Attention Allocation for Human Multi-Robot Control: Cognitive Analysis based on Behavior Data and Hidden States

Human multi-robot interaction exploits both the human operator’s high-level decision-making skills and the robotic agents’ vigorous computing and motion abilities. While controlling multi-robot teams, an operator’s attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator’s attentional resources, a robot with self reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots’ normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot’s request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots’ failures) tasks, regardless of the system’s reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users’ underlying cognitive strategies and may fail to reflect the user’s true intent. As understanding users’ intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators’ underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments

Quantitative Mechanical Properties of the Relaxed Biceps and Triceps Brachii Muscles in Patients with Subacute Stroke: A Reliability Study of the Myoton-3 Myometer

Objective. Test-retest reliability of the myotonometer was investigated in patients with subacute stroke. Methods. Twelve patients with substroke (3 to 9 months poststroke) were examined in standardized testing position twice, 60 minutes apart, with the Myoton-3 myometer to measure tone, elasticity, and stiffness of relaxed bilateral biceps and triceps brachii muscles. Intrarater reliability of muscle properties was determined using intraclass correlation coefficient (ICC), the standard error of measurement (SEM), and the minimal detectable change (MDC). Results. Intrarater reliability of muscle properties of bilateral biceps and triceps brachii muscles were good (ICCs = 0.79–0.96) except for unaffected biceps tone (ICC = 0.72). The SEM and MDC of bilateral biceps and triceps brachii muscles indicated small measurement error (SEM% <10%, MDC% <25%). Conclusion. The Myoton-3 myometer is a reliable tool for quantifying muscle tone, elasticity, and stiffness of the biceps and triceps brachii in patients with subacute stroke

Biochanin A, a Phytoestrogenic Isoflavone with Selective Inhibition of Phosphodiesterase 4, Suppresses Ovalbumin-Induced Airway Hyperresponsiveness

The present study investigated the potential of biochanin A, a phytoestrogenic isoflavone of red clover (Triflolium pratense), for use in treating asthma or chronic obstructive pulmonary disease (COPD). Biochanin A (100 μmol/kg, orally (p.o.)) significantly attenuated airway resistance (RL), enhanced pause (Penh), and increased lung dynamic compliance (Cdyn) values induced by methacholine (MCh) in sensitized and challenged mice. It also significantly suppressed an increase in the number of total inflammatory cells, neutrophils, and eosinophils, and levels of cytokines, including interleukin (IL)-2, IL-4, IL-5, and tumor necrosis factor (TNF)-α in bronchoalveolar lavage fluid (BALF) of the mice. However, it did not influence interferon (IFN)-γ levels. Biochanin A (100 μmol/kg, p.o.) also significantly suppressed the total and ovalbumin (OVA)-specific immunoglobulin E (IgE) levels in the serum and BALF, and enhanced the total IgG2a level in the serum of these mice. The PDE4H/PDE4L value of biochanin A was calculated as >35. Biochanin A did not influence xylazine/ketamine-induced anesthesia. Biochanin A (10~30 μM) significantly reduced cumulative OVA (10~100 μg/mL)-induced contractions in the isolated guinea pig trachealis, suggesting that it inhibits degranulation of mast cells. In conclusion, red clover containing biochanin A has the potential for treating allergic asthma and COPD

False Data Injection Attack on Atmospheric Electric Field in Thunderstorm Warning

Thunderstorm warning plays an important role in lightning prevention and disaster mitigation. In practical applications, thunderstorm warning system is also vulnerable to attacks, such as False Data Injection Attack (FDIA). However, there is a lack of research on False Data Injection Attack for thunderstorm warning. Therefore, this paper put forwards a FDIA method based on principal component analysis (PCA) for atmospheric electric field (AEF), which is usually used for thunderstorm warning. In the FDIA scenario, the AEF-based thunderstorm warning algorithm is also introduced with electric field differential index (EFDI). Finally, experiments are conducted based on AEF data collected by an atmospheric electric field meter (AEFM) about the real thunderstorm. The experimental results show that FDIA seriously interferes with the results of the AEF-based thunderstorm warning

Increased spinal prodynorphin gene expression in reinflammation-associated hyperalgesia after neonatal inflammatory insult

<p>Abstract</p> <p>Background</p> <p>Neuroplasticity induced by neonatal inflammation is the consequence of a combination of activity-dependent changes in neurons. We investigated neuronal sensitivity to a noxious stimulus in a rat model of neonatal hind-paw peripheral inflammation and assessed changes in pain behaviour at the physiological and molecular levels after peripheral reinflammation in adulthood.</p> <p>Results</p> <p>A decrease in paw withdrawal latency (PWL) after a heat stimulus was documented in rats that received inflammatory injections in their left hind paws on postnatal day one (P1) and a reinflammation stimulus at postnatal 6-8 weeks of age, compared with normal rats. An increase in the expression of the prodynorphin (<it>proDYN</it>) gene was noted after reinflammation in the spinal cord ipsilateral to the afferents of the neonatally treated hind paw. The involvement of the activation of extracellular signal-regulated kinases (ERK) in peripheral inflammatory pain hypersensitivity was evidenced evident by the increase in phospho-ERK (pERK) activity after reinflammation.</p> <p>Conclusions</p> <p>Our results indicate that peripheral inflammation in neonates can permanently alter the pain processing pathway during the subsequent sensory stimulation of the region. Elucidation of the mechanism underlying the developing pain circuitry will provide new insights into the understanding of the early pain behaviours and the subsequent adaptation to pain.</p

A Bilevel Optimization Model Based on Edge Computing for Microgrid

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments