Multistable behavior above synchronization in a locally coupled Kuramoto model

A system of nearest neighbors Kuramoto-like coupled oscillators placed in a ring is studied above the critical synchronization transition. We find a richness of solutions when the coupling increases, which exists only within a solvability region (SR). We also find that they posses different characteristics, depending on the section of the boundary of the SR where the solutions appear. We study the birth of these solutions and how they evolve when {K} increases, and determine the diagram of solutions in phase space.Comment: 8 pages, 10 figure

Increased CO₂ loss from vegetated drained lake tundra ecosystems due to flooding

Tundra ecosystems are especially sensitive to climate change, which is particularly rapid in high northern latitudes resulting in significant alterations in temperature and soil moisture. Numerous studies have demonstrated that soil drying increases the respiration loss from wet Arctic tundra. And, warming and drying of tundra soils are assumed to increase CO2 emissions from the Arctic. However, in this water table manipulation experiment (i.e., flooding experiment), we show that flooding of wet tundra can also lead to increased CO2 loss. Standing water increased heat conduction into the soil, leading to higher soil temperature, deeper thaw and, surprisingly, to higher CO2 loss in the most anaerobic of the experimental areas. The study site is located in a drained lake basin, and the soils are characterized by wetter conditions than upland tundra. In experimentally flooded areas, high wind speeds (greater than ~4 m s−1) increased CO2 emission rates, sometimes overwhelming the photosynthetic uptake, even during daytime. This suggests that CO2 efflux from C rich soils and surface waters can be limited by surface exchange processes. The comparison of the CO2 and CH4 emission in an anaerobic soil incubation experiment showed that in this ecosystem, CO2 production is an order of magnitude higher than CH4 production. Future increases in surface water ponding, linked to surface subsidence and thermokarst erosion, and concomitant increases in soil warming, can increase net C efflux from these arctic ecosystems

Mean-field analysis of the majority-vote model broken-ergodicity steady state

We study analytically a variant of the one-dimensional majority-vote model in which the individual retains its opinion in case there is a tie among the neighbors' opinions. The individuals are fixed in the sites of a ring of size $L$ and can interact with their nearest neighbors only. The interesting feature of this model is that it exhibits an infinity of spatially heterogeneous absorbing configurations for $L \to \infty$ whose statistical properties we probe analytically using a mean-field framework based on the decomposition of the $L$-site joint probability distribution into the $n$-contiguous-site joint distributions, the so-called $n$-site approximation. To describe the broken-ergodicity steady state of the model we solve analytically the mean-field dynamic equations for arbitrary time $t$ in the cases n=3 and 4. The asymptotic limit $t \to \infty$ reveals the mapping between the statistical properties of the random initial configurations and those of the final absorbing configurations. For the pair approximation ($n=2$) we derive that mapping using a trick that avoids solving the full dynamics. Most remarkably, we find that the predictions of the 4-site approximation reduce to those of the 3-site in the case of expectations involving three contiguous sites. In addition, those expectations fit the Monte Carlo data perfectly and so we conjecture that they are in fact the exact expectations for the one-dimensional majority-vote model

Processing ANN Traffic Predictions for RAN Energy Efficiency

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed

Processing conditions and properties of continuous fiber reinforced GF/PP thermoplastic matrix composites manufactured from different pre-impregnated materials

The aim of the present work was to study and compare the processing conditions and final mechanical properties of continuous glass-fiber reinforced polypropylene composites (GF/PP) manufactured by using available thermoplastic preimpregnated materials produced by different methods.To assess the quality of the three different GF/PP pre-impregnated materials, final manufactured composite parts were submitted to mechanical testing and microscopy analysis. The obtained properties were compared between each other and to those theoretical ones that can be predicted by using the Classical amination Theory (CLT).Fundação para a Ciência e a Tecnologia (FCT

Medial dorsal cutaneous nerve entrapment following inversion ankle sprain

The medial dorsal cutaneous nerve is one of the terminal branches of the superficial peroneal nerve that provides sensory innervation to the dorsum of the foot. It may be prone to injury by direct blow, iatrogenic surgical lesion or in rare situations secondary to ankle sprains. The authors report a case of persistent ankle pain in a female patient caused by a post- traumatic compressive neuropathy of the medial dorsal cutaneous nerve secondary to an ankle sprain which was successfully surgically treated with complete resolution of the symptoms

Metaheuristic Approaches for Hydropower System Scheduling

This paper deals with the short-term scheduling problem of hydropower systems. The objective is to meet the daily energy demand in an economic and safe way. The individuality of the generating units and the nonlinearity of their efficiency curves are taken into account. The mathematical model is formulated as a dynamic, mixed integer, nonlinear, nonconvex, combinatorial, and multiobjective optimization problem. We propose two solution methods using metaheuristic approaches. They combine Genetic Algorithm with Strength Pareto Evolutionary Algorithm and Ant Colony Optimization. Both approaches are divided into two phases. In the first one, to maximize the plant’s net generation, the problem is solved for each hour of the day (static dispatch). In the second phase, to minimize the units’ switching on-off, the day is considered as a whole (dynamic dispatch). The proposed methodology is applied to two Brazilian hydroelectric plants, in cascade, that belong to the national interconnected system. The nondominated solutions from both approaches are presented. All of them meet demand respecting the physical, electrical, and hydraulic constraints