Search Results (19,841)

Incidences of kidney cancers are steadily increasing. The surgical resection of renal tumors remains the treatment of choice, and different techniques provide similar oncological outcomes. Minimally invasive methods, especially partial nephrectomy (PN), have emerged as the preferred method of tumor resection, both in traditional and robot-assisted laparoscopy. PN may be performed as an open or laparoscopic operation. On-clamp PN is a variant of PN that includes the clamping of renal vessels; off-clamp PN is performed without any ischemia. Objectives: To assess the short-term loss of eGFR after on-clamp and off-clamp PN. Methods: Data from 2021 to 2024 were retrospectively collected from a hospital database. The patients included in the study had a diagnosed kidney tumor that was confirmed by MRI or CT imaging. The patients were divided into two groups depending on the type of treatment they received: on-clamp PN or off-clamp PN. Hematocrit (HCT), hemoglobin (Hb) and eGFR were measured and compared. Results: Both groups had comparable preoperative HTC, Hb, and eGFR. eGFR loss 24 h after the procedure was 35.4% lower in the off-clamp group compared to the on-clamp group (p = 0.027). Conclusions: Off-clamp PN is a safe and viable method for kidney tumor resection, both in traditional and robot-assisted laparoscopy. This technique results in a smaller perioperative loss of eGFR, which relates to better short-term functional outcomes than on-clamp PN. Full article

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch). Full article

Industrial robots are slowly finding their way into manufacturing companies. This paper examines the impact of robots on productivity, exports, quality, sustainability and labor in European manufacturing companies. There is little research on the use of industrial robots and their impact in developed countries. Most research relates to Chinese companies, and often, the data are outdated. The data in this paper come from the European Manufacturing Survey project, which was conducted in 2022 and includes 476 manufacturing companies. The results of the impact of industrial robots on quality, labor productivity, exports and green technologies are determined using a T-test between companies that use industrial robots and those that do not. However, the impact of higher investment in environmental technologies by industrial robot users was examined by a two-stage OLS regression analysis with control variables representing the contextual characteristics of the companies. The results show positive effects on all of the variables. The results show that the greater use of robots occurs in industries with low-to-medium technology intensity, that robots contribute to labor productivity and exports and that companies that use robots also tend to use environmentally friendly technologies. Full article

The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by significantly enhancing decision-making processes through data-driven insights. This study investigates the influence of using ML, particularly supervised and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experiences of middle-level management within e-government in Jordan, the findings underscore that ML positively impacts the rational decision-making process in e-government. It enables more efficient and effective data gathering, improves the accuracy of data analysis, enhances the speed and accuracy of evaluating decision alternatives, and improves the assessment of potential risks. Additionally, this study reveals that trust plays a critical role in determining the effectiveness of ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance. The findings highlight the necessity of cultivating trust to ensure the successful deployment of ML in public administration, thereby enabling a more effective and sustainable digital transformation. Despite certain limitations, the outcomes of this study offer substantial insights for researchers and government policymakers alike, contributing to the advancement of sustainable practices in the e-government domain. Full article

In this paper, using a honeycomb-velcro structure to generate a novel jamming gripper is explored. Each finger of the gripper consists of multi-layers with a honeycomb sandwich structure acting as a core wrapped by a fabric sheet and sealed by a latex membrane. This structure can transit between unjammed (flexible) and jammed (rigid) states thanks to the vacuum pressure. Various materials of honeycomb structure, fabric, and reinforcements are investigated to seek optimal combinations for making the jamming fingers. Then, such fingers are deployed in experiments to evaluate the stiffness and the surface friction with different loads in terms of with or without vacuum. Vacuum pressure boosts the stiffness and friction of all the jamming fingers compared with the without-vacuum case. Attached to a gripper, the jamming finger shows good performance in diverse manipulation with food, a metal component, a toy, a can, and a bottle. Furthermore, the variable-stiffness finger under vacuum pressure can be utilized to perform assembly and installation operations such as pushing a bolt into an aligned hole. Full article

