A bibliometric analysis on tobacco regulation investigators Background: To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) has identified research priorities under the umbrella of tobacco regulatory science (TRS). As a newly integrated field, the current boundaries and landscape of TRS research are in need of definition. In this work, we conducted a bibliometric study of TRS research by applying author topic modeling (ATM) on MEDLINE citations published by currently-funded TRS principle investigators (PIs). Results: We compared topics generated with ATM on dataset collected with TRS PIs and topics generated with ATM on dataset collected with a TRS keyword list. It is found that all those topics show a good alignment with FDA's funding protocols. More interestingly, we can see clear interactive relationships among PIs and between PIs and topics. Based on those interactions, we can discover how diverse each PI is, how productive they are, which topics are more popular and what main components each topic involves. Temporal trend analysis of key words shows the significant evaluation in four prime TRS areas.The results show that ATM can efficiently group articles into discriminative categories without any supervision. This indicates that we may incorporate ATM into author identification systems to infer the identity of an author of articles using topics generated by the model. It can also be useful to grantees and funding administrators in suggesting potential collaborators or identifying those that share common research interests for data harmonization or other purposes. The incorporation of temporal analysis can be employed to assess the change over time in TRS as new projects are funded and the extent to which new research reflects the funding priorities of the FDA.To facilitate the implementation of the Family Smoking Prevention and Tobacco Control Act (FSPTCA) of 2009, the Federal Drug Agency (FDA) Center for Tobacco Products (CTP) was formed to oversee tobacco regulatory activities. Its responsibilities include setting performance standards, reviewing premarket applications for new and modified risk tobacco products, requiring new warning labels, and establishing and enforcing advertising and promotion restrictions. In order to meet these responsibilities, the CTP has identified research priories for tobacco regulatory science (TRS) in order to inform and guide the CTP's regulatory decision-making. While tobacco researchers BioData Mining In this work, we employed author topic modeling to conduct a bibliometric analysis on the publications of principle investigators on tobacco. We only reported topic interpretations and observations for the TRSAwardeeSet, as our primary interests were the TRS investigators. In fact, the KWset were diverse in both author and topics, and thus a more in-depth exploration is needed to understand this dataset. Nonetheless, we did temporal trend analysis based on the results of ATM for KWset. It showed us how the significance of key words in different topics evolved over time.. Author topic modeling has been shown to be an effective approach in modeling corpus of computer sciences as well as more general ones, like publicly available emails, collections of diverse research articles. No research is done in modeling a constraint domain like tobacco regulations yet. The results show that this approach can efficiently cluster collections of articles into discriminative categories without any supervision. More interestingly, it can associate topics to authors in a high accuracy. This indicates that we may incorporate author topic modeling into author identification systems to infer the identity of an author of articles using topics generated by the model.. The relevance of this analysis to TRS is multiple folds. First, this analysis is a 'proof of concept' that it can be beneficial to assess the change over time in TRS as new projects are funded and collaborative science in this area changes. This is particularly important because the FDA must use the data from funded research to inform their regulatory decision-making, so if there are 'holes' in the types of research being conducted or published, a bibliometric analysis with ATM could be helpful for the FDA to make decisions. The results can be used to assess the extent to which new research reflects the funding priorities of the FDA.. Second, ATM outcomes can be used by investigators to assess who is conducting research in a particular research domain in order to foster collaborative science [25, 26] . Again, this is very important for the FDA to know given their need to make regulatory decisions. For example, if the FDA is contemplating a regulation that would lead to reductions in nicotine within cigarettes, assessing who is conducting research that can inform that regulatory process is important. Similarly, if the FDA needs to conduct rapid research to address an emerging issue, they can use this type of data to identify likely research teams to carry out that research. Since many issues in tobacco regulatory science require trans-disciplinary science, which cannot be addressed through the research of a single discipline, the ability to assess who is doing relevant research can lead to the development of unique teams that have the best potential to address those complex problems rapidly.. Third, these analyses begin to demonstrate the evolving research productivity of investigators, which we anticipate will occur to a greater extent as publications increase due to FDA funding. For example, we found that 'cessation' and 'treatment' clustered even though that topic is not really included in tobacco regulatory science. This clustering seems to reflect that some leading scientists who conducted research on tobacco treatment have successfully either shifted or expanded their research focus on tobacco regulatory science. Future analyses can further delineate how scientists transition into tobacco regulatory science research, particularly as a result of new funding, to better understand both the scientific expertise relevant to the TRS field, and also to understand the impact in other fields of scientists following the increased funding in TRS.. By fostering collaborative science in TRS, it becomes possible to speed advances in that science by fostering communication between scientists that can avoiding unneeded duplication and impact decision-making on new science that can benefit regulatory decision-making. Articles' yearly distributions Figure 1 shows that from the year 2000 onward, there were about 10,000 newly published articles related to tobacco regulatory science each year. Among them, TRS awardees contributed about 10%. TCORS awardee publications made up half of TRS awardee contributions.. The total number of articles have seen a slight increase from year-toyear over the past 10 years, with the exception of 2007 to 2009, where there was a large jump in numbers. Yet, after 2009, the number becomes stable. This may be related to the short-term grants funded during economic stimulus efforts (ARRA grants) [16] , which had some different publication and research dissemination stipulations than more traditional grants.. We also investigated the distribution based on Mesh headings [17] , which is a comprehensive controlled vocabulary for the purpose of indexing journal articles and books in the life sciences (illustrated in Figure 2 ). The general trend looks quite similar to that in Figure 1 , in that there were slight increases from year-to-year. However, a few differences can be observed as well. Among the literature retrieved using the keyword search queries, based on CTP research interest areas, a more diverse range of research topics can be observed over time. Due to this large array of diversity, only 10-14% are from TRS-funded researchers, with TCORS awardee publications again constituting half of TRS researcher contributions.

