 during embryonic, fetal, and early postnatal development. The spatiotemporal expression of LC3B, LAMP2A, and GRP78 indicates the important role of autophagy during the early stages of renal development. In addition, our data suggest a disturbance in autophagy processes in the glomeruli and tubuli of diabetic kidneys as an important factor in the pathogenesis of diabetic kidney disease.

Natural killer (NK) cells are essential components of the innate immune system, with their activity significantly regulated by Killer cell Immunoglobulin-like Receptors (KIRs). The diversity and structural complexity of KIR genes present significant challenges for accurate genotyping, essential for understanding NK cell functions and their implications in health and disease. Traditional genotyping methods struggle with the variable nature of KIR genes, leading to inaccuracies that can impede immunogenetic research. These challenges extend to high-quality phased assemblies, which have been recently popularized by the Human Pangenome Consortium. This paper introduces BAKIR (Biologically-informed Annotator for KIR locus), a tailored computational tool designed to overcome the challenges of KIR genotyping and annotation on high-quality, phased genome assemblies. BAKIR aims to enhance the accuracy of KIR gene annotations by structuring its annotation pipeline around identifying key functional mutations, thereby improving the identification and subsequent relevance of gene and allele calls. It uses a multi-stage mapping, alignment, and variant calling process to ensure high-precision gene and allele identification, while also maintaining high recall for sequences that are significantly mutated or truncated relative to the known allele database. BAKIR has been evaluated on a subset of the HPRC assemblies, where BAKIR was able to improve many of the associated annotations and call novel variants. BAKIR is freely available on GitHub, offering ease of access and use through multiple installation methods, including pip, conda, and singularity container, and is equipped with a user-friendly command-line interface, thereby promoting its adoption in the scientific community.