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Flux balance analysis	Mathematical description	The steady-state assumption dates to the ideas of material balance developed to model the growth of microbial cells in fermenters in bioprocess engineering. During microbial growth, a substrate consisting of a complex mixture of carbon, hydrogen, oxygen and nitrogen sources along with trace elements are consumed to generate biomass.
Flux balance analysis	Mathematical description	The material balance model for this process becomes: Input=Output+Accumulation If we consider the system of microbial cells to be at steady state then we may set the accumulation term to zero and reduce the material balance equations to simple algebraic equations. In such a system, substrate becomes the input to the system which is consumed and biomass is produced becoming the output from the system. The material balance may then be represented as: Input=Output Input−Output=0 Mathematically, the algebraic equations can be represented as a dot product of a matrix of coefficients and a vector of the unknowns. Since the steady-state assumption puts the accumulation term to zero. The system can be written as: A⋅x=0 Extending this idea to metabolic networks, it is possible to represent a metabolic network as a stoichiometry balanced set of equations. Moving to the matrix formalism, we can represent the equations as the dot product of a matrix of stoichiometry coefficients (stoichiometric matrix S ) and the vector of fluxes v as the unknowns and set the right hand side to 0 implying the steady state.
Flux balance analysis	Mathematical description	S⋅v=0 Metabolic networks typically have more reactions than metabolites and this gives an under-determined system of linear equations containing more variables than equations. The standard approach to solve such under-determined systems is to apply linear programming.
Flux balance analysis	Mathematical description	Linear programs are problems that can be expressed in canonical form: maximize subject to and x≥0 where x represents the vector of variables (to be determined), c and b are vectors of (known) coefficients, A is a (known) matrix of coefficients, and (⋅)T is the matrix transpose. The expression to be maximized or minimized is called the objective function (cTx in this case). The inequalities Ax ≤ b are the constraints which specify a convex polytope over which the objective function is to be optimized.
Flux balance analysis	Mathematical description	Linear Programming requires the definition of an objective function. The optimal solution to the LP problem is considered to be the solution which maximizes or minimizes the value of the objective function depending on the case in point. In the case of flux balance analysis, the objective function Z for the LP is often defined as biomass production. Biomass production is simulated by an equation representing a lumped reaction that converts various biomass precursors into one unit of biomass.
Flux balance analysis	Mathematical description	Therefore, the canonical form of a Flux Balance Analysis problem would be: maximize subject to and lowerbound≤v≤upperbound where v represents the vector of fluxes (to be determined), S is a (known) matrix of coefficients. The expression to be maximized or minimized is called the objective function ( cTv in this case). The inequalities lowerbound≤v and v≤upperbound define, respectively, the minimal and the maximal rates of flux for every reaction corresponding to the columns of the S matrix. These rates can be experimentally determined to constrain and improve the predictive accuracy of the model even further or they can be specified to an arbitrarily high value indicating no constraint on the flux through the reaction.
Flux balance analysis	Mathematical description	The main advantage of the flux balance approach is that it does not require any knowledge of the metabolite concentrations, or more importantly, the enzyme kinetics of the system; the homeostasis assumption precludes the need for knowledge of metabolite concentrations at any time as long as that quantity remains constant, and additionally it removes the need for specific rate laws since it assumes that at steady state, there is no change in the size of the metabolite pool in the system. The stoichiometric coefficients alone are sufficient for the mathematical maximization of a specific objective function.
Flux balance analysis	Mathematical description	The objective function is essentially a measure of how each component in the system contributes to the production of the desired product. The product itself depends on the purpose of the model, but one of the most common examples is the study of total biomass. A notable example of the success of FBA is the ability to accurately predict the growth rate of the prokaryote E. coli when cultured in different conditions. In this case, the metabolic system was optimized to maximize the biomass objective function. However this model can be used to optimize the production of any product, and is often used to determine the output level of some biotechnologically relevant product. The model itself can be experimentally verified by cultivating organisms using a chemostat or similar tools to ensure that nutrient concentrations are held constant. Measurements of the production of the desired objective can then be used to correct the model.
Flux balance analysis	Mathematical description	A good description of the basic concepts of FBA can be found in the freely available supplementary material to Edwards et al. 2001 which can be found at the Nature website. Further sources include the book "Systems Biology" by B. Palsson dedicated to the subject and a useful tutorial and paper by J. Orth. Many other sources of information on the technique exist in published scientific literature including Lee et al. 2006, Feist et al. 2008, and Lewis et al. 2012.
