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The transportation demand is rapidly growing in metropolises, resulting in chronic traffic congestions in dense downtown areas. Adaptive traffic signal control as the principle part of intelligent transportation systems has a primary role to effectively reduce traffic congestion by making a real-time adaptation in response to the changing traffic network dynamics. Reinforcement learning (RL) is an effective approach in machine learning that has been applied for designing adaptive traffic signal controllers. One of the most efficient and robust type of RL algorithms are continuous state actor-critic algorithms that have the advantage of fast learning and the ability to generalize to new and unseen traffic conditions. These algorithms are utilized in this paper to design adaptive traffic signal controllers called actor-critic adaptive traffic signal controllers (A-CATs controllers). The contribution of the present work rests on the integration of three threads: (a) showing performance comparisons of both discrete and continuous A-CATs controllers in a traffic network with recurring congestion (24-hour traffic demand) in the upper downtown core of Tehran city, (b) analyzing the effects of different traffic disruptions including opportunistic pedestrians crossing, parking lane, non-recurring congestion, and different levels of sensor noise on the performance of A-CATS controllers, and (c) comparing the performance of different function approximators (tile coding and radial basis function) on the learning of A-CATs controllers. To this end, first an agent-based traffic simulation of the study area is carried out. Then six different scenarios are conducted to find the best A-CATs controller that is robust enough against different traffic disruptions. We observe that the A-CATs controller based on radial basis function networks (RBF (5)) outperforms others. This controller is benchmarked against controllers of discrete state Q-learning, Bayesian Q-learning, fixed time and actuated controllers; and the results reveal that it consistently outperforms them.

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Dried Nerium oleander leaves at single lethal dose of 110 mg/kg body weight were administered orally to six native male sheep. Clinical signs of toxicosis in sheep began to appear about 30 min after receiving the oleander and included decrease of the heart rate followed by cardiac pauses and tachyarrhythmias; ruminal atony, mild to moderate tympany, abdominal pain, polyuria and polakiuria. Electrocardiography revealed bradycardia, atrio-ventricular blocks, depression of S-T segments, ventricular premature beats and tachycardia, and ventricular fibrillation. Five sheep died within 4-12 h and one survived. At necropsy there were varying degrees of haemorrhages in different organs and gastroenteritis. Histopathological examination of tissue sections revealed myocardial degeneration and necrosis, degeneration and focal necrosis of hepatocytes, necrosis of tubular epithelium in kidneys, oedema in the lungs, and ischemic changes in the cerebrum.

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The prevalence of Anaplasma infection was studied in cattle, sheep, and goats in the Mashhad area from 1999 to 2002. A total of 160 cattle from 32 farms and 391 sheep and 385 goats from 77 flocks were clinically examined for the presence of Anaplasma spp. in blood smears. The study revealed that 19.37% of cattle were infected with Anaplasma marginale and 80.3% of sheep and 38.92% of goats were infected with Anaplasma ovis. Prevalence of Anaplasma infection between male and female and between different age groups of cattle, sheep, and goats were statistically nonsignificant. Seasonally, the prevalence of Anaplasma infection in sheep and goats reached its highest level in summer, while a decrease was observed in autumn, and reached the lowest level in winter. The seasonal prevalence of Anaplasma infection in cattle was not significantly different. Symptomatic cases were not observed in any of the cattle, sheep, and goats. The ranges of anaplasmatemia in infected cattle, sheep, and goats were 0.005-0.5%, 0.01-3%, and 0.01-3%, respectively.

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