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Six-dimensional pose estimation task predicts its 3D rotation matrix and 3D translation matrix in the world coordinate system by inputting the color image or depth image of the target object. Existing methods usually use deep neural networks to directly predict or regress object poses based on keypoint methods. The prediction results usually have deviations depending on whether the surface shape of the object is prominent or not and the size of the object. To solve this problem, we propose the six-dimensional pose estimation based on multi-task parameter sharing (PMP) framework to incorporate object category information into the pose estimation network through the form of an object classification auxiliary task. First, we extract the image features and point cloud features of the target object separately, and fuse them point by point; then, we share the confidence of each keypoint in pose estimation task and the knowledge of the classification task, get the key points with higher confidence, and predict the object pose; finally, the obtained object pose is passed through an iterative optimization network to obtain the final pose. The experimental results on the LineMOD dataset show that the proposed method can improve the accuracy of pose estimation and narrow the gap in the prediction accuracy of objects with different shapes. We also tested on a new dataset of small-scale objects, which contains object RGBD images and accurate 3D point cloud information. The proposed method is applied to the grasping experiment on the UR5 robotic arm, which satisfies the real-time pose estimation results during the grasping process. [ABSTRACT FROM AUTHOR]

Objectives: Single position lateral fusion with robotic assistance eliminates the need for surgical staging while harnessing the precision of robotic adjuncts. We expand on this technique by demonstrating the technical feasibility of placing bilateral pedicle screws with S2-alar-iliac (S2AI) fixation while in the lateral position. Methods: A cadaveric study was performed using 12 human specimens. A retrospective clinical series was also performed for patients who had undergone robot-assisted placement of S2AI screws in lateral decubitus between June 2020 and June 2022. Case demographics, implant placement time, implant size, screw accuracy, and complications were recorded. Early postoperative radiographic outcomes were reported. Results: In the cadaveric series, a total of 126 screws were placed with robotic assistance in 12 cadavers of which 24 screws were S2AI. There were four breaches from pedicle screws and none with S2AI screws for an overall accuracy rate of 96.8%. In the clinical series, four patients (all male, mean age 65.8 years) underwent single position lateral surgery with S2AI distal fixation. Mean BMI was 33.6 and mean follow-up was 20.5 months. Mean radiographic improvements were lumbar lordosis 12.3 ± 4.7°, sagittal vertical axis 1.5 ± 2.1 cm, pelvic tilt 8.5 ± 10.0°, and pelvic incidence—lumbar lordosis mismatch 12.3 ± 4.7°. A total of 42 screws were placed of which eight screws were S2AI. There were two breaches from pedicle screws and none from S2AI screws for an overall accuracy rate of 95.2%. No repositioning or salvage techniques were required for the S2AI screws. Conclusions: We demonstrate here the technical feasibility of single position robot-assisted placement of S2-alar-iliac screws in the lateral decubitus position for single position surgery. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to examine the effects of intraoperative technology use on the rate of using polyethylene liners 15 mm or greater during primary total knee arthroplasty (TKA). Methods: There were 103,295 implants from 16,386 primary unilateral TKAs performed on 14,253 patients at a single institution between 1 January 2018, and 30 June 2022, included in the current study. Robotic assistance and navigation guidance were used in 1274 (8%) and 8345 (51%) procedures, respectively. The remaining 6767 TKAs (41%) were performed manually. Polyethylene liners were manually identified and further subcategorised by implant thickness. Patients who underwent robotic‐assisted TKA were younger (p < 0.001) and more likely to be male (p < 0.001) compared to patients who underwent navigation‐guided or manual TKAs. Results: Average polyethylene liner thickness was similar between groups (10.5 ± 1.5 mm for robotic‐assisted TKAs, 10.9 ± 1.8 mm for navigation‐guided TKAs and 10.8 ± 1.8 mm for manual TKAs). The proportions of polyethylene liners 15 mm or greater used were 4.9%, 3.8% and 1.9% for navigation‐guided, manual and robotic‐assisted procedures, respectively (p < 0.001). Multivariate regression analyses demonstrated that navigation‐guided (odds ratio [OR]: 2.6, 95% confidence Interval [CI]: [1.75–4.07], p < 0.001) and manual (OR: 2.0, 95% CI: [1.34–3.20], p = 0.001) procedures were associated with an increased use of polyethylene liners 15 mm or greater. Conclusion: Robotic‐assisted TKA was associated with a lower proportion of polyethylene liners 15 mm or greater used compared to navigation‐guided and manual TKA. These findings suggest that robotic assistance can reduce human error via a more precise cutting system, limit over‐resection of the tibia and flexion‐extension gap mismatch and ultimately allow for more appropriately sized implants. Level of Evidence: Level III, retrospective cohort study. [ABSTRACT FROM AUTHOR]

Automated handwritten digit identification has become essential for many daily operations such as verifying the authenticity of a cheque or deciphering a postal code. Researchers have been putting a lot of effort into figuring out how to automatically recognize and sort handwritten digits because of the advent of robotic technology in recent decades. Due to the complexity of the Arabic language and a lack of publicly available Arabic handwritten digit datasets, previous research has primarily concentrated on automating the recognition of English and European digits on availability of relevant datasets. Arabic handwritten recognition also plays a significant role in electronic-learning (e-learning) systems. It has not taken into account the recognition of handwritten Arabic digits. Arabic handwritten digits vary in size, form, slant, and image noise, which can cause changes in numeral topology. These factors make it difficult to classify and recognize Arabic handwritten digits. In order to overcome these challenges, we implemented a biologically plausible technique to classify Arabic handwritten digits. The objective of the research is to address the unique challenges of style, size, shape, slant variations, and image noise of Arabic handwritten digits in classification and recognition. To categorize Arabic handwritten digits, we employed convolutional spiking neural network (CSNN) and spike neural network (SNN) models. The reason for using SNN and SCNN is that the second-generation neural networks accumulate accolades on various computer vision tasks, i.e., pattern recognition, segmentation, and classification. However, there are certain challenges, i.e., energy inefficiency and computational cost, presented by these networks. The spiking neural network models also address these issues. These models are low-power, high-performance neural network models. We trained the spiking neural network model on Arabic digit datasets using a rate-based, non-spiking algorithm, i.e., backpropagation and soft-LIF, then integrated them with a spiking neural network. We have also used an STDP (spike time-dependent plasticity)-based spiking neural network for Arabic digit recognition. We attenuate the spiking variability in CSNN by adding noise during training, compensating the training errors, and bringing robustness against the spiking variability. We experimented with soft-LIF, a non-spiking version of the LIF neuron, and a variant of STDP rules for Arabic digit classification. Experimental results illustrate that CSNN achieves high performance at a significantly low energy and computational cost. We attained 98.98% and 91.16% recognition rates for the Arabic digit dataBase (ADBase) dataset using the convolutional spiking model and STDP spiking model, respectively. [ABSTRACT FROM AUTHOR]

