General network design: A unified view of combined location and network design problems

This paper presents a unified framework for the general network design problem which encompasses several classical problems involving combined location and network design decisions. In some of these problems the service demand relates users and facilities, whereas in other cases the service demand relates pairs of users between them, and facilities are used to consolidate and re-route flows between users. Problems of this type arise in the design of transportation and telecommunication systems and include well-known problems such as location-network design problems, hub location problems, extensive facility location problems, tree-star location problems and cycle-star location problems, among others. Relevant modeling aspects, alternative formulations and possible algorithmic strategies are presented and analyzed.     

Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem

We consider the problem of restoring services provided by infrastructure systems after an extreme event disrupts them. This research proposes a novel integrated network design and scheduling problem that models these restoration efforts. In this problem, work groups must be allocated to build nodes and arcs into a network in order to maximize the cumulative weighted flow in the network over a horizon. We develop a novel heuristic dispatching rule that selects the next set of tasks to be processed by the work groups. We further propose families of valid inequalities for an integer programming formulation of the problem, one of which specifically links the network design and scheduling decisions. Our methods are tested on realistic data sets representing the infrastructure systems of New Hanover County, North Carolina in the United States and lower Manhattan in New York City. These results indicate that our methods can be used in both real-time restoration activities and long-term scenario planning activities. Our models are also applied to explore the effects on the restoration activities of aligning them with the goals of an emergency manager and to benchmark existing restoration procedures.     

Selling to customers with both Veblen and network effects

For the products that provide not only intrinsic value from their functions but also stylish consumption experience, there often exist both Veblen and network effects. Some customers are more likely to purchase the product if fewer customers can afford it, while others might appreciate the existence of more peers. Focusing on these products, we study the market equilibrium under rational expectations. The optimal pricing and quantity decisions reveal interesting insights about the effects of such mixed consumption externalities.     

A unified network performance measure with importance identification and the ranking of network components

In this paper, we propose the first network performance measure that can be used to assess the efficiency of a network in the case of either fixed or elastic demands. Such a measure is needed for many different applications since only when the performance of a network can be quantifiably measured can the network be appropriately managed. Moreover, as we demonstrate, the proposed performance measure, which captures flow information and behavior, allows one to determine the criticality of various nodes (as well as links) through the identification of their importance and ranking. We present specific networks for which the performance/efficiency is computed along with the importance rankings of the nodes and links. The new measure can be applied to transportation networks, supply chains, financial networks, electric power generation and distribution networks as well as to the Internet and can be used to assess the vulnerability of a network to disruptions.     

An unfeasibility view of neural network learning

We define the notion of a continuously differentiable perfect learning algorithm for multilayer neural network architectures and show that such algorithms do not exist provided that the length of the data set exceeds the number of involved parameters and the activation functions are logistic, tanh or sin.     

A neural fuzzy control system with structure and parameter learning

A general connectionist model, called neural fuzzy control network (NFCN), is proposed for the realization of a fuzzy logic control system. The proposed NFCN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The NFCN can be constructed from supervised training examples by machine learning techniques, and the connectionist structure can be trained to develop fuzzy logic rules and find membership functions. Associated with the NFCN is a two-phase hybrid learning algorithm which utilizes unsupervised learning schemes for structure learning and the backpropagation learning scheme for parameter learning. By combining both unsupervised and supervised learning schemes, the learning speed converges much faster than the original backpropagation algorithm. The two-phase hybrid learning algorithm requires exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a reinforcement neural fuzzy control network (RNFCN) is further proposed. The RNFCN is constructed by integrating two NFCNs, one functioning as a fuzzy predictor and the other as a fuzzy controller. By combining a proposed on-line supervised structure-parameter learning technique, the temporal difference prediction method, and the stochastic exploratory algorithm, a reinforcement learning algorithm is proposed, which can construct a RNFCN automatically and dynamically through a reward-penalty signal (i.e., “good” or “bad” signal). Two examples are presented to illustrate the performance and applicability of the proposed models and learning algorithms.     

Free trial or no free trial: Optimal software product design with network effects

A common business strategy to promote product adoption in software industry is to provide a free trial version with limited functionalities of the commercial product to increase the installed user base. The increase of user base will lead to higher value of the software because of positive network effects. However, offering a free trial version may cannibalize some demand of the commercial software. This paper examines the tradeoff between network effects and the cannibalization effect, and aims to uncover the conditions under which firms should introduce the free trial product. We find that when network intensity is strong, it is more profitable for a software monopoly to offer free trial than to segment the market with two versions of different qualities. In addition, this paper solves the joint decision problem of finding the optimal quality for the firm’s free trial software and the optimal price of its commercial product.     

