Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis and Evaluation of Assemble-to-Order Systems with Stochastic Sequential Lead Times

We consider the multiproduct and multicomponent assemble-to-order (ATO) systems where the replenishment lead times of the components are stochastic, sequential, and independent of the system state. The component inventories are either controlled by the continuous-time base-stock policies, namely, a base-stock ATO system, or by the continuous-time batch-ordering policies, namely, a batch-ordering ATO system. This paper develops the following results: First, for a base-stock ATO system with a single end product and renewal demand arrivals, we characterize the probability distribution of the delivery lead time, i.e., the time it takes to satisfy a demand. The exact analysis allows us to provide simple proofs for the important system properties. Second, for a base-stock ATO system with multiple end products and demand following independent Poisson processes, we characterize the dependence among the stockout delays of the components. We show that a multiproduct ATO system can be decomposed into multiple single-product subsystems with each subsystem corresponding to one product. The analysis allows us to develop two numerical methods to evaluate the performance of the base-stock ATO systems of medium to large sizes. A hypothetical example inspired by a real-world problem is presented. Third, for a batch-ordering ATO system, we develop efficient numerical methods for performance evaluation based on Monte Carlo simulation. Given the sample size, the number of products, and the reorder points, the computational complexity of the methods is no more than that of sorting a set of real numbers, where the set size equals to the sum of the batch sizes of all components. Finally, we characterize the impact of the dependence among the components on various ATO systems, and discuss the limits of the approach.
Multisensor placement in 3D environments via visibility estimation and derivative-free optimization

This paper proposes a complete system for robotic sensor placement in initially unknown arbitrary three-dimensional environments. The system uses a novel approach for computing the quality of acquisition of a mobile sensor group in such environments. The quality of acquisition is based on a geometric model of a camera which allows accurate sensor models and simple occlusion computation. The proposed system combines this new metric with a global derivative-free optimization algorithm to find simultaneously the number of sensors and their configuration to sense accordingly the environment. The presented framework compares favourably with current techniques working in two-dimensional environments. Furthermore, simulation and experimental results demonstrate the ability of the system to cope with full three-dimensional environments, a domain still unexplored by previous methods.
Crystal Structures of a Piscine Betanodavirus: Mechanisms of Capsid Assembly and Viral Infection

Betanodaviruses cause massive mortality in marine fish species with viral nervous necrosis. The structure of a T = 3 Grouper nervous necrosis virus-like particle (GNNV-LP) is determined by the ab initio method with non-crystallographic symmetry averaging at 3.6 Å resolution. Each capsid protein (CP) shows three major domains: (i) the N-terminal arm, an inter-subunit extension at the inner surface; (ii) the shell domain (S-domain), a jelly-roll structure; and (iii) the protrusion domain (P-domain) formed by three-fold trimeric protrusions. In addition, we have determined structures of the T = 1 subviral particles (SVPs) of (i) the delta-P-domain mutant (residues 35−217) at 3.1 Å resolution; and (ii) the N-ARM deletion mutant (residues 35−338) at 7 Å resolution; and (iii) the structure of the individual P-domain (residues 214−338) at 1.2 Å resolution. The P-domain reveals a novel DxD motif asymmetrically coordinating two Ca2+ ions, and seems to play a prominent role in the calcium-mediated trimerization of the GNNV CPs during the initial capsid assembly process. The flexible N-ARM (N-terminal arginine-rich motif) appears to serve as a molecular switch for T = 1 or T = 3 assembly. Finally, we find that polyethylene glycol, which is incorporated into the P-domain during the crystallization process, enhances GNNV infection. The present structural studies together with the biological assays enhance our understanding of the role of the P-domain of GNNV in the capsid assembly and viral infection by this betanodavirus.
Performance of Fly ASH Based Concrete with Fiber under Elevated Temperature

Data mining (DM) is the process of exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules.One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. Frequent itemsets play an important role in many data mining tasks. The task of finding the frequent itemsets is a fundamental problem in data mining. In this paper we examine the problem of finding frequent itemsets using Hyper-structure mining of frequent patterns in large databases (H-Mine) algorithm on Mushroom Dataset. The performance of the algorithm is evaluated with respect to execution time and frequent pattern generation. The Mushroom dataset contains characteristics of various species of mushrooms, and was originally obtained from the UCI Repository of Machine Learning Databases. H-Mine is one of the efficient algorithm for mining frequent itemsets in a set of transactions. H-mine uses the advantage of the H-struct data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has very limited and precisely predictable space overhead and runs really fast in memory-based setting. In this paper we describe the implementation of this algorithm with respect to both execution time and frequent pattern generation.
A 0.4-20 mu m spectroscopic study of the clumped wind of WR 147

