Search Results (15)

A new genus of Ectinosomatidae is recorded in association with an Ascidia from a permanent submerged coral community in Barra Beach, Salvador City, Bahia State (Brazil). The new taxon belongs to a smaller group of genera in the family Ectinosomatidae based on the geniculate maxilla and the prehensile first swimming leg. This group is composed of Bradiellopsis, Chaulionyx, Halophytophilus, and Sigmatidium. The new genus differs from the others due to distinct aspects of the mandible gnathobasis, which features sharp processes on the pars incisiva and pars molaris followed by a short spinulated area and a serrulated seta; the presence of a two-segmented endopod of the first swimming leg, with the first endopodal segment longer than all exopodites combined; and a short, quadratic second endopodal segment with two strong curved claws. In addition, it differs from other genera due to the distinct armature of the first to fourth swimming legs and the presence of fused exopod and baseoendopod on the fifth swimming leg. Cruscollatus gen.nov. lives specifically within the digestive tract of Eudistoma vannamei Millar, 1977, an Ascidia species endemic to northeastern Brazil. This study reports the first documented association between a harpacticoid copepod of the family Ectinosomatidae (order Harpacticoida) and ascidian hosts (Ascidia). Full article

Background: Sarcopenia is a comorbidity associated with heart failure, which aggravates its prognosis. Objectives: To analyze the differential characteristics of polypathological patients with acute heart failure (AHF) based on the presence of low muscle strength, as well as to study whether this condition is associated with a worse prognosis. Methods: An observational study of 377 patients with a diagnosis of acute heart failure from the prospective multicentric PROFUND-IC registry was carried out. The main variable is low muscle strength, which is assessed with dynamometry or prehensile strength. Epidemiological and anthropometric characteristics, as well as associated comorbidities, were analyzed. Likewise, the etiology of the AHF episode, the number of admissions in the previous year, and the NYHA scale were also included. Finally, scores on functionality, treatment established, and mortality and readmission rates were studied. Quantitative variables are described as mean, and standard deviation, and qualitative variables are expressed as absolute numbers and percentages. A descriptive and bivariate analysis was performed according to the presence of low muscle strength (handgrip <27 kg in men and <16 kg in women), using the Welch test for quantitative measures and Chi-square for qualitative variables. In addition, Kaplan-Meier curves of readmission and mortality and a logistic regression analysis were also performed. Results: 377 patients were included (56% female, mean age 83 years). 310 (82.23%) had low muscle strength. Those with low muscle strength were older (84 vs. 78 years, p < 0.001), with more cognitive impairment (11.9% vs. 0%, p = 0.021), worse functional class (p = 0.016), lower scores in the Barthel index and Rockwood scale (p < 0.001), and higher in the PROFUND index (p < 0.001). They had higher rates of readmission and mortality without statistically significant differences. The PROFUND index is significantly associated with low muscle strength (OR 1.19, CI (1.09–1.31), p < 0.001). Conclusions: Elderly polypathological patients with acute heart failure and low muscle strength have a higher PROFUND index and a lower probability of survival per year. Full article

This research study represents a polydexterous deep reinforcement learning-based pick-and-place framework for industrial clutter scenarios. In the proposed framework, the agent tends to learn the pick-and-place of regularly and irregularly shaped objects in clutter by using the sequential combination of prehensile and non-prehensile robotic manipulations involving different robotic grippers in a completely self-supervised manner. The problem was tackled as a reinforcement learning problem; after the Markov decision process (MDP) was designed, the off-policy model-free Q-learning algorithm was deployed using deep Q-networks as a Q-function approximator. Four distinct robotic manipulations, i.e., grasp from the prehensile manipulation category and inward slide, outward slide, and suction grip from the non-prehensile manipulation category were considered as actions. The Q-function comprised four fully convolutional networks (FCN) corresponding to each action based on memory-efficient DenseNet-121 variants outputting pixel-wise maps of action-values jointly trained via the pixel-wise parametrization technique. Rewards were awarded according to the status of the action performed, and backpropagation was conducted accordingly for the FCN generating the maximum Q-value. The results showed that the agent learned the sequential combination of the polydexterous prehensile and non-prehensile manipulations, where the non-prehensile manipulations increased the possibility of prehensile manipulations. We achieved promising results in comparison to the baselines, differently designed variants, and density-based testing clutter. Full article

