Evaluating the bioaccumulation of nickel and vanadium and their effects on the growth of Artemia urmiana and A. franciscana

Although there is growing evidence that metals can be toxic to various aquatic species, there is still insufficient knowledge to integrate this information in environmental risk assessment procedures. In this study, we have investigated bioaccumulation and effects of nickel and vanadium on mortality and growth of Artemia urmiana and Artemia franciscana. The in 24 h of A. urmiana and A. franciscana exposed to nickel and vanadium were 0.0072, 0.0114 mg/l and 0.0107 and 0.011 mg/l respectively. In growth experiments, the length of animals was considered as growth index. Results indicates that the mean length of animals in (0.001, 0.002 and 0.003 mg/l) Ni and V on first, 5th, 7th and 11th days of life significantly decreases in comparison with control groups (p<0.05).Bioaccumulation of Ni and V in the same concentration, after 24 h in nauplius and also in adults of A. urmiana and A. fransicana were statistically significantly higher than of the control groups (P < 0.05). Both species accumulate nickel and vanadium in their bodies. However A. urmiana is more resistant to the heavy metals. Results show, nickel is less toxic than vanadium on Artemia

Compact Q-Learning Optimized for Micro-robots with Processing and Memory Constraints

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of a safewandering task with the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one-step Qlearning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floatingpoint and multiplication operators. Finally, quality of learning is compared to a floating-point based algorithm

Compact Q-Learning for Micro-robots

Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of safe-wandering and light following tasks on the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one step Q-learning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floating-point and multiplication operators

Enriching Remote Control Applications with Fog Computing

Fog computing has emerged in the recent years as a paradigm tailored to serve geo-distributed applications requiring low latency. Remote Control (RC) applications allow a mobile device to control another device from remote. To enrich Quality of Experience (QoE) of RC applications, in this paper we investigate the use of fog computing as a viable platform to offload computation of tasks that would be expensive if performed locally on a mobile device. The proposed approach, supported with next 5G communication systems, will enable a Tactile Internet experience. In this paper we study and compare offload policies to accommodate tasks in the fog platform and analyze the requirements to minimize outages

Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms

Constrained submodular maximization problems have long been studied, with near-optimal results known under a variety of constraints when the submodular function is monotone. The case of non-monotone submodular maximization is less understood: the first approximation algorithms even for the unconstrainted setting were given by Feige et al. (FOCS '07). More recently, Lee et al. (STOC '09, APPROX '09) show how to approximately maximize non-monotone submodular functions when the constraints are given by the intersection of p matroid constraints; their algorithm is based on local-search procedures that consider p-swaps, and hence the running time may be n^Omega(p), implying their algorithm is polynomial-time only for constantly many matroids. In this paper, we give algorithms that work for p-independence systems (which generalize constraints given by the intersection of p matroids), where the running time is poly(n,p). Our algorithm essentially reduces the non-monotone maximization problem to multiple runs of the greedy algorithm previously used in the monotone case. Our idea of using existing algorithms for monotone functions to solve the non-monotone case also works for maximizing a submodular function with respect to a knapsack constraint: we get a simple greedy-based constant-factor approximation for this problem. With these simpler algorithms, we are able to adapt our approach to constrained non-monotone submodular maximization to the (online) secretary setting, where elements arrive one at a time in random order, and the algorithm must make irrevocable decisions about whether or not to select each element as it arrives. We give constant approximations in this secretary setting when the algorithm is constrained subject to a uniform matroid or a partition matroid, and give an O(log k) approximation when it is constrained by a general matroid of rank k.Comment: In the Proceedings of WINE 201

Fluctuation of gonadosomatic index during oocyte development in the narrow-clawed crayfish Astacus leptodactylus (Eschscholtz, 1823) in Aras Dam Lake, Iran

This study was carried out with the aim of examining the seasonal reproductive cycle of the female crayfish Astacus leptodactylus from Aras Dam Lake, Western-Azerbaijan, Iran. Gonadosomatic index (GSI), and oocyte size were measured in females sampled seasonally in June, August, November (2011), January (2012). Development of the oocytes was categorized according to the diameter and the presence/absence of yolk granules. The ovary development was accompanied by increasing levels gonadosomatic index and egg diameter. Ovarian development histologicaly related to the seasonal GSI . This index was low in June (0.61±0.05) when oocytes started developing and reached the highest value in November (13.53±0.25), when vitellogenic oocytes were abundant in the mature ovary. Our results highlight the relationship between the ovary development and the GSI and egg diameter in the crayfish A. leptodactylus during the reproductive cycle and held important implications for the management of aquatic species. Thus, investigation of female A. leptodactylus reproduction is highly significant for fisheries managers as well as environmentalists concerned with perpetuating crayfish stocks

Aberrant Frequency Related Change-Detection Activity in Chronic Tinnitus

Tinnitus is the perception of sound without the occurrence of an acoustic event. The deficit in auditory sensory or echoic memory may be the cause of the perception of tinnitus. This study considered the mismatch negativity (MMN) to investigate the potential difference between and within groups of persons with normal hearing (NH) and tinnitus. Using an auditory multi-feature paradigm to elicit the MMN, this study considered the MMN peak amplitude at two central frequencies for two MMN subcomponents. These central frequencies were 1 and 5 kHz, which the latter was closer to the perceived tinnitus frequency in the group with tinnitus. The deviants were higher frequency, lower frequency, higher intensity, lower intensity, duration, location (left), location (right), and gap. The pure tone audiometry (PTA) test and distortion product otoacoustic emissions (DPOAE) test showed no meaningful difference between the two groups. For the frontal subcomponent, the mean amplitudes of the MMN peak for the two groups illustrated less negative meaningful MMN peak amplitudes in the group of persons with tinnitus. For the supratemporal component at 5 kHz central frequency, amplitudes were lower for the group of persons with tinnitus, whereas for the central frequency of 1 kHz, most deviants exhibited meaningful differences. Additionally, within-group comparisons indicated that mean amplitudes for both groups were more negative at the central frequency of 1 kHz for the frontal MMN subcomponent. In comparison, the supratemporal component illustrated a lower peak amplitude at 5 kHz central frequency in the group of persons with tinnitus and no difference in the NH group, which is a unique observation of this study. Results of the between-groups are in accordance with previous studies and within-group comparisons consider the probability of decreasing the change detection capability of the brain. The results of this study indicate that increasing the frequency of the stimuli close to the tinnitus perceived frequencies decreases the prediction error, including the prediction error of the silence. Such a decrease may cause the prediction error of the spontaneous neural activity in the auditory pathway to exceed the silence prediction error, and as a result, increases the probability of occurrence of tinnitus in higher frequencies according to the predictive coding model. © Copyright © 2020 Asadpour, Jahed and Mahmoudian

Cooperative Q-learning: The Knowledge Sharing Issue

A group of cooperative and homogeneous Q-learning agents can cooperate to learn faster and gain more knowledge. In order to do so, each learner agent must be able to evaluate the expertness and the intelligence level of the other agents and to assess the knowledge and the information it gets from them. In addition, the learner needs a suitable method to properly combine its own knowledge and what it gains from the other agents according to their relative expertness. In this paper, some expertness measuring criteria are introduced. Also, a new cooperative learning method, called Weighted Strategy Sharing (WSS) is given. In WSS, based on the amount of its teammate expertness, each agent assigns a weight to their knowledge and utilizes it accordingly. WSS and the expertness criteria are tested on two simulated Hunter-Prey and Object Pushing systems