We present a detailed spectroscopic study of the WN8(h)+B0.5 V binary system WR 147 using a non-LTE model that incorporates advances in line blanketing and allows for clumping in the wind. Ground-based optical and near-infrared observations, and high-resolution spectra obtained with the ISO Short wavelength Spectrometer are combined for this study. Previously derived interstellar extinction values of EB-V similar or equal to 4.1 mag at R-V similar or equal to 2.7 are confirmed here. The distance to WR 147 is revised slightly upwards to 650 pc, based on Galactic and LMC WN8-9 calibrators in the It band. An infrared extinction curve for this sightline reveals absorption bands at 9.7 and 18 mu m that are stronger than expected for the average ISM, but the nature and location of the intervening material is unknown.He line profiles of the WN8 star exhibit clear evidence for clumping in the wind. We find that a volume filling factor f of 0.1 yields line profile which match observations (emphasizing the electron scattering wings), though values of f in the range of 0.04 to 0.25 are reasonable. This leads to a mass-loss rate range of (1.5-3.7)x 10(-5) M. , yr(-1), substantially lower than derived ft om optical or radio observations under assumptions of homogeneity. Additionally including the effects of line-blanketing, the wind performance factor, M upsilon(infinity)/(L-* /c), is reduced to similar to 2.5. Hydrogen is severely depleted, but present at 9% by mass. Observations of the [Ca IV] 3.21 mu m, [S IV] 10.51 mu m, and [Ne III] 15.56 mu m fine structure lines, from which we measure a terminal wind velocity of similar to 950 kms(-1), allow us to place constraints on the surface abundances of these elements. In all cases we find good agreement with cosmic abundances (adjusted to a hydrogen-poor environment) and thus with evolutionary predictions for WN stars.
Forthcoming in Political Studies Advocacy Strategies in Global Governance: Inside vs. Outside Lobbying

As political authority shifts to the global level, non-governmental organisations (NGOs) increasingly attempt to influence policy-making within international organisations (IOs). This article examines the nature and sources of NGOs’ advocacy strategies in global governance. We advance a two-fold theoretical argument. First, NGO advocacy can be described in terms of inside and outside strategies, similar to interest group lobbying in American and European politics. Second, NGOs’ chosen combination of inside and outside strategies can be explained by their organisational goals and membership base. Empirically, this argument is corroborated through a large-n analysis of original data from structured interviews with 303 NGO representatives active in relation to the United Nations (UN), complemented by 19 semistructured interviews with UN and state officials. The article’s findings have implications for the theory and practice of NGO involvement in global governance.
Examining Language in Context: The Need for New Research and Practice Paradigms in the Testing of English-Language Learners

Concerns about how to ensure the valid and equitable assessment of English-language learners (ELLs) and other students from culturally non-mainstream backgrounds are longstanding. This article proposes that new paradigms in the research and practice related to ELL testing are needed to address the complexities of language and culture more effectively. Three main areas are identified as key to this paradigm shift: test review, test development, and treatment of language as a source of measurement error. Research examples are provided that illustrate that the proposed paradigm shift is not only necessary but also possible. The authors propose the combined use of generalizability theory and research designs in which ELLs are given the same items in both English and their native languages—an approach that has the potential to reveal more fine-grained understandings of the interactions among first and second language profi-ciency, student content knowledge, and the linguistic and content demands of test items.
The Young Netherlands Twin Register (YNTR): Longitudinal Twin and Family Studies in Over 70,000 Children

The Netherlands Twin Register (NTR) began in 1987 with data collection in twins and their families, including families with newborn twins and triplets. Twenty-five years later, the NTR has collected at least one survey for 70,784 children, born after 1985. For the majority of twins, longitudinal data collection has been done by age-specific surveys. Shortly after giving birth, mothers receive a first survey with items on pregnancy and birth. At age 2, a survey on growth and achievement of milestones is sent. At ages 3, 7, 9/10, and 12 parents and teachers receive a series of surveys that are targeted at the development of emotional and behavior problems. From age 14 years onward, adolescent twins and their siblings report on their behavior problems, health, and lifestyle. When the twins are 18 years and older, parents are also invited to take part in survey studies. In sub-groups of different ages, in-depth phenotyping was done for IQ, electroencephalography , MRI, growth, hormones, neuropsychological assessments, and cardiovascular measures. DNA and biological samples have also been collected and large numbers of twin pairs and parents have been genotyped for zygosity by either micro-satellites or sets of short nucleotide polymorphisms and repeat polymorphisms in candidate genes. Subject recruitment and data collection is still ongoing and the longitudinal database is growing. Data collection by record linkage in the Netherlands is beginning and we expect these combined longitudinal data to provide increased insights into the genetic etiology of development of mental and physical health in children and adolescents.
Knowledge and the Maintenance of Entrepreneurial Capability