A basic process in cancer is the breaching of basement-membrane barriers to permit tissue invasion. Cancer cells can use proteases and physical mechanisms to produce initial holes in basement membranes, but how cells squeeze through this barrier into matrix environments is not well understood. We used a 3D invasion model consisting of cancer-cell spheroids encapsulated by a basement membrane and embedded in collagen to characterize the dynamic early steps in cancer-cell invasion across this barrier. We demonstrate that certain cancer cells extend exceptionally long (~30–100 μm) protrusions through basement membranes via actin and microtubule cytoskeletal function. These long protrusions use integrin adhesion and myosin II-based contractility to pull cells through the basement membrane for initial invasion. Concurrently, these long, organelle-rich protrusions pull surrounding collagen inward while propelling cancer cells outward through perforations in the basement-membrane barrier. These exceptionally long, contractile cellular protrusions can facilitate the breaching of the basement-membrane barrier as a first step in cancer metastasis. Full article

Robotic manipulation in cluttered environments is one of the challenges roboticists are currently facing. When the objects to handle are delicate fresh fruits, grasping is even more challenging. Detecting and localizing fruits with the accuracy necessary to grasp them is very difficult due to the large variability in the aspect and dimensions of each item. This paper proposes a solution that exploits a state-of-the-art neural network and a novel enhanced 6D pose estimation method that integrates the depth map with the neural network output. Even with an accurate localization, grasping fruits with a suitable force to avoid slippage and damage at the same time is another challenge. This work solves this issue by resorting to a grasp controller based on tactile sensing. Depending on the specific application scenario, grasping a fruit might be impossible without colliding with other objects or other fruits. Therefore, a non-prehensile manipulation action is here proposed to push items hindering the grasp of a detected fruit. The pushing from an initial location to a target one is performed by a model predictive controller taking into account the unavoidable delay in the perception and computing pipeline of the robotic system. Experiments with real fresh fruits demonstrate that the overall proposed approach allows a robot to successfully grasp apples in various situations. Full article

In this study, we develop a framework for an intelligent and self-supervised industrial pick-and-place operation for cluttered environments. Our target is to have the agent learn to perform prehensile and non-prehensile robotic manipulations to improve the efficiency and throughput of the pick-and-place task. To achieve this target, we specify the problem as a Markov decision process (MDP) and deploy a deep reinforcement learning (RL) temporal difference model-free algorithm known as the deep Q-network (DQN). We consider three actions in our MDP; one is ‘grasping’ from the prehensile manipulation category and the other two are ‘left-slide’ and ‘right-slide’ from the non-prehensile manipulation category. Our DQN is composed of three fully convolutional networks (FCN) based on the memory-efficient architecture of DenseNet-121 which are trained together without causing any bottleneck situations. Each FCN corresponds to each discrete action and outputs a pixel-wise map of affordances for the relevant action. Rewards are allocated after every forward pass and backpropagation is carried out for weight tuning in the corresponding FCN. In this manner, non-prehensile manipulations are learnt which can, in turn, lead to possible successful prehensile manipulations in the near future and vice versa, thus increasing the efficiency and throughput of the pick-and-place task. The Results section shows performance comparisons of our approach to a baseline deep learning approach and a ResNet architecture-based approach, along with very promising test results at varying clutter densities across a range of complex scenario test cases. Full article