O B I O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Beating oscillations of magneto-optical spectra in simple hexagonal graphite The magneto-optical properties of simple hexagonal graphite exhibit rich beating oscillations, which are dominated by the field strength and photon energy. The former has a strong effect on the intensity, the energy range of the beating and the number of groups, and the latter modulates the total group numbers of the oscillation structures. The single-particle and collective excitations are simultaneously presented in the magnetoreflectance spectra and can be precisely distinguished. For the loss function and reflectance, the beating pattern of the first group displays stronger intensities and broader energy range than other groups. Simple hexagonal graphite possesses unique magneto-optical characteristics that can serve to identify other bulk graphites. Graphene-based materials have attracted many researchers to investigate their physical properties due to their potential for novel applications. The material properties demonstrate their strong dependence on the stacking configurations [1] [2] [3] [4] [5] [6] , layer numbers [7] [8] [9] [10] [11] [12] , and interlayer atomic interactions [13] [14] [15] . Graphite is composed of van der Waals coupled graphene layers [16, 17] . Three prototypes of periodical stacking along the z-direction exist: AA-stacked simple hexagonal graphite (SHG), AB-stacked Bernal graphite (BG), and ABC-stacked rhombohedral graphite (RHG). The graphites discovered in nature are mainly composed of BG and RHG. Recently, SHG has been successfully synthesized in the laboratory [18] . The interlayer couplings play an important role in determining the lowenergy electronic properties; thus, different periodic stacking configurations exhibit their own unique characteristics. It is known that the neighboring electronic states congregate and form the Landau subbands (LSs) along k z in a perpendicular uniform magnetic field B=B 0 z. The magneto-electronic properties demonstrate very interesting phenomena, e.g., the anisotropy of low-energy electronic structures [16, [19] [20] [21] , the de Haas-van Alphen effect [22, 23] , quantum Hall effect [24] [25] [26] [27] . In this work, we mainly focus on obtaining the magneto-optical properties of SHG by means of evaluating the dielectric function ε(ω, B 0 ).. Comparisons with BG and RHG are also made.. The LSs of graphites present many important features. SHG possesses very strong k z -dependent energy dispersions with a broad band width about 1 eV, and each LS can be described by a simple relationship with k z [19, 25] . Many LSs cross the Fermi level (E F = 0) [19, 28] . Moreover, the excitations related to the densely low-lying LSs own wide energy ranges which can overlap for different optical transition channels. On the contrary, RHG exhibits weak k z -dependent dispersions with a narrow band width (∼10 meV) [21] .. Only one LS crosses E F , and there is no coexisting energies for different optical excitations.. The LS can be characterized by the approximate solution [29] . The energy dispersion of BG has a band width of ∼0.2 eV, which lies between that of SHG and RHG, and two LSs cross E F [30, 31] . The low-lying LSs are complex and cannot be easily described by k z .. The characteristics of LSs would be reflected in the magneto-optical spectra.. The magneto-optical properties are closely associated with the stacking configurations of graphites [28, [31] [32] [33] . The low-energy magneto-optical absorption spectrum of SHG is dominated by intraband and interband optical excitations which induce a multi-channel threshold peak, several two-channel peaks, and many double-peak structures [28] . In the magneto-optical absorption spectra of BG, the prominent peaks originate from the interband excitations at both the K and H points. The peaks associated with the K point display double-peak structures [20, 30] . Moreover, the field evolution of the absorption lines for the K-point type shows an approximately linear dependence, while the dependence of the H point is square-root like [20, 30, 34] . The magnetoreflectance R(ω, B 0 ) spectra of BG displays irregular oscillations [35, 36] . Up to now, no theoretical calculations and experimental measurements for the magneto-optical absorption or reflection of RHG have been performed .. The magneto-optical properties are evaluated based on the Peierls tight-binding model, which can be exactly diagonalized even with the inclusions of field-induced Peierls phases and important atomic interactions in the Hamiltonian [28, 31, 37] . This study shows that the beating patterns of the dielectric function can be formed mainly owing to the strong overlap of different optical transition channels in a wide frequency range. Such beating patterns are also exhibited in the higher-frequency absorption spectrum, loss function, and reflectance.. The single-particle and collective excitations can be precisely identified, respectively, based on the shoulders (peaks) and dips of specific structures in the magnetoreflectance spectra.. The regular beating magneto-optical spectra can be controlled by the field strength and the photon energy, which provide a theoretical basis for future experiments to clarify the optical responses of the graphite configurations.. The generalized tight-binding model deserves a closer examination in numerical calculations. We developed this model to study the magneto-electronic and -optical properties by the exact diagonalization method. In studying the magneto-electronic properties, the earlier work can only cope with eigenvalues and eigenfunctions at strong magnetic field strength [38] because the Hamiltonian matrix gets too large as the field strength decreases.. For example, this matrix is 40000 × 40000 for monolayer graphene at 7.9 T. By means of rearranging the tight-binding functions, it is possible to transform the huge matrix into a band-like one. Therefore, the eigenvalues and the wave functions can be efficiently solved at weaker field strength (∼ 1T) [37] . In this work, the magneto-optical absorption spectra, which are determined by three large matrices due to the Hamiltonian, the initial state and the final state, can be obtained by using the localized features of the magnetic wave functions. The PC clusters are sufficient in calculating numerical data. The acquired features of LS spectra and the reliable characterization of the LS wave functions provide a guideline for other physical properties, such as Coulomb excitations and transport properties. As for the discussion of the optical properties in our previously published works, the generalized tight-binding method has been successfully applied to investigate the magneto-optical absorption spectra of few-layer graphenes. The optical selection rules are well defined through the detailed analysis on the wave functions. It is also applicable to bulk graphite with layers stacked in any sequence. Furthermore, the generalized tight-binding model can be used in the cases of spatially modulated fields and combined magnetic and electric fields. The magneto-optical properties of SHG demonstrate rich spectra with beating structures, owing to the strong k z -dependent LS and the wide overlapping ranges of energy for different transition channels. The single-particle excitations and collective excitations appear simultaneously in the magneto-optical spectra and can be precisely identified. The beating pattern of both the loss function and reflectance in Group I exhibit stronger intensities and wider energy ranges than those in other groups. As field strength increases, the plasmon peaks of the loss function and the dips of the reflectance are intensified, but the number of groups of beating structures is diminished. Moreover, the photon energy can modulate the total number of groups of the oscillation structures. The unique magneto-optical properties, the beating oscillations and the largest variation in reflectance spectrum, could be confirmed by magneto-optical spectroscopy measurements [35, 36, 49, 50] . For calculation purposes, the geometric structure of simple hexagonal graphite is regarded as a stacking sequence of infinite layers of graphene with an AA-stacked configuration along z. All honeycomb structures in SHG have the same projections on the x-y plane. The interlayer distance is I c = 3.50Å [18] and the C-C bond length is b ′ = 1.42. A. A primitive unit cell consists of two atoms. The associated hopping integrals γ i 's taken into account are the one intralayer atomic interaction (γ 0 = 2.519 eV) and three interlayer atomic interactions (γ 1 = 0.361 eV; γ 2 = 0.013 eV; γ 3 = −0.032 eV) [15] .. When SHG is subjected to a B 0 z, the path integral of the vector potential induces a periodical Peierls phase (details in Ref. [19] ). The phase term of the associated period is inversely proportional to the magnetic flux (Φ = 3 √ 3b ′2 B 0 /2) through a hexagon. To satisfy the integrity of the primitive cell, the ratio R B = Φ 0 /Φ (Φ 0 = hc/e flux quantum) has to be a positive integer. As a result, the extended rectangular unit cell has 4R B carbon atoms. The main features of the electronic properties can be directly manifested by optical excitations. As materials absorb photons, electrons are excited from occupied states to unoccupied states. Within the relaxation-time approximation [39] , the transverse dielectric function at zero temperature is expressed as. where ǫ 0 = 2.4 is the background dielectric constant [40] . can be employed to study the absorption spectrum, loss function, and reflectance.

