Search Results (2,759)

Background/Objectives: Hand loss or severe impairment significantly reduces quality of life by restricting essential daily activities and professional tasks. Despite advances in prosthetics, challenges remain in affordability, accessibility, and usability. This study aimed to design and develop a low-cost, ergonomic bionic hand prototype that integrates sustainable fabrication, intuitive control, and modular electronics. Methods: A user-centred design process guided by iterative prototyping, anatomical modelling, and functional validation. The prototype was manufactured using 3D printing techniques and assembled with modular electronic components. The design included segmented fingers, independent thumb articulation, and a tendon-like actuation system driven by micro-motors. Control was implemented through an ESP32-based board and a Bluetooth-enabled mobile application. Durability was preliminarily assessed through 500 grasp–release cycles. Results: Experimental validation confirmed the feasibility of both precision and power grips. The pinch grip successfully lifted objects to 120 g, and the power grip up to 85 g, corresponding to effective output forces of approximately 1.2 N and 0.83 N, respectively. The final prototype weighed ~350 g and maintained reliable performance during 500 grasp–release cycles. Conclusions: The developed bionic hand demonstrates that an affordable, ergonomic, and functional prosthetic can be achieved through sustainable 3D printing and accessible electronics. Future work will focus on enhancing actuation strength, long-term durability, and integration of sensory feedback, with the long-term objective of clinical testing and scalable production. Full article

This paper presents the design, modeling, and experimental validation of a capacitive tactile sensor specifically conceived to sense shear-driven contact dynamics in robotic manipulation. The proposed device is a layered flexible capacitive structure, in which controlled tangential interactions are induced. The electrode design maximizes sensitivity to shear motion and promotes an isotropic response with respect to slip direction, thereby addressing two key limitations that affect the majority of existing slip-sensing technologies. An analytical model was developed to describe the essential relationship between shear-induced displacements and the electrical response, providing insight into the design parameters and supporting the selection of geometry and materials. To test the sensor in real conditions, a dedicated capacitive readout circuit based on high-frequency excitation and synchronous demodulation was developed to robustly acquire capacitance variations while rejecting static offsets and parasitic effects. Several formulations for the interposed dielectric layer material were investigated, including viscous fluids and composite mixtures with high-permittivity nanoparticles, with the aim of improving electrical sensitivity while preserving mechanical stability. Experimental results obtained under controlled loading and sliding conditions demonstrate that the sensor is highly sensitive to changes in contact state and tangential interaction dynamics. The sensor responded consistently to both load-induced shear and slip-related phenomena, enabling the reliable monitoring of contact dynamics rather than binary slip detection. A proof-of-concept integration into a robotic finger confirms the suitability of the proposed approach for grasp monitoring. Full article

As critical end-effectors enabling the practical deployment of marine robotic systems, soft hands face persistent challenges including multi-finger asynchronization, unbalanced force distribution, and insufficient anti-disturbance robustness, compounded by constraints from soft material nonlinearity and harsh marine environmental disturbances. To address these limitations, this paper proposes a dexterous grasping method integrating coupled dynamical systems and adaptive force planning control, designed to enhance operational reliability in complex marine environments. An intermediate dynamic layer is embedded to ensure precise multi-finger synchronization, a hybrid force planning algorithm balances force uniformity and constraint satisfaction, and an adaptive controller synergizes with a Neo-Hookean model to compensate for nonlinear deviations. Simulations and physical experiments demonstrate that the method delivers excellent grasping stability and accuracy for uneven mass distribution targets such as cylinders and spheres, while balancing synchronization precision, constraint compliance, and anti-disturbance capability. Compared with the traditional coupled dynamical systems (DSs), the constraint violation is reduced by up to 18.2%, the friction force is increased by 4.0%, and the force distribution uniformity is improved by approximately 5.1%.Compared with the particle swarm optimization (PSO) strategy, the constraint violation is reduced by up to 50.5%, the friction force is increased by 40.9%, and the force distribution uniformity is also improved by about 5.1%. This work fills a key gap in balancing multiple performance metrics for marine soft hands, providing a reliable technical solution to accelerate the real-world deployment of marine robotic systems. Full article

