Enriching the tactical network design of express service carriers with fleet scheduling characteristics

Express service carriers provide time-guaranteed deliveries of parcels via a network consisting of nodes and hubs. In this, nodes take care of the collection and delivery of parcels, and hubs have the function to consolidate parcels in between the nodes. The tactical network design problem assigns nodes to hubs, determines arcs between hubs, and routes parcels through the network. Afterwards, fleet scheduling creates a schedule for vehicles operated in the network. The strong relation between flow routing and fleet scheduling makes it difficult to optimise the network cost. Due to this complexity, fleet scheduling and network design are usually decoupled. We propose a new tactical network design model that is able to include fleet scheduling characteristics (like vehicle capacities, vehicle balancing, and drivers’ legislations) in the network design. The model is tested on benchmark data based on instances from an express provider, resulting in significant cost reductions.     

Adaptive behaviour of fishers to external perturbations: simulation of the Tasmanian rock lobster fishery

The rock lobster, Jasus edwardsii, lies on a global “hotspot” for climate change in the southeastern Australian state of Tasmania. The short-term effects of climate change are predicted to lead to an increasing exploitable biomass in the south and declining biomass in the north of the state. The future of the fishery is highly uncertain due to climate change, but also due to insecurities linked to the market conditions. The market for Tasmanian rock lobster is driven by the demand of a single market, China, which absorbs 75 % of the catch. This study examines how fishers can adapt to external perturbations that affect the social and economic viability of the fleet and the ecological dynamics of the stock. Three fleet dynamic models of increasing complexity are used to investigate the effects of climate change and lobster price changes on the fishery. There could be local depletion leading to negative short-term profit for the fleet if it is static and the proportion of the total catch taken in each region of the fishery does not respond to climate-induced-changes. Better outcomes would occur if the fleet adapts dynamically to environmental conditions, and fishing effort follows stock abundance, which would counter-act the short-term effects of climate change. Only a model with explicit representation of economic drivers can fully capture the local economic and social impacts of large scale global perturbations.     

Electric Motorcycles in Thailand: A Life Cycle Perspective

Battery electric motorcycles offer the potential to reduce greenhouse gas emissions and fossil fuel consumption in road transportation, but result in problem shifting when considering potential environmental impacts during vehicle production and disposal. This study evaluates the life cycle environmental impacts of implementing lithium‐ion and lead‐acid battery electric motorcycles in Thailand's motorcycle fleet to meet the government's motorcycle energy reduction target of 2,791 kilotonnes oil equivalent, compared to conventional motorcycles. A stock‐turnover model is used to determine the market growth of electric motorcycles on the fleet from 2015 to 2030 to meet the energy reduction target. The total costs of ownership of each motorcycle are analyzed on a single vehicle basis to compare the cost benefits. The results of the study reveal that the environmental performance of an electric motorcycle fleet is sensitive to variations in use‐phase energy consumption, the electricity mix consumed, and battery disposal scenarios. Realization of Thailand's Power Development Plan can reduce total impacts of the electric motorcycle fleet to global warming by 6% to 10%. It is crucial that batteries from electric motorcycles are recycled to avoid 98% of impacts to toxicity. Lead‐acid battery electric motorcycles are a more affordable option for consumers in Thailand compared to conventional motorcycles and lithium‐ion battery electric motorcycles. Recommendations are made to improve the environmental performance of electric motorcycle implementation strategies in Thailand.     

CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×.     

The Ecuadorian Artisanal Fishery for Large Pelagics: Species Composition and Spatio-Temporal Dynamics

The artisanal fisheries of Ecuador operate within one of the most dynamic and productive marine ecosystems of the world. This study investigates the catch composition of the Ecuadorian artisanal fishery for large pelagic fishes, including aspects of its spatio-temporal dynamics. The analyses of this study are based on the most extensive dataset available to date for this fishery: a total of 106,963 trip-landing inspection records collected at its five principal ports during 2008 ‒ 2012. Ecuadorian artisanal fisheries remove a substantial amount of biomass from the upper trophic-level predatory fish community of the eastern tropical Pacific Ocean. It is estimated that at least 135 thousand metric tons (mt) (about 15.5 million fish) were landed in the five principal ports during the study period. The great novelty of Ecuadorian artisanal fisheries is the “oceanic-artisanal” fleet component, which consists of mother-ship (nodriza) boats with their towed fiber-glass skiffs (fibras) operating with pelagic longlines. This fleet has fully expanded into oceanic waters as far offshore as 100°W, west of the Galapagos Archipelago. It is estimated that nodriza operations produce as much as 80% of the total catches of the artisanal fishery. The remainder is produced by independent fibras operating in inshore waters with pelagic longlines and/or surface gillnets. A multivariate regression tree analysis was used to investigate spatio-environmental effects on the nodriza fleet (n = 6,821 trips). The catch species composition of the nodriza fleet is strongly influenced by the northwesterly circulation of the Humboldt Current along the coast of Peru and its associated cold waters masses. The target species and longline gear-type used by nodrizas change seasonally with the incursion of cool waters (< 25°C) from the south and offshore. During this season, dolphinfish (Coryphaena hippurus) dominates the catches. However, in warmer waters, the fishery changes to tuna-billfish-shark longline gear and the catch composition becomes much more diverse.     

