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Tele-operated social robots (telerobots) offer an innovative means of allowing children who are medically restricted to their homes (MRH) to return to their local schools and physical communities. Most commercially available telerobots have three foundational features that facilitate child-robot interaction: remote mobility, synchronous two-way vision capabilities, and synchronous two-way audio capabilities. We conducted a comparative analysis between the Toyota Human Support Robot (HSR) and commercially available telerobots, focusing on these foundational features. Children who used these robots and these features on a daily basis to attend school were asked to pilot the HSR in a simulated classroom for learning activities. As the HSR has three additional features that are not available on commercial telerobots: (1) pan-tilt camera, (2) mapping and autonomous navigation, and (3) robot arm and gripper for children to "reach" into remote environments, participants were also asked to evaluate the use of these features for learning experiences. To expand on earlier work on the use of telerobots by remote children, this study provides novel empirical findings on (1) the capabilities of the Toyota HSR for robot-mediated learning similar to commercially available telerobots and (2) the efficacy of novel HSR features (i.e., pan-tilt camera, autonomous navigation, robot arm/hand hardware) for future learning experiences. We found that among our participants, autonomous navigation and arm/gripper hardware were rated as highly valuable for social and learning activities.

Disentangling the sources of visual motion in a dynamic scene during self-movement or ego motion is important for autonomous navigation and tracking. In the dynamic image segments of a video frame containing independently moving objects, optic flow relative to the next frame is the sum of the motion fields generated due to camera and object motion. The traditional ego-motion estimation methods assume the scene to be static, and the recent deep learning-based methods do not separate pixel velocities into object- and ego-motion components. We propose a learning-based approach to predict both ego-motion parameters and object-motion field (OMF) from image sequences using a convolutional autoencoder while being robust to variations due to the unconstrained scene depth. This is achieved by: 1) training with continuous ego-motion constraints that allow solving for ego-motion parameters independently of depth and 2) learning a sparsely activated overcomplete ego-motion field (EMF) basis set, which eliminates the irrelevant components in both static and dynamic segments for the task of ego-motion estimation. In order to learn the EMF basis set, we propose a new differentiable sparsity penalty function that approximates the number of nonzero activations in the bottleneck layer of the autoencoder and enforces sparsity more effectively than L1- and L2-norm-based penalties. Unlike the existing direct ego-motion estimation methods, the predicted global EMF can be used to extract OMF directly by comparing it against the optic flow. Compared with the state-of-the-art baselines, the proposed model performs favorably on pixelwise object- and ego-motion estimation tasks when evaluated on real and synthetic data sets of dynamic scenes.

Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.

Currently, developing materials with high power capability is crucial to the rapid deployment of electric vehicles. The Li4Ti5O12 (LTO) anode material with zero strain rate upon lithium insertion and extraction. The successful implementation of this anode material depends on strategies to overcome the low electronic and ionic conductivity. In this study, Li4Ti5O12 was synthesized using solid‐state approach in which different phases (anatase and rutile) of TiO2 as precursors were used. X‐ray photoelectron spectroscopy (XPS) and Raman analysis confirmed the formation of oxygen vacancies with the existence of Ti3+ ions, which is believed to greatly enhance the electronic conductivity of Li4Ti5O12. The electrochemical performance revealed that the rutile TiO2 precursor concentration significantly affects the resultant Li4Ti5O12 capacity and power capability. The Li4Ti5O12 synthesized using 80% anatase and 20% rutile TiO2 (N2‐LTO) exhibits a specific capacity of 169, 145, 140, 133, 122, 101, and 84 mAh g−1 at 1C, 3C, 5C, 7C, 10C, 20C, and 30C. In addition, LFP||N2‐LTO pouch‐cell was fabricated which showed promising results with 95% capacity retention after 1000 cycles at 1C rate. The oxygen vacancies created due to the presence of Ti3+/Ti4+ mixed valances and reduced particle size for faster lithium‐ion diffusion significantly enhanced the overall electrochemical performance. [ABSTRACT FROM AUTHOR]

In this paper, an attempt was made to synthesize LaNiO3, CeNiO3, and BaNiO3, and Ce3+ and Ba2+ co-substituted LaNiO3. These samples were further subjected to various material characterization techniques in order to evaluate their physio-chemical properties. Scanning electron microscope (SEM) images showed large chunks of aggregated nanoparticles with minute voids. The EDX-derived atomic composition deviated from the nominal composition suggesting the occurrence of multiple phases. In addition, transmission electron microscope (TEM) images revealed that the samples exhibit uneven spherical shape with a high degree of aggregation. The Fourier transformed-infrared (FT-IR) spectra of the synthesized samples show vibrations of the BO6 octahedral indicating the presence of Ni–O bonds. In addition, metal-carboxyl vibrations were identified from the peaks at 1400 and 860 cm−1. Optical diffuse reflectance spectra (DRS) showed certain peaks originating from the O2− (2p) → Ni2+ (3d) charge transfer. The X-ray powder diffraction (XRPD) analysis revealed the existence of multiple phases for the samples CeNiO3, BaNiO3, and La(Ce, Ba)NiO3. Moreover, La(Ce, Ba)NiO3 contained four phases showing that the co-substitution of Ba2+ and Ce3+ into LaNiO3 may require more sophisticated methodologies. The sample BaNiO3 showed maximum weight loss, due to the existence of carbonate phase. The dielectric properties decreased with increasing frequency, while the ac electrical conductivity enhanced with increasing frequencies obeying the Maxwell–Wagner two-layer model in accordance with Koop's phenomenological theory. [ABSTRACT FROM AUTHOR]