O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Client-Led Information System Creation (CLIC): navigating the gap Abstract. This paper offers a new framework to facilitate an interpretive approach to client-led information system development, referred to as CLIC (Client-Led Information System Creation). The challenge of moving seamlessly through a process of information systems (IS) design is still the subject of much research in the IS field. Attempts to address the difficulties of ‘bridging the gap’ between a client's business needs and an information system definition have hitherto not provided a coherent and practical approach. Rather than attempting to bridge the gap, this paper describes an approach to managing this gap by facilitating the clients’ navigating through the information system design process (or inquiry process) in a coherent manner. The framework has been developed through practice, and the paper provides an example of navigating through the design phase taken from an Action Research field study in a major UK bank. In this paper we describe a framework for thinking developed through practice to facilitate a client led approach to information system design, underpinned by interpretivist principles. We refer to this approach as: Client Led Information system Creation (CLIC). The CLIC framework is a development of the work undertaken by Stowell for the past 18 years (Liang et al, 1998; Stowell, 1985 Stowell, , 1991 Stowell, , 2000 Stowell and West, 1994; Stowell et al, 1997) . We suggest that using this framework facilitates a coherent approach to sense making and the creation of a shared appreciation (Vickers, 1965) , from the exploration of the problem situation right through to the design for an information system and if appropriate, the application of different methods for designing and creating software. The flexibility facilitated through the CLIC framework is important as it can provide the opportunity to support both the clients and the software developers in using their preferred methods of computerbased information system development.. Various authors have discussed the difficulty of moving from some means of exploring a problem situation to creating a specification for an information system (Avison et al, 1998; Boland, 1985; Dietz et al, 1998; Doyle et al, 1993; Lai, 2000; Liang et al, 1998; Mathiassen and Nielsen, 2000; Miles, 1988; Reijswoud, et al, 1999; Savage and Mingers, 1996; Stowell and West, 1994) . Most of the research in this field has been directed at providing some sort of 'bridge' between clients' requirements and a logical specification. The focus on providing a bridge between what the client wants and a specification for a technology-based information system has resulted in a concern to map, or trace, requirements from one type of model to another. Even within the work of researchers working from an interpretivist perspective, there is an underpinning concern to translate information system requirements directly from one sort of model to the next. Avison et al, 1998; Guo et al, 2000; Liang et al, 1998; Mathiassen and Nielsen, 2000; Savage and Mingers, 1996 and West, 1994 have all offered approaches that suggest different ways of translating requirements directly from one sort of model to another. We suggest that participants in the design process need to be offered support in order to better appreciate the difference (or gap) between the purposeful action they wish to take and the support that will be required in order to implement that action.. The work presented here offers a different approach. We have applied the definition of an information system first suggested by Checkland and Scholes (1990, p. 54) , that is: an information system is a system to serve purposeful action. This definition is underpinned by the assumption that the information system, (or serving system), is different to the 'system to be served', (or the purposeful action that people wish to take). The system to be served must be considered and conceptualised in some way before the serving system can be considered (Checkland and Scholes, 1990; Winter et al, 1995) .. It is important to note that any models created during the design process are regarded as intellectual devices to facilitate participants in first, expressing ideas about what purposeful action might bring improvement to a problem situation, and then to express ideas about what sort of serving system might be needed, if such action were indeed taken. The models are relevant to the situation (Checkland, 1995) , and are used as devices to facilitate debate and as one means of expressing ideas to help people think through the consequences and implications of design decisions. This is a direct application of Checkland's insight that Systems thinking "….transfers systemicity from the world to the process of inquiry into the world" (Checkland, 1983, p. 672, his italics) . The focus when using this approach is to use appropriate methods and tools in a coherent and flexible manner to manage the process of sense-making.. The models depicted in Figures 2, 3 and 4 of this paper have been previously published as the starting point for illustrating one means of navigating through the design process, using a computer-aided design tool called SOMATiK (Champion and Stowell, 2002) . The models are reproduced in this paper to illustrate the CLIC framework for thinking to guide the design process and also to illustrate a way of navigating through the design process using different methods and tools to specify a technology-based information system (Unified Modelling Language). The resultant specification presented here was accepted by the business clients. We believe that the flexibility for using different methods and tools in a coherent manner, achieved through the application of the CLIC framework, is fundamental to its practical value for business and industry.. The CLIC framework for guiding information system design is informed by the work of Vickers (1965 Vickers ( , 1983 and is undertaken from an interpretivist perspective.. That is, we "…act according to the assumption that social reality is socially constructed, continuously [and we…] use explicit intellectual devices consciously to explore; understand and act in the situation in question" (Holwell, 1997, p. 401) . In order to be consistent with these principles of inquiry, we have used an Action Research approach in our work.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O


Clustering analysis prioritization of automobile logistics services Purpose -The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers. Design/methodology/approach -In total, 98 automobile and auto-parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two-stage clustering approach combined with Ward's minimum-variance method and the K-means algorithm, the logistics companies prioritize their services to better satisfy groups of customers with specific preferences. Findings -Four distinctive groups of manufacturers are identified using the two-stage clustering approach. The clusters separate logistic preferences and outsourcing patterns of after market parts suppliers, original equipment service parts suppliers, original equipment manufacturer parts suppliers, and tier one car makers. The paper finds that distribution and delivery services hold the highest percentage of services outsourced among the manufacturers. Originality/value -This paper models logistic services as customizable services and develops a data system methodology to define the profiles of automobile manufacturers and their preferred logistic services. Through the analysis of service preferences and clustering, the paper identifies the key logistic services that can be customized for members of the automobile supply chain. A case is provided which demonstrates how a logistics company can provide customized service designs for specific target markets and customers. 