Background: Many surgeons use online videos to learn. However, these videos vary in content, quality, and educational value. In the setting of recent work questioning the safety of robotic-assisted cholecystectomies, we aimed (1) to identify highly watched online videos of robotic-assisted cholecystectomies, (2) to determine whether these videos demonstrate suboptimal techniques, and (3) to compare videos based on platform. Methods: Two authors searched YouTube and a members-only Facebook group to identify highly watched videos of robotic-assisted cholecystectomies. Three members of the Society of American Gastrointestinal and Endoscopic Surgeons Safe Cholecystectomy Task Force then reviewed videos in random order. These three members rated each video using Sanford and Strasberg's six-point criteria for critical view of safety (CVS) scoring and the Parkland grading scale for cholecystitis. We performed regression to determine any association between Parkland grade and CVS score. We also compared scores between the YouTube and Facebook videos using a t test. Results: We identified 50 videos of robotic-assisted cholecystectomies, including 25 from YouTube and 25 from Facebook. Of the 50 videos, six demonstrated a top-down approach. The remaining 44 videos received a mean of 2.4 of 6 points for the CVS score (SD = 1.8). Overall, 4 of the 50 videos (8%) received a passing CVS score of 5 or 6. Videos received a mean of 2.4 of 5 points for the Parkland grade (SD = 0.9). Videos on YouTube had lower CVS scores than videos on Facebook (1.9 vs. 2.8, respectively), though this difference was not significant (p = 0.09). By regression, there was no association between Parkland grade and CVS score (p = 0.13). Conclusion: Publicly available and closed-group online videos of robotic-assisted cholecystectomy demonstrated inadequate dissection and may be of limited educational value. Future work should center on introducing measures to identify and feature videos with high-quality techniques most useful to surgeons. [ABSTRACT FROM AUTHOR]

Background: Sleeve gastrectomy has become a gold standard in addressing medically refractory obesity. Robotic platforms are becoming more utilized, however, data on its cost-effectiveness compared to laparoscopy remain controversial (1–3). At NYU Langone Health, many of the bariatric surgeons adopted robotic surgery as part of their practices starting in 2021. We present a retrospective cost analysis of laparoscopic sleeve gastrectomy (LSG) vs. robotic sleeve gastrectomy (RSG) at New York University (NYU) Langone Health campuses. Methods: All adult patients ages 18–65 who underwent LSG or RSG from 202 to 2023 at NYU Langone Health campuses (Manhattan, Long Island, and Brooklyn) were evaluated via electronic medical records and MBSAQIP 30-day follow-up data. Patients with prior bariatric surgery were excluded. Complication-related ICD-10/CPT codes are collected and readmission costs will be estimated from ICD codes using the lower limit of CMS transparent NYU standard charges (3). Direct charge data for surgery and length of stay cost data were also obtained. Statistical T-test and chi-squared analysis were used to compare groups. Results: Direct operating cost data at NYU Health Campuses demonstrated RSG was associated with 4% higher total charges, due to higher OR charges, robotic-specific supplies, and more post-op ED visits. Conclusions: RSG was associated with higher overall hospital charges compared to LSG, though there are multiple contributing factors. More research is needed to identify cost saving measures. This study is retrospective in nature, and does not include indirect costs nor reimbursement. Direct operating costs, per contractual agreement with suppliers, are only given as percentages. Data are limited to 30-day follow-up. [ABSTRACT FROM AUTHOR]

Introduction: As our population ages, older adults are being considered for anti-reflux surgery (ARS). Geriatric patients typically have heightened surgical risk, and literature has shown mixed results regarding postoperative outcomes. We sought to evaluate the safety and efficacy of robotic ARS in the geriatric population. Methods: We conducted a single-institution review of ARS procedures performed between 2009 and 2023. Patients ≥ 65 were assigned to the geriatric cohort. We compared operative details, lengths of stay (LOS), readmissions, reoperations, and complications between the two cohorts. The gastroesophageal reflux disease health-related quality of life (GERD-HRQL) survey and review of clinic notes were used to evaluate ARS efficacy. Results: 628 patients were included, with 190 in the geriatric cohort. This cohort had a higher frequency of diabetes (16.3% vs 5.9% p < 0.0001), hypertension (50.0% vs 21.5% p < 0.0001), and heart disease (17.9% vs 2.3% p < 0.0001). Geriatric patients were more likely to exhibit hiatal hernias on imaging (51.6% vs 34.2% p < 0.0001) and were more likely to have large hernias (30.0% vs 7.1% p < 0.0001). Older adults were more likely to undergo Toupet fundoplications (58.4% vs 41.3%, p < 0.0001), Collis gastroplasties (9.5% vs 2.7% p < 0.0001), and relaxing incisions (11.6% vs 1.4% p < 0.0001). Operative time was longer for geriatric patients (132.0 min vs 104.5 min p < 0.0001). There were no significant differences in LOS, readmissions, or reoperations between cohorts. Geriatric patients exhibited lower rates of complications (7.4% vs. 14.6%, p = 0.011), but similar complication grades. Both groups had significant reduction in symptom scores from preoperative values. There were no significant differences in the reported symptoms between cohorts at any follow-up timepoint. Conclusion: Geriatric robotic ARS patients tend to do as well as younger adults regarding postoperative and symptomatic outcomes, despite presenting with larger hiatal hernias and shorter esophagi. Clinicians should be aware of possible need for lengthening procedures or relaxing incisions in this population. [ABSTRACT FROM AUTHOR]

Background: Although robotic pancreatectomy may facilitate an earlier functional recovery, the impact of a robotic pancreatectomy program during its early experience on the timing of return to intended oncologic therapy (RIOT) after surgery is unknown. Methods: In this retrospective cohort study, we used propensity score matching with a 1:2 ratio to compare patients who underwent robotic or open surgery (distal pancreatectomy or pancreatoduodenectomy) for pancreatic ductal adenocarcinoma (PDAC) during the first 3 years of our robotic pancreatectomy experience (January 2018–December 2021). Generalized estimating equations modeling was used to evaluate the effect of surgical approach on early RIOT, defined as adjuvant chemotherapy initiation within 8 weeks after surgery, and late RIOT, defined as initiation within 12 weeks after surgery. Results: The matched cohort included 26 patients who underwent robotic pancreatectomy and 52 patients who underwent open pancreatectomy. Rates of receipt of adjuvant chemotherapy were 96.2% and 78.9%, respectively. Rate of early RIOT in the robotic group (73.1% was higher than that in the open group (44.2%; P = 0.018). In multivariable analysis, a robotic approach was associated with early RIOT (odds ratio, 3.54; 95% confidence interval 1.08–11.62; P = 0.038). Surgical approach did not impact late RIOT (odds ratio, 3.21; 95% confidence interval 0.71–14.38; P = 0.128). Conclusions: Compared with open pancreatectomy, robotic pancreatectomy did not delay RIOT. In fact, odds of early RIOT were increased, which supports the oncological safety of our robotic pancreatectomy program during its implementation. [ABSTRACT FROM AUTHOR]

