Water requirement and yield of fig trees under different drip irrigation management.

This work aimed to study the effect of drip irrigation management on growth and yield of the 'Roxo de Valinhos' fig tree (Ficus carica L.), at three years old, and to determine crop coefficients (Kc) and its water requirement (ETc) under Baixada Fluminense climate and soil conditions, state of Rio de Janeiro, Brazil. The study was carried out in the experimental area of SIPA (Sistema Integrado de Produção Agroecológica) in Seropédica, Rio de Janeiro State, from July 2011 to May 2012. The experimental area was divided in two blocks, named B1 (sandy clay loam texture) and B2 (loamy sand texture). In each block, irrigation frequencies (IF) of two (T1) and four days (T2) were evaluated, as well as the irrigation absence (T3). Irrigation management and water consumption determination were performed through the soil water balance, using the TDR technique. Plant growth was not affected by IF, differing only in the number of produced internodes. For both soil textures, the mean Kc was 0.60, with a significant difference (p<0.05) only for IF. The estimated mean yield showed no significant differences between both textural classes, ranging from 6,612 kg ha-1 (T3) to 8,554 kg ha-1 (T1). This study indicates the importance of irrigation frequency in the irrigation management of fig trees cultivated in soils with different physical characteristics

Activated Random Walkers: Facts, Conjectures and Challenges

We study a particle system with hopping (random walk) dynamics on the integer lattice $\mathbb Z^d$. The particles can exist in two states, active or inactive (sleeping); only the former can hop. The dynamics conserves the number of particles; there is no limit on the number of particles at a given site. Isolated active particles fall asleep at rate $\lambda > 0$, and then remain asleep until joined by another particle at the same site. The state in which all particles are inactive is absorbing. Whether activity continues at long times depends on the relation between the particle density $\zeta$ and the sleeping rate $\lambda$. We discuss the general case, and then, for the one-dimensional totally asymmetric case, study the phase transition between an active phase (for sufficiently large particle densities and/or small $\lambda$) and an absorbing one. We also present arguments regarding the asymptotic mean hopping velocity in the active phase, the rate of fixation in the absorbing phase, and survival of the infinite system at criticality. Using mean-field theory and Monte Carlo simulation, we locate the phase boundary. The phase transition appears to be continuous in both the symmetric and asymmetric versions of the process, but the critical behavior is very different. The former case is characterized by simple integer or rational values for critical exponents ($\beta = 1$, for example), and the phase diagram is in accord with the prediction of mean-field theory. We present evidence that the symmetric version belongs to the universality class of conserved stochastic sandpiles, also known as conserved directed percolation. Simulations also reveal an interesting transient phenomenon of damped oscillations in the activity density

Prediction techniques on FPGA for latency reduction on tactile internet

Tactile Internet (TI) is a new internet paradigm that enables sending touch interaction information and other stimuli, which will lead to new human-to-machine applications. However, TI applications require very low latency between devices, as the system’s latency can result from the communication channel, processing power of local devices, and the complexity of the data processing techniques, among others. Therefore, this work proposes using dedicated hardware-based reconfigurable computing to reduce the latency of prediction techniques applied to TI. Finally, we demonstrate that prediction techniques developed on field-programmable gate array (FPGA) can minimize the impacts caused by delays and loss of information. To validate our proposal, we present a comparison between software and hardware implementations and analyze synthesis results regarding hardware area occupation, throughput, and power consumption. Furthermore, comparisons with state-of-the-art works are presented, showing a significant reduction in power consumption of ≈1300× and reaching speedup rates of up to ≈52×

A hybrid MPPT algorithm based on DE-IC for photovoltaic systems under partial shading conditions

This paper presents a hybrid maximum power point tracking (MPPT), which combines a metaheuristic algorithm and a traditional MPPT method applied in a photovoltaic system operating under partial shading conditions. The MPPTs based on traditional methods are not able to track the global maxi-mum power point (GMPP) when partial shadings occur. Thus, MPPT algorithms based on metaheuristic algorithms, which are used for global optimization, have presented efficiency to extract the maximum power from photovoltaic arrays. However, these methods are random, resulting in large power oscillations in transients of small variations in solar irradiance. Therefore, this paper proposes the metaheuristic algorithm called Differential Evolution (DE) to seek and track the GMPP. After the DE convergence, the MPPT algorithm is switched to Incremental Conductance (IC) in order to refine the tracking. The effectiveness of the algorithm is proved through simulation results. Furthermore, comparative analyses are provided for each algorithm (DE and IC) to evaluate their performances in the PV system