Solving environmental odor issues can be confounded by many analytical, technological, and socioeconomic factors. Considerable know-how and technologies can fail to properly identify odorants responsible for the downwind nuisance odor and, thereby, focus on odor mitigation strategies. We propose enabling solutions to environmental odor issues utilizing troubleshooting techniques developed for the food, beverage, and consumer products industries. Our research has shown that the odorant impact-priority ranking process can be definable and relatively simple. The initial challenge is the prioritization of environmental odor character from the perspective of the impacted citizenry downwind. In this research, we utilize a natural model from the animal world to illustrate the rolling unmasking effect (RUE) and discuss it more systematically in the context of the proposed environmental odorant prioritization process. Regardless of the size and reach of an odor source, a simplification of odor character and composition typically develops with increasing dilution downwind. An extreme odor simplification-upon-dilution was demonstrated for the prehensile-tailed porcupine (P.T. porcupine); its downwind odor frontal boundary was dominated by a pair of extremely potent character-defining odorants: (1) ‘onion’/‘body odor’ and (2) ‘onion’/‘grilled’ odorants. In contrast with the outer-boundary simplicity, the near-source assessment presented considerable compositional complexity and composite odor character difference. The ultimate significance of the proposed RUE approach is the illustration of naturally occurring phenomena that explain why some environmental odors and their sources can be challenging to identify and mitigate using an analytical-only approach (focused on compound identities and concentrations). These approaches rarely move beyond comprehensive lists of volatile compounds emitted by the source. The novelty proposed herein lies in identification of those few compounds responsible for the downwind odor impacts and requiring mitigation focus. Full article

Robotic manipulation of a bulky object is challenging due to the limited kinematics and payload of the manipulator. In this study, a robot realizes the manipulation of general-shaped bulky objects utilizing the contact with the environment. We propose a hierarchical manipulation planner that effectively combined three manipulation styles, namely, pivoting, tumbling, and regrasping. In our proposed method, we first generate a set of superimposed planar segments on the object surface to obtain an object pose in stable contact with the table, and a set of points on the object surface for the end-effectors (EEFs) of a dual-arm manipulator to stably grasp the object. Object manipulation can be realized by solving a graph, considering the kinematic constraints of pivoting and tumbling. For pivoting, we consider two supporting styles: stable support (SP) and unstable support (USP). Our proposed method manipulates large and heavy objects by selectively using the two different support styles of pivoting and tumbling according to the conditions on the table area. In addition, it can effectively avoid the limitation arising due to the arm kinematics by regrasping the object. We experimentally demonstrate that a dual-arm manipulator can move an object from the initial to goal position within a limited area on the table, avoiding obstacles placed on the table. Full article

To achieve human-level object manipulation capability, a robot must be able to handle objects not only with prehensile manipulation, such as pick-and-place, but also with nonprehensile manipulation. To study nonprehensile manipulation, we studied robotic batting, a primitive form of nonprehensile manipulation. Batting is a challenging research area because it requires sophisticated and fast manipulation of moving objects and requires considerable improvement. In this paper, we designed a batting system for dynamic manipulation of a moving ball and proposed several algorithms to improve the task performance of batting. To improve the recognition accuracy of the ball, we proposed a circle-fitting method that complements color segmentation. This method enabled robust ball recognition against illumination. To accurately estimate the trajectory of the recognized ball, weighted least-squares regression considering the accuracy according to the distance of a stereo vision sensor was used for trajectory estimation, which enabled more accurate and faster trajectory estimation of the ball. Further, we analyzed the factors influencing the success rate of ball direction control and applied a constant posture control method to improve the success rate. Through the proposed methods, the ball direction control performance is improved. Full article

This paper proposes a hybrid singulation approach combining dynamic scattering and pushing techniques. Using the impulse-based dynamic model, a guideline is provided to decide whether scattering or pushing is conducted for object singulation. The Soma cube consisting of seven blocks is used as the test example. The target is to singulate all the blocks. The dynamic scattering technique was initially applied to separate blocks in the formation. However, scattering alone does not provide target singulation in all the cases. So we combine the quasi-static pushing technique to complete the singulation of all the blocks. In pushing, image segmentation based on principal component analysis (PCA) algorithm was employed to singulate multiple blocks in clutter and prehensile manipulation was used to remove isolated blocks. Several 2-D formations of the Soma cube are used as the test cases. To validate the effectiveness of our approach, we have conducted comparative analysis which clearly shows that the hybrid singulation achieves singulation in much less time as compared to the pure pushing approach. Full article

