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Carbon-capture-and-storage (CCS) is an important technology to reduce CO2 emissions. A commercial method to establish the CCS on a large scale is to sequestrate CO2 in depleted oil reservoirs and to combine it with enhanced oil recovery (EOR) operations. In this way, the CO2 emission is reduced, as well as the oil production increases. The joint CCS-EOR planning problem determines the optimum allocation of existing CO2 to depleted reservoirs and the scheduling of the EOR operations. This paper presents a deterministic MIP model which is a modification of an existing model in the literature. Then, this model is extended to a two-stage stochastic model in which the parameters expressing the initial oil yields and the periodic depletion factor of oil yields associated with reservoirs are uncertain, and the uncertainty is realized as soon as the operation of the reservoir is started. Our stochastic model is computationally more efficient than the existing model in the literature, due to the reduction of binary variables, as well as the absence of “non-anticipativity constraints”. Instead, our stochastic model is less realistic. The proposed models are examined over two case studies taken from the literature. The obtained results confirm the higher effectiveness of our stochastic model. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the electroencephalographic profile of elite and non-elite basketball players seconds before and during the basketball free throw. Sixteen male subjects in the elite group (national team/premier league players with an average age of 22.06 ± 1.56) and 16 male non-elite subjects (university players with an average age of 22.37 ± 1.45) voluntarily participated in this research. Electroencephalographic data were measured from 28 cortical areas using a mobile wireless device. ANOVA with repeated measures were also performed to investigate the characteristics of theta, alpha, and beta frequency bands. The findings showed the higher cortical activity of the elite group. Different frequency bands exhibited similar asymmetry patterns, suggesting the higher activity of the left hemisphere in most of the homologous sites. Moreover, the activity of frequency bands in the left hemisphere rose by approaching the moment of throw. Furthermore, the activity of a limited number of right hemisphere sites increased by getting closer to the moment of action. In general, hemispheric asymmetry in favor of the left hemisphere has a cortical pattern, reflecting high-performance activities. In addition, the characteristics of different frequency bands of hemispheres are directed toward increasing cognitive processing, attention focusing, and inhibiting irrelevant information. [ABSTRACT FROM AUTHOR]

Summary: The central nervous system receives a vast amount of sensory inputs, and it should be able to discriminate and recognize different kinds of multisensory information. Winner‐take‐all (WTA) consists of a simple recurrent neural network carrying out discrimination of input signals through competition. This paper presents a real‐time scalable digital hardware implementation of the spiking WTA network. The need for concurrent computing, real‐time performance, proper accuracy, and the reconfigurable device has led to the field‐programmable gate array (FPGA) as the target hardware platform. A set of techniques is employed to lessen memory and resource usage. The proposed architecture consists of multiprocessing elements, which share hardware resources between a specific number of neurons. We introduce a novel connectivity array for neurons (dedicated to the WTA network) to cut down memory usage. Also, a multiplier‐less method in the neuron model and a novel tree adder in the synapse processing unit are designed to improve computational efficiency. The proposed network simulates 4,500 neurons in real time on a Xilinx Artix‐7 FPGA, while a scalable architecture facilitates the implementation of up to 20,000 neurons on this device. The pipeline structure can guarantee real‐time performance for large‐scale networks. Based on simulation and physical synthesis results, the presented network mimics biological WTA dynamics and consumes efficient hardware resources. [ABSTRACT FROM AUTHOR]

Introduction: The present study was designed to investigate the effect of visual and skill training on learning forehand drive in table tennis and motor-perceptual abilities (reaction time, coincidence-anticipation timing, eye-hand coordination, and depth perception). Materials and Methods: Forty volunteer female students (Mean±SD age: 21.50±0.78 years) were selected and randomly assigned to one of four groups (each group had 10 participants): visual and tennis training group, visual training group, tennis training group, and control group. Motor perceptual abilities (reaction time, coincidence-anticipation timing, eye-hand coordination, and depth perception) and forehand drive performance were measured before and after the training period, and also after 24 h retention period. After the pretest, including the accuracy of the kicks test for assessment of forehand drive in table tennis and motorperceptual test, the experimental groups underwent four weeks (three sessions per week) of visual training, table tennis forehand training, or both. The control group followed their normal daily life for the whole study period. Then, they participated in the posttest and 24 h later in the retention test of kick accuracy. Results: The results revealed that visual and table tennis training, visual training, and table tennis training had a significant effect on the reaction time (P=0.001), coincidence-anticipation timing (P=0.001) and eye-hand coordination (error time) (P=0.01). Moreover, visual and tennis training and table tennis training had a significant effect on the acquisition (P=0.001) and retention of forehand drive (P=0.005). Besides, the post hoc LSD (Least Significant Difference) test showed that visual and tennis training had a more significant impact on the learning forehand drive. Visual training and tennis training had a significant effect on eye-hand coordination (number of errors). The three types of training programs were not effective in the depth of perception. Conclusion: Visual training can be used as a supplementary program in the athletes' training schedule. [ABSTRACT FROM AUTHOR]

Human intelligence relies on the vast number of neurons and their interconnections that form a parallel computing engine. If we tend to design a brain-like machine, we will have no choice but to employ many spiking neurons, each one has a large number of synapses. Such a neuronal network is not only compute-intensive but also memory-intensive. The performance and the configurability of the modern FPGAs make them suitable hardware solutions to deal with these challenges. This paper presents a scalable architecture to simulate a randomly connected network of Hodgkin-Huxley neurons. To demonstrate that our architecture eliminates the need to use a high-end device, we employ the XC7A200T, a member of the mid-range Xilinx Artix®-7 family, as our target device. A set of techniques are proposed to reduce the memory usage and computational requirements. Here we introduce a multi-core architecture in which each core can update the states of a group of neurons stored in its corresponding memory bank. The proposed system uses a novel method to generate the connectivity vectors on the fly instead of storing them in a huge memory. This technique is based on a cyclic permutation of a single prestored connectivity vector per core. Moreover, to reduce both the resource usage and the computational latency even more, a novel approximate two-level counter is introduced to count the number of the spikes at the synapse for the sparse network. The first level is a low cost saturated counter implemented on FPGA lookup tables that reduces the number of inputs to the second level exact adder tree. It, therefore, results in much lower hardware cost for the counter circuit. These techniques along with pipelining make it possible to have a high-performance, scalable architecture, which could be configured for either a real-time simulation of up to 5120 neurons or a large-scale simulation of up to 65536 neurons in an appropriate execution time on a cost-optimized FPGA. [ABSTRACT FROM AUTHOR]