An experimental design framework for Markovian gene regulatory networks under stationary control policy

Background

A fundamental problem for translational genomics is to find optimal therapies based on gene regulatory intervention. Dynamic intervention involves a control policy that optimally reduces a cost function based on phenotype by externally altering the state of the network over time. When a gene regulatory network (GRN) model is fully known, the problem is addressed using classical dynamic programming based on the Markov chain associated with the network. When the network is uncertain, a Bayesian framework can be applied, where policy optimality is with respect to both the dynamical objective and the uncertainty, as characterized by a prior distribution. In the presence of uncertainty, it is of great practical interest to develop an experimental design strategy and thereby select experiments that optimally reduce a measure of uncertainty.

Results

In this paper, we employ mean objective cost of uncertainty (MOCU), which quantifies uncertainty based on the degree to which uncertainty degrades the operational objective, that being the cost owing to undesirable phenotypes. We assume that a number of conditional probabilities characterizing regulatory relationships among genes are unknown in the Markovian GRN. In sum, there is a prior distribution which can be updated to a posterior distribution by observing a regulatory trajectory, and an optimal control policy, known as an “intrinsically Bayesian robust” (IBR) policy. To obtain a better IBR policy, we select an experiment that minimizes the MOCU remaining after applying its output to the network. At this point, we can either stop and find the resulting IBR policy or proceed to determine more unknown conditional probabilities via regulatory observation and find the IBR policy from the resulting posterior distribution. For sequential experimental design this entire process is iterated. Owing to the computational complexity of experimental design, which requires computation of many potential IBR policies, we implement an approximate method utilizing mean first passage times (MFPTs) – but only in experimental design, the final policy being an IBR policy.

Conclusions

Comprehensive performance analysis based on extensive simulations on synthetic and real GRNs demonstrate the efficacy of the proposed method, including the accuracy and computational advantage of the approximate MFPT-based design.

     

Recent Advances in Intervention in Markovian Regulatory Networks

Markovian regulatory networks constitute a class of discrete state-space models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therapeutic strategies. The salient translational goal is to design therapeutic strategies that desirably modify network dynamics via external signals that vary the expressions of a control gene. The objective of an intervention strategy is to reduce the likelihood of the pathological cellular function related to a disease. The task of finding an effective intervention strategy can be formulated as a sequential decision making problem for a pre-defined cost of intervention and a cost-per-stage function that discriminates the gene-activity profiles. An effective intervention strategy prescribes the actions associated with an external signal that result in the minimum expected cost. This strategy in turn can be used as a treatment that reduces the long-run likelihood of gene expressions favorable to the disease. In this tutorial, we briefly summarize the first method proposed to design such therapeutic interventions, and then move on to some of the recent refinements that have been proposed. Each of these recent intervention methods is motivated by practical or analytical considerations. The presentation of the key ideas is facilitated with the help of two case studies.Key Words: Regulatory networks, markovian decision processes, translational genomics, systems biology.     

Nonlinear costs and optimal fleet capacity in deterministic and stochastic fisheries

This paper presents a bioeconomic model for determining optimal fisheries investment strategies in both deterministic and stochastic environments. An analytic approach is followed; formulas are derived to describe the effects of depreciation, discounting, and randomness on the optimal fleet capacity, for the general case of nonlinear fishing costs as well as the less complex linear case. To accomplish this, fish stock dynamics are simplified, by assuming that current stock sizes are independent of past escapements. The extremity of this assumption is lessened through the use of an “opportunity cost of escapement” penalty function to account subjectively for the de facto cost of low escapements.     