Flux balance analysis	Model preparation and refinement	The key parts of model preparation are: creating a metabolic network without gaps, adding constraints to the model, and finally adding an objective function (often called the Biomass function), usually to simulate the growth of the organism being modelled.
Flux balance analysis	Model preparation and refinement	Metabolic network and software tools Metabolic networks can vary in scope from those describing a single pathway, up to the cell, tissue or organism. The main requirement of a metabolic network that forms the basis of an FBA-ready network is that it contains no gaps. This typically means that extensive manual curation is required, making the preparation of a metabolic network for flux-balance analysis a process that can take months or years. However, recent advances such as so-called gap-filling methods can reduce the required time to weeks or months.
Flux balance analysis	Model preparation and refinement	Software packages for creation of FBA models include: Pathway Tools/MetaFlux, Simpheny, MetNetMaker, COBRApy, CarveMe, MIOM, or COBREXA.jl.Generally models are created in BioPAX or SBML format so that further analysis or visualization can take place in other software although this is not a requirement.
Flux balance analysis	Model preparation and refinement	Constraints A key part of FBA is the ability to add constraints to the flux rates of reactions within networks, forcing them to stay within a range of selected values. This lets the model more accurately simulate real metabolism. The constraints belong to two subsets from a biological perspective; boundary constraints that limit nutrient uptake/excretion and internal constraints that limit the flux through reactions within the organism. In mathematical terms, the application of constraints can be considered to reduce the solution space of the FBA model. In addition to constraints applied at the edges of a metabolic network, constraints can be applied to reactions deep within the network. These constraints are usually simple; they may constrain the direction of a reaction due to energy considerations or constrain the maximum speed of a reaction due to the finite speed of all reactions in nature.
Flux balance analysis	Model preparation and refinement	Growth media constraints Organisms, and all other metabolic systems, require some input of nutrients. Typically the rate of uptake of nutrients is dictated by their availability (a nutrient that is not present cannot be absorbed), their concentration and diffusion constants (higher concentrations of quickly-diffusing metabolites are absorbed more quickly) and the method of absorption (such as active transport or facilitated diffusion versus simple diffusion).
Flux balance analysis	Model preparation and refinement	If the rate of absorption (and/or excretion) of certain nutrients can be experimentally measured then this information can be added as a constraint on the flux rate at the edges of a metabolic model. This ensures that nutrients that are not present or not absorbed by the organism do not enter its metabolism (the flux rate is constrained to zero) and also means that known nutrient uptake rates are adhered to by the simulation. This provides a secondary method of making sure that the simulated metabolism has experimentally verified properties rather than just mathematically acceptable ones.
Flux balance analysis	Model preparation and refinement	Thermodynamical reaction constraints In principle, all reactions are reversible however in practice reactions often effectively occur in only one direction. This may be due to significantly higher concentration of reactants compared to the concentration of the products of the reaction. But more often it happens because the products of a reaction have a much lower free energy than the reactants and therefore the forward direction of a reaction is favored more.
Flux balance analysis	Model preparation and refinement	For ideal reactions, −∞<vi<∞ For certain reactions a thermodynamic constraint can be applied implying direction (in this case forward) 0<vi<∞ Realistically the flux through a reaction cannot be infinite (given that enzymes in the real system are finite) which implies that, max Experimentally measured flux constraints Certain flux rates can be measured experimentally ( vi,m ) and the fluxes within a metabolic model can be constrained, within some error ( ε ), to ensure these known flux rates are accurately reproduced in the simulation.
Flux balance analysis	Model preparation and refinement	vi,m−ε<vi<vi,m+ε Flux rates are most easily measured for nutrient uptake at the edge of the network. Measurements of internal fluxes is possible using radioactively labelled or NMR visible metabolites.
Flux balance analysis	Model preparation and refinement	Constrained FBA-ready metabolic models can be analyzed using software such as the COBRA toolbox (available implementations in MATLAB and Python), SurreyFBA, or the web-based FAME. Additional software packages have been listed elsewhere. A comprehensive review of all such software and their functionalities has been recently reviewed.An open-source alternative is available in the R (programming language) as the packages abcdeFBA or sybil for performing FBA and other constraint based modeling techniques.