Perovskite-type oxides La1-xCaxFeO3 (x = 0.0–1.0) were synthesized using combustion technique. The samples have been investigated using scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, ultraviolet–visible diffuse reflectance spectroscopy (DRS), X-ray powder diffraction (XRPD) using Rietveld refinement, thermogravimetric analysis (TGA) and dielectric spectroscopy. The SEM images showed spherical-shaped agglomerates having a non-uniform distribution of size. The EDX results suggested that the experimental chemical composition was in good accordance with the nominal values. FT-IR revealed multiple peaks in the range of 580–510 cm−1 due to asymmetric stretching of the octahedron. The reflectance spectra showed characteristic d → d transitions arising from the Fe 3d and the highest optical band gap energy ( E g ) of 2.57 eV was exhibited by LaFeO3, while the lowest E g (1.41 eV) was shown by La1-xCaxFeO3 with x = 0.6. The results suggest that these samples are suitable for photocatalytic applications. From phase evaluation of the diffraction patterns, it was found that five distinct phases exist in the series. A phase transformation from orthorhombic ( Pnma ) to cubic ( P m 3 ¯ m ) was found when x was increased from 0.4 to 0.6. TGA showed that LaFeO3 was the most stable with a residual mass of ~ 97 wt% at 900 ∘ C compared to CaFeO3 (~ 70 wt% at 900 ∘ C ). The evaluation of frequency-dependent (20 Hz–20 MHz) dielectric properties agreed with the Maxwell–Wagner two-layer model. After Ca2+ substitution, tan δ at 20 Hz significantly reduced from ~ 103 for x = 0 to ~ 100 for x = 1.0. The high polarization observed was due to e− hopping between Fe2+ ↔ Fe3+. The electrical models of the samples show non-Debye type relaxation behavior, while the ac conductivity enhanced with increasing frequency which was in accordance with Koop's phenomenological theory. [ABSTRACT FROM AUTHOR]

Epoxy composites were prepared by doping nano Zirconia Toughened Alumina (ZTA) which were synthesized by solution combustion method into epoxy resin and hardener. Initially ZTA nanopowder was characterized to check its purity, morphology and to confirm its metal-oxide bonding using XRD, SEM and FTIR respectively. The thermal properties such as TGA and DTG were also analysed. The polymer composites were obtained by uniformly dispersing ZTA nanopowder into epoxy using an ultrasonicator. Polymer composites of various concentrations viz, 0.5, 1, 1.5, 2 and 2.5 wt% were synthesized, all concentrations were prepared on weight basis. All the polymer composites were tested for compression properties, flexural properties and tensile properties. Best results for all the mechanical properties were obtained for epoxy with 1.5 wt% ZTA composites. Electrical properties such as breakdown voltage and breakdown strength were analysed and outstanding results were observed for epoxy with 2.5 wt% ZTA composite. [ABSTRACT FROM AUTHOR]

Objective: Lung hypoplasia associated with congenital diaphragmatic hernia (CDH) results in respiratory insufficiency and pulmonary hypertension after birth. We have investigated whether aerating the lung before removing placental support (physiologically based cord clamping (PBCC)), improves the cardiopulmonary transition in lambs with a CDH.Methods: At ≈138 days of gestational age, 17 lambs with surgically induced left-sided diaphragmatic hernia (≈d80) were delivered via caesarean section. The umbilical cord was clamped either immediately prior to ventilation onset (immediate cord clamping (ICC); n=6) or after achieving a target tidal volume of 4 mL/kg, with a maximum delay of 10 min (PBCC; n=11). Lambs were ventilated for 120 min and physiological changes recorded.Results: Pulmonary blood flow (PBF) increased following ventilation onset in both groups, but was 19-fold greater in PBCC compared with ICC lambs at cord clamping (19±6.3 vs 1.0±0.5 mL/min/kg, p<0.001). Cerebral tissue oxygenation was higher in PBCC than ICC lambs during the first 10 min after cord clamping (59%±4% vs 30%±5%, p<0.001). PBF was threefold higher (23±4 vs 8±2 mL/min/kg, p=0.01) and pulmonary vascular resistance (PVR) was threefold lower (0.6±0.1 vs 2.2±0.6 mm Hg/(mL/min), p<0.001) in PBCC lambs compared with ICC lambs at 120 min after ventilation onset.Conclusions: Compared with ICC, PBCC prevented the severe asphyxia immediately after birth and resulted in a higher PBF due to a lower PVR, which persisted for at least 120 min after birth in CDH lambs. [ABSTRACT FROM AUTHOR]

Distributed denial of service (DDoS) attack is a coordinated attack, generally performed on a massive scale on the availability of services of a target system or network resources. Owing to the continuous evolution of new attacks and ever-increasing number of vulnerable hosts on the Internet, many DDoS attack detection or prevention mechanisms have been proposed. In this paper, we present a comprehensive survey of DDoS attacks, detection methods and tools used in wired networks. The paper also highlights open issues, research challenges and possible solutions in this area. [ABSTRACT FROM AUTHOR]