Classification of Different Tympanic Membrane Conditions Using Fused Deep Hypercolumn Features and Bidirectional LSTM

Objectives: Middle ear inflammatory diseases are global health problem that can have serious consequences such as hearing loss and speech disorders. The high cost of medical devices such as oto-endoscope and oto-microscope used by the specialists for the diagnosis of the disease prevents its widespread use. In addition, the decisions of otolaryngologists may differ due to the subjective visual examinations. For this reason, computer-aided middle ear disease diagnosis systems are needed to eliminate subjective diagnosis and high cost problems. To this aim, a hybrid deep learning approach was proposed for automatic recognition of different tympanic membrane conditions such as earwax plug, myringosclerosis, chronic otitis media and normal from the otoscopy images.Materials and methods: In this study we used public Ear Imagery dataset containing 880 otoscopy images. The proposed approach detects keypoints from the otoscopy images and following the obtained keypoint positions, extracts hypercolumn deep features from 5 different layers of the VGG 16 model. Classification of tympanic membrane conditions were realized by feeding the deep hypercolumn features to Bi-LSTM network in the form of non-time related data.Results: The performance of the proposed model was evaluated in three different color spaces as Red-Green-Blue (RGB), Hue-Saturation-Value (HSV) and Haematoxylin-Eosin-Diaminobenzidine (HED). The proposed model achieved acceptable results in all color spaces, moreover it showed a very successful performance in classifying tympanic membrane conditions especially in RGB space. Experimental studies showed that the proposed model achieved Acc of 99.06%, Sen of 98.13% and Spe of 99.38%.Conclusion: As a result, a robust model with high sensitivity was obtained for classification of tympanic membrane conditions and it was shown that Bi-LSTM network, which is generally used with time-related data, could also be used successfully with non-time related data for diagnosis of tympanic membrane conditions.     

Systematic Reverse Engineering of Network Topologies: A Case Study of Resettable Bistable Cellular Responses

A focused theme in systems biology is to uncover design principles of biological networks, that is, how specific network structures yield specific systems properties. For this purpose, we have previously developed a reverse engineering procedure to identify network topologies with high likelihood in generating desired systems properties. Our method searches the continuous parameter space of an assembly of network topologies, without enumerating individual network topologies separately as traditionally done in other reverse engineering procedures. Here we tested this CPSS (continuous parameter space search) method on a previously studied problem: the resettable bistability of an Rb-E2F gene network in regulating the quiescence-to-proliferation transition of mammalian cells. From a simplified Rb-E2F gene network, we identified network topologies responsible for generating resettable bistability. The CPSS-identified topologies are consistent with those reported in the previous study based on individual topology search (ITS), demonstrating the effectiveness of the CPSS approach. Since the CPSS and ITS searches are based on different mathematical formulations and different algorithms, the consistency of the results also helps cross-validate both approaches. A unique advantage of the CPSS approach lies in its applicability to biological networks with large numbers of nodes. To aid the application of the CPSS approach to the study of other biological systems, we have developed a computer package that is available in Information S1.     

A Low Dimensional Approach on Network Characterization

In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.     

A New Tradeoff Model for Fuzzy Supply Chain Network Design and Optimization

This article proposes a novel mixed integer linear programming model for solving a fuzzy supply chain network (SCN) design problem. This problem includes fuzzy parameters, choosing suppliers according to their quality of raw materials, and the supplier's engagement contracts. There is a tradeoff between raw material quality, and its purchasing and reprocessing costs. If a decision-maker (DM) wishes to work with a supplier that supplies a low-quality raw material, this raw material may be in need of reprocessing. To avoid the reprocessing costs, a supplier that provides a high-quality raw material can be chosen, but in this case the DM faces a high purchasing cost. An integrated fuzzy SCN system that consists of multiple suppliers, manufacturers, distribution centers, and retailers is considered in order to address problems under the aforementioned tradeoffs. Finally, concluding remarks and suggestions for future work are presented.     

西北太平洋柔鱼渔业生物经济模型及其管理策略

西北太平洋柔鱼是中国大陆、日本及中国台湾鱿钓船队共同开发的主要经济头足类之一,如何兼顾鱿钓船队的渔业利益,并综合考虑生态效益、经济效益和社会效益,科学制定渔业管理目标是确保柔鱼资源可持续利用的重要研究内容。根据中国大陆、日本及中国台湾鱿钓船队的渔获统计数据及相关经济参数,构建基于多船队的生物经济模型,模拟了50年内(1997—2047年)各捕捞船队开发柔鱼资源过程中的动态变化,并探讨了在10种不同的管理方案下各船队捕捞努力量、柔鱼资源量、渔获产量及其渔业利润随时间的动态变化。结果表明,在所有设计方案下,随着捕捞努力量的动态变化,资源量在前10a均呈急剧下降趋势,随后经长期波动后逐渐趋于稳定并维持在较低水平。从长期效益来看,方案9(提高中国台湾船队捕捞系数)的累计产量及累计利润中等,但能兼顾各国或地区船队间的经济利益和社会效益,可建议以方案9作为柔鱼渔业的参考管理目标;从柔鱼资源保护角度来看,方案4(提高中国大陆船队作业成本)的资源量保持最好,可以对方案4进行适当的优化作为参考管理目标,即提高中国大陆船队作业成本,同时适当降低日本船队的可捕系数,可确保柔鱼资源量保持在更高的水平上。     