Flux balance analysis	Model preparation and refinement	Objective function FBA can give a large number of mathematically acceptable solutions to the steady-state problem (Sv→=0) . However solutions of biological interest are the ones which produce the desired metabolites in the correct proportion. The objective function defines the proportion of these metabolites. For instance when modelling the growth of an organism the objective function is generally defined as biomass. Mathematically, it is a column in the stoichiometry matrix the entries of which place a "demand" or act as a "sink" for biosynthetic precursors such as fatty acids, amino acids and cell wall components which are present on the corresponding rows of the S matrix. These entries represent experimentally measured, dry weight proportions of cellular components. Therefore, this column becomes a lumped reaction that simulates growth and reproduction. Therefore, the accuracy of experimental measurements plays an essential role in the correct definition of the biomass function and makes the results of FBA biologically applicable by ensuring that the correct proportion of metabolites are produced by metabolism.
Flux balance analysis	Model preparation and refinement	When modeling smaller networks the objective function can be changed accordingly. An example of this would be in the study of the carbohydrate metabolism pathways where the objective function would probably be defined as a certain proportion of ATP and NADH and thus simulate the production of high energy metabolites by this pathway.
Flux balance analysis	Model preparation and refinement	Optimization of the objective/biomass function Linear programming can be used to find a single optimal solution. The most common biological optimization goal for a whole-organism metabolic network would be to choose the flux vector v→ that maximises the flux through a biomass function composed of the constituent metabolites of the organism placed into the stoichiometric matrix and denoted biomass or simply vb max s.t.
Flux balance analysis	Model preparation and refinement	Sv→=0 In the more general case any reaction can be defined and added to the biomass function with either the condition that it be maximised or minimised if a single “optimal” solution is desired. Alternatively, and in the most general case, a vector c→ can be introduced, which defines the weighted set of reactions that the linear programming model should aim to maximise or minimise, max s.t.
Flux balance analysis	Model preparation and refinement	In the case of there being only a single separate biomass function/reaction within the stoichiometric matrix c→ would simplify to all zeroes with a value of 1 (or any non-zero value) in the position corresponding to that biomass function. Where there were multiple separate objective functions c→ would simplify to all zeroes with weighted values in the positions corresponding to all objective functions.
Flux balance analysis	Model preparation and refinement	Reducing the solution space – biological considerations for the system The analysis of the null space of matrices is implemented in software packages specialized for matrix operations such as Matlab and Octave. Determination of the null space of S tells us all the possible collections of flux vectors (or linear combinations thereof) that balance fluxes within the biological network. The advantage of this approach becomes evident in biological systems which are described by differential equation systems with many unknowns. The velocities in the differential equations above - v1 and v2 - are dependent on the reaction rates of the underlying equations. The velocities are generally taken from the Michaelis–Menten kinetic theory, which involves the kinetic parameters of the enzymes catalyzing the reactions and the concentration of the metabolites themselves. Isolating enzymes from living organisms and measuring their kinetic parameters is a difficult task, as is measuring the internal concentrations and diffusion constants of metabolites within an organism. Therefore, the differential equation approach to metabolic modeling is beyond the current scope of science for all but the most studied organisms. FBA avoids this impediment by applying the homeostatic assumption, which is a reasonably approximate description of biological systems.
Flux balance analysis	Model preparation and refinement	Although FBA avoids that biological obstacle, the mathematical issue of a large solution space remains. FBA has a two-fold purpose. Accurately representing the biological limits of the system and returning the flux distribution closest to the natural fluxes within the target system/organism. Certain biological principles can help overcome the mathematical difficulties. While the stoichiometric matrix is almost always under-determined initially (meaning that the solution space to Sv→=0 is very large), the size of the solution space can be reduced and be made more reflective of the biology of the problem through the application of certain constraints on the solutions.
Flux balance analysis	Extensions	The success of FBA and the realization of its limitations has led to extensions that attempt to mediate the limitations of the technique. Flux variability analysis The optimal solution to the flux-balance problem is rarely unique with many possible, and equally optimal, solutions existing. Flux variability analysis (FVA), built into some analysis software, returns the boundaries for the fluxes through each reaction that can, paired with the right combination of other fluxes, estimate the optimal solution. Reactions which can support a low variability of fluxes through them are likely to be of a higher importance to an organism and FVA is a promising technique for the identification of reactions that are important.
Flux balance analysis	Extensions	Minimization of metabolic adjustment (MOMA) When simulating knockouts or growth on media, FBA gives the final steady-state flux distribution. This final steady state is reached in varying time-scales. For example, the predicted growth rate of E. coli on glycerol as the primary carbon source did not match the FBA predictions; however, on sub-culturing for 40 days or 700 generations, the growth rate adaptively evolved to match the FBA prediction.Sometimes it is of interest to find out what is the immediate effect of a perturbation or knockout, since it takes time for regulatory changes to occur and for the organism to re-organize fluxes to optimally utilize a different carbon source or circumvent the effect of the knockout. MOMA predicts the immediate sub-optimal flux distribution following the perturbation by minimizing the distance (Euclidean) between the wild-type FBA flux distribution and the mutant flux distribution using quadratic programming. This yields an optimization problem of the form.
