Massive Search Research Articles

An innovative four-layer heuristic for scheduling multi-mode projects under multiple resource constrains

In this paper, an innovative four-layer heuristic is presented for scheduling multi-mode projects under multiple resource constraints. For this purpose, a biased-random sampling technique, a local search, a decomposition method, and an evolutionary search mechanism, each in a separate layer, are combined, with each layer passing its output to the next layer for improvement. The procedure has been designed based on the fact that what makes the scheduling of multi-mode projects hard to solve is a massive search space of modes compounded with the starting times of activities. That is why the procedure is aimed at balancing explorationversusexploitation in searching a massive search space. On the one hand, it exploits promising areas further and, on the other hand, it searches unexplored areas for expanding its range. Since the first layer provides an initial solution, and each of the other three layers can either improve the result of its previous layer or keep it unchanged, solutions never deteriorate and hence promising areas are exploited. Moreover, unexplored areas are searched effectively because each layer explores solution space differently than its previous layer. Based on whether or not an improvement each layer can make to the result of its previous layer, the effect of the corresponding layer on the performance of the procedure has been measured.

Parameter estimation in models of biological oscillators: an automated regularised estimation approach

BackgroundDynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i.e. massive search spaces), (ii) convergence to local optima (due to multimodality of the cost function), (iii) overfitting (fitting the noise instead of the signal) and (iv) a lack of identifiability. As a consequence, the use of standard estimation methods (such as gradient-based local ones) will often result in wrong solutions. Overfitting can be particularly problematic, since it produces very good calibrations, giving the impression of an excellent result. However, overfitted models exhibit poor predictive power.Here, we present a novel automated approach to overcome these pitfalls. Its workflow makes use of two sequential optimisation steps incorporating three key algorithms: (1) sampling strategies to systematically tighten the parameter bounds reducing the search space, (2) efficient global optimisation to avoid convergence to local solutions, (3) an advanced regularisation technique to fight overfitting. In addition, this workflow incorporates tests for structural and practical identifiability.ResultsWe successfully evaluate this novel approach considering four difficult case studies regarding the calibration of well-known biological oscillators (Goodwin, FitzHugh–Nagumo, Repressilator and a metabolic oscillator). In contrast, we show how local gradient-based approaches, even if used in multi-start fashion, are unable to avoid the above-mentioned pitfalls.ConclusionsOur approach results in more efficient estimations (thanks to the bounding strategy) which are able to escape convergence to local optima (thanks to the global optimisation approach). Further, the use of regularisation allows us to avoid overfitting, resulting in more generalisable calibrated models (i.e. models with greater predictive power).

Understanding the community interest of breast cancer in Indonesia: a digital epidemiology study using Google trends

Introduction. Breast cancer was count for 30.5% of all cancers diagnosed in Indonesia. Although preventable, breast cancer is mostly diagnosed in advanced stages and caused leading dead among females. Given the increasing growth of online information seeking behavior, adequate cancer health promotion through virtual setting is needed to tackle the massive increasing burden of breast cancer. Therefore this study aims to explore the community interest in breast cancer using Google Trends. Method. 5 years ( from September 2013 to August 2018) information searches for breast cancer from Google were retrieved in Indonesian language. Data were downloaded at national and sub-region level to examine the pattern and distribution of queries. Results. Sporadic traces of information searches related to breast cancer from Google Trends presented the pattern and distribution of queries. Massive search happened in July 2015 and June 2017 following the died of Indonesian celebrity who suffered from breast cancer. However, the cancer awareness month every October does not impact the number of information searches. Considering the high influence of celebrities, many studies reveal the positive impact of celebrities involvement in health promotion. Celebrity can attract the public attention to health messages and increase the agreement of vaccination and screening for cancer. Thereby, celebrities involvement and availability of qualified breast cancer online information should be increased to win the breast cancer health promotion program in the digital era. Online information related to breast cancer could be disseminated through targetted risk population using Mobile JKN that has been downloaded by 1.5 million members. Conclusion. Google Trends could be potentially used as a novel tool to measure the dynamics of community interest in breast cancer in Indonesia. Therefore, adequate cancer health promotion through a virtual setting is the key to tackling the massive increasing burden of breast cancer in the digital era.