1. Introduction Automobile and components manufacturers recognize the strategic importance of integrated delivery systems for the global market. Current research over the last decade has demonstrated many ways to integrate and synchronize materials and information flows, enhance supply chain efficiency, and reduce logistic management costs for supply chain intermediaries. An important development is the outsourcing of logistics services. Researches show that third party logistics (3PL) providers enhance the availability of products and materials on-demand and facilitate the consolidation of orders (Sohail and Sohal, 2003; Carbone and Stone, 2005; Kim et al., 2008) . Using good quality 3PL services, the supply chain intermediaries increase information sharing, build more robust working relations, and decrease the uncertainty of the product life cycle (Trappey et al., 2007; Govindan et al., 2010) . The scope of logistics management has been extended from intra-organization coordination to inter-organizational coordination and combines customized services for the building of long-term customer loyalty (Wang and Sang, 2005; Gol, 2007; Sihn and Schmitz, 2007) .This research provides a methodology for 3PL companies to customize services for the automobile and components manufacturers. For the first phase of the study, the industrial customers for 3PL firms are surveyed to collect the outsourcing status quo of logistics activities, logistics service preference attributes, and general industrial demographic information. For the second phase, a two-stage clustering method classifies customers into distinct clusters so that specific logistic services can be defined for members of a given cluster. Once the customers' preferences and product distribution attributes are known, the data clustering provides decision support for logistic services that will improve logistics effectiveness and lower distribution costs. A survey is used to collect the customer and service preference data and the K-means algorithm clusters customers into sub-segments according to their logistics service attributes. The 3PL then uses the clusters to create customized delivery and distribution services that better satisfy the requirements and preferences of each customer group.This research paper is organized as follows. Section 2 reviews and discusses the background literature. The proposed two-stage clustering method and the decision support methodologies are described and discussed in Section 3. Section 4 presents the case implementation. The concluding remarks for implementation and further design of customized services are provided in Section 5. Automobile and components manufacturers recognize the strategic importance of integrated delivery systems for the global market. Current research over the last decade has demonstrated many ways to integrate and synchronize materials and information flows, enhance supply chain efficiency, and reduce logistic management costs for supply chain intermediaries. An important development is the outsourcing of logistics services. Researches show that third party logistics (3PL) providers enhance the availability of products and materials on-demand and facilitate the consolidation of orders (Sohail and Sohal, 2003; Carbone and Stone, 2005; Kim et al., 2008) . Using good quality 3PL services, the supply chain intermediaries increase information sharing, build more robust working relations, and decrease the uncertainty of the product life cycle (Trappey et al., 2007; Govindan et al., 2010) . The scope of logistics management has been extended from intra-organization coordination to inter-organizational coordination and combines customized services for the building of long-term customer loyalty (Wang and Sang, 2005; Gol, 2007; Sihn and Schmitz, 2007) .. This research provides a methodology for 3PL companies to customize services for the automobile and components manufacturers. For the first phase of the study, the industrial customers for 3PL firms are surveyed to collect the outsourcing status quo of logistics activities, logistics service preference attributes, and general industrial demographic information. For the second phase, a two-stage clustering method classifies customers into distinct clusters so that specific logistic services can be defined for members of a given cluster. Once the customers' preferences and product distribution attributes are known, the data clustering provides decision support for logistic services that will improve logistics effectiveness and lower distribution costs. A survey is used to collect the customer and service preference data and the K-means algorithm clusters customers into sub-segments according to their logistics service attributes. The 3PL then uses the clusters to create customized delivery and distribution services that better satisfy the requirements and preferences of each customer group.. This research paper is organized as follows. Section 2 reviews and discusses the background literature. The proposed two-stage clustering method and the decision support methodologies are described and discussed in Section 3. Section 4 presents the case implementation. The concluding remarks for implementation and further design of customized services are provided in Section 5. This research proposes the provision of customized logistics services derived from customer preferences. The study provides detailed observations about customers in four clusters and their preferences. According to the cluster results, the implementation of real-time logistic data and decision management (e.g. RFID tags and readers) will best facilitate real-time merchandise and service tracking, a quicker response to customers' requests and complaints, and provide service crew information and support. As a result, the 3PLs can support standardized services for specific customer cluster to enhance customer's satisfaction.. The satellite manufacturing partnership is regarded as the central manufacturer with warehouses and many satellite suppliers that implement JIT for reducing logistics and production time and cost. The ability to better understand and customize the needs of logistic services is greatly desired industry innovation. The automobile industry and other manufacturing industries as well, require these types of innovations to enhance productivity and reduce costs across the supply chain. In summary, even though most logistics activities between channel intermediaries are currently managed by manufacturers, these services can be outsourced to better enable manufacturers to focus on their core business operations. This paper provides the method to segment the 3PLs' potential customers in automobile industry (AM, OES, OEM suppliers and central car makers) while identifying and prioritizing their logistic service needs. Thus, 3PLs can improve their service offering, particularly for high-priority services, with the support of information systems that apply the advanced analytical tools described in this paper. As indicated in the survey reports of Lieb (2008) and Lieb and Butner (2007) , the three challenges for 3PL providers are to find and keep managerial talent, invest in information technology, and reduce the cost of services. Confronted with these challenges, building long-term collaborative working relationships between 3PL providers and their industrial customers via prioritized or customized services are critically important for the success of 3PL operations and the supply chains they serve. The objectives of future research will study the advancement of logistic services that match the diverse needs of segmented customers in industrial domains.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


iPCA: an integration information system for patient controlled analgesia using wireless techniques Recently, pain management has become an important aspect of medical care. Patient Controlled Analgesia (PCA) is a particularly effective and flexible treatment that allows patients to adjust the dosages of the analgesics that control their pain after surgery. However, PCA requires manual operation for sustained attention, and should be analysed with other pathology information for synergic usage. In this paper, we propose an integrated information system for pain management, called Integrated Patient Controlled Analgesia (iPCA). The system integrates various PCA-related devices by wireless techniques to provide real-time information for medial staffs, and offer more efficient and convenient pain control for both in-hospital and at-home patients. To evaluate our system, we have developed a prototype and conducted a series of field trails at a large private hospital in southern Taiwan. The results show that the iPCA system indeed works well with patients, and can provide better post-operative pain control by efficiently collecting PCA usage information.Keywords: wireless sensor networks; network integration; patient controlled analgesia; home healthcare; data mining.Reference to this paper should be made as follows: Yang, S-F., Ku, T-H., Jeng, A.A-K., Jan, R-H., Tseng, Y-C., Wang, K-C. and Hu, Y-J. (2013) 'iPCA: an integration information system for patient controlled analgesia using wireless techniques', Int. The advances of information and network technologies have shifted the objectives of many innovations from improving hardware efficiency to providing better services. Medical care is one of the potential applications, which aims to create a safer, healthier and more comfortable environment where provides better treatment for patients and offer instant information or even decision support for health workers. Such as an on-line virtual clinic system developed by Chang et al. (2004) , it enables remote diagnosis via video and voice devices; Yang et al. (2006) employed a radio system to trace each patient's position. Similarly, a variety of remote medical treatment systems were studied by Reske et al. (2002) , Fensli et al. (2004) , Lin et al. (2004) , Lee et al. (2007) , Liu (2011) and Kahn et al. (2011) . A survey of wireless-sensor-network-based healthcare systems can be found in Huo et al. (2011) .. In particular, pain management is a critical segment in modern medical care. Constant pain may both physically and mentally affect our life and make people feel more intolerable than the illness itself, especially for those who have undergone surgeries. Intramuscular (IM) opioid injection is a commonly used treatment for postoperative pain relief (Ballantyne et al., 1993) . It takes roughly 10 to 20 minutes to take effect. But different surgeries cause different degrees of pain and pain endurance varies among people. Without the correct analgesic dosage beforehand, it is hard to meet the patient's need by this method.. Patient Controlled Analgesia (PCA) is an electronic analgesic delivery system that makes direct and flexible pain treatment by allowing patients to administer their own pain relief (Walder et al., 2001; Dolin et al., 2002) . PCA also allows caregivers to regulate the patient's operation by configuring, such as, the delivery rate, delivery limit, concentration units, bolus volume and loading dose of the device. It has been widely used in hospitals and considered the most effective way for postoperative analgesia, especially for major surgery.. However, there are still several reasons making PCA inconvenient for the users: First, the medical staff has to constantly monitor the patients for at least the first two to twenty four hours so as to ensure PCA devices were properly used (Taber and Venes, 2009) . It involves several manual operations, e.g. collecting patient's PCA data, printing out analgesia usage, entering readings into appropriate databases, and etc. Second, PCA data are usually exploited with pathology information obtained from other devices. For instance, whether the current PCA control requires a readjustment after the initial setting is based on the patient's physical states (e.g. heartbeat rate, blood pressure, and SpO 2 ) and discomfort symptoms (e.g. vomiting, dizziness, fever and weakness). Such information should be quickly and correctly collected from the corresponding device as needed. Moreover, it is awkward for medical staffs if they have to frequently add or remove different devices using cable lines. The wired connections also restrict patients from walking freely in wards and hospital.. Recently, pain management also plays a vital role in home healthcare. Home healthcare, as opposed to inhospital care, is a prevalent tendency that provides healthcare, including medication, for patients at homes as if they remain at hospital. Providing pain management inhome can not only assist the post-hospitalisation recovery process during the initial weeks after discharge but also improve the quality of life for chronic pain patients.. In this paper, we propose an integrated information system for pain management, called Integrated Patient Controlled Analgesia (iPCA). The system integrates various PCA-related devices by wireless techniques to provide realtime information for medial staffs, and offer more efficient and convenient pain control for both in-hospital and athome patients. We have implemented a prototype and conducted a series of field trails at a large private hospital in southern Taiwan. The results show that our system can significantly reduce the time required for collecting PCArelated information.. The rest of this paper is organised as follows: In Section 2, we overview the architecture and application scenarios of the iPCA system. The major designs of the proposed system are presented in Section 3. In section 4, we detail the implementation information. Section 5 presents the on-site field trials, and the concluding remarks are given in Section 6. In this paper, we have proposed an integrated pain control system (iPCA) that integrates various pain control devices using the wireless sensor, Wi-Fi, and 3G networks, which to provide real-time information for medial staffs, and offer more efficient and convenient pain control for both inhospital and at-home patients. We have also developed treelearning based predictors to predict the total anaesthetic does and to make the readjustment decision. Through a series of field trials in CCH, we have examined that our iPCA system can work well with patients and significantly reduce the collection time of PCA data.. In the future research, we plan to extend the work in the following directions. First, we will integrate other medical instruments with iPCA to collect a wider variety of vital health signs, and use them as additional descriptive attributes that can provide the medical staff with more accurate predictions. Second, we will incorporate authentication and encryption into iPCA to address the issues of privacy and security. With the architecture of the integrated system proposed in this paper, we hope not only to improve its performance and expand its applicability, but also to link the research on sensor networks with real-world applications.

O O O O O O B I I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I B I B I I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Breakout Local Search for the Max-Cut Problem Given an undirected graph G = (V, E) where each edge of E is weighted with an integer number, the maximum cut problem (Max-Cut) is to partition the vertices of V into two disjoint subsets so as to maximize the total weight of the edges between the two subsets. As one of Karp's 21 NP-complete problems, Max-Cut has attracted considerable attention over the last decades. In this paper, we present Breakout Local Search (BLS) for Max-Cut. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. The proposed algorithm shows excellent performance on the set of well-known maximum cut benchmark instances in terms of both solution quality and computational time. Out of the 71 benchmark instances, BLS is capable of finding new improved results in 33 cases and attaining the previous best-known result for 35 instances, within a computational time ranging from less than one second to 5.6 hours for the largest instance with 20000 vertices. The maximum cut problem (Max-Cut) is one of Karp's 21 NP-complete problems with numerous practical applications [12] . Let G = (V, E) be an undirected graph with the set of vertices V and the set of edges E, each edge (i, j) ∈ E being associated with a weight w ij . Max-Cut consists in partitioning the vertices of V into two disjoint subsets V 1 and V 2 such that the total weight of the edges whose endpoints belong to different subsets is maximized, i.e., f (V 1 , V 2 ) = max i∈V 1 ,j∈V 2 w ij .. (. Given its theoretical and practical importance, Max-Cut has received considerable attention over the last decades. Well-known exact methods for MaxCut, such as the Branch and Price procedure [17] , are capable of solving to optimality medium size instances (i.e., |V | = 500). For larger instances, different heuristic methods have been proposed including global equilibrium search [21] , projected gradient approach [6] , rank-2 relaxation heuristic [7] , and greedy heuristics [11] . Other well-known algorithms are based on popular metaheuristics such as variable neighbourhood search [8] , tabu search [1, 16] , scatter search [19] , grasp [22] , and different hybrid approaches [8, 22] .. In this work, we propose a new heuristic algorithm for Max-Cut, using the Breakout Local Search (BLS) [4, 5] . Based on the framework of Iterated Local Search (ILS) [18] , BLS combines local search (i.e., the steepest descent) with a dedicated