Vision-based robot control remains a significant challenge due to the sample inefficiency and prolonged training times associated with traditional deep reinforcement learning methods. We propose a novel approach inspired by biological gene regulation, leveraging Gene Regulatory Networks (GRNs) for efficient and robust robot control. In our approach, robot states are encoded as gene expression levels, and evolutionary optimization is used to learn GRN parameters that map raw visual inputs to motor commands. We evaluate this method on the KukaDiverseObjectEnv benchmark, where robots must grasp diverse objects using only RGB images. Our GRN-based controller achieves a $57.5\%$ success rate while reducing training time by $13.7\times$ compared to Proximal Policy Optimization baselines. It also outperforms NEAT, standard reinforcement learning algorithms, and deep Q-learning in terms of both efficiency and performance. The controller maintains $91.8\%$ performance under noisy visual conditions. This bio-inspired design naturally enables hierarchical control via expression cascades, computational efficiency through bounded dynamics, and temporal reasoning without explicit memory modules. Full article

This study claims that only by revisiting Buber’s entire oeuvre does one fully grasp his position on the relation between politics, religion, and ethics. I argue that Buber’s writings in the thirties are an act of resistance against national socialism and that his consistent political resistance before, in, and after this period appears in many of his writings. Buber was as a political thinker, not only in his exegesis, but also in his dialogical philosophy, in his view on Judaism and Zionism, in his translation project with Rosenzweig, and in his creative reinterpretation of Hasidism. Rereading these interrelated writings allows us to rediscover Buber as a political thinker whose humanist and social concept of religion allowed him to resist a politics disconnected from a dialogical ethics. Full article

This paper presents the design of a high-efficiency apple harvesting robot based on a hybrid pneumatic-electric drive system, capable of operating around the clock. The robotic system comprises a mobile platform with two degrees of freedom (DOF) and a five-DOF PRRRP manipulator for fruit picking. To meet the harvesting requirements, a spoon-shaped end-effector with pneumatic control was developed, enabling precise manipulator control and flexible grasping. The robot’s vision system integrates machine vision and deep neural network approaches. Additionally, an industrial computer and AC servo drivers were employed to control the manipulator and end-effector. An integrated nighttime illumination system allowed for all-weather operation. Initial experiments were conducted in a controlled laboratory. Subsequently, comprehensive identification and harvesting tests were performed in both laboratory and field environments to validate system robustness. Experimental results validated the effectiveness of the proposed system, demonstrating an apple harvesting success rate of 81% and an average harvesting time of 7.81 s per apple. The system achieved a fruit damage rate of less than 5% during field experiments, demonstrating its potential for gentle handling. The primary innovation of this work lies in its hybrid drive architecture and adaptive vision strategy, which together offer a cost-effective and robust solution for all-weather automated harvesting, addressing key limitations of high cost and environmental sensitivity in existing robotic harvesters. Full article

Fast and universal grasping remains a critical challenge for robotic hands operating in unstructured and industrial environments. Conventional pin-array-based robotic hands exhibit strong adaptability to objects with diverse geometries, yet their grasping speed is often limited by centralized motor-driven actuation. To address this limitation, this paper presents the development of a fast-acting cluster-tube self-adaptive robotic hand (CTSA-FA hand), which transforms traditional active actuation into a passive energy-storage-and-release-driven grasping mechanism. A spring-cam-based structure is introduced to enable rapid energy release during the grasping phase, significantly reducing the gathering time. A theoretical model of the CTSA-FA hand is established, including cam trajectory planning and mechanical analysis, to guide parameter design and performance optimization. A physical prototype is developed and experimentally validated. Experimental results demonstrate that the proposed CTSA-FA hand can complete a grasp-release frequency of approximately 3 Hz, corresponding to a grasping time of about 0.25 s per cycle, while maintaining robust adaptive grasping performance. These characteristics indicate that the proposed design is well suited for applications requiring fast and universal grasping, particularly in intelligent mining equipment and industrial automation scenarios. Full article