Precise and complete 3D representations of architectural structures or industrial sites are essential for various applications, including structural monitoring or cadastre. However, acquiring these datasets can be time-consuming, particularly for large objects. Mobile scanning systems offer a solution for such cases. In the case of complex scenes, multiple scanning systems are required to obtain point clouds that can be merged into a comprehensive representation of the object. Merging individual point clouds obtained from different sensors or at different times can be difficult due to discrepancies caused by moving objects or changes in the scene over time, such as seasonal variations in vegetation. In this study, we present the integration of point clouds obtained from two mobile scanning platforms within a built-up area. We utilized a combination of a quadruped robot and an unmanned aerial vehicle (UAV). The PointNet++ network was employed to conduct a semantic segmentation task, enabling the detection of non-ground objects. The experimental tests used the Toronto 3D dataset and DALES for network training. Based on the performance, the model trained on DALES was chosen for further research. The proposed integration algorithm involved semantic segmentation of both point clouds, dividing them into square subregions, and performing subregion selection by checking the emptiness or when both subregions contained points. Parameters such as local density, centroids, coverage, and Euclidean distance were evaluated. Point cloud merging and augmentation enhanced with semantic segmentation and clustering resulted in the exclusion of points associated with these movable objects from the point clouds. The comparative analysis of the method and simple merging was performed based on file size, number of points, mean roughness, and noise estimation. The proposed method provided adequate results with the improvement of point cloud quality indicators. Full article

Untethered magnetic soft robots show great potential for biomedical and small-scale micromanipulation applications due to their high flexibility and ability to cause minimal damage. However, most current research on these robots focuses on marine and reptilian biomimicry, which limits their ability to move in unstructured environments. In this work, we design a quadruped soft robot with a magnetic top cover and a specific magnetization angle, drawing inspiration from the common locomotion patterns of quadrupeds in nature and integrating our unique actuation principle. It can crawl and tumble and, by adjusting the magnetic field parameters, it adapts its locomotion to environmental conditions, enabling it to cross obstacles and perform remote transportation and release of cargo. Full article

Humanoid robots are increasingly being utilized in various activities involving humans, as they can facilitate certain tasks and provide benefits to users. Humanoid service robots possess capabilities akin to human performance, often proving advantageous due to their operational speed and immunity to fatigue. Within the scope of this study, Pepper, a humanoid robot renowned for its fidelity in mimicking human gestures and behavior, serves as the focal point. Tasked with aiding office occupants in object manipulation and relocation, Pepper underwent a targeted reliability assessment. This assessment encompassed the development of a reliability block diagram (RBD), alongside meticulous analyses of individual components and system functionality across diverse operational scenarios. Full article

Background: With the advent of da Vinci SP, surgical methods using da Vinci SP are becoming popular in thyroid surgery. The authors previously reported on a new surgical method called the single-port robotic areolar (SPRA) approach, which evolved from the previous bilateral axillary breast approach (BABA). This paper reports a comparative analysis of SPRA and BABA over one year. Methods: The data on SPRA and BABA thyroid surgery performed at the authors’ hospital from December 2022 to December 2023 were analyzed. Results: 111 SPRA and 159 BABA surgeries were performed. SPRA was performed overwhelmingly on women (1 man vs. 110 women), and the body mass index (BMI) was lower in SPRA patients (23.63 ± 3.49 vs. 25.71 ± 4.39, p < 0.001). The proportion of total thyroidectomy was significantly higher in BABA patients, and a modified radical neck dissection (MRND) was only performed using the BABA method. The time for flap formation before robot docking was significantly shorter in SPRA patients (12.08 ± 3.99 vs. 18.34 ± 5.84 min, p < 0.001). Postoperative drain amount was also significantly lower in SPRA patients (53.87 ± 35.45 vs. 81.74 ± 30.26 mL, p < 0.001). Hospital stay after surgery was significantly shorter with SPRA (3.04 ± 0.48 vs. 3.36 ± 0.73 days, p < 0.001). Thyroglobulin levels after a total thyroidectomy (0.06 ± 0.13 vs. 0.45 ± 0.78, p = 0.002) and stimulated Tg level before the RAI (1.03 ± 0.74 vs. 5.01 ± 13.63, p = 0.046) were significantly lower in the SPRA group. No significant differences were observed between the two groups according to the postoperative complications, including vocal cord palsy and hypoparathyroidism. Conclusions: Based on the authors’ experience, SPRA is a less invasive robot thyroid surgery method than BABA. Full article