and adaptive diversification mechanism. Its basic idea is to use local search to discover local optima and employ adaptive perturbations to continually move from one attractor to another in the search space. The continual exploration of new search areas is achieved by alternating between random and directed, and weak and strong perturbations depending on the current search state. Despite its simplicity, BLS shows excellent performance on the set of well-known Max-Cut instances in terms of both solution quality and computational time. Out of 71 benchmark instances, the proposed approach is capable of improving the previous best-known solutions in 33 cases and reaching the previous best-known results for 35 instances, within a computational time ranging from less than one seconds to 5.6 hours for the largest instance with 20000 vertices.. In the next section, we present in details the breakout local search approach for the Max-Cut problem. Section 3 shows extensive computational results and comparisons with the state-of-art Max-Cut algorithms. In Section 4, we provide a parameter sensitivity analysis and justify the parameter settings used to obtain the reported results. Moreover, we investigate the efficiency of the proposed diversification mechanism of BLS, and highlight the importance of excluding diversification schemes during the local search phase. Conclusions are given in the last section. In this paper, we presented the Breakout Local Search approach for the MaxCut problem. The BLS alternates between a local search phase (to find local optima) and a perturbation-based diversification phase (to jump from a local optimum to another local optimum). The diversification phase is of an extreme importance for the performance of BLS since the local search alone is unable to escape a local optimum. The diversification mechanism of the proposed approach adaptively controls the jumps towards new local optima according to the state of the search. This is achieved by varying the magnitude of a jump and selecting the most suitable perturbation for each diversification phase.. Experimental evaluations on a popular set of benchmark instances showed that despite its simplicity, our approach outperforms all the current Max-Cut algorithms in terms of solution quality. Out of the 71 benchmark instances, BLS improves the current best results in 33 cases and attains the previous best-known result for 35 instances. Moreover, the computing time required by BLS to reach the reported results competes very favorably compared to other state-of-the-art approaches. To attain its best results reported in the paper, BLS needs less than one second to 10 minutes for the graphs with up to 3000 vertices, and 0.2 to 5.6 hours for the large and very large instances with 5000 to 20000 vertices.. We also provided experimental evidences to highlight the importance of the adaptive perturbation strategy employed by the proposed BLS approach, and the benefit of separating completely diversification from intensification during the local search phases. The general BLS procedure combines some features from several well-established metaheuristics: iterated local search [18] , tabu search [10] and simulated annealing [15] . We briefly discuss the similarities and differences between our BLS approach and these methods.. Following the general framework of ILS, BLS uses local search to discover local optima and perturbation to diversify the search. However, BLS distinguishes itself from most ILS algorithms by the combination of multiple perturbation strategies triggered according to the search status, leading to variable levels of diversification. Moreover, with BLS each locally optimal solution returned by the local search procedure is always accepted as the new starting solution no matter its quality, which completely eliminates the acceptance criterion component of ILS.. A further distinction of BLS is the way an appropriate perturbation strategy is selected at a certain stage of the search. As explained in Section 2.3.1, BLS applies a weak perturbation with a higher probability P as long as the search progresses towards improved solutions. As the number of consecutively visited non-improving local optima ω increases, indicating a possible search stagnation, we progressively decrease the probability for a weak perturbation and increase the probability for a strong perturbation. The idea of this adaptive change of probability finds its inspiration from simulated annealing and enables a better balance between an intensified and diversified search.. To direct the search towards more promising regions of the search space, BLS employs perturbation strategies based on the notion of tabu list that is borrowed from tabu search. The tabu list enables BLS to perform moves that do not deteriorate too much the solution quality in a way that the search does not return to the previous local optimum. However, BLS does not consider the tabu list during its local search (descent) phases while each iteration of tabu search is constrained by the tabu list. As such, BLS and tabu search may explore different trajectories during their respective search, leading to different local optima. In fact, one of the keys to the effectiveness of BLS is that it completely excludes diversification during local search, unlike tabu search and simulated annealing for which the intensification and diversification are always intertwined. We argue that during local search, diversification schemes are unnecessary and the compromise between search exploration and exploitation is critical only once a local optimum is reached. Other studies supporting this idea can be found in [2, 13] . We show computational evidences to support this assumption in Section 4.2.2.. As we will see in the next section, BLS is able to attain highly competitive results on the set of well-known benchmarks for the Max-Cut problem in comparison with the state of the art algorithms.

O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O


A model reduction method for biochemical reaction networks Background: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. Results: We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%.The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment. In this paper, we have outlined a method for model reduction of biochemical reaction networks that are asymptotically stable around a steady state. The principle behind our model reduction method is to couple the dynamics of certain complexes to the dynamics of the neighbouring complexes in such a way that the net rate of inflow into the complex is equal to the net rate of outflow from it. We provide an algorithm for choosing the complexes to be deleted in such a way that the transient behaviour of the significant species of the reduced model is close to that of the original model. Apart from a reduction in the number of state variables, our model reduction method also leads to a reduction in the number of reactions and parameters of the model. This in turn leads to an improved computational effort required in order to analyze the model. Our model reduction procedure ensures that the reduced model mimics the full model well under the conditions of a specific dynamic event. A different reduced model with a different set of deleted complexes may be produced by our procedure under the conditions of a different dynamic event.. In Additional file 1, we give a list of some well-known enzyme kinetic rate laws for which our model reduction method is applicable. Some of the rate laws provided in this list are rate laws for irreversible reactions. However our method is also applicable for the reversible version of these rate laws. This is because a reversible reaction can be split into its forward and reverse reaction components and if our method is applicable to each of these components then it is applicable to the given reversible reaction. For example, since our method is applicable to reactions governed by irreversible mass action kinetics, it is also applicable for reactions governed by reversible mass action kinetics. The list provided in Additional file 1 is not an exhaustive list of all enzyme kinetic rate laws for which our method is applicable. We have checked that our method is applicable to all the rate laws that are included in the COPASI software [34] .. We have applied our method on a yeast glycolysis model and a beta-oxidation model in rat liver and have observed a good agreement between the transient behaviours of the