Research on Optimal Matching Scheme of Public Resource Management Based on the Computational Intelligence Model

The management of public resources means that people’s governments at all levels and other public administrative subjects should use certain means and methods, follow certain principles, rationally allocate and utilize public resources, and maximize their functions and benefits. Under the background of limited human resources, this study adheres to the principle of maximizing the benefits of human resources and rationally allocates the use of human resources. In this study, this kind of resource allocation problem is regarded as a linear programming problem by specifying the benefit function, and then, genetic algorithm, ant colony algorithm, and hybrid genetic-ant colony algorithm are used to solve the problem; the cost and time consumption of different algorithms under different scales are evaluated. Finally, it is found that genetic algorithm is superior to ant colony algorithm when the task scale is small and the effect of genetic algorithm is lower than ant colony algorithm with the expansion of task scale, whereas the improved hybrid genetic-ant colony algorithm is better than ordinary algorithm in general.

Analyzing the Impact of Agrophotovoltaic Power Plants on the Amenity Value of Agricultural Landscape: The Case of the Republic of Korea

Recently, in Korea, there have been some disturbing events forcing a trade-off between the expansion of agrophotovoltaic (APV) power plants and the agricultural policy to enhance the public benefit function of agriculture. Under this context, this study attempts to examine the public perception of agricultural landscape and the APV power plants and to analyze the impact of APV power plants on the amenity value of the agricultural landscape. The results of the analysis based on the choice experiment method shows that the marginal willingness-to-pay for a rural tourism accommodation with a ‘agricultural landscape view’ is USD 64.37 higher compared to ‘agrophotovoltaics panel view.’ This implies that the value of the agricultural landscape decreases when solar panels are installed on farmland, signifying the detrimental impact of the APV power plants on the multi-functionality of agriculture. If the installation of APVs is expanded to farmlands nationwide, the amenity value of agricultural landscape is estimated to decrease by USD 1.70 billion or 55.0% of the total estimated amenity value in Korea.

A benefit function for community detection based on edge and path of length two

In the field of community detection in complex networks, the most commonly used approach to this problem is the maximization of the benefit function known as “modularity”. In this study, it is found that the path of length two have the similar property as the edge, which is denser within communities and sparser between different communities. In order to take both edge and path of length two into consideration simultaneously, a self-loop is added to each node of the network and a novel benefit function has been defined. To divide the network into two communities, a second eigenvector method is proposed based on maximization of our new benefit function. Experimental results obtained by applying the method to karate club network and dolphin social network show the feasibility of our benefit function and the effectiveness of our algorithm.

Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users post many messages, because they aim at being recognized as influencers, typically on specific topics. How a user can discover influencers concerned with her/his interest? Micro-blog apps and web sites lack a functionality to recommend users with influencers, on the basis of the content of posted messages. In this paper, we envision such a scenario and we identify the problem that constitutes the basic brick for developing a recommender of (possibly influencer) users: training a classification model by exploiting messages labeled with topical classes, so as this model can be used to classify unlabeled messages, to let the hidden topic they talk about emerge. Specifically, the paper reports the investigation activity we performed to demonstrate the suitability of our idea. To perform the investigation, we developed an investigation framework that exploits various patterns for extracting features from within messages (labeled with topical classes) in conjunction with the mostly-used classifiers for text classification problems. By means of the investigation framework, we were able to perform a large pool of experiments, that allowed us to evaluate all the combinations of feature patterns with classifiers. By means of a cost-benefit function called “Suitability”, that combines accuracy with execution time, we were able to demonstrate that a technique for discovering topics from within messages suitable for the application context is available.