The advent of collaborative and soft robotics has reduced the mandatory adoption of safety barriers, pushing human–robot interaction to previously unreachable levels. Due to their reciprocal advantages, integrating these technologies can maximize a device’s performance. However, simplifying assumptions or elementary geometries are often required due to non-linear factors that identify analytical models for designing soft pneumatic actuators for collaborative and soft robotics. Over time, various approaches have been employed to overcome these issues, including finite element analysis, response surface methodology (RSM), and machine learning (ML) algorithms. Based on the latter, in this study, the bending behavior of an externally reinforced soft pneumatic actuator was characterized by the changing geometric and functional parameters, realizing a Bend dataset. This was used to train 14 regression algorithms, and the Bilayered neural network (BNN) was the best. Three different external reinforcements, excluded for the realization of the dataset, were tested by comparing the predicted and experimental bending angles. The BNN demonstrated significantly lower error than that obtained by RSM, validating the methodology and highlighting how ML techniques can advance the prediction and mechanical design of soft pneumatic actuators. Full article

Magnesium alloy ZK60 shows great promise as a medical metal material, but its corrosion resistance in the body is inadequate. Hydroxyapatite (HA), the primary inorganic component of human and animal bones, can form chemical bonds with body tissues at the interface, promoting the deposition of phosphorus products and creating a dense calcium and phosphorus layer. To enhance the properties of ZK60, HA was added to create HA/ZK60 composite materials. These composites, fabricated using the advanced technique of LPBF, demonstrated superior corrosion resistance and enhanced bone inductive capabilities compared to pristine ZK60. Notably, the incorporation of 3 wt% led to a significant reduction in bulk porosity, achieving a value of 0.8%. The E_corr value increased from −1.38 V to −1.32 V, while the minimum I_corr value recorded at 33.9 μA·cm⁻². Nano-HA achieved the lowest volumetric porosity and optimal corrosion resistance. Additionally, these composites significantly promoted osteogenic differentiation in bone marrow stromal cells (BMSCs), as evidenced by increased alkaline phosphatase (ALP) activity and robust calcium nodule formation, highlighting their excellent biocompatibility and osteo-inductive potential. However, when increasing the HA content to 6 wt%, the bulk porosity rose significantly to 3.3%. The E_corr value was −1.3 V, with the I_corr value being approximately 50 μA·cm⁻². This increase in porosity and weaker interfacial bonding, ultimately accelerated electrochemical corrosion. Therefore, a carefully balanced amount of HA significantly enhances the performance of the ZK60 magnesium alloy, while excessive amounts can be detrimental. Full article

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation. Full article

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional–integral–derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method. Full article

Birds have remarkable flight capabilities due to their adaptive wing morphology. However, studying live birds is time-consuming and laborious, and obtaining information about the complete wingbeat cycle is difficult. To address this issue and provide a complete dataset, we recorded comprehensive motion capture wing trajectory data from five free-flying pigeons (Columba livia). Five key motion parameters are used to quantitatively characterize wing kinematics: flapping, sweeping, twisting, folding and bending. In addition, the forelimb skeleton is mapped using an open-chain three-bar mechanism model. By systematically evaluating the relationship of joint degrees of freedom (DOFs), we configured the model as a 3-DOF shoulder, 1-DOF elbow and 2-DOF wrist. Based on the correlation analysis between wingbeat kinematics and joint movement, we found that the strongly correlated shoulder and wrist roll within the stroke plane cause wing flap and bending. There is also a strong correlation between shoulder, elbow and wrist yaw out of the stroke plane, which causes wing sweep and fold. By simplifying the wing morphing, we developed three flapping wing robots, each with different DOFs inside and outside the stroke plane. This study provides insight into the design of flapping wing robots capable of mimicking the 3D wing motion of pigeons. Full article

In today’s society, robots are increasingly being developed and playing an important role in many fields of industry. Combined with advances in artificial intelligence, sensors, and design principles, these robots are becoming smarter, more flexible, and especially capable of interacting more naturally with humans. In that context, a comprehensive humanoid robot with human-like actions and emotions has been designed to move flexibly like a human, performing movements to simulate the movements of the human neck and head so that the robot can interact with the surrounding environment. The mechanical design of the emotional humanoid robot head focuses on the natural and flexible movement of human electric motors, including flexible suitable connections, precise motors, and feedback signals. The feedback control parts, such as the neck, eyes, eyebrows, and mouth, are especially combined with artificial skin to create a human-like appearance. This study aims to contribute to the field of biomimetic humanoid robotics by developing a comprehensive design for a humanoid robot head with human-like actions and emotions, as well as evaluating the effectiveness of the motor and feedback control system in simulating human behavior and emotional expression, thereby enhancing natural interaction between robots and humans. Experimental results from the survey showed that the behavioral simulation rate reached 94.72%, and the emotional expression rate was 91.50%. Full article