original and the reduced models. In these cases, our model reduction method has also helped us significantly in improving our understanding of the dynamics of the networks, for example by identification of the close-toequilibrium reactions of the yeast glycolysis model and of the reaction pathways with comparatively low fluxes in the rat liver beta-oxidation model. In the case of the yeast glycolysis model, we found that the order of deletion of complexes (metabolites) is not the same as the order of their convergence times. This shows that in this case, our model reduction approach will perform differently from a time-scale separation technique where metabolites with faster convergence times are assumed to attain rapid steady state. As observed in the two examples of metabolic reaction networks, our model reduction method helps in removing redundancies in the given network (in the yeast glycolysis network, redundant reactions were coupled and in the rat liver beta-oxidation model, redundant pathways were removed).. Recently, there has been an interest in kinetic modelling of large genome-scale networks. For example, [35] gives a method for building a parameterized genome-scale kinetic model of a network consisting of 956 reactions and 820 metabolites. We envisage that our model reduction method will be particularly useful to reduce the complexity of such large genome-scale kinetic models in the future. Likewise, it can also be useful in reducing multiscale models of biochemical reaction networks which are computationally burdensome to analyze. In a largescale kinetic model of a biochemical reaction network, our model reduction method can be used to reduce all pathways except the ones of interest into which we would like to zoom-in and analyze.. Currently, we are using our reduced model of the rat liver beta-oxidation model that we explained in an earlier section in order to check for possible bifurcations. In this respect, the reduced model provides an advantage over the full model as it requires lesser computational effort while checking for bifurcation. Our model reduction method involves rewriting of the complex graph corresponding to a given network. In the literature, there are other model reduction methods that involve graph rewriting like QSSA and quasi-equilibrium procedures, the method of [21] and the graph rewriting of monomolecular reaction networks described in [3] . The difference between these methods and ours is that these involve rewriting of the species-reactions graph unlike ours where the complex graph is rewritten.. In order to come up with a reasonably good reduced model, we follow an iterative procedure as described in the subsection titled "Error integral". This iterative procedure involves computation of error integrals of a number of reduced models which are obtained by deletion M) where M is the number of species that must still be present in the reduced model (and hence, are not considered in the subset of complexes for deletion). In our example, M = 14 (corresponding to all Acyl-carnitines in the cytosol and mitochondria) and the total simulations using our procedure are 299. More precisely, if there are N number of species, M number of important species and if L is the dimension of the reduced species, then the total number of simulations is. . Although our procedure is time consuming, it is simple and systematic, ensures that the reduced model closely mimics the transients of the original model and is scalable to a large model. We are currently investigating the computation of error bounds based on the algebraic property of the Laplacian matrices and we foresee that this knowledge might allow us to directly obtain the optimal set of complexes to be deleted.. The cut-off value of the error integral at which we stop our iterative model reduction procedure can be set according to the desired closeness to the original model. For the two models discussed in this paper, this value has been set at 0.1 and it has been found that if this value is set at 0.2, then the resulting comparison plots of the transient behaviours of the original vs the reduced models reveal significant visual differences. Another method of coming up with a cut-off value for the error integral is to look for a sudden, steep increase in the error integral histogram. However in the case where the error-integral histogram shows uniform increase with an increase in the number of complexes deleted, this method cannot be used.. Note that in principle, complexes can be deleted to reduce any biochemical model, whether asymptotically stable or not. However, for the computation of error integral as described in the paper, it is necessary that the original network is asymptotically stable around a steady state, since the computation makes use of the steady state concentrations of some species of the network.. In some cases, deletion of certain complexes can drastically change the dynamics of the system. An example is the following. Assume that X 1 , X 2 , . . . , X 10 are distinct species of the network. Consider the following reaction network consisting of three reversible reactions:. Now consider deletion of the complex X 4 + X 5 from the above reaction network. This deletion leads to the deletion of the second reaction of the network. Since the first and the third reaction of the network do not have common species, deletion of the second reaction leads to the first and the third reactions occuring independently of each other. This will lead to the reduced model exhibiting a behaviour which is not close to the behaviour of the original model. Although one can quantify the difference between the behaviours using error integrals, avoiding such deletions will reduce the computational effort in making the choice of complexes to be deleted. Examples of such deletions are deletion of complexes TRIO or BPG in the yeast glycolysis model and deletion of complex C16 Acyl Carnitine in the beta oxidation model. We remark here that one should update a reduced model when major changes are made to the original model. For example, if we consider the rat liver betaoxidation model of a MCAD-deficient animal, this model will not have the enzyme MCAD. In this case, the dynamic behaviour of the reduced model of Figure 7 may be far from that of the original model of Figure 5 without the enzyme MCAD.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


On Feedback Vertex Set: New Measure and New Structures We present a new parameterized algorithm for the feedback vertex set problem (fvs) on undirected graphs. We approach the problem by considering a variation of it, the disjoint feedback vertex set problem (disjoint-fvs), which finds a feedback vertex set of size k that has no overlap with a given feedback vertex set F of the graph G. We develop an improved kernelization algorithm for disjoint-fvs and show that disjoint-fvs can be solved in polynomial time when all vertices in G\F have degrees upper bounded by three. We then propose a new branch-andsearch process on disjoint-fvs, and introduce a new branch-and-search measure. The process effectively reduces a given graph to a graph on which disjoint-fvs becomes polynomial-time solvable, and the new measure more accurately evaluates the efficiency of the process. These algorithmic and combinatorial studies enable us to develop an O * (3.83 k )-time parameterized algorithm for the general fvs problem, improving all previous algorithms for the problem. All graphs in our discussion are undirected and simple, i.e., they contain neither selfloops nor multiple edges. A feedback vertex set (FVS) F in a graph G is a set of vertices in G whose removal results in an acyclic graph. The problem of finding a minimum feedback vertex set in a graph is one of the classical NP-complete problems [17] . It has been intensively studied for several decades. The problem is known to be solvable in time O(1.7548 n ) for a graph of n vertices [14] , and admit a polynomialtime approximation algorithm of ratio 2 [1, 3] .. An important application of the feedback vertex set problem is Bayesian inference in artificial intelligence [2, 3] , where the size k of a minimum FVS F (i.e., the number of vertices in F) of a graph can be expected to be fairly small. This motivated the study of the parameterized version of the problem, which we will name fvs: given a graph G and a parameter k, either construct a FVS of size bounded by k in G or report no such a FVS exists. Parameterized algorithms for fvs have been extensively studied that find a FVS of size k in a graph of n vertices in time f (k)n O (1) for a fixed function f (thus, the algorithms become practically efficient when the value k is small). The existence of such an algorithm for fvs is implied in [13] . The first group of constructive algorithms for this problem was given by Downey and Fellows [10] and by Bodlaender [4] . Since then a chain of improvements has been obtained (see Table 1 ). 