Background: Robotic suturing training is in increasing demand and can be done using suture-pads or robotic simulation training. Robotic simulation is less cumbersome, whereas a robotic suture-pad approach could be more effective but is more costly. A training curriculum with crossover between both approaches may be a practical solution. However, studies assessing the impact of starting with robotic simulation or suture-pads in robotic suturing training are lacking. Methods: This was a randomized controlled crossover trial conducted with 20 robotic novices from 3 countries who underwent robotic suturing training using an Intuitive Surgical® X and Xi system with the SimNow (robotic simulation) and suture-pads (dry-lab). Participants were randomized to start with robotic simulation (intervention group, n = 10) or suture-pads (control group, n = 10). After the first and second training, all participants completed a robotic hepaticojejunostomy (HJ) in biotissue. Primary endpoint was the objective structured assessment of technical skill (OSATS) score during HJ, scored by two blinded raters. Secondary endpoints were force measurements and a qualitative analysis. After training, participants were surveyed regarding their preferences. Results: Overall, 20 robotic novices completed both training sessions and performed 40 robotic HJs. After both trainings, OSATS was scored higher in the robotic simulation-first group (3.3 ± 0.9 vs 2.5 ± 0.8; p = 0.049), whereas the median maximum force (N) (5.0 [3.2–8.0] vs 3.8 [2.3–12.8]; p = 0.739) did not differ significantly between the groups. In the survey, 17/20 (85%) participants recommended to include robotic simulation training, 14/20 (70%) participants preferred to start with robotic simulation, and 20/20 (100%) to include suture-pad training. Conclusion: Surgical performance during robotic HJ in robotic novices was significantly better after robotic simulation-first training followed by suture-pad training. A robotic suturing curriculum including both robotic simulation and dry-lab suturing should ideally start with robotic simulation. [ABSTRACT FROM AUTHOR]

Aims: To evaluate the safety profile of robotic cholecystectomy performed within the United Kingdom (UK) Robotic Hepatopancreatobiliary (HPB) training programme. Methods: A retrospective evaluation of prospectively collected data from eleven centres participating in the UK Robotic HPB training programme was conducted. All adult patients undergoing robotic cholecystectomy for symptomatic gallstone disease or gallbladder polyp were considered. Bile duct injury, conversion to open procedure, conversion to subtotal cholecystectomy, length of hospital stay, 30-day re-admission, and post-operative complications were the evaluated outcome parameters. Results: A total of 600 patients were included. The median age was 53 (IQR 65–41) years and the majority (72.7%; 436/600) were female. The main indications for robotic cholecystectomy were biliary colic (55.5%, 333/600), cholecystitis (18.8%, 113/600), gallbladder polyps (7.7%, 46/600), and pancreatitis (6.2%, 37/600). The median length of stay was 0 (IQR 0–1) days. Of the included patients, 88.5% (531/600) were discharged on the day of procedure with 30-day re-admission rate of 5.5% (33/600). There were no bile duct injuries and the rate of conversion to open was 0.8% (5/600) with subtotal cholecystectomy rate of 0.8% (5/600). Conclusion: The current study confirms that robotic cholecystectomy can be safely implemented to routine practice with a low risk of bile duct injury, low bile leak rate, low conversion to open surgery, and low need for subtotal cholecystectomy. [ABSTRACT FROM AUTHOR]

Climate change is causing issues in all areas of the economy, and robotic and artificial intelligence development have the potential to prevent these risks. We investigate quantile spillovers using quantile vector autoregression (QVAR), robotics, artificial intelligence development, and carbon risk. Using global data on a global daily dataset from carbon emission future (CEF), Global X Robotics & artificial intelligence (BOTZ), iShares Robotics and Artificial Intelligence Multisector (IRBO), First Trust Nasdaq Artificial Intelligence and Robotics (ROBT) from April 1, 2019, to October 28, 2022, our study illustrates that there exists considerable dynamic connectedness between robotic, artificial intelligence development, and carbon risk. The COVID-19 epidemic and the Russia–Ukraine war caused a brief change in direct connectivity over quantiles. CEF consistently receives shocks from the system, while IRBO and ROBT transmit significant shocks to systems. The dynamic net pairwise directional connectivity in the context of quantiles demonstrates that the common eigen factor (CEF) mainly absorbs the influences of the three other variables, especially during COVID-19. This dynamic net pairwise directional connectivity across quantiles demonstrates how unknown events, such as the Ukraine–Russia conflict and the COVID-19 epidemic, affect investor mood as well as both environmentally unfriendly and environmentally beneficial cryptocurrencies. Hence, our paper calls for policy designs that maximize the positive effects of robotic and AI development on the carbon emission market. [ABSTRACT FROM AUTHOR]

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Artificial intelligence has not achieved defining features of biological intelligence despite models boasting more parameters than neurons in the human brain. In this perspective article, we synthesize historical approaches to understanding intelligent systems and argue that methodological and epistemic biases in these fields can be resolved by shifting away from cognitivist brain-as-computer theories and recognizing that brains exist within large, interdependent living systems. Integrating the dynamical systems view of cognition with the massive distributed feedback of perceptual control theory highlights a theoretical gap in our understanding of nonreductive neural mechanisms. Cell assemblies—properly conceived as reentrant dynamical flows and not merely as identified groups of neurons—may fill that gap by providing a minimal supraneuronal level of organization that establishes a neurodynamical base layer for computation. By considering information streams from physical embodiment and situational embedding, we discuss this computational base layer in terms of conserved oscillatory and structural properties of cortical-hippocampal networks. Our synthesis of embodied cognition, based in dynamical systems and perceptual control, aims to bypass the neurosymbolic stalemates that have arisen in artificial intelligence, cognitive science, and computational neuroscience. [ABSTRACT FROM AUTHOR]

Collaborative robotics possesses the potential to revolutionise industrial automation by offering affordable and accessible solutions with reasonable skill requirements. However, identifying the most valuable and appropriate applications for this technology remains a challenge. This study conducted a comprehensive literature review to analyse the existing collaborative robotics applications, and the results showed that only a limited number of applications can be considered true collaboration, with even fewer classified as intelligent collaboration. The study comprised a survey designed to offer valuable insights to not only enhance the state-of-the-art analysis in the identification of existing challenges in the field of collaborative robotics but also to provide motivation to guide future advancements. By leveraging the survey results, researchers and practitioners will be better equipped to navigate the complex landscape of collaborative robotics and develop innovative solutions to tackle the identified challenges. This study also informs on the latest research and development in the field. [ABSTRACT FROM AUTHOR]

The success of deep learning in computer vision and natural language processing communities can be attributed to the training of very deep neural networks with millions or billions of parameters, which can then be trained with massive amounts of data. However, a similar trend has largely eluded the training of deep reinforcement learning (RL) algorithms where larger networks do not lead to performance improvement. Previous work has shown that this is mostly due to instability during the training of deep RL agents when using larger networks. In this paper, we make an attempt to understand and address the training of larger networks for deep RL. We first show that naively increasing network capacity does not improve performance. Then, we propose a novel method that consists of (1) wider networks with DenseNet connection, (2) decoupling representation learning from the training of RL, and (3) a distributed training method to mitigate overfitting problems. Using this three-fold technique, we show that we can train very large networks that result in significant performance gains. We present several ablation studies to demonstrate the efficacy of the proposed method and some intuitive understanding of the reasons for performance gain. We show that our proposed method outperforms other baseline algorithms on several challenging locomotion tasks. [ABSTRACT FROM AUTHOR]

