A Contagion Model of Emergency Airplane Evacuations

Motivated by the Asiana Flight 214 crash in San Francisco this summer, this project focuses on modeling an emergency airplane evacuation. Our models are based on the Particle Swarm Optimization (PSO) algorithm, where each agent\u27s position is compared to a fitness function that describes the current environment. Each agent moves according to its knowledge of its own previous best position and the group\u27s current best position. The static environment is modeled by a potential function that describes the layout of the airplane that includes the exits and physical barriers such as the seats. We model the interactions within the swarm by an attraction-repulsion force. Finally, we chose to incorporate the spread of an emotion such as fear or panic that influences the behavior of agents within the swarm. Our project includes an analysis of how the parameters and scaling of different parts of the model affect the swarm behavior. We also compared simulations with and without fear to study the impact of emotion on individual behavior as well as the ability of the entire group to safely exit the aircraft. We hope that this will lead to increased understanding of how panicked crowds behave in evacuation situations and that this will lead to better, safer evacuation designs

Downlink Rate Analysis for Virtual-Cell Based Large-Scale Distributed Antenna Systems

Despite substantial rate gains achieved by coordinated transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which, as recently demonstrated in \cite{Dai_TWireless}, could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few closest base-station (BS) antennas to form its virtual cell, that is, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity

Low-Complexity Beam Allocation for Switched-Beam Based Multiuser Massive MIMO Systems

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave (mmWave) frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K logN). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N=K

Sled-pull Training Improves Maximal Horizontal Velocity in Collegiate Male and Female Soccer Players

The force velocity profile (FvP), which details the capacity to sprint and accelerate, is a determinant of success in soccer. To date, no data exist that details the FvP of male and female collegiate Division I soccer players. Further, there is limited insight on how training interventions may modify the FvP of either males or females. PURPOSE: The aim of this investigation was to compare FvP between collegiate male and female athletes and assess the efficacy of a 12-week sled pull training intervention. METHODS: 17 male (20.17 ± 1.38 yrs) and 12 female (19.75 ± 1.05 yrs) soccer players participated in a 12-week sled pull training intervention. FvP was measured prior, during, and after training using a 30m sprint to assess maximal horizontal force (F0), maximal horizontal speed (V0), and maximal power output (Pmax). RESULTS: The intervention improved 30m sprint times of men by 11.86% (pre: 4.35 ± 0.17s, post: 4.27 ± 0.17, p0 in both men (pre: 7.98 ± 0.36 m/s, post: 8.09 ± 0.35 m/s, p0 or Pmax. CONCLUSION: This is the first study to detail FvP in both male and female collegiate soccer players. A 12-week sled pull training intervention improves 30m sprint times and V0 in both male and female collegiate athletes, but does not improve F0 and Pmax. Thus, the sled pull intervention should be modified or paired with other training that specifically targets force and power development

Isolation, Purification, Identification and Hypolipidemic Activity of Lipase Inhibitory Peptide from Chlorella pyrenoidosa

In this study, pancrelipase inhibitory peptides (PES) from an enzymatic protein hydrolysate of Chlorella pyrenoidosa were isolated and purified by ultrafiltration and Sephadex gel chromatography. The in vivo hypolipidemic activity of PES was evaluated by fat deposition and the levels of triglyceride (TG) and total cholesterol (TC) in Caenorhabditis elegans fed a high sugar diet. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify the peptide sequence of PES, and molecular docking was used to select potential pancreatic lipase inhibitory peptides, and the pancreatic lipase inhibitory activity of the synthesized peptides was verified. The results showed that PES had good hypolipidemic activity at a concentration of 1 mg/mL; it inhibited lipid deposition by 22.5%, and reduced the levels of TG and TC by 27.4% and 29.4%, respectively. In total, 999 peptides were identified, and four potential lipase inhibitory peptides were obtained. Among them, FLGPF had the best inhibitory effect on pancreatic lipase, with an inhibition rate of 50.12% at 8 mg/mL. The inhibition was reversible and non-competitive, with an inhibition constant of 5.23 mg/mL. Molecular docking showed that FLGPF could better bind to human pancreatic triacylglycerol lipase (PTL) via π-hydrogen, π-cation and hydrogen bond interactions. This study can provide a theoretical reference for the development and utilization of C. pyrenoidosa protein-derived hypolipidemic peptide

A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

Conventionally, the resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to be obtained in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever-increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of current scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then, the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive multiple-input-multiple-output (MIMO) systems shows that the proposed machine-learning based resource allocation outperforms conventional methods