Flud: A Hybrid Crowd–Algorithm Approach for Visualizing Biological Networks

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

Determination of Biomass Power Plant Location Using GIS-Based Heuristic Methods: A Case Study in Turkey

Biomass conversion to bioenergy has always been necessary to ensure the most efficient use of the limited biomass resource and enable economic viability. Evaluating biomass transportation cost, electricity transmission cost and heat transferring cost between power plant location/s and supply/demand points and selection of an optimum power plant capacity is an important issue for a robust supply chain design. In this study, we employed designing optimum biomass to the bioenergy supply chain for agricultural activities using Geographic Information System and Simulated Annealing algorithm to overcome a real-world problem in Bismil District of Diyarbakır/Turkey. Our goal is to define a potential investment location/s on the trigeneration system by comparing the trade-offs between the raw material/end-product transportation costs and facility/s and pipeline installation costs. To determine possible locations for power plants, distance matrices were retrieved from suitable candidate power plant locations and agricultural parcel, settlement and the nearest high voltage electricity line from the Geographic Information System. The results showed that establishing one power plant is feasible. The net present value of a potential investment is almost 260 million Euros and the re-payment period is 1.33 years.

Genetic Diversity and Structure of Core Collection of Huangqi (Astragalus) Developed by Genomic Simple Sequence Repeat Markers

Huangqi (Astragalus) is a versatile herb that possesses several therapeutic effects against a variety of diseases, especially lung diseases. The aim of this study was to establish a core collection of Astragalus germplasm resources based on molecular 10 SSR markers. Based on 380 samples of Astragalus collected from different areas, five different methods were utilized to construct the core collection of Astragalus, including PowerCore-based M strategy, CoreFinder-based M strategy, Core Hunter-based stepwise sampling, PowerMarker-based simulated annealing algorithm based on allele maximization, and PowerMarker-based simulated annealing algorithm based on maximizing genetic diversity. Of the constructed Astragalus core collections, the CoreFinder-based M strategy was found to be the most suitable approach as it reserved all the alleles and most of the genetic diversity parameters were higher than those of the initial collection. Additional analyses demonstrated that the genetic diversity of the core collection matched the properties of the initial collection. Further, the phylogenetic trees indicated that the population structure of the core collection was similar to that of the initial collection. In addition, our results showed that the optimal grouping value of K was 2. The construction of a core collection is beneficial for the understanding, management, and utilization of Astragalus. Moreover, this study will act as a valuable reference for constructing core collections for other plants or fungi.

Optimization of Cooperative Offloading Model with Cost Consideration in Mobile Edge Computing

The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.); therefore, we proposed a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost; and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

Optimization of Emergency Transportation Organization of Holiday Tourism Traffic

The purpose of the optimization of holiday traffic emergency traffic organization is to solve the problem of serious traffic jams in holiday scenic spots. Based on the prediction of traffic volume and traffic mode division in the future years of the scenic spot, the traffic accident route is analyzed to provide theoretical support for the emergency traffic organization and planning of the scenic spot. This article takes the Shijiazhuang Jinta Bay scenic area as the research object, based on the traffic volume of the Jinta Bay tourist scenic area from 2009 to 2016, analyzes the traffic environment of the scenic area, predicts the traffic demand, and builds a one-way traffic organization double-layer optimization model. The simulated annealing algorithm is used to solve the model, an emergency transportation organization optimization plan is formulated, and the feasibility of the plan is verified through VISSIM simulation. The results of the study show that the one-way traffic organization method reduces the average vehicle delay by 32.2% and the average queue length by 14.5%. The one-way traffic organization based on branch diversion can more effectively solve the main road jamming and congestion caused by traffic accidents, prevent the occurrence of secondary accidents, and reduce the economic losses of scenic area managers. At the same time, the purpose of ensuring the tourist quality of tourists and the economic interests of scenic spot management departments is ensured.