In engineering materials, surface anisotropy is known in certain textured patterns that appear during the manufacturing process. In biology, there are numerous examples of mechanical systems which combine anisotropic surfaces with the motion, elicited due to some actuation using muscles or stimuli-responsive materials, such as highly ordered cellulose fiber arrays of plant seeds. The systems supplemented by the muscles are rather fast actuators, because of the relatively high speed of muscle contraction, whereas the latter ones are very slow, because they generate actuation depending on the daily changes in the environmental air humidity. If the substrate has ordered surface profile, one can expect certain statistical order of potential trajectories (depending on the order of the spatial distribution of the surface asperities). If not, the expected trajectories can be statistically rather random. The same presumably holds true for the artificial miniature robots that use actuation in combination with frictional anisotropy. In order to prove this hypothesis, we developed numerical model helping us to study abovementioned cases of locomotion in 2D space on an uneven terrain. We show that at extremely long times, these systems tends to behave according to the rules of ballistic diffusion. Physically, it means that their motion tends to be associated with the "channels" of the patterned substrate. Such a motion is more or less the same as it should be in the uniform space. Such asymptotic behavior is specific for the motion in model regular potential and would be impossible on more realistic (and complex) fractal reliefs. However, one can expect that in any kind of the potential with certain symmetry (hexagonal or rhombic, for example), where it is still possible to find the ways, the motion along fixed direction during long (or even almost infinite) time intervals is possible. [ABSTRACT FROM AUTHOR]

Background: Ventriculoperitoneal (VP) shunts are commonly used for managing hydrocephalus, with mechanical dysfunction being the most common cause of complications that require revision. A VP shunt placed using a real-time three-dimensional (3D) robotic C-arm navigation system may have better outcomes and fewer complications. Methods: In this technical note, we introduced the workflow of the use of the real-time 3D robotic C-arm navigation system for ventriculoperitoneal shunting. Conclusion: The real-time 3D robotic C-arm can provide a more precise approach to the target. Furthermore, this technique may lower the risk of complications and increase the success rate of shunt placements. [ABSTRACT FROM AUTHOR]

Robotics, an emerging tool in STEM education, promises a multitude of educational benefits, ranging from hands-on learning experiences to fostering critical thinking and collaborative skills. However, the integration of robotics in classrooms is primarily contingent upon science teachers' inclination and willingness to adopt this technology. To bridge this gap, this research aimed to explore the adoption of robotics within STEM education by science teachers, by merging the Theory of Planned Behavior, Technology Acceptance Model, and Self-Determination Theory into a unified conceptual framework. Our hypothesis-driven approach tested using data from 605 science teachers across various regions in Turkey. The results indicate a significant positive correlation between science teachers' perceptions of the usefulness and ease of use of robotics and their intent to adopt it in their classrooms. Moreover, intrinsic motivational factors such as perceived autonomy and relatedness played a pivotal role in influencing teachers' behavioral intention towards robotics. The findings revealed that, compared to existing theories, our unified model offers a heightened understanding of the determinants influencing teachers' intentions to use robotics in STEM education. Ultimately, our research not only paves the way for a theoretically enriched understanding of technology adoption in STEM but also presents actionable insights for educators, curriculum designers, and policymakers aiming to capitalize on the myriad opportunities offered by robotics in contemporary education. [ABSTRACT FROM AUTHOR]

Simulation-based robotic surgery training may help surgeons gain operative skills and experience in the simulation environment. This bibliometric analysis examined the development of simulation-based training for robotic surgical education. Articles pertaining to robotic surgical simulation training that were included in the Web of Science Core Collection up to April 25, 2024, were included. The temporal patterns in published paper numbers were evaluated using Microsoft Excel software, and the data regarding co-authorship and keyword co-occurrence were analyzed and visualized using the VOSviewer and SCImago Graphica tools. A total of 594 papers on simulation-based training for robotic surgical education were evaluated in this study. The United States and United Kingdom were the leading contributors in this field. The most published authors were Professor Ahmed Kamran (23 publications) and Prokar Dasgupta (22 publications). The highest number of papers was published in the journal titled "Surgical Endoscopy and Other Interventional Techniques." The most common keywords were "virtual reality," "curriculum," "robotic surgery simulator," "assessment," and "learning curve." Our study offers a detailed overview of international research on simulation-based training for robotic surgical education, including the publishing countries, institutions, authors, journals, and research hotspots. It also methodically summarizes the state of knowledge in the area, and provides definite directions and concepts for further in-depth analysis. [ABSTRACT FROM AUTHOR]

Various researchers have reported that learners still have a limited conceptual knowledge of linear motion which may affect their learning achievement and motivation towards this subject matter. For this purpose, an investigation was conducted to find out the effect of the Project-Based Learning (PBL) approach with educational robotics on senior students' conceptualunderstanding of linear motion and learning motivation. This study used a quasi-experimental non-equivalent group design with a quantitative research approach. Participants were 78 senior students selected purposively from one school whose teachers have been trained on PBL by Rwanda Quality Basic Education for Human Capital Development project. A linear motion achievement test and a 5-point Likert motivation scale questionnaire were administered to the same groups of students. Descriptive and inferential statistics were used to analyse data quantitatively using Ms Excel and SPSS. Results have shown that students who were taught through PBL had more motivation toward learning linear motion and conceptualized better linear motion. The study recommends implementing Project-Based Learning (PBL) approaches, including educational robotics, and providing professional development for teachers to improve senior students' conceptual understand and motivation in linear motion. [ABSTRACT FROM AUTHOR]

This study investigated the effect of project-based Arduino robot application on students' computational thinking skills development in robotics programming course using the quasi-experimental research design. The participants consist of 73 students in tertiary institutions in South-East Nigeria that offer Computer and Robotics Education programme. There was no sampling as the entire second-year trainee teachers of computer and robotics education in the selected institutions participated in the study. The instrument used for data collection was the Robotics Programming Computational Thinking Scale (RPCTS). Mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) was used to test the hypotheses at an alpha level of 0.05. The result of the study showed that students who were taught using Project-based Arduino Robot Application (PARA) had higher mean computational thinking scores than those taught using the conventional lecture method, and there was a significant difference in the mean computational thinking scores of students based on method of instruction. Also, the male students taught robotics programming using PARA had higher mean computational thinking scores compared to their female counterparts, and there was a significant difference in the mean computational thinking scores of male and female students taught robotics programming using PARA. Schools and other stakeholders in education should support the training of teachers on how to develop and used project-based approaches in teaching/learning robotics programming-related courses. [ABSTRACT FROM AUTHOR]