This paper intends to bridge the austrian theory of entrepreneurship and the recent developments of the theory of the firm, namely the Knowledge Based Approach. The KBA and the theory of entrepreneurship share a common focus on knowledge and coordination. It is argued herein that by examining the cognitive foundations of entrepreneurship it is possible to connect the KBA and the theory of entrepreneurship. Entrepreneurship is by essence a matter of opportunities. An entrepreneurial opportunity is the result of the connection of two radically different forms of knowledge: knowledge of feasibility and knowledge of relevance. These two categories of knowledge define the domain of alertness, that is, the set of opportunities that the entrepreneur is able to perceive. Knowledge is thus a key element of opportunity identification and development. In the modern firm, this knowledge is dispersed among several individuals. To achieve long term growth, firms must maintain their ability to find new profit opportunities, and must thus use efficiently the entrepreneurial knowledge they have at their disposal. In order to do so, they may use two different strategies: entrepreneurial synthesis and entrepreneurial combination. Each strategy leads to different distributions of knowledge within the firm.
Incremental cluster evolution tracking from highly dynamic network data

Dynamic networks are commonly found in the current web age. In scenarios like social networks and social media, dynamic networks are noisy, are of large-scale and evolve quickly. In this paper, we focus on the cluster evolution tracking problem on highly dynamic networks, with clear application to event evolution tracking. There are several previous works on data stream clustering using a node-by-node approach for maintaining clusters. However, handling of bulk updates, i.e., a subgraph at a time, is critical for achieving acceptable performance over very large highly dynamic networks. We propose a subgraph-by-subgraph incremental tracking framework for cluster evolution in this paper. To effectively illustrate the techniques in our framework, we consider the event evolution tracking task in social streams as an application, where a social stream and an event are modeled as a dynamic post network and a dynamic cluster respectively. By monitoring through a fading time window, we introduce a skeletal graph to summarize the information in the dynamic network, and formalize cluster evolution patterns using a group of primitive evolution operations and their algebra. Two incremental computation algorithms are developed to maintain clusters and track evolution patterns as time rolls on and the network evolves. Our detailed experimental evaluation on large Twitter datasets demonstrates that our framework can effectively track the complete set of cluster evolution patterns from highly dynamic networks on the fly.
Transduction Recursive Auto-Associative Memory: Learning Bilingual Compositional Distributed Vector Representations of Inversion Transduction Grammars

We introduce TRAAM, or Transduction RAAM, a fully bilingual generalization of Pollack’s (1990) monolingual Recursive Auto-Associative Memory neural network model, in which each distributed vector represents a bilingual constituent—i.e., an instance of a transduction rule, which specifies a relation between two monolingual constituents and how their subconstituents should be permuted. Bilingual terminals are special cases of bilingual constituents, where a vector represents either (1) a bilingual token —a token-totoken or “word-to-word” translation rule —or (2) a bilingual segment—a segmentto-segment or “phrase-to-phrase” translation rule. TRAAMs have properties that appear attractive for bilingual grammar induction and statistical machine translation applications. Training of TRAAM drives both the autoencoder weights and the vector representations to evolve, such that similar bilingual constituents tend to have more similar vectors.
Sentence Realisation from Bag of Words with Dependency Constraints

In this paper, we present five models for sentence realisation from a bag-of-words containing minimal syntactic information. It has a large variety of applications ranging from Machine Translation to Dialogue systems. Our models employ simple and efficient techniques based on n-gram Language modeling. We evaluated the models by comparing the synthesized sentences with reference sentences using the standard BLEU metric (Papineni et al., 2001). We obtained higher results (BLEU score of 0.8156) when compared to the state-of-art results. In future, we plan to incorporate our sentence realiser in Machine Translation and observe its effect on the translation accuracies.
On access control requirements for inter-organizational workflow

The rapid development in networks technologies has significantly enhanced connectivity and interactions between organizations. In fact, several organizations gather to cooperate and put in common their resources and skills to reach a common goal. Inter-Organizational Workflow aims to support cooperation between business processes running in different organizations. The Access Control service remains a major challenge to ensure safe and reliable exchange of data. Indeed, such collaborative systems are highly vulnerable to security threats, especially for confidential and sensitive data. So organizations must take into account the security needs and particularly those related to access control. That's why it's important to study the security requirements in such systems. This paper studies different Inter-Organizational Workflow Access Control models, explores the main requirements in such environments and gives a comparative study.
A Local Search Heuristic for Biobjective Intersecting Geometric Graphs