Alternative operation models for using a feed‐in terminal as a part of the forest chip supply system for a CHP plant

The fuel supply of forest chips has to adapt to the annual fluctuations of power and heat generation. This creates inefficiency and unbalances the capacity utilization of the fuel supply fleet in the direct fuel supplies from roadside storages to power and heat generation. Terminals can offer an alternative approach for the fleet management of fuel supplies in terms of smoothing the unbalanced fleet use towards more even year‐round operations. The aim of the study was to compare the supply costs of a conventional direct forest chip supply to an alternative fuel supply with the use of a feed‐in terminal using the discrete‐event simulation method. The influences of the terminal location, terminal investment cost, outbound terminal transport method, terminal truck utilization and quality changes of terminal‐stored forest chips for the fuel supply cost were studied in the case environment. By introducing a feed‐in terminal and a shuttle truck for the transports of terminal‐stored forest chips, the total supply cost was 1.4% higher than the direct fuel supply scenario. In terminal scenarios, the supply costs increased 1–2% if the cost of the terminal investment increased 30%, the distance to the terminal increased from 5 to 30 km or the total annual use of a terminal truck decreased 1500 h. Moreover, a 1 per cent point per month increase in the dry matter loss of terminal‐stored chips increased the total supply cost 1%. The study revealed that with the relatively low additional cost, the feed‐in terminal can be introduced to the conventional forest chip supply. Cost compensation can be gained through the higher annual use of a fuel supply fleet and more secured fuel supply to power plants by decreasing the need for supplement fuel, which can be more expensive at a time of the highest fuel demand.     

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.     

Metabolic Pathfinding Using RPAIR Annotation

Metabolic databases contain information about thousands of small molecules and reactions, which can be represented as networks. In the context of metabolic reconstruction, pathways can be inferred by searching optimal paths in such networks. A recurrent problem is the presence of pool metabolites (e.g., water, energy carriers, and cofactors), which are connected to hundreds of reactions, thus establishing irrelevant shortcuts between nodes of the network. One solution to this problem relies on weighted networks to penalize highly connected compounds. A more refined solution takes the chemical structure of reactants into account in order to differentiate between side and main compounds of a reaction. Thanks to an intensive annotation effort at KEGG, decompositions of reactions into reactant pairs (RPAIR) categorized by their role (main, trans, cofac, ligase, and leave) are now available.The goal of this article is to evaluate the impact of RPAIR data on pathfinding in metabolic networks. To this end, we measure the impact of different parameters concerning the construction of the metabolic network: mapping of reactions and reactant pairs onto a graph, use of selected categories of reactant pairs, weighting schemes for compounds and reactions, removal of highly connected metabolites, and reaction directionality. In total, we tested 104 combinations of parameters and identified their optimal values for pathfinding on the basis of 55 reference pathways from three organisms.The best-performing metabolic network combines the biochemical knowledge encoded by KEGG RPAIR with a weighting scheme penalizing highly connected compounds. With this network, we could recover reference pathways from Escherichia coli with an average accuracy of 93% (32 pathways), from Saccharomyces cerevisiae with an average accuracy of 66% (11 pathways), and from humans with an average accuracy of 70% (12 pathways). Our pathfinding approach is available as part of the Network Analysis Tools.     

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.     

A PR-quadtree based multi-dimensional indexing for complex query in a cloud system

The state-of-the-art indexing mechanisms for distributed cloud data management systems can not support complex queries, such as multi-dimensional query and range query. To solve this problem, we propose a multi-dimensional indexing mechanism named PR-Chord to support complex queries. PR-Chord is composed of the global index named PR-Index and the Chord network. The multi-dimensional space formed by the range of the multi-dimensional data is divided into hyper-rectangle spaces equally. The PR-Index is a hierarchical index structure based on the improved PR quadtree to index these spaces. The complex query is transformed into the query of leaf nodes of PR-Index. We design the algorithms of query, insertion and deletion to support complex queries. Since PR-Index does not store the multi-dimensional data, its maintenance cost is zero. PR-Chord has the advantages of load balancing and simple algorithm. The experiment results demonstrate that PR-Chord has good query efficiency.