Project-based Learning (PBL) provides an effective environment for collaborative engineering design education. However, it is difficult to assess students' engagement and provide process-oriented feedback on their collaboration due to limited resources and scalability challenges. This paper presents an empirical study examining the application of Machine Learning (ML) to evaluate student engagement in PBL. A supervised ML model (SML) was developed using Natural Language Processing (NLP) to predict engagement levels using conversation data in online chat groups and comparing them to the ground truth metrics. Eleven teams of engineering students participated in the PBL courses on systems engineering while employing text-based discussions. Communication messages were manually labelled for active engagement metrics collected in our prior research. The SML was constructed with Gradient Boosting to classify engagement levels. The model was trained using more than 5,000 manually classified text messages, and it demonstrated an accuracy range of 0.75 to 0.81 on held-out test data. The model's predictions for complete team communication showed a meaningful association with the actual levels of engagement as determined by log data and surveys. Moreover, the model reproduces general patterns of team engagement and collaboration. According to the findings, conversational data can be a scalable source for ML-driven engagement assessment. This shows how AI has the potential to scale up process-oriented assessment in PBL. Future research should look towards instructor-AI dashboards and individualized feedback systems. The work adds an empirical evaluation of AI for automated engagement analysis, extending earlier research on feedback systems to tackle PBL issues. [ABSTRACT FROM AUTHOR]

Mobile bed to move patients in hospitals is a critical aspect in a healthcare system. Typical hospital beds often require significant manpower and impose physical stresses on caregivers. This paper explores potential solutions to improve hospital bed mobility, focusing on the integration of robotic technologies. Existing literatures on the subject are reviewed, including studies on hospital bed design structures, ergonomic considerations, and implementations of robotics. Different types of wheels suitable for robot movements are discussed, covering conventional wheels, omniwheels, mecanum wheels, swerve drives, and powered fifth wheels. Each wheel type is described and its advantages and disadvantages are highlighted. The comparison of these wheel types helps identify the most suitable option for modifying hospital beds while maintaining their maneuverability. Furthermore, the paper draws inspiration from many applications of mobile robots in other industries, such as automated guided vehicles (AGVs) and autonomous mobile robots (AMRs), to explore potential innovations for the healthcare. By leveraging robotic technologies, hospitals can enhance efficiency and alleviate the burden on healthcare workers. [ABSTRACT FROM AUTHOR]

Standardised proficiency-based progression is the cornerstone of safe robotic skills acquisition, however, is currently lacking within surgical training curricula. Expert consensuses have defined a modular pathway to accredit surgeons. This study aimed to address the lack of a formal, pre-clinical core robotic skills, proficiency-based accreditation curriculum in the UK. Novice robotic participants underwent a four-day pre-clinical core robotic skills curriculum incorporating multimodal assessment. Modifiable-Global Evaluative Assessment of Robotic Skills (M-GEARS), VR-automated performance metrics (APMs) and Objective Clinical Human Reliability Analysis (OCHRA) error methodology assessed performance at the beginning and end of training. Messick's validity concept and a curriculum evaluation model were utilised. Feedback was collated. Proficiency-based progression, benchmarking, tool validity and reliability was assessed through comparative and correlational statistical methods. Forty-seven participants were recruited. Objective assessment of VR and dry models across M-GEARS, APMs and OCHRA demonstrated significant improvements in technical skill (p < 0.001). Concurrent validity between assessment tools demonstrated strong correlation in dry and VR tasks (r = 0.64–0.92, p < 0.001). OCHRA Inter-rater reliability was excellent (r = 0.93, p < 0.001 and 81% matched error events). A benchmark was established with M-GEARS and for the curriculum at 80%. Thirty (63.82%) participants passed. Feedback was 5/5 stars on average, with 100% recommendation. Curriculum evaluation fulfilled all five domains of Messick's validity. Core robotic surgical skills training can be objectively evaluated and benchmarked to provide accreditation in basic robotic skills. A strategy is necessary to enrol standardised curricula into national surgical training at an early stage to ensure patient safety. [ABSTRACT FROM AUTHOR]

The "Robotic Curriculum for young Surgeons" (RoCS) was launched 03/2020 to address the increasing importance of robotics in surgical training. It aims to provide residents with foundational robotic skills by involving them early in their training. This study evaluated the impact of RoCS' integration into clinical routine on patient outcomes. Two cohorts were compared regarding the implementation of RoCS: Cohort 1 (before RoCS) included all robot-assisted procedures between 2017 and 03/2020 (n = 174 adults) retrospectively; Cohort 2 (after RoCS) included all adults (n = 177) who underwent robotic procedures between 03/2020 and 2021 prospectively. Statistical analysis covered demographics, perioperative parameters, and follow-up data, including mortality and morbidity. Subgroup analysis for both cohorts was organ-related (upper gastrointestinal tract (UGI), colorectal (CR), hepatopancreaticobiliary system (HPB)). Sixteen procedures were excluded due to heterogeneity. In-hospital, 30-, 90-day morbidity and mortality showed no significant differences between both cohorts, including organ-related subgroups. For UGI, no significant intraoperative parameter changes were observed. Surgery duration decreased significantly in CR and HPB procedures (p = 0.018 and p < 0.001). Estimated blood loss significantly decreased for CR operations (p = 0.001). The conversion rate decreased for HPB operations (p = 0.005). Length of hospitalization decreased for CR (p = 0.015) and HPB (p = 0.006) procedures. Oncologic quality, measured by histopathologic R0-resections, showed no significant changes. RoCS can be safely integrated into clinical practice without compromising patient safety or oncologic quality. It serves as an effective training pathway to guide robotic novices through their first steps in robotic surgery, offering promising potential for skill acquisition and career advancement. [ABSTRACT FROM AUTHOR]

Background: With the improvements in laparoscopic or robotic surgical techniques and instruments, a growing number of surgeons have attempted to complete all digestive tract reconstruction intracorporeally; these procedures include totally robotic gastrectomy (TRG) and totally laparoscopic gastrectomy (TLG). This study aimed to evaluate the safety and feasibility of the TRG and compare the short-term outcomes of the TRG and TLG in patients with gastric cancer. Methods: Between January 2018 and June 2023, 346 consecutive patients who underwent TRG or TLG at a high-volume academic gastric cancer specialty center were included. 1:1 propensity score matching (PSM) was performed to reduce confounding bias. The surgical outcomes, postoperative morbidity, and surgical burden were compared in PSM cohort. Results: After PSM, a well-balanced cohort of 194 patients (97 in each group) was included in the analysis. The total operation time of the TRG group was significantly longer than that of the TLG group (244.9 vs. 213.0 min, P < 0.001). There was no significant difference in the effective operation time between the 2 groups (217.8 vs. 207.2 min, P = 0.059). The digestive tract reconstruction time of the TRG group was significantly shorter than that of the TLG group (39.4 vs. 46.7 min, P < 0.001). The mean blood loss in the TRG group was less than that in the TLG group (101.1 vs. 126.8 mL, P = 0.014). The TRG group had more retrieved lymph nodes in the suprapancreatic area than that in the TLG group (16.6 vs 14.2, P = 0.002). The TRG group had a lower surgery task load index (38.9 vs. 43.1, P < 0.001) than the TLG group. No significant difference was found in terms of postoperative morbidity between the 2 groups (14.4% vs. 16.5%, P = 0.691). Conclusion: This study demonstrated that TRG is a safe and feasible procedure, and is preferable to TLG in terms of invasion and ergonomics. The TRG may maximize the superiority of robotic surgical systems and embodies the theory of minimally invasive surgery. [ABSTRACT FROM AUTHOR]

