Micro- and nanorobots (MNRs) propelled by external actuations have broad potential in biomedical applications. Among the numerous external excitations, ultrasound (US) features outstanding practical significance with merits of its noninvasiveness, tunability, penetrability, and biocompatibility. Attributing to various physiochemical effects of US, it can propel the MNRs with sophisticated structures through asymmetric acoustic streaming, bubble oscillation, and so on. In this review, we introduce several advanced and representative US-propelled MNRs with inhomogeneous density distribution, asymmetric shape, hollow cavity, etc. The potential biomedical applications of these cutting-edge MNRs are also presented, including intracellular delivery, harmful substances collection, and so on. Furthermore, we conclude the advantages and limitations of US-propelled MNRs and prospect their future developments in multidisciplinary fields.

The care sector has become one of the test beds for developing robotic technologies, which have been promised to mitigate problems with aging populations and labor shortages. Despite these promises, the practical application of such technologies have been met with limited success. Apart from technical limitations, other challenges exist in the way we approach designing these technologies. Critical to the development in the care sector is understanding the complexity of the contexts, the needs and goals of diverse actors, and how these are socio-materially scaffolded. This paper presents a study conducted at the intersection of a value sensitive design and speculative design to understand these sensitivities. Based on the data collected in interviews (n = 6) and card workshops (n = 6) from care workers and residents in mobile care and care home contexts in Austria, we developed five themes capturing situated practices and understandings of good care as built on trust-developing routines, negotiations between different actors, affective and reciprocal dimension of care, care worker self-care, and material mediations. Subsequently, we created six speculative vignettes which serve as rhetorical devices to emphasize the tensions that arise with any technological intervention entering and reshaping existing care practices and relations. We argue that our approach can support robot designers to develop a rich understanding of the values and tensions in the specific context under study from the before design and development begin.

Robot navigation has transitioned from avoiding static obstacles to adopting socially aware navigation strategies for coexisting with humans. Consequently, socially aware navigation in dynamic, human-centric environments has gained prominence in the field of robotics. One of the methods for socially aware navigation, the reinforcement learning technique, has fostered its advancement. However, defining appropriate reward functions, particularly in congested environments, holds a significant challenge. These reward functions, crucial for guiding robot actions, necessitate intricate human-crafted design due to their complex nature and inability to be set automatically. The multitude of manually designed reward functions contains issues such as hyperparameter redundancy, imbalance, and inadequate representation of unique object characteristics. To address these challenges, we introduce a transformable Gaussian reward function (TGRF). The TGRF possesses two main features. First, it reduces the burden of tuning by utilizing a small number of hyperparameters that function independently. Second, it enables the application of various reward functions through its transformability. Consequently, it exhibits high performance and accelerated learning rates within the deep reinforcement learning (DRL) framework. We also validated the performance of TGRF through simulations and experiments.

BACKGROUND: The Da Vinci robot-assisted surgery technique has been widely used in laparoscopic mesangectomy for rectal cancer. However, the short-term efficacy of these procedures compared to traditional laparoscopic surgery remains controversial. The purpose of this study was to compare and analyze the short- and medium-term efficacy of Da Vinci robot and laparoscopic surgery in total mesangectomy (TME) for rectal cancer, so as to provide guidance and reference for clinical practice.
OBJECTIVE: To investigate the safety and long-term efficacy of robotic and laparoscopic total mesorectal resection for the treatment of rectal cancer.
METHODS: The clinicopathologic data of 240 patients who underwent TME for rectal cancer in the Anorectal Department of People\'s Hospital of Xinjiang Uygur Autonomous Region from August 2018 to March 2023 were retrospectively analyzed. Among them, 112 patients underwent laparoscopic TME (L-TME) group, and 128 patients underwent robotic TME (R-TME) group. The intraoperative, postoperative, and follow-up conditions of the two groups were compared.
RESULTS: The conversion rate of the L-TME group was greater than that of the R-TME group (5.4% vs 0.8%, χ 2 = 4.417, P = 0.036). The complication rate of the L-TME group was greater than that of the R-TME group (32.1% vs 17.2%, χ 2 = 7.290, P = 0.007). The percentage of positive annular margins in the L-TME group was greater than that in the R-TME group (7.1% vs 1.6%, χ 2 = 4.658, P = 0.031). The 3-year disease-free survival (DFS) rate and overall survival (OS) rate of the L-TME group were lower than those of the R-TME group (74.1% vs 85.2%, χ 2 = 4.962, P = 0.026; 81.3% vs 91.4%, χ 2 = 5.494, P = 0.019); in patients with American Joint Committee on Cancer stage III DFS rate and OS rate in the L-TME group were significantly lower than those in the R-TME group (52.5% vs 76.1%, χ 2 = 5.799, P = 0.016; 65.0% vs 84.8%, χ 2 = 4.787, P = 0.029).
CONCLUSIONS: Compared with the L-TME group, the R-TME group had a better tumor prognosis and was more favorable for patients with rectal cancer, especially for patients with stage III rectal cancer.