Depression and its Main Determinants Among Iranian Operating Room Personnel: A Systematic Review and Meta-Analysis

Background: Most nurses, especially operating room personnel, seems to be more likely to be affected by mood disorders than other social strata. The present study attempted to systematically review the prevalence of depression and its main determinants among operating room personnel in Iran. Methods: The method of this systematic review is documenting in a published protocol in the International Prospective Register of Systematic Reviews (PROSPERO) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. After this massive search, titles and abstracts of retrieved documents have screened and all irrelevant articles excluded. Two reviewers screened the documents and selected all relevant studies and assessed included articles separately. Results: Totally, 12 citations found in the initial literature search where four citations excluded, as they did not meet the inclusion criteria. The final number of studies available for analysis was 12 including a total of 373 operating room personnel (86 men and 287 women, mean the age of 27.71 years ranged from 20 to 36 years). The pooled prevalence of depression among operating room personnel was estimated to be 45.3%. In this regard, 27.0% of personnel suffered from severe depression. A significant heterogeneity found in the overall analysis of the overall prevalence of depression and its severe pattern. Conclusion: A notable number of operating room personnel in Iran suffer from depression even in its severe condition emphasizing the importance of the managerial approach to minimize its adverse effects on their performance as well as to improve their quality of life

A likelihood-free Bayesian derivation method for service variants

Application programming interfaces (API), allowing systems to be accessed by the services they expose, have proliferated on the Internet and gained strategic interest in the IT industry. However, integration opportunities for larger, enterprise systems are hampered by complex and overloaded operations of their interfaces, having hundreds of parameters and multiple levels of nesting, corresponding to multiple business entities. Static (code) analysis techniques have been proposed to analyse service interfaces of enterprise systems. They support the derivation of business entities and relationships from the parameters of interface operations, allowing the restructure of operations, based on individual entities. In this paper, we extend the repertoire of static interface analysis to derive service variants, whereby subsets of operation parameters correspond to multiple nested business entity subtypes of variants. Specifically, we apply a Monte Carlo sampling method, based on likelihood-free Bayesian sampling, to traverse large parameter spaces, based on higher probabilistic tree search, to efficiently find subsets of parameters related to prospective subtypes. The results demonstrate a method with significant success rates in massive search spaces, as applied to the FedEx Shipment interface whose operations have in excess of 1000 parameters.

Image Compression Based on Mapping Image Fractals to Rational Numbers

The advent of the Internet era and advances in telecommunications has seen image compression attract a great deal of research. Enhancements to the quality and ratio of image compression have been achieved through approaches, such as neural networks and discrete transforms. However, other heuristic and bio-inspired methods, such as genetic algorithms, are still in the developmental stages. In this paper, we introduce a new image compression mechanism that exploits the relationship between rational numbers and their corresponding quotient representation. Each sub-image is mapped to a fractional number based on its RGB representation, and this fraction is then reduced to an efficient quotient. The appeal of using genetic algorithms is explained by the massive search needed to find a close fraction that can be reduced to a short quotient. We enhance the search by pre-calculating all possible rational numbers for a given set of numerators and denominators. Experimental results show that a considerable compression ratio can be achieved when the least significant bits of each byte are altered. Hence, the image quality is preserved while achieving a high compression ratio.

Systematic Search for Chemical Reactions in Gas Phase Contributing to Methanol Formation in Interstellar Space.