The success of weed control is critical for our food security. Non-chemical weed control is a promising technique in sustainable agriculture to ensure the food security. In this review, multiple directed energy weed control methods are reviewed with a specific focus on laser and optical radiation weed control. The mechanisms of the weed control in terms of adverse ablation, radiation thermal effects, and molecular-level damages are systematically reviewed. In particular, the underlying mathematical models determining the dose and response relationship of the weed control are also analyzed for a rigorous study of the physical law of the control process. Challenges of applying the techniques into practice are also illustrated to guide practical weed control applications. [ABSTRACT FROM AUTHOR]

Machine metaphors abound in life sciences: animals as automata, mitochondria as engines, brains as computers. Philosophers have criticized machine metaphors for implying that life functions mechanically, misleading research. This approach misses a crucial point in applying machine metaphors to biological phenomena: their reciprocity. Analogical modeling of machines and biological entities is not a one-way street where our understanding of biology must obey a mechanical conception of machines. While our understanding of biological phenomena undoubtedly has been shaped by machine metaphors, the resulting insights have likewise altered our understanding of what machines are and what they can do. [ABSTRACT FROM AUTHOR]

Wireless charging for UAVs (unmanned aerial vehicles) is a significant development in the field of robotics and aerial technology. It has the potential to revolutionize the way UAVs are used and deployed, by eliminating the need for tethers and cables to provide power. However, electromagnetic force will be generated due to the interaction between coupled magnetic field and moving charge. Especially in the space isolation environment, the coil structure will produce non-contact free movement under the action of electromagnetic force, which has a side effect on the safe operation of the system. This paper analyses the characteristics of electromagnetic force on the coil structure, proposes an optimization method to reduce the electromagnetic force, and verified the optimized structure by simulation. According to the results, the electromagnetic force on the optimized coil structure is significantly lower than that of the traditional coil structure. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to compare the degree of accuracy in implant positioning and limb alignment offered by two robot‐assisted (RA) systems: an image‐based robot‐assisted (IBRA) versus an imageless robot‐assisted (ILRA) system for the treatment of medial knee osteoarthritis with unicompartmental knee arthroplasty (UKA). Methods: This retrospective radiographic study included medial UKAs performed between 2011 and 2023. Radiographic measurements taken preoperatively and at 1‐year postoperative control visit focusing on hip‐knee‐ankle angle (HKA), posterior tibial slope (PTS), tibial component coronal alignment relative to Cartier's angle and restoration of proper joint line (JL) height were analyzed. Outliers for postoperative measurements were defined as follows: HKA <175° or >180°, PTS <2° or >8°, >3° or <−3° alterations in Cartier's angle and ±2 mm changes in the height of the joint line. Results: The final sample consisted of 292 medial UKAs: 95 (32.5%) with an IBRA system and 197 (67.5%) with an ILRA system. Implant positioning and limb alignment were more accurate in the group of patients treated with IBRA, HKA (77.9% vs. 67.5%, p = 0.07), PTS (93.7% vs. 82.7%, p = 0.01), restoration of tibial varus relative to Cartier's angle (87.4% vs. 65%, p < 0.001) and restoration of JL height (81.1% vs. 69.5%, p = 0.04). Conclusion: Medial UKA surgery using an IBRA system was associated with a higher degree of accuracy in implant positioning and postoperative limb alignment as compared to an ILRA system. This is a valuable contribution to help communicate the advantages of using this surgical technique and improve its reproducibility. Level of Evidence: Level III, Retrospective cohort study. [ABSTRACT FROM AUTHOR]

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically 'acceptable futures' framed by an 'ethics of expectations.' This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies. [ABSTRACT FROM AUTHOR]

People meeting a robot for the first time do not know what it is capable of and therefore how to interact with it—what actions to produce, and how to produce them. Despite social robotics' long-standing interest in the effects of robots' appearance and conduct on users, and efforts to identify factors likely to improve human–robot interaction, little attention has been paid to how participants evaluate their robotic partner in the unfolding of actual interactions. This paper draws from qualitative analyses of video-recorded interactions between a robot and groups of participants, in the framework of ethnomethodology and conversation analysis. We analyse the particular ways in which participants shape their embodied actions, how they can reproduce a prior action that failed to obtain a response from the robot; and how they explore the robot's embodied nature. We find a set of recurrent methods or practices, showing that robot-recipient design displays not only participants' initial assumptions about the robot's competences, but also more importantly perhaps their continuous assessment of the robot's behaviour, and their attempts to adapt to it. Participants locally produce and constantly revise their understanding of the robot as a more or less competent co-participant, drawing from its past, current, and projected conduct and responsiveness. We discuss the implications of these findings for research in robotics and human–robot interactions, and the value of the approach to shed new light on old questions by paying attention to the quality of gesture and the sequential organisation of interaction. [ABSTRACT FROM AUTHOR]

Image segmentation is a crucial task in computer vision and image processing, with numerous segmentation algorithms being found in the literature. It has important applications in scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, image compression, among others. In light of this, the widespread popularity of deep learning (DL) and machine learning has inspired the creation of fresh methods for segmenting images using DL and ML models respectively. We offer a thorough analysis of this recent literature, encompassing the range of ground-breaking initiatives in semantic and instance segmentation, including convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid-based methods, recurrent networks, visual attention models, and generative models in adversarial settings. We study the connections, benefits, and importance of various DL- and ML-based segmentation models; look at the most popular datasets; and evaluate results in this Literature. [ABSTRACT FROM AUTHOR]

Purpose: Endovascular aneurysm repair has emerged as the standard therapy for abdominal aortic aneurysms. In 9–30% of cases, retrograde filling of the aneurysm sac through patent branch arteries may result in persistence of blood flow outside the graft and within the aneurysm sac. This condition is called an endoleak type II, which may be treated by catheter-based embolization in case of continued sac enlargement. If an endovascular access is not possible, percutaneous targeting of the perfused nidus remains the only option. However, this can be very challenging due to the difficult access and deep puncture with risk of organ perforation and bleeding. Innovative targeting techniques such as robotics may provide a promising option for safe and successful targeting. Methods: In nine consecutive patients, percutaneous embolization of type II endoleaks was performed using a table-mounted micro-robotic targeting platform. The needle path from the skin entry to the perfused nidus was planned based on the C-arm CT image data in the angio-suite. Entry point and path angle were aligned using the joystick-operated micro-robotic system under fluoroscopic control, and the coaxial needle was introduced until the target point within the perfused nidus was reached. Results: All punctures were successful, and there were no puncture-related complications. The pre-operative C-arm CT was executed in 11–15 s, and pathway planning required 2–3 min. The robotic setup and sterile draping were performed in 1–2 min, and the alignment to the surgical plan took no longer than 30 s. Conclusion: Due to the small size, the micro-robotic platform seamlessly integrated into the routine clinical workflow in the angio-suite. It offered significant benefits to the planning and safe execution of double-angulated deeply localized targets, such as type II endoleaks. [ABSTRACT FROM AUTHOR]