Flux balance analysis	Extensions	min \|\|vw−vd\|\|2s.t.S⋅vd=0 where vw represents the wild-type (or unperturbed state) flux distribution and vd represents the flux distribution on gene deletion that is to be solved for. This simplifies to: min 12vdTIvd+(−vw)⋅vds.t.S⋅vd=0 This is the MOMA solution which represents the flux distribution immediately post-perturbation.
Flux balance analysis	Extensions	Regulatory on-off minimization (ROOM) ROOM attempts to improve the prediction of the metabolic state of an organism after a gene knockout. It follows the same premise as MOMA that an organism would try to restore a flux distribution as close as possible to the wild-type after a knockout. However it further hypothesizes that this steady state would be reached through a series of transient metabolic changes by the regulatory network and that the organism would try to minimize the number of regulatory changes required to reach the wild-type state. Instead of using a distance metric minimization however it uses a mixed integer linear programming method.
Flux balance analysis	Extensions	Dynamic FBA Dynamic FBA attempts to add the ability for models to change over time, thus in some ways avoiding the strict steady state condition of pure FBA. Typically the technique involves running an FBA simulation, changing the model based on the outputs of that simulation, and rerunning the simulation. By repeating this process an element of feedback is achieved over time.
Flux balance analysis	Comparison with other techniques	FBA provides a less simplistic analysis than Choke Point Analysis while requiring far less information on reaction rates and a much less complete network reconstruction than a full dynamic simulation would require. In filling this niche, FBA has been shown to be a very useful technique for analysis of the metabolic capabilities of cellular systems.
Flux balance analysis	Comparison with other techniques	Choke point analysis Unlike choke point analysis which only considers points in the network where metabolites are produced but not consumed or vice versa, FBA is a true form of metabolic network modelling because it considers the metabolic network as a single complete entity (the stoichiometric matrix) at all stages of analysis. This means that network effects, such as chemical reactions in distant pathways affecting each other, can be reproduced in the model. The upside to the inability of choke point analysis to simulate network effects is that it considers each reaction within a network in isolation and thus can suggest important reactions in a network even if a network is highly fragmented and contains many gaps.
Flux balance analysis	Comparison with other techniques	Dynamic metabolic simulation Unlike dynamic metabolic simulation, FBA assumes that the internal concentration of metabolites within a system stays constant over time and thus is unable to provide anything other than steady-state solutions. It is unlikely that FBA could, for example, simulate the functioning of a nerve cell. Since the internal concentration of metabolites is not considered within a model, it is possible that an FBA solution could contain metabolites at a concentration too high to be biologically acceptable. This is a problem that dynamic metabolic simulations would probably avoid. One advantage of the simplicity of FBA over dynamic simulations is that they are far less computationally expensive, allowing the simulation of large numbers of perturbations to the network. A second advantage is that the reconstructed model can be substantially simpler by avoiding the need to consider enzyme rates and the effect of complex interactions on enzyme kinetics.
Mobile app development	Mobile app development	Mobile app development is the act or process by which a mobile app is developed for one or more mobile devices, which can include personal digital assistants (PDA), enterprise digital assistants (EDA), or mobile phones. Such software applications are specifically designed to run on mobile devices, taking numerous hardware constraints into consideration. Common constraints include CPU architecture and speeds, available memory (RAM), limited data storage capacities, and considerable variation in displays (technology, size, dimensions, resolution) and input methods (buttons, keyboard, touch screens with/without styluses). These applications (or 'apps') can be pre-installed on phones during manufacturing or delivered as web applications, using server-side or client-side processing (e.g., JavaScript) to provide an "application-like" experience within a web browser.Mobile app development has been steadily growing, in revenues and jobs created. A 2013 analyst report estimates there are 529,000 direct app economy jobs within the EU then 28 members (including the UK), 60 percent of which are mobile app developers.
Mobile app development	Overview	In order to facilitate the development of applications for mobile devices, and consistency thereof, various approaches have been taken.
Mobile app development	Overview	Most companies that ship a product (e.g. Apple, iPod/iPhone/iPad) provide an official software development kit (SDK). They may also opt to provide some form of Testing and/or Quality Assurance (QA). In exchange for being provided the SDK or other tools, it may be necessary for a prospective developer to sign a some form of non-disclosure agreement, or NDA, which restricts the sharing of privileged information.
Mobile app development	Overview	As part of the development process, mobile user interface (UI) design is an essential step in the creation of mobile apps. Mobile UI designers consider constraints, contexts, screen space, input methods, and mobility as outlines for design. Constraints in mobile UI design in constraints include the limited attention span of the user and form factors, such as a mobile device's screen size for a user's hand(s). Mobile UI context includes signal cues from user activity, such as the location where or the time when the device is in use, that can be observed from user interactions within a mobile app. Such context clues can be used to provide automatic suggestions when scheduling an appointment or activity or to filter a list of various services for the user.
Mobile app development	Overview	The user is often the focus of interaction with their device, and the interface entails components of both hardware and software. User input allows for the users to manipulate a system, and device's output allows the system to indicate the effects of the users' manipulation. Overall, mobile UI design's goal is mainly for an understandable, user-friendly interface. Functionality is supported by mobile enterprise application platforms or integrated development environments (IDEs). Developers of mobile applications must also consider a large array of devices with different screen sizes, hardware specifications, and configurations because of intense competition in mobile hardware and changes within each of the platforms. Today, mobile apps are usually distributed via an official online outlet or marketplace (e.g. Apple - The App Store, Google - Google Play) and there is a formalized process by which developers submit their apps for approval and inclusion in those marketplaces. Historically, however, that was not always the case. Mobile UIs, or front-ends, rely on mobile back-ends to support access to enterprise systems. The mobile back-end facilitates data routing, security, authentication, authorization, working off-line, and service orchestration. This functionality is supported by a mix of middleware components including mobile app server, mobile backend as a service (MBaaS), and service-oriented architecture (SOA) infrastructure.
Mobile app development	Platform	The software development packages needed to develop, deploy, and manage mobile apps are made from many components and tools which allow a developer to write, test, and deploy applications for one or more target platforms. Front-end development tools Front-end development tools are focused on the user interface and user experience (UI-UX) and provide the following abilities: UI design tools SDKs to access device features Cross-platform accommodations/supportNotable tools are listed below. First-Party First party tools include official SDKs published by, or on behalf of, the company responsible for the design of a particular hardware platform (e.g. Apple, Google, etc) as well as any third-party software that is officially supported for the purpose of developing mobile apps for that hardware. Second Party Third Party Back-end servers Back-end tools pick up where the front-end tools leave off, and provide a set of reusable services that are centrally managed and controlled and provide the following abilities: Integration with back-end systems User authentication-authorization Data services Reusable business logicAvailable tools are listed below.
Mobile app development	Platform	Security add-on layers With bring your own device (BYOD) becoming the norm within more enterprises, IT departments often need stop-gap, tactical solutions that layer atop existing apps, phones, and platform component. Features include App wrapping for security Data encryption Client actions Reporting and statistics System software Many system-level components are needed to have a functioning platform for developing mobile apps.
Mobile app development	Platform	Criteria for selecting a development platform usually contains the target mobile platforms, existing infrastructure and development skills. When targeting more than one platform with cross-platform development it is also important to consider the impact of the tool on the user experience. Performance is another important criteria, as research on mobile apps indicates a strong correlation between application performance and user satisfaction. Along with performance and other criteria, the availability of the technology and the project's requirement may drive the development between native and cross-platform environments. To aid the choice between native and cross-platform environments, some guidelines and benchmarks have been published. Typically, cross-platform environments are reusable across multiple platforms, leveraging a native container while using HTML, CSS, and JavaScript for the user interface. In contrast, native environments are targeted at one platform for each of those environments. For example, Android development occurs in the Eclipse IDE using Android Developer Tools (ADT) plugins, Apple iOS development occurs using Xcode IDE with Objective-C and/or Swift, Windows and BlackBerry each have their own development environments.
Mobile app development	Platform	Mobile app testing Mobile applications are first tested within the development environment using emulators and later subjected to field testing. Emulators provide an inexpensive way to test applications on mobile phones to which developers may not have physical access. The following are examples of tools used for testing application across the most popular mobile operating systems. Google Android Emulator - an Android emulator that is patched to run on a Windows PC as a standalone app, without having to download and install the complete and complex Android SDK. It can be installed and Android compatible apps can be tested on it. The official Android SDK Emulator - a mobile device emulator which mimics all of the hardware and software features of a typical mobile device (without the calls). TestiPhone - a web browser-based simulator for quickly testing iPhone web applications. This tool has been tested and works using Internet Explorer 7, Firefox 2 and Safari 3.