Production and Pricing Strategies of Energy-Saving Products in the Presence of Duopolistic Manufacturers

This paper considers optimal production and pricing strategies of energy-saving products in the presence of duopolistic manufacturers. First, we analyze the free competition case by a Bertrand game. A sufficient condition for guaranteeing the existence and the uniqueness of the equilibrium solution is proposed. The change rate of the benefit function of environment with regard to purchasing preference proportions is examined. Second, we investigate the case in the presence of energy-saving incentive. A two-layer decision model is constructed by considering the decision order of each participant. Optimal strategies between the two cases are compared. We provide theoretical foundations for the government to formulate policies of energy-saving incentive under a financial budget constraint. Finally, a numerical example is presented to verify the obtained conclusions and make some supplements.

Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening

We review simple methods for evaluating 4 types of biomarkers. First, we discuss the evaluation of surrogate endpoint biomarkers (to shorten a randomized trial) using 2 statistical and 3 biological criteria. Second, we discuss the evaluation of prognostic biomarkers (to predict the risk of disease) by comparing data collection costs with the anticipated net benefit of risk prediction. Third, we discuss the evaluation of predictive markers (to search for a promising subgroup in a randomized trial) using a multivariate subpopulation treatment effect pattern plot involving a risk difference or responders-only benefit function. Fourth, we discuss the evaluation of cancer screening biomarkers (to predict cancer in asymptomatic persons) using methodology to substantially reduce the sample size with stored specimens.

Formation of Stable and Efficient Social Storage Cloud

In this paper, we study the formation of endogenous social storage cloud in a dynamic setting, where rational agents build their data backup connections strategically. We propose a degree-distance-based utility model, which is a combination of benefit and cost functions. The benefit function of an agent captures the expected benefit that the agent obtains by placing its data on others’ storage devices, given the prevailing data loss rate in the network. The cost function of an agent captures the cost that the agent incurs to maintain links in the network. With this utility function, we analyze what network is likely to evolve when agents themselves decide with whom they want to form links and with whom they do not. Further, we analyze which networks are pairwise stable and efficient. We show that for the proposed utility function, there always exists a pairwise stable network, which is also efficient. We show that all pairwise stable networks are efficient, and hence, the price of anarchy is the best that is possible. We also study the effect of link addition and deletion between a pair of agents on their, and others’, closeness and storage availability.

An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks

Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.

An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover

In this paper, the Minimum Cost Submodular Cover problem is studied, which is to minimize a modular cost function such that the monotone submodular benefit function is above a threshold. For this problem, an evolutionary algorithm EASC is introduced that achieves a constant, bicriteria approximation in expected polynomial time; this is the first polynomial-time evolutionary approximation algorithm for Minimum Cost Submodular Cover. To achieve this running time, ideas motivated by submodularity and monotonicity are incorporated into the evolutionary process, which likely will extend to other submodular optimization problems. In a practical application, EASC is demonstrated to outperform the greedy algorithm and converge faster than competing evolutionary algorithms for this problem.

A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks

In wireless multi-hop networks, instead of using the traditional store-and-forward method, the relay nodes can exploit the network coding idea to encode and transmit the packets in the distributed coding-aware routing (DCAR) mechanisms, which can decrease the transmission number and achieve higher throughput. However, depending on the primary coding conditions of DCAR, the DCAR-type schemes may not only detect more coding opportunities, but also lead to an imbalanced distribution of the network load. Especially, they are not energy efficient in more complex scenarios, such as wireless ad-hoc networks. In this paper, to solve these shortcomings, we propose a constrained coding-aware routing (CCAR) mechanism with the following benefits: (1) by the constrained coding conditions, the proposed mechanism can detect good coding opportunities and assure a higher decoding probability; (2) we propose a tailored “routing + coding” discovery process, which is more lightweight and suitable for the CCAR scheme; and (3) by evaluating the length of the output queue, we can estimate the states of coding nodes to improve the efficient coding benefit. To those ends, we implement the CCAR scheme in different topologies with the ns-2 simulation tool. The simulation results show that a higher effective coding benefit ratio can be achieved by the constrained coding conditions and new coding benefit function. Moreover, the CCAR scheme has significant advantages regarding throughput, average end-to-end delay, and energy consumption.