The development of social assistive robots for supporting healthcare provision faces a lack of an ethical approach that adequately addresses the normatively relevant challenges regarding its deployment. Current ethical reflection is primarily informed by an individual-centered perspective focused on robots' implications for their end-users and thereby limited to the dyadic human–robot interaction sphere. Considering that this is tightly correlated to the restricted understanding of core ethical concepts upon which reflection stands, this paper delves into the concept of freedom from a philosophical perspective to unfold its full normative breadth for a critical assessment of technological development. By bringing to the fore the political-structural dimension of freedom and, in turn, elaborating the political dimension of technology, the undertaken philosophical approach discloses freedom as a transversal ethical concept for a normative reflection on technology. Thereby, it broadens the scope of ethical attention beyond the sphere of human–robot interaction and turns attention to the so far overlooked structural dimension of human–robot relations. Drawing on conceptions of freedom as non-domination, among others, the paper approaches social assistive robotics and reexamines the terrain of relevant issues for its development. Since freedom is one major issue upon which current concerns revolve, the undertaken analysis substantially enriches the ongoing ethical discussion on social assistive robotics' implications for human freedom. In this way, this work contributes to going beyond the current individual-centered ethical perspective by laying conceptual grounds for a comprehensive ethical approach to social assistive robotics' development. [ABSTRACT FROM AUTHOR]

This research introduces the novel "Kraken-Gear" mechanism, emphasizing the advantages of additive polymer 3D printing in high-ratio gearbox systems for lightweight robotic applications, such as surgical instruments. The innovative kinematic solution provides high torsional system stiffness, substantial gear ratios, and backlash-free transmission. Leveraging the "hot lithography" additive manufacturing method ensures precise and warp-free gearbox components. Targeting medical technology, the gearbox meets stringent requirements: backlash-free, minimal vibration, high precision, and torque, with minimized weight for ergonomic comfort and fatigue mitigation. Computational simulations assess forces and stresses, highlighting the potential of additive manufacturing for cost-effective and functionally efficient gearbox fabrication. Nevertheless, careful material selection remains imperative for optimal functionality, especially in demanding medical applications. In summary, this research underscores a promising approach to gearbox fabrication, emphasizing the critical role of material selection and simulation-based assessments for optimal performance. [ABSTRACT FROM AUTHOR]

Current practice in simulation and implementation of robot controllers is usually undertaken with guidance from high-level design diagrams and pseudocode. Thus, no rigorous connection between the design and the development of a robot controller is established. This paper presents a framework for designing robotic controllers with support for automatic generation of executable code and automatic property checking. A state-machine based notation, RoboChart, and a tool (RoboTool) that implements the automatic generation of code and mathematical models from the designed controllers are presented. We demonstrate the application of RoboChart and its related tool through a case study of a robot performing an exploration task. The automatically generated code is platform independent and is used in both simulation and two different physical robotic platforms. Properties are formally checked against the mathematical models generated by RoboTool, and further validated in the actual simulations and physical experiments. The tool not only provides engineers with a way of designing robotic controllers formally but also paves the way for correct implementation of robotic systems. [ABSTRACT FROM AUTHOR]

In recent years, the interaction of a human operator with teleoperated robotic systems has been much improved. One of the factors influencing this improvement is the addition of force feedback to complement the visual feedback provided by traditional graphical user interfaces. However, the users of these systems performing tasks in isolated and safe environments are often inexperienced and occasional users. In addition, there is no common framework to assess the usability of these systems, due to the heterogeneity of applications and tasks, and therefore, there is a need for new usability assessment methods that are not domain specific. This study addresses this issue by proposing a measure of usability that includes five variables: user efficiency, user effectiveness, mental workload, perceived usefulness, and perceived ease of use. The empirical analysis shows that the integration of haptic feedback improves the usability of these systems for non-expert users, even though the differences are not statistically significant; further, the results suggest that mental workload is higher when haptic feedback is added. The analysis also reveals significant differences between participants depending on gender. [ABSTRACT FROM AUTHOR]

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Copyright of Manuelle Medizin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 103 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures. Enabling complex shape morphing in engineering systems remains a significant challenge. In this work, authors demonstrate that a transformer-like hierarchical origami metastructure can self-reconfigure into over 1000 versatile structures with fewer than 3 actuation degrees of freedom and simple control. [ABSTRACT FROM AUTHOR]

The hinotori™ Surgical Robot System (hinotori™, Medicaroid, Kobe, Japan) is increasingly being utilized primarily in urology and adult surgery; however, data on its application in pediatric surgery are lacking. This preclinical study aimed to evaluate the limitations of this system for accurate suturing in small cavities designed for pediatric and neonatal applications. Two trained operators performed simple ligature sutures (easy task [ET]) and hepaticojejunostomy sutures (difficult task [DT]) within five differently sized boxes, ranging from 5123 to 125 mL. The suture time, number of internal and external instrument/instrument collisions, instrument/box collisions, and suture accuracy were evaluated. The suture accuracy was assessed using the A-Lap Mini endoscopic surgery skill assessment system. As a result, an increase in the number of collisions and extended suturing times were observed in boxes with volumes smaller than 215 mL. Despite these variations, there were no significant differences between the boxes, and all tasks were precisely performed in all boxes (p = 0.10 for the ET and p = 1.00 for the DT). These findings demonstrate the capability of the hinotori™ system to perform precise suturing techniques within tightly confined simulated neonatal cavities as small as 125 mL. To advance the integration of pediatric robotic surgery utilizing the hinotori™ system, additional trials comparing it with conventional laparoscopic and thoracoscopic techniques using pediatric and animal models are necessary to assess its clinical safety and applicability. [ABSTRACT FROM AUTHOR]

This study aimed to assess the surgical outcomes of single-layer versus double-layer renorrhaphy in robot-assisted laparoscopic partial nephrectomy (RAPN) performed by an experienced surgeon.We enrolled 655 patients who underwent RAPN between January 2019 and June 2023. After excluding those with multiple tumors or incomplete medical records, 554 patients were included in the final analysis. We compared surgical outcomes between single-layer renorrhaphy (outer cortical closure) and double-layer renorrhaphy (inner layer suture and outer cortical closure), adjusting for preoperative factors such as sex, age, body mass index, American Society of Anesthesiologists score, preoperative estimated glomerular filtration rate (eGFR), preoperative complications (diabetes or hypertension), surgical approach (transperitoneal or retroperitoneal), tumor size, and tumor complexity, using propensity score matching. Of the 554 patients analyzed, 59 were classified into the single-layer group and 495 into the double-layer group. Before matching, the double-layer group had more complex and larger tumors. After matching, 58 patients were included in each group. The single-layer group showed significantly shorter operative times (91 min vs. 100 min, p = 0.0361), lower estimated blood loss (EBL) (7.9 ml vs. 27 ml, p = 0.021), and better preservation of eGFR (− 1.2% vs. − 5.1%, p = 0.0313). Other outcomes, such as perioperative complications and postoperative hospital stay, were not significantly different between the groups. Single-layer renorrhaphy demonstrated better surgical outcomes, including shorter operative time, lower EBL, and better preservation of kidney function, compared to double-layer renorrhaphy. These findings suggest that single-layer reconstruction may optimize RAPN outcomes in selected patients. [ABSTRACT FROM AUTHOR]