In this work, we consider the problem of constructing spanning trees from two geometric graphs corresponding to two nets, each with multiple terminals, with a goal to minimize the total edge cost and the number of intersections among the edges of the two trees. Such an optimization problem is computationally hard for which no efficient algorithm or good heuristic is known to exist. Additionally, in a biobjective setting, the major challenge to solve a problem is to obtain many representative diverse solutions across the (near-) optimal Pareto-front. We present a local search based heuristic to find near-optimal Pareto-front in the feasible solution space. Each element of this solution set is a tuple of two spanning trees corresponding to the given geometric graphs. The heuristic is shown to give superior results over the existing stochastic technique
Towards a Classification Framework of Business Intelligence Value Research

Business Intelligence and Analytics (BI&A) and the topic of ‘big data’ have sparked renewed interest in the discipline of Business Intelligence (BI) and the challenges associated with the implementation thereof. Although the benefits of the powerful analytical capability of BI&A are obvious, not all organisations have reached the maturity of dealing with big data. For some organisations, the focus remains on implementing ‘traditional’ BI capabilities based on structured data (also known as BI&A 1.0) such as data warehousing. For this reason, the value of such implementations remains a burning issue, especially in tough economic times. As a result, the authors embarked on a journey of investigating the current published academic material focusing on the value added by BI projects to organisations. Various focus areas have been identified and presented in a conceptual classification framework. This provisional framework makes an important academic contribution as it provides the reader with various approaches to identify, evaluate and justify the value of such projects either prior to, during or after projects; monitor the potential value in the course of the project; identify gaps in current research and identify future research opportunities.
A Bayesian multilevel model for estimating the diet/disease relationship in a multicenter study with exposures measured with error: The EPIC study

In a multicenter study, the overall relationship between diet and cancer risk can be broken down into: (a) within‐center relationships, which reflect the relationships at the individual level in each of the centers, and (b) a between‐center relationship, which captures the association between exposure and disease risk at the aggregate level. In this work, we propose the use of a Bayesian multilevel model that takes into account the within‐ and between‐center levels of evidence, using information at the individual and aggregate level. Correction for measurement error is performed in order to correct for systematic between‐center measurement error in dietary exposure, and for attenuation biases in relative risk estimates within centers. The estimation of the parameters is carried out in a Bayesian framework using Gibbs sampling. The model entails a measurement, an exposure, and a disease component. Within the European Prospective Investigation into Cancer and Nutrition (EPIC) the association between lipid intake, assessed through dietary questionnaire and 24‐hour dietary recall, and breast cancer incidence was evaluated. This analysis involved 21 534 women and 334 incident breast cancer cases from the EPIC calibration study. In this study, total energy intake was positively associated with breast cancer incidence at the aggregate level, whereas no effect was observed for fat. At the individual level, height was positively related to breast cancer incidence, whereas a weaker association was observed for fat. The use of multilevel models, which constitute a very powerful approach to estimating individual vs aggregate levels of evidence should be considered in multicenter studies. Copyright © 2008 John Wiley & Sons, Ltd.
Comparative Analysis and Validation Methodologies of GC and HPLC for Analysis of Cholesterol in Meat Products Gisely

This study validated different extraction methodologies and compared the quantification of cholesterol by gas chromatography (GC) and high performance liquid chromatography (HPLC) in mg per 100 g of Italian-type salami and traditional bologna. The GC method used was direct saponification of the samples without derivatizations and the HPLC method was used to extract of the lipid samples. The GC limits of detection and quantification obtained for cholesterol were, respectively, 0.001 and 0.003 mg·g. The HPLC values were 0.005 mg·g and 0.016 mg·g. The GC recovery rate was 97.10 ± 0.13 and that of HPLC was 93.33 ± 0.22. Comparison of the cholesterol quantity found using the two chromatographic techniques shows that both are capable of quantifying cholesterol in the foods. With regard to costs, analysis time, the cost/benefit relationship was better with gas chromatography than that obtained with high performance liquid chromatography.
Are Photographs Worth More Than a Thousand Words? Examining the Effects of Photographic–Textual and Textual-Only Frames on Public Attitude Toward Nuclear Energy and Nanotechnology

This study examines the impact of photographic–textual and risk–benefit frames on the level of visual attention, risk perception, and public support for nuclear energy and nanotechnology in Singapore. Using a 2 (photographic–textual vs. textual-only frames) × 2 (risk vs. benefit frames) × 2 (nuclear energy vs. nanotechnology) between-subject design with eye-tracking data, the results showed that photographic–textual frames elicited more attention and did have partial amplification effect. However, this was observable only in the context of nuclear energy, where public support was lowest when participants were exposed to risk frames accompanied by photographs. Implications for theory and practice were discussed.