A massive search for chemical routes leading to methanol formation in gas phase has been conducted using computational chemistry, at the CBS-QB3 level of theory. The calculations were performed at five different temperatures (100, 80, 50, 20, and 10 K) and at three pressures (0.1, 0.01, and 0.001 atm) for each temperature. The search was focused on identifying reactions with the necessary features to be viable in the interstellar medium (ISM). A searching strategy was applied to that purpose, which allowed to reduce an initial set of 678 possible reactions to a subset of 11 chemical routes that are recommended, for the first time, as potential candidates for contributing to methanol formation in the gas phase of the ISM. They are all barrier-less, and thus they are expected to take place at collision rates. Hopefully, including these reactions in the currently available models, for the gas-phase methanol formation in the ISM, would help improving the predicted fractional abundance of this molecule in dark clouds. Further investigations, especially those dealing with grain chemistry and electronic excited states, would be crucial to get a complete picture of the methanol formation in the ISM.

Toward continuous pattern detection over evolving large graph with snapshot isolation

This paper studies continuous pattern detection over large evolving graphs, which plays an important role in monitoring-related applications. The problem is challenging due to the large size and dynamic updates of graphs, the massive search space of pattern detection and inconsistent query results on dynamic graphs. This paper first introduces a snapshot isolation requirement, which ensures that the query results come from a consistent graph snapshot instead of a mixture of partial evolving graphs. Second, we propose an SSD (single sink directed acyclic graph) plan friendly to vertex-centric-distributed graph processing frameworks. SSD plan can guide the message transformation and transfer among graph vertices, and determine the satisfaction of the pattern on graph vertices for the sink vertex. Third, we devise strategies for major steps in the SSD evaluation, including the location of valid messages to achieve snapshot isolation, AO-List to determine the satisfaction of transition rule over dynamic graph, and message-on-change policy to reduce outgoing messages. The experiments on billion-edge graphs using Giraph, an open source implementation of Pregel, illustrate the efficiency and effectiveness of our method.

Abstract A17: Assessing the similarity and dissimilarity between primary and metastatic melanoma using gene expression data

Abstract The Cancer Genome Atlas (TCGA) consortium measured genome-wide gene expression using RNA-seq for 336 skin cutaneous melanoma (SKCM) samples among which 272 were clinically classified as metastatic SKCM tumors and the remaining 64 as primary SKCM tumors. We aimed to identify gene signatures that separate the primary SKCM from the metastatic SKCM samples. Our initial analysis showed that the primary and metastatic SKCM samples shared enough similarity at the gene expression level so that misclassification rates were unacceptably high. We reasoned that some of the primary SKCM tumors plausibly might have evolved to resemble the metastatic tumors in their gene expression. This idea led us to propose an alternative computational method to find a gene signature for accurate classification but does so while making explicit allowance for allegiance switching moving samples from one group to the other, e.g., primary to metastatic or vice versa. Based on an iterative stochastic search algorithm that delivers nearly optimal gene signatures for classification, our alternative algorithm is rooted in the groups defined by clinical classification but allows for switching between groups when a sample is clearly discordant with other group members based on its gene expression profile. We began by seeking such near-optimal partitioning of the 336 samples into the primary and metastatic groups based on the gene expression data using the clinical classification as the guide/basis. Specifically, our algorithm gives each of the 336 samples a small but equal probability to be switched to the other group at each iteration (e.g., from metastatic to primary, or vice versa). We carried out a massive computational search for gene signatures (a set of 20 genes) that provide a near optimal partitioning of the groups while keeping the clinical classification for most of the 336 samples but reassigning a few to the other group. Distinguishing between the newly re-assigned primary and metastatic partitioning now possible based on gene expression data. The search carried out 5,000 independent runs of our alternative stochastic search algorithm to generate 5,000 near-optimal gene signatures and 5,000 sets of near-optimal partitioning of the groups. By examining how often a sample was assigned to the primary and metastatic groups, we could estimate the proportion of runs where the sample was classified as a primary or metastatic SKCM tumor. We found that nearly all the clinically classified metastatic tumors were consistently assigned to the metastatic tumor group in 90-100% of the runs whereas the clinically classified primary SKCM tumors were often reassigned to the metastatic tumor group in proportions ranging from 2% to 80%. This result suggests that the gene expression profiles of many primary tumors resemble those of metastatic tumors to various degrees. Gene ontology analysis of the 500 most frequently selected genes (those appearing most frequently in the 5,000 gene signatures) suggested that the top-ranked genes are enriched in ectoderm and epidermis development, epithelia and epidermal cell differentiation, kerationization, and regulation of inflammatory and defense response. In summary, we have developed a unique computational method that not only assesses the relevance of genes in sample classification but also classifies each sample probabilistically to uncover the true tumor status. Our analysis may provide useful information for treatment and disease management. Citation Format: Yuanyuan Li, Juno Krahn, Leping Li. Assessing the similarity and dissimilarity between primary and metastatic melanoma using gene expression data. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Melanoma: From Biology to Therapy; Sep 20-23, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(14 Suppl):Abstract nr A17.