Wearable sensor systems with transmitting capabilities are currently employed for the biometric screening of exercise activities and other performance data. Such technology is generally wireless and enables the non-invasive monitoring of signals to track and trace user behaviors in real time. Examples include signals relative to hand and finger movements or force control reflected by individual grip force data. As will be shown here, these signals directly translate into task, skill, and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. The present study draws from thousands of such sensor data recorded from multiple spatial locations. The individual grip force profiles of a highly proficient left-hander (expert), a right-handed dominant-hand-trained user, and a right-handed novice performing an image-guided, robot-assisted precision task with the dominant or the non-dominant hand are analyzed. The step-by-step statistical approach follows Tukey’s “detective work” principle, guided by explicit functional assumptions relating to somatosensory receptive field organization in the human brain. Correlation analyses (Person’s product moment) reveal skill-specific differences in co-variation patterns in the individual grip force profiles. These can be functionally mapped to from-global-to-local coding principles in the brain networks that govern grip force control and its optimization with a specific task expertise. Implications for the real-time monitoring of grip forces and performance training in complex task-user systems are brought forward. Full article

The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward. Full article

If we define a mode as a set of specific configurations that hold the same constraint, and if we investigate their transitions beforehand, we can efficiently probe the configuration space by using a manipulation planner. However, when multiple mobile robots together manipulate an object by using the non-prehensile method, the candidates for the modes and their transitions become enormous because of the numerous contacts among the object, the environment, and the robots. In some cases, the constraints on the object, which include a combination of robot contacts and environmental contacts, are incapable of guaranteeing the object’s stability. Furthermore, some transitions cannot appear because of geometrical and functional restrictions of the robots. Therefore, in this paper, we propose a method to narrow down the possible modes and transitions between modes by excluding the impossible modes and transitions from the viewpoint of statics, kinematics, and geometry. We first generated modes that described an object’s contact set from the robots and the environment while ignoring their exact configurations. Each multi-contact set exerted by the robots and the environment satisfied the condition necessary for the force closure on the object along with gravity. Second, we listed every possible transition between the modes by determining whether or not the given robot could actively change the contacts with geometrical feasibility. Finally, we performed two simulations to validate our method on specific manipulation tasks. Our method can be used in various cases of non-prehensile manipulations by using mobile robots. The mode transition graph generated by our method was used to efficiently sequence the manipulation actions before deciding the detailed configuration planning. Full article

The hand represents one of the most remarkable expressions of humanization of the anterior limb. The anterior limb, at first ambulatory, underwent continuous evolution acquiring innumerable new functions. In the course of human evolution the hand has undergone continual structural and functional adaptations, characterized, among others, by enrichment of peripheral innervation and further development of the thumb. This development was accompanied by important changes in the brain and the relocation of the eyes, together allowing the muscle control and stereoscopic vision, necessary for a controlled grip. The anatomy of the hand is complex, intricate, and fascinating. Its integrity is absolutely essential for our everyday functional living. It is intimately correlated with the brain, both in the evolution of the species and in the development of the individual. Actually, we can state that we “think” and “feel” with our hands, hence, their contribution is essential to the mental processes of thought and feeling. The aim of this review is to evaluate the most typical hand quality, the prehensility and hence, the possibility of manoeuvring tools. Our attention is mainly focused on the hand anatomy and prehensility during pushing and pulling motions. In particular, our attention is directed toward the relationship existing between the hand prehensility and the volume of the object to be gripped. As an example, we use a grip of the paddle and, pushing and pulling motions during kayak paddling. Indeed, we are firmly convinced that the prehensility plays a crucial role not only in performing the stylistically correct paddling, but especially in realizing a more effective and powerful paddle stroke. This review highlights a great link existing between biomechanical and anatomical notions and sporting performance. Full article