The effectiveness of deep learning methods in image segmentation has led to interest in their deployment for 3D point cloud segmentation, particularly in the context of pre-grasp identification of a unique object amongst distractors. However, existing 3D object datasets are not ideal for training and evaluation of these methods. Datasets developed for grasp planning are often CAD models that are too clean for sim-to-real transfer. Real-world datasets can lack texture information or have been collected using sets of objects and/or specialized sensor setups that are hard to reproduce. In this work, we introduce the MiniMarket80 dataset to address this gap.The dataset consists of 1200 colored point cloud partial views, each of 80 standard grocery objects, collected with widely used Realsense RGB-D cameras (D415 and D435) under variable lighting conditions. We also provide a complete pipeline to generate a per-object segmentation dataset from these partial views suitable for use in training. We use this dataset to evaluate 11 state-of-the-art point cloud segmentation methods. Only four of these are able to (partially) segment the target object in a real-world test, still producing significant false positives and false negatives. Full article

Off-policy reinforcement learning is usually used to train the grasping task model of the manipulator. However, in the training process, it is difficult to collect enough successful experience data and rewards for learning and training; that is, there is a problem of sparse rewards. Hindsight experience replay (HER) allows the agent to relabel the completed states. However, not all failed experiences have the same effect on learning and training. Facing the many transitions generated by the environment during operation, adopting a random uniform sampling method from the experience replay buffer will result in low data utilization and slow convergence. This paper proposes using a prioritized sampling method to sample the relabelled transitions, and then combines various off-policy reinforcement learning algorithms with it for training in simulated environments. This paper uses the prioritized sampling method, which allows the agent to access more important transitions earlier and accelerate the convergence of training. The results demonstrate that hindsight experience replay with prioritization (PHER) exhibits significantly faster convergence compared to other methods. Full article

Using the Multiconfiguration Dirac–Hartree–Fock method as implemented in the General Relativistic Atomic Structure Package, the magnetic dipole and electric quadrupole hyperfine structure constants were determined for the ground and first excited levels of ^135,137Ba II isotopes, as well as for ¹³⁷Ba I and ⁸⁷Sr II, to assess the robustness of the developed model. This study builds upon and extends previous investigations by examining the levels involved in resonance lines, with the aim of resolving persistent discrepancies in the hyperfine structure of ¹³⁷Ba II and ⁸⁷Sr II. New code developments such as the use of natural orbitals, as well as the addition of polarization effects and Configuration State Function Generators, as implemented in GRASPG, were tested for these heavy elements. The developed strategy allowed us to achieve encouraging results that satisfactorily agree with experiments for all studied levels but ${}^{2}D_{5/2}$ in the ¹³⁷Ba II isotope. This disagreement was also observed in ¹³⁵Ba II isotope as well as in ⁸⁷Sr II. With two valence electrons, ¹³⁷Ba I is definitely more complex, requiring a multireference approach. Even with the latter, the theory–observation disagreement observed for the hyperfine structure of the low-lying levels remains large in comparison with the alkali-like systems. Possible ongoing developments to remediate this issue are discussed in the conclusions. Full article

Sustainability, as meeting present needs without compromising future generations, is challenged by resource scarcity and economic uncertainty. An organization’s environment affects its functioning and also its resilience. While often studied at the organizational level, achieving the interconnected Sustainable Development Goals (SDGs) requires network-level cooperation. This led to asking the main question: How to develop business network resilience and sustainability in a context of scarcity of resources facing SDGs? To find the answer on that research problem, a bibliometric (5981 abstracts) and widespread systematic literature review (SLR, with 94 full text papers) was conducted. After the review of the state-of-the-art in terms of resilience, sustainability, business network, and SDGs, the ARA model (actors-resources-actions) was employed, to conceptually grasp how relational resources allow the evolution towards sustainable and resilient business networks. The analysis demonstrates that relational resources—such as trust, knowledge sharing, and collaborative partnerships—are pivotal. The study concludes that business networks have to strengthen multi-stakeholder cooperation for sustainable development, focusing on relational resources. These resources enable coordinated actions, foster resource stewardship, and enhance adaptive capacity within the network, directly supporting SDG implementation, particularly SDG 17 (Partnerships for the Goals). That SDG is like organizational umbrella for the remaining 16 SDGs and there is a need to contribute to systemic sustainable development, moving beyond isolated organizational efforts to achieve broader impact. Full article