Bayesian network approach to multinomial parameter learning using data and expert judgments

One of the hardest challenges in building a realistic Bayesian Network (BN) model is to construct the node probability tables (NPTs). Even with a fixed predefined model structure and very large amounts of relevant data, machine learning methods do not consistently achieve great accuracy compared to the ground truth when learning the NPT entries (parameters). Hence, it is widely believed that incorporating expert judgments can improve the learning process. We present a multinomial parameter learning method, which can easily incorporate both expert judgments and data during the parameter learning process. This method uses an auxiliary BN model to learn the parameters of a given BN. The auxiliary BN contains continuous variables and the parameter estimation amounts to updating these variables using an iterative discretization technique. The expert judgments are provided in the form of constraints on parameters divided into two categories: linear inequality constraints and approximate equality constraints. The method is evaluated with experiments based on a number of well-known sample BN models (such as Asia, Alarm and Hailfinder) as well as a real-world software defects prediction BN model. Empirically, the new method achieves much greater learning accuracy (compared to both state-of-the-art machine learning techniques and directly competing methods) with much less data. For example, in the software defects BN for a sample size of 20 (which would be considered difficult to collect in practice) when a small number of real expert constraints are provided, our method achieves a level of accuracy in parameter estimation that can only be matched by other methods with much larger sample sizes (320 samples required for the standard machine learning method, and 105 for the directly competing method with constraints).     

The network structure and systemic risk in the global non-life insurance market

This paper contributes to the literature on systemic risk by assessing the systemic importance of insurers in the global non-life insurance market. First, we estimate the bilateral reinsurance claims matrix using the aggregate outstanding reinsurance data from ISIS and theoretically analyze the interconnectedness in the global reinsurance network using network indicators. The robustness of the estimated matrix is fully assured by sensitivity analysis. Second, we theoretically analyze the contagious defaults introducing the Eisenberg–Noe framework. Reinsurers play a dominant role in the reinsurance network and most of them are included in our data sample. The network analysis finds that some reinsurers with large centrality measures are central in the hierarchical structure of the network. The default analysis shows the occurrences of many stand-alone defaults and only one contagious default via the global reinsurance network after the global financial crisis. In addition, one stress test based on a hypothetical severe stress scenario predicts a few occurrences of contagious defaults in the future. It follows from these analyses that systemic risk via the global reinsurance network is relatively restricted in the global non-life insurance market. In conclusion, our methodology would help supervisory authorities develop an assessment approach for interconnectedness in the global reinsurance network and aid the implementation of insurer stress tests for default contagion.     

Two-agent scheduling with position-based deteriorating jobs and learning effects

Scheduling with deteriorating jobs and learning effects has been widely studied. However, multi-agent scheduling with simultaneous considerations of deteriorating jobs and learning effects has hardly been considered until now. In view of this, we consider a two-agent single-machine scheduling problem involving deteriorating jobs and learning effects simultaneously. In the proposed model, given a schedule, we assume that the actual processing time of a job of the first agent is a function of position-based learning while the actual processing time of a job of the second agent is a function of position-based deterioration. The objective is to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. We develop a branch-and-bound and several simulated annealing algorithms to solve the problem. Computational results show that the proposed algorithms are efficient in producing near-optimal solutions.     

Families,experiments, and nature: Learning science through project-based learning

This study brings together the research focused on science education through project-based learning (PBL). This learning project was carried out in a rural learning community and an attempt was made to adapt to the natural resources of the area by organizing educational outings, experimental activities, and encouraging the participation of families. The overall objective is to test the effectiveness of applying the PBL teaching methodology for learning science in a rural learning community. The methodology used has been qualitative, specifically, the participating research has been used and the information has been compiled in a field notebook. The results show that the didactic proposal had good results; showing that, in conclusion, science teaching today should be inclined toward more innovative educational methodologies such as PBL.     

Measuring agility of networked organizational structures via network entropy and mutual information

While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.     

Inspecting the foundations of claims about cognitive demand and student learning: A citation analysis of Stein and Lane (1996)

Educational research communities bear responsibility for establishing a substantial body of evidence to support claims that drive the field. For example, one commonly accepted claim is that there is a relationship between the cognitive demand of mathematical task enactments and students’ learning. One study that is often cited in association with this claim is Stein and Lane (1996), and in 44% of those citations, Stein and Lane (1996) is the sole reference. Citation analysis reveals that many of these claims go beyond the warrants provided by the Stein and Lane study, either by granting more confidence in the relationship than the study design allows or by phrasing the claim as a causal relationship between cognitive demand and student learning. A few other studies are occasionally cited in conjunction with Stein and Lane (1996) and are summarized in this article, but there remains a need for replication studies to provide better empirical support for claims about cognitive demand and student learning and to refine our shared understanding.     

Statistical learning control of uncertain systems: theory and algorithms

It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some “solved” control problems is prohibitive. Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can “approximately” answer these questions “most of the time”, and have “high confidence” in the correctness of the answers.     

The effect of the 4MAT method (learning styles and brain hemispheres) of instruction on achievement in mathematics

The purpose of this study was to determine the efficiency of 4MAT method of instruction in which learning style and cerebral hemispheres are taken into account in teaching the binary operation and its properties in mathematics. The sample of this study comprised 58 ninth grade students in two separate classes in a high school. One of the classes was selected as the experimental group in which 4MAT method of instruction was used; and the other class was selected as the control group in which the traditional teaching was given, and this selection was performed randomly. The data have been obtained primarily from three scales, namely ‘mathematical knowledge test’, ‘mathematical attitude scale’ and ‘knowledge test on binary operation and its properties’. It has been determined that 4MAT method of instruction was more efficient than the traditional method in teaching of the binary operation subject in mathematics.     