1 All algorithms summarized in Table 1 are deterministic. There is also an active research line on randomized parameterized algorithms for fvs, based on very different algorithmic techniques. A randomized algorithm of time O * (4 k ) for fvs has been known for more than a decade [2] . More recently, Cygan et al. [7] developed an improved randomized algorithm of time O * (3 k ). As pointed out in [7] , however, the techniques employed by this randomized algorithm do not seem to be easily derandomized.. The main result of the current paper is a deterministic algorithm of time O * (3.83 k ) for fvs. We give an outline to explain how our algorithm achieves the improvement over previous algorithms. As most recent algorithms, our algorithm is based on the technique of iterative compression [25] , which reduces the fvs problem to a closely related disjoint feedback vertex set problem (disjoint-fvs). On an instance (G, k, F), where F is a FVS in the graph G and k is the parameter, the disjoint-fvs problem asks whether there is a FVS F of size k in G such that F ∩ F = ∅.. The disjoint-fvs problem can be solved based on a branch-and-search process on vertices w in G \ F, whose complexity depends on the number of neighbors of w that are in F [6] . In particular, the more neighbors w has in F, the more effective the branching on w is. A major step of the fastest algorithm [6] , before our algorithm, is to show that such a branch-and-search process can always branch on a vertex in G \ F that has at least two neighbors in F. Therefore, in order to further speedup this process, we should branch only on vertices in G \ F that have more than two neighbors in F. For this, however, two issues must be addressed: (1) during the branch-and-search process, we must be able to continuously maintain the condition that such vertices always exist; and (2) when the branch-and-search process cannot be further applied, we must be able to efficiently solve the problem for the remaining structure.. To address issue (2), we develop a polynomial-time algorithm for the disjointfvs problem for instances (G, k, F) in which all vertices in G \ F have degree upper bounded by three. This algorithm is based on a nontrivial reduction from disjoint-fvs to a polynomial-time solvable matroid matching problem, the cographic matroid parity problem [22] . This result, however, does not give a direct solution to issue (1) : vertices in G \ F that have degree larger than three in G do not necessarily have more than two neighbors in F. To resolve this problem, we observe that there are always vertices in G \ F on which a branching may not be very effective but will produce structures in G \ F that are favored for the polynomial-time algorithm we developed for addressing issue (2) . To catch this observation, we use the measure-based method and introduce a new measure to evaluate the effectiveness of our branch-andsearch process more accurately. These new techniques, combined with the iterative compression method, yield an improved algorithm for the fvs problem.. The main results of this paper are summarized as follows: (i) a new technique that produces an improved kernelization algorithm for the disjoint-fvs problem, which is based on a branch-and-search algorithm for the problem. This, to our best knowledge, is the first time such a technique is used in the literature of kernelization; (ii) a polynomial-time algorithm that solves a restricted version of the disjointfvs problem; (iii) a new branch-and-search process that effectively reduces an input instance of disjoint-fvs to an instance that is solvable by the algorithm developed in (ii); and (iv) a new measure that more accurately evaluates the efficiency of the branch-and-search process in (iii). We developed an O * (3.83 k )-time parameterized algorithm for the fvs problem. Our algorithm was obtained by a nontrivial combination of several known techniques in algorithm research and their generalizations. This includes iterative compression, branch-and-search, and efficient algorithms for graphs of low vertex-degrees. For branch-and-search processes for dealing with the fvs problem, we introduced new branching rules and new branching measures, which allow us to more effectively reduce a general instance into a polynomial-time solvable instance of the problem and to more accurately evaluate the efficiency of the branch-and-search process. For efficient algorithms for graphs of low vertex-degrees, we use a nontrivial reduction that transforms the fvs problem to a polynomial-time solvable version of the matroid matching problem. Note that using matroid matching to solve the fvs problem for 3-regular graphs has been observed previously [15, 27, 28] , while we extended the techniques to solve the disjoint-fvs problem on a larger graph class in which not all vertices are required to have degree bounded by 3.. Further faster algorithms for fvs have drawn much attention in the recent research in parameterized computation [9] . Following our approach with a new reduction rule introduced, Kociumaka and Pilipczuk [20] have announced a revision of our algorithm that has an improved running time O * (3.62 k ) for the fvs problem. On the other hand, the study on the lower bound of the fvs problem has made significant progress. Based on the Strong Exponential Time Hypothesis (see [21] ), Cygan et al. [7] have reported a lower bound on the complexity of the fvs problem in terms of the pathwidth pw of a graph, which states that the fvs problem cannot be solved in time O * ((3 − ) pw ) for any positive constant > 0. This result does not yet directly lead to a lower bound for the fvs problem in terms of the parameter k, which is the size of the objective FVS (to see this, observe that the ladder graph P l × P 2 has a pathwidth 2 but its minimum FVS has a size l/2 , where P i denotes the simple path of i vertices). On the other hand, studying the complexity of the fvs problem in terms of graph pathwidth or treewidth seems to have very interesting connection to the complexity of the original fvs problem. For example, the O * (3 tw )-time randomized algorithm for the fvs problem proposed in [7] , where tw is the treewidth of the input graph, directly implies an O * (3 k )-time randomized algorithm for fvs. In particular, this has motivated an interesting open problem whether there is a deterministic O * (3 k )-time algorithm for the fvs problem [9] .. It is interesting to observe that the research on parameterized algorithms and that on approximation algorithms for the fvs problem have undergone a similar process. Early algorithms used the cycle packing-covering duality, and hence ended with O * (log k O(k) )-time parameterized algorithms [19, 23] and O(log n)-ratio approximation algorithms [12] , respectively. Later algorithms turned to the observation on graph vertex-degrees, which resulted in O * (2 O(k) )-time parameterized algorithms [6, 8] and constant-ratio approximation algorithms [1, 3] , respectively. However, constant-ratio approximation algorithms for fvs do not seem to rely on a process that is related to the iterative compression process [25] , which, on the other hand, seems to have played a critical role in the development of all O * (2 O(k) )-time parameterized algorithms for the fvs problem. A parameterized algorithm based on iterative compression for the fvs problem runs in time O * ((1 + α) k ) , where α is a constant such that the disjoint-fvs problem can be solved in time O * (α k ). Since the disjoint-fvs problem is NP-hard, the constant α has to be larger than 1. In other words, using the iterative compression technique excludes the possibility of solving the fvs problem in time O * (2 k ). An interesting research direction and a possible approach to developing further improved algorithms for the fvs problem is to explore new algorithmic techniques that are not based on iterative compression. would like to thank anonymous referees for thoughtful and detailed comments, which led to an improved presentation.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O