To address the challenges of harvest crate localization caused by varying illumination, partial occlusion, and background interference in unstructured farmland environments, as well as the high costs and low efficiency associated with traditional manual harvesting, this paper proposes FAL-YOLO, a lightweight keypoint detection model. Using YOLOv8n-Pose as the baseline framework, the model integrates a C2f-ContextGuided backbone and a Slim-Neck feature fusion layer. Furthermore, a LSCD-LQE lightweight detection head is designed, and an Inner-MPDIoU loss function is introduced to enhance keypoint detection performance under complex backgrounds and occluded conditions. Experimental results on the self-constructed farmland harvest crate dataset indicate that FAL-YOLO requires only 1.71 M parameters and 4.5 GFLOPs of computational cost, representing reductions of 44.5% and 45.8% compared to YOLOv8n-Pose, while achieving an mAP@0.5 of 94.9%, corresponding to an improvement of 1.2%. Additionally, by establishing correspondences between keypoints and the 3D model through the PnP algorithm, the 3D pose of the crate can be reconstructed, providing reliable spatial input for robotic arm manipulation. The results demonstrate that FAL-YOLO achieves an effective balance between model lightweightness and detection accuracy, providing an efficient solution for automatic identification and grasping of harvest crates in farmland environments. Full article

Bromodomain (BRD)-containing proteins are gaining attention as key targets in epigenetic drug development. BRDs bind to acetylated lysine residues on histones and other proteins, significantly impacting transcriptional regulation and chromatin remodeling. As our grasp of bromodomain structures and biochemistry deepens, the momentum behind developing small-molecule inhibitors for these BRD domains is triggered and potent inhibitors targeting different family members of BRDs are proposed. In addition, computational simulations have also played a significant role in advancing inhibitor design for the BRD family. This review delves into recent breakthroughs in small-molecule BRD receptor inhibitors and computational studies, spotlighting their biological impact and therapeutic potential, and outlining the research road ahead. This review is expected to provide guidance for future drug design of BRD inhibitors. Full article

For communities on the margins of hostile or indifferent power structures, the political order can be experienced as a force whose acts are not motivated by reasons in accord with recognizable norms. Power, then, as a social phenomenon, is naturalized in the sense that it is dehumanized. Derrida explored some of this territory in his final seminar, the Beast and the Sovereign. Power becomes a latent animality, structuring social life as it removes itself from mechanisms of accountability. At the same time, the Black church ritual, in the United States and elsewhere, provides an experience of a self-sustaining power, whose invisibility is taken as coextensive with its omnipresence. The act of worship becomes a project of counter-habituation whereby power can be constituted as just and life-affirming. Simone Weil’s spiritual writings on the necessity of God’s love can be of some assistance here, but her concern with “decreation” is on its face a self-erasing theological enterprise, the sociopolitical implications of which would seem to put it at odds with a movement, among marginalized people, toward increased recognition. A look at the relation between Weil’s writing method—which I analyze as a kind of endophrasis—and Edmund Husserl’s transcendental understanding of the self provides a way to reorganize our understanding of the sociocultural project supported by the ritual. To grasp the counter-habituating project of the ritual, we must see it as founded in non-linguistic thinking and post-linguistic acts. These acts are, in part, improvisational, which is a key to habituating the recognition of higher-order necessity through free activity. They bring the worshiper “through” culturally determined linguistic acts to another kind of experience, in which the freedom to worship an invisible God is manifest. Full article

Traditional dexterous hands can readily grasp objects but face limitations in dexterous manipulation due to complex control systems and high actuation demands. This paper presents a novel dexterous hand designed to address these challenges. The hand consists of four fingers, each equipped with two mecanum wheels at the fingertips to allow for the omnidirectional manipulation of objects. Continuous rotation of the mecanum wheels enables unbounded motion of grasped objects without the need for finger gaiting. Object pose adjustment is achieved by controlling the rotation of mecanum wheels, thus significantly reducing operational complexity and enhancing manipulative agility. Furthermore, to address the control difficulty of multi-finger coordinated motion, a four-finger coupled mechanism is implemented, resulting in a dexterous hand with three degrees of freedom. Kinematic models of omnidirectional manipulation are established for typical geometric objects, including a flat plate, a cuboid, a sphere, and a cylinder. Simulations confirm the correctness of the kinematic models. Experimental results show that the hand can achieve omnidirectional manipulation of objects. Finally, the extended functionality of the dexterous hand is briefly presented, which allows it to be reconfigured into an omnidirectional mobile robot. Full article