Matching Research of Binary Image Based on Canny Operator and SIFT Algorithm

A matching method based on Canny operator and SIFT algorithm for binary image is proposed. The Canny operator was applied on source image to detect edge and generate binary image. SIFT algorithm was used to match and recognize the binary images. The results of experiment show that the method we proposed ,under the premise of exact recognition, not only hold the great mass of key information, but also reduce the volume of data in source image. This method has good application prospects in massive image search ,terrain matching and so on.

By All Means, Prosper Giant Economies

Multinationals have grown dramatically in the last two decades, reflecting rapid post-war economic growth and technological advancements. Multinational Companies (MNC's) are making substantial efforts to succeed by launching massive search for raw material and exploring new markets for their products; shifting the relative economic power of major industrial countries. Multinational Companies (MNC's) assert worldwide influence over other business entities and even on governments of the host countries. This research studies the contribution of multinationals towards national exchequer of the third world countries and deliberates upon the manner in which the MNC's understandably watch over their vested interests. The concern of the researcher is to ascertain the impact of the TNC's on the prosperity of the society, and that on the material and spiritual dimensions of the consumer personality. The scholar suggests measures that need to be taken to safeguard interests of both the giants and the pygmies. This research examines the current state of foreign investment in the developing countries of Asia, particularly Pakistan and investigates the extent, content and quality of life of affected by this investment in the host countries. Data are primarily drawn from the World Investment Report (UNCTAD) and the World Economic Forum. The research paper argues that activities of transnational corporations require regulations and laws that avoid environmental disasters and ensure the health and wellbeing of their workers. The scholar urges the giant economies to integrate social and ethical concerns in their business operations on a voluntary basis.

Distributed Service Discovery Algorithm Based on Ant Colony Algorithm

UDDI is a universal description, discovery and integration protocol. As a public registry of Web service, it is designed to store information about each company and its service. Traditional centralized service discovery structure of UDDI service registration center does not apply to large- scale service discovery. When all the services register to a center, the service bottleneck, failure of single point and the poor scalability defects will occur. In addition, traditional service matching mechanisms are mainly based on keywords method which lacks of semantic description and makes the service publisher and demanders cannot reach a common semantic understanding. This will lead to the problems of semantic conflicts and low accuracy that seriously affects the precision and recall of service matching. To address these shortcomings of the centralized service discovery structure of UDDI, we propose a distributed semantic service registration center which is in the construction of loosely coupled P2P network enabled the progressive massive search. In the P2P distributed network, there can be a large number of nodes to store the registration information which is suitable for large-scale service because of the adaptivity, scalability and good fault tolerance characteristics. In order to reduce the number of concurrent transmitted packets, the advanced ant colony algorithm is introduced to forward packets by probabilistic choice. The results comparison with the traditional algorithm is given through the simulation experiments and it has shown that the proposed method has good performance for the distributed service discovery Index Terms—UDDI, P2P distributed network, Semantic, Ant colony algorithm