The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning capabilities by combining symbolic reasoning with connectionist learning. We survey the literature on neuro-symbolic AI during the last two decades, including books, monographs, review papers, contribution pieces, opinion articles, foundational workshops/talks, and related PhD theses. Four main features of neuro-symbolic AI are discussed, including representation, learning, reasoning, and decision-making. Finally, we discuss the many applications of neuro-symbolic AI, including question answering, robotics, computer vision, healthcare, and more. Scalability, explainability, and ethical considerations are also covered, as well as other difficulties and limits of neuro-symbolic AI. This study summarizes the current state of the art in neuro-symbolic artificial intelligence. [ABSTRACT FROM AUTHOR]

Electronic skins with deep and comprehensive liquid information detection are desired to endow intelligent robotic devices with augmented perception and autonomous regulation in common droplet environments. At present, one technical limitation of electronic skins is the inability to perceive the liquid sliding information as realistically as humans and give feedback in time. To this critical challenge, in this work, a self-powered bionic droplet electronic skin is proposed by constructing an ingenious co-layer interlaced electrode network and using an overpass connection method. The bionic skin is used for droplet environment reconnaissance and converts various dynamic droplet sliding behaviors into electrical signals based on triboelectricity. More importantly, the two-dimensional sliding behavior of liquid droplets is comprehensively perceived by the e-skin and visually fed back in real-time on an indicator. Furthermore, the flow direction warning and intelligent closed-loop control of water leakage are also achieved by this e-skin, achieving the effect of human neuromodulation. This strategy compensates for the limitations of e-skin sensing droplets and greatly narrows the gap between artificial e-skins and human skins in perceiving functions. Electronic skins are limited in perceiving liquid sliding information as realistically as humans and provide real-time feedback. Here, Xu et al. developed a self-powered bionic droplet electronic skin with co-layer interlaced electrode networks and overpass connection technology to sense complex water motion. [ABSTRACT FROM AUTHOR]

To achieve precise grasping and spreading of irregular sheet-like soft objects (such as leather) by robots, this study addresses several challenges, including the irregularity of leather edges and the ambiguity of feature recognition points. To tackle these issues, this paper proposes an innovative method that involves alternately grasping the lowest point twice and using planar techniques to effectively spread the leather. We improved the YOLOV8 algorithm by incorporating the BIFPN network structure and the WIOU loss function, and trained a dedicated dataset for the lowest grasping points and planar grasping points, thereby achieving high-precision recognition. Additionally, we determined the optimal posture for grasping the lowest point and constructed an experimental platform, successfully conducting multiple rounds of leather grasping and spreading experiments with a success rate of 72%. Through an in-depth analysis of the failed experiments, this study reveals the limitations of the current methods and provides valuable guidance for future research. [ABSTRACT FROM AUTHOR]

Aim: To study the published literature on robotic-assisted pull-through procedures for anorectal malformation. Method: A PubMed search was done on 10th April 2024, with the words "robotic AND Anorectal malformation". The articles were screened for relevance and the data were compiled on the safety, feasibility, technical details, and limitations of robotic-assisted procedures in children for anorectal malformation. Results: The search robotic and anorectal malformation gave ten articles. Two were excluded as they were not relevant. Two articles were added from cross-reference. Ten publications on robotic-assisted procedures for anorectal malformation were studied, describing procedures in thirty-three cases. The youngest child operated was 3 months old. All except one case were done in males. Most articles were from the US and the Kingdom of Saudi Arabia (KSA). The principles involved in robotic-assisted anorectoplasty (RAARP) were the same as that of laparoscopic procedures. Complications reported included pelvic abscess, epididymo-orchitis, and stricture of pelvic tunnel or conversion to open. The magnification and endo-wrist technology of robotics facilitated the sharp dissection and ligation at origin of fistula. The mean operating time was 228.7 min (docking and console time), shortest being 86 min and mean hospital stay was 7 days. The number of ports used varied from 3 to 4 with 8.5 mm being the most commonly used umbilical port and 8 mm as working ports, although in one article, a 12 mm port was used for telescope. The prolonged operating time and cost are the two factors to be addressed in RAARP. Conclusion: Robotic surgery is feasible in infants with ARM and safe in expert hands. Robotics is a very effective tool with its better ergonomics, tremor filtration, 3D magnification, and dexterity. Increasing awareness and referral to high-volume centers can tide over the cost factor, and good training of the surgeons can reduce the operative time. [ABSTRACT FROM AUTHOR]

This manuscript offers a detailed description of our successful tips for mastering transanal robotic surgery. It covers various aspects, including patient positioning, management of abdominal pressures to maintain a stable pneumorectum, platform positioning, camera alignment, trocar positioning to minimize collisions, instruments used, and approaches to tumor resection. [ABSTRACT FROM AUTHOR]

Purpose: Precision Prostatectomy (PP) is a viable treatment option for men with unilateral dominant cancer who are interested in preserving functional outcomes. To date, the data published about the outcomes of this technique has come from a single center only (Henry Ford - HF). We present the surgical, functional, and oncological outcomes of the first series of patients to undergo PP outside of HF, to demonstrate the safety and reproducibility of the technique. Methods: Between 2022 and 2023, PP was offered to select patients who were interested in preserving their functional status. Men who underwent PP were followed at 3 monthly intervals; information regarding their functional status was simultaneously obtained. Men who had biochemical recurrence were advised to undergo remnant biopsy. If residual cancer was detected, then remnant removal was performed. Results: The median age and median PSA of the study group was 63 years and 6.89 ng/ml respectively. The median operative and console times were 196.5 and 154 minutes. No intra-operative complications were noted. Three patients had a total of three post-operative complications. Three patients had biochemical recurrence; cancer was not detected in any of these patients on postoperative biopsies of the prostatic remnant. At 12 months, 91% of patients reported using 0 pads/day and 90.9% of pre-operatively potent patients were potent at 12 months. Conclusion: PP is a safe and reproducible technique that can ensure cancer control and preservation of functional status in select patients. Further studies with large sample sizes and longer follow-up are required to ascertain the long-term outcomes of this surgical technique. [ABSTRACT FROM AUTHOR]

The focus of this paper is on investigating the use of visual communication between two humanoid robots, in order to enhance the coordination of tasks between them. The problem continues to be an interesting and fruitful area of research from the days of using multiple robot manipulator arms for manufacturing as well as space robotics to current research in medical robotics. The approach here is to employ several off-the-shelf algorithms, software and hardware such as the NAO robot and support software, including Choregraphe, OpenCV to capture and process images, the SVM to classify objects in images, and the Python programming environment. Five robotic actions were studied and three modes. The experiments used one robot as the "viewer" and the second robot as the "subject" being analyzed. Results show that the visual communication system has an accuracy of 90% in correctly identifying the five movements. This research has shown an original solution, as a model that can enable robots to run in the complex service tasks consisting of multiple connected actions in a dynamic environment. This methodology can also let the intelligent operation of the robots serve in different scenes according to their actual requirements. This research focuses on enhancing the prototype robot vision function and development of additional value for consolidation manageable platform that increases service robots in the home environment of intelligent control capability. [ABSTRACT FROM AUTHOR]