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies-such as plant genetic engineering-shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany-a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

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Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC (Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm\'s feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

There is an inherent tension between the nursing profession\'s empathetic, human-centric ethos and the impersonal nature of digital technology. However, digital solutions such as robot carers could offer convenience, dignity and reduced feelings of burden, so there is potentially a significant misalignment between nursing concepts of care and contemporary patient needs. The notion of \'care\' should be reconceptualised to include digital advancements, aligning practice with changing patient expectations and technological progress. A strategy to do this could involve a philosophical overhaul of nursing care models, integration of advanced patient-centric technologies, comprehensive education and training, collaborative development of nursing technologies, showcasing successful digital integration and policy advocacy for digital care models. This transformation is essential if nursing is to stay relevant and effective in the digital era, bridging the traditional care and modern healthcare needs while maintaining its core ethic of care.

Microplastics have been detected from water and soil systems extensively, with increasing evidence indicating their detrimental impacts on human and animal health. Concerns surrounding microplastic pollution have spurred the development of advanced collection and characterization methods for studying the size, abundance, distribution, chemical composition, and environmental impacts. This paper offers a comprehensive review of artificial intelligence (AI)-empowered technologies for the collection and characterization of microplastics. A framework is presented to streamline efforts in utilizing emerging robotics and machine learning technologies for collecting, processing, and characterizing microplastics. The review encompasses a range of AI technologies, delineating their principles, strengths, limitations, representative applications, and technology readiness levels, facilitating the selection of suitable AI technologies for mitigating microplastic pollution. New opportunities for future research and development on integrating robots and machine learning technologies are discussed to facilitate future efforts for mitigating microplastic pollution and advancing AI technologies.

UNASSIGNED: Asking questions is common in conversations, and while asking questions, we need to listen carefully to what others say and consider the perspective our questions adopt. However, difficulties persist in verifying the effect of asking questions on older adults\' cognitive function due to the lack of a standardized system for conducting experiments at participants\' homes.
UNASSIGNED: This study examined the intervention effect of cognitive training moderated by robots on healthy older adults. A focus on the feasibility of the intervention at participants\' homes was also maintained. Feasibility was evaluated by considering both the dropout rate during the intervention and the number of questions posed to each participant during the experiment.
UNASSIGNED: We conducted a randomized controlled trial with 81 adults older than 65 years. Participants were recruited through postal invitations and then randomized into 2 groups. The intervention group (n=40) received sessions where participants listened to photo-integrated stories and posed questions to the robots. The control group (n=41) received sessions where participants listened to photo-integrated stories and only thanked the robots for confirming participation. The participants participated in 12 dialogue sessions for 2-3 weeks. Scores of global cognitive functioning tests, recall tests, and verbal fluency tasks measured before and after the intervention were compared between the 2 groups.
UNASSIGNED: There was no significant intervention effect on the Telephone Interview for Cognitive Status-Japanese scores, recall tests, and verbal fluency tasks. Additionally, our study successfully concluded with no participant dropouts at follow-up, confirming the feasibility of our approach.
UNASSIGNED: There was no statistically significant evidence indicating intervention benefits for cognitive functioning. Although the feasibility of home-based interventions was demonstrated, we identified areas for improvement in the future, such as setting up more efficient session themes. Further research is required to identify the effectiveness of an improved cognitive intervention involving the act of asking questions.