Token list based information search in a multi-dimensional massive database

Finding proximity information is crucial for massive database search. Locality Sensitive Hashing (LSH) is a method for finding nearest neighbors of a query point in a high-dimensional space. It classifies high-dimensional data according to data similarity. However, the "curse of dimensionality" makes LSH insufficiently effective in finding similar data and insufficiently efficient in terms of memory resources and search delays. The contribution of this work is threefold. First, we study a Token List based information Search scheme (TLS) as an alternative to LSH. TLS builds a token list table containing all the unique tokens from the database, and clusters data records having the same token together in one group. Querying is conducted in a small number of groups of relevant data records instead of searching the entire database. Second, in order to decrease the searching time of the token list, we further propose the Optimized Token list based Search schemes (OTS) based on index-tree and hash table structures. An index-tree structure orders the tokens in the token list and constructs an index table based on the tokens. Searching the token list starts from the entry of the token list supplied by the index table. A hash table structure assigns a hash ID to each token. A query token can be directly located in the token list according to its hash ID. Third, since a single-token based method leads to high overhead in the results refinement given a required similarity, we further investigate how a Multi-Token List Search scheme (MTLS) improves the performance of database proximity search. We conducted experiments on the LSH-based searching scheme, TLS, OTS, and MTLS using a massive customer data integration database. The comparison experimental results show that TLS is more efficient than an LSH-based searching scheme, and OTS improves the search efficiency of TLS. Further, MTLS per forms better than TLS when the number of tokens is appropriately chosen, and a two-token adjacent token list achieves the shortest query delay in our testing dataset.

Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

AbstractWe introduce the anisotropic clustering of location uncertainty distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, we apply six different validation procedures in order to select the corresponding best fault network. Two of the validation steps (cross validation and Bayesian information criterion (BIC) process the fit residuals, while the four others look for solutions that provide the best agreement with independently observed focal mechanisms. Tests on synthetic catalogs allow us to qualify the performance of the fitting method and of the various validation procedures. The ACLUD method is able to provide solutions that are close to the expected ones, especially for the BIC and focal mechanism‐based techniques. The clustering method complemented by the validation step based on focal mechanisms provides good solutions even in the presence of a significant spatial background seismicity rate. Our new fault reconstruction method is then applied to the Landers area in Southern California and compared with previous clustering methods. The results stress the importance of taking into account undersampled subfault structures as well as of the spatially inhomogeneous location uncertainties.

Structure and magnetism of disordered carbon

Motivated by the observation of ferromagnetism in carbon foams, we perform a massive search for (meta)stable disordered structures of elemental carbon by a generate and test approach. We use the density functional based program SIESTA to optimize the structures and calculate the electronic spectra and spin densities. About 1% of the 24 000 optimized structures present magnetic moments, a necessary but not sufficient condition for intrinsic magnetic order. We analyse the results using elements of graph theory. Although the relation between the structure and the occurrence of magnetic moments is not yet fully clarified, we give some minimal requirements for this possibility, such as the existence of three-fold coordinated atoms surrounded by four-fold coordinated atoms. We discuss in detail the most promising structures.

Marker detection in rectal cancer chemoradiation response using an exhaustive combinatorics approach.

347 Background: Our objective was to detect a set of markers that predicted tumor response to chemoradiationtherapy (CRT) in a prospective phase II protocol. Our hypothesis was that certain combinations of markers had a synergistic effect on those patients with a complete response (pCR) versus other responses. Methods: 132 rectal cancer patients’ tumors were evaluated for mutations and polymorphisms in 23 genes. The factors were selected prospectively as having the potential to influence CRT outcome. Our selected risk factors were transformed to yes/no values from predetermined cutoffs. We performed a massive computational combinatorics search for those combinations of factors associated with tumor response (3 million possible combinations). Our best sets of candidates were those with the largest association with tumor response to CRT (CR vs other) by Fisher’s Exacttests after adjusting for False Discovery Rate. Results: 33 of 132 (25%) patients achieved a pCR and 99 (75%) patients had non-pCR. Our results showed that sets of markers may work alone or synergistically. For example, some markers may not be associated with CRT response by themselves, but are highly correlated to outcome in the presence of other markers. Interactions such as we modeled here are often not considered by informatics software due to computational complexity. Conclusions: A combinatorics approach is computationally expensive but is able to detect marker combinations that are more complex (i.e., nonlinear) than those commonly used in popular informatics software. Our methods are readily extensible to any marker detection scenario, given sufficient computational resources. [Table: see text]