Single-commodity robust network design problem: Complexity,instances and heuristic solutions

We study a single-commodity Robust Network Design problem (RND) in which an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. In each scenario, a subset of the nodes is exchanging flow. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. Previously conducted computational investigations on the problem motivated the study of the complexity of some special cases and we present complexity results on them, including hypercubes. In turn, these results lead to the definition of new instances (random graphs with {−1, 0, 1} balances) that are computationally hard for the natural flow formulation. These instances are then solved by means of a new heuristic algorithm for RND, which consists of three phases. In the first phase the graph representing the network is reduced by heuristically deleting a subset of the arcs, and a feasible solution is built. The second phase consists of a neighborhood search on the reduced graph based on a Mixed-Integer (Linear) Programming (MIP) flow model. Finally, the third phase applies a proximity search approach to further improve the solution, taking into account the original graph. The heuristic is tested on the new instances, and the comparison with the solutions obtained by Cplex on a natural flow formulation shows the effectiveness of the proposed method.     

Impact of computer animations in cognitive learning: differentiation

In mathematic courses, construction of some concepts by the students in a meaningful way may be complicated. In such circumstances, to embody the concepts application of the required technologies may reinforce learning process. Onset of learning process over daily life events of the student's environment may lure their attention and may enable them to gain from the preliminary knowledge. Therefore, a good initiation may be realized in the course of meaningful learning. The underlying meaning of the abstract concepts by computer animations may be accomplished in class environments. That study is conducted searching out to discover the effects of animations over the learning process in mathematic courses. The study was performed over the 58 university freshman students selected randomly. Thirty-two students constituted the experiment group and 26 students constituted the control group. Computer animations-aided instruction model in constructive form were applied on the experiment group and non-computer-aided instruction model in constructive form were implemented on the control group. Student academic success via a test method developed by explored group with confidence rate .819 (Cronbach's alpha) revealed that data were evaluated by two-way variance analyses. The findings provided from the final test shows that the experiment group students were significantly higher according to the control group students in terms of academic success average scores. Computer animations were observed to be significant to assimilate the derivative concept in a discrete way over the students, to appeal their attention, animations of real life events observed to transform the abstract meanings in the events to a concrete manner. Students of whom the concrete stage is constructed meaningfully found to be tactful in reaching to semi-abstract and abstract stages.     

Reducing mosquito-borne disease outbreak size: The relative importance of contact and transmissibility in a network model

The complex biological and environmental factors involved in the transmission of mosquito-borne diseases in humans have made their control elusive in many instances. Conceptual models contribute to gain insight and help to reduce the risk of taking poor managerial decisions. The focus of this paper is to compare, using a contact network model, the impact that perturbation of the number infectious contacts and of transmissibility have on the size of an outbreak. We illustrate the analysis on a contact network parametrized with data that associates humans and the mosquito Culex quinquefasciatus, a vector for lymphatic filariasis. The model suggests that, if the values corresponding to transmissibility and number of infectious contacts is relatively large, variations in the size of an outbreak are significantly in favor of control measures to reduce infectious contacts.     

组织学习能力的个性优势识别与评价研究

组织学习能力是个多维概念,定期识别在哪个维度上具备个性优势并在此基础上对组织学习能力状况做出评价将有利于促进组织绩效的提升。本文按照目标规划的思想,依据2-范数距离,提出一种组织学习能力的个性优势识别方法,该方法从尊重每一位被评价者的意见出发识别其个性优势特征,并在此基础上给出综合评价结论。在此基础上以国内某大型软件企业的20个事业部为例进行了应用研究,组织学习能力由发现能力、发明能力、选择能力、执行能力、推广能力、反思能力、获取知识能力、输出知识能力、建立知识库能力九个维度组成。研究结果表明,基于个性优势识别的组织学习能力评价方法符合“人本管理”思想,易于被评价主体接受,且具有客观性的特点,同时可以产生标杆效应。     

Rule learning: Ordinal prediction based on rough sets and soft-computing

This work promotes a novel point of view in rough set applications: rough sets rule learning for ordinal prediction is based on rough graphical representation of the rules. Our approach tackles two barriers of rule learning. Unlike in typical rule learning, we construct ordinal prediction with a mathematical approach, rough sets, rather than purely rule quality measures. This construction results in few but significant rules. Moreover, the rules are given in terms of ordinal predictions rather than as unique values. This study also focuses on advancing rough sets theory in favor of soft-computing. Both theoretical and a designed architecture are presented. The features of our proposed approach are illustrated using an experiment in survival analysis. A case study has been performed on melanoma data. The results demonstrate that this innovative system provides an improvement of rule learning both in computing performance for finding the rules and the usefulness of the derived rules.