2P1-B03 身体環境保持負荷指標に基づく脚腕協調段差昇降行動の実現(ヒューマノイド)

Climbing motion with hands and feet must be considered from the standpoints of torque, friction, balance, collision, and geometory. Massive search of constraint's space is needed. In this paper, forcusing on the necessity of the mechanical optimization for climbing higher objects, the body retention load index (BRLI) which considers mechanical constrains like friction, balance and torque is introduced, and based on that, it becomes possible to decide where hands and feet to be placed and what kinds of poses are better. We confirm effectiveness of our proposed method through simulation results and finally show the experiments in which a HRP-2 robot climbed a ladder and a car.

Probabilistic Biological Network Alignment

Interactions between molecules are probabilistic events. An interaction may or may not happen with some probability, depending on a variety of factors such as the size, abundance, or proximity of the interacting molecules. In this paper, we consider the problem of aligning two biological networks. Unlike existing methods, we allow one of the two networks to contain probabilistic interactions. Allowing interaction probabilities makes the alignment more biologically relevant at the expense of explosive growth in the number of alternative topologies that may arise from different subsets of interactions that take place. We develop a novel method that efficiently and precisely characterizes this massive search space. We represent the topological similarity between pairs of aligned molecules (i.e., proteins) with the help of random variables and compute their expected values. We validate our method showing that, without sacrificing the running time performance, it can produce novel alignments. Our results also demonstrate that our method identifies biologically meaningful mappings under a comprehensive set of criteria used in the literature as well as the statistical coherence measure that we developed to analyze the statistical significance of the similarity of the functions of the aligned protein pairs.

Treatment Update of Sensitized Pediatric Kidney Transplant Recipients: A Review

Sensitization of recipients is an increasing problem in children. Some case series in children exist comparing the diverse desensitizing protocols. These protocols include intravenous immunoglobulin, cytomegalovirus immune globulin, plasmapheresis, and some adjunctive therapies such as rituximab. Desensitizing protocols have advantages and disadvantages. Clinical trials are required to determine suitable protocols for sensitized pediatric recipients. We performed a systematic review of these protocols in children. A massive search was done in PubMed, Embase, and the Cochrane library system. The results of these studies are compared.

Algorithms for Joint Spectrum Allocation and Cooperation Set Partition in Cognitive Radio Networks

The coexistence of multi-primary users and multi-secondary users in cooperative cognitive radio networks motivate the study to propose a joint spectrum allocation and cooperation set partition problem, which so far has not been addressed before. The problem is formulated as a 0-1 integer non-linear programming model. Due to its NP-hardness, the study proposes a suboptimal Centralized Genetic Algorithm (CGA) to show its convergence by modeling it as a homogeneous finite Markov chain. The study then extends CGA to a fully Distributed Genetic Algorithm (DGA) that consists of two phases. The core techniques include a minimum dominate set based cluster partition, a spectrum pre-allocation algorithm in phase 1, and an inter-cluster cooperation set negotiation and cluster fitness refinement algorithm in phase 2. A Fast-Convergent DGA (FDGA) is also devised to reduce the system configuration time. Extensive experiments by simulations demonstrate that in terms of the fitness that reflects the performance of the proposed algorithms: (1) CGA is shown to perform as well as 92% of the optimal solution by brutal search under small network sizes; (2) As the network size increases, due to the massive search space CGA has to deal, DGA and FDGA instead outperform CGA with 20% on average when achieving the same algorithm termination condition; (3) FDGA delivers similar results as DGA while reducing the configuration time significantly, which is more suitable for large-scale networks.