Mobile app development	Platform	iPhoney - gives a pixel-accurate web browsing environment and it is powered by Safari. It can be used while developing web sites for the iPhone. It is not an iPhone simulator but instead is designed for web developers who want to create 320 by 480 (or 480 by 320) websites for use with iPhone. iPhoney will only run on OS X 10.4.7 or later.
Mobile app development	Platform	BlackBerry Simulator - There are a variety of official BlackBerry simulators available to emulate the functionality of actual BlackBerry products and test how the device software, screen, keyboard and trackwheel will work with application. Windows UI Automation - To test applications that use the Microsoft UI Automation technology, it requires Windows Automation API 3.0. It is pre-installed on Windows 7, Windows Server 2008 R2 and later versions of Windows. On other operating systems, you can install using Windows Update or download it from the Microsoft Web site.
Mobile app development	Platform	MobiOne Developer - a mobile Web integrated development environment (IDE) for Windows that helps developers to code, test, debug, package and deploy mobile Web applications to devices such as iPhone, BlackBerry, Android, and the Palm Pre. MobiOne Developer was officially declared End of Life by the end of 2014.Tools include eggPlant: A GUI-based automated test tool for mobile app across all operating systems and devices.
Mobile app development	Platform	Ranorex: Test automation tools for mobile, web and desktop apps. Testdroid: Real mobile devices and test automation tools for testing mobile and web apps.
Mobile app development	Patents	Many patent applications are pending for new mobile phone apps. Most of these are in the technological fields of business methods, database management, data transfer, and operator interface.
Big memory	Big memory	Big memory computers are machines with a large amount of random-access memory (RAM). The computers are required for databases, graph analytics, or more generally, high-performance computing, data science and big data. Some database systems are designed to run mostly in memory, rarely if ever retrieving data from disk or flash memory. See list of in-memory databases.
Big memory	Details	The performance of big memory systems depends on how the central processing units (CPUs) access the memory, via a conventional memory controller or via non-uniform memory access (NUMA). Performance also depends on the size and design of the CPU cache.
Big memory	Details	Performance also depends on operating system (OS) design. The huge pages feature in Linux and other OSes can improve the efficiency of virtual memory. The transparent huge pages feature in Linux can offer better performance for some big-memory workloads. The "Large-Page Support" in Microsoft Windows enables server applications to establish large-page memory regions which are typically three orders of magnitude larger than the native page size.
UNIQUAC	UNIQUAC	In statistical thermodynamics, UNIQUAC (a portmanteau of universal quasichemical) is an activity coefficient model used in description of phase equilibria. The model is a so-called lattice model and has been derived from a first order approximation of interacting molecule surfaces. The model is, however, not fully thermodynamically consistent due to its two-liquid mixture approach. In this approach the local concentration around one central molecule is assumed to be independent from the local composition around another type of molecule.
UNIQUAC	UNIQUAC	The UNIQUAC model can be considered a second generation activity coefficient because its expression for the excess Gibbs energy consists of an entropy term in addition to an enthalpy term. Earlier activity coefficient models such as the Wilson equation and the non-random two-liquid model (NRTL model) only consist of enthalpy terms.
UNIQUAC	UNIQUAC	Today the UNIQUAC model is frequently applied in the description of phase equilibria (i.e. liquid–solid, liquid–liquid or liquid–vapor equilibrium). The UNIQUAC model also serves as the basis of the development of the group contribution method UNIFAC, where molecules are subdivided into functional groups. In fact, UNIQUAC is equal to UNIFAC for mixtures of molecules, which are not subdivided; e.g. the binary systems water-methanol, methanol-acryonitrile and formaldehyde-DMF.
UNIQUAC	UNIQUAC	A more thermodynamically consistent form of UNIQUAC is given by the more recent COSMOSPACE and the equivalent GEQUAC model.
UNIQUAC	Equations	Like most local composition models, UNIQUAC splits excess Gibbs free energy into a combinatorial and a residual contribution: GE=(GE)C+(GE)R The calculated activity coefficients of the ith component then split likewise: ln ln ln ⁡γiR The first is an entropic term quantifying the deviation from ideal solubility as a result of differences in molecule shape. The latter is an enthalpic correction caused by the change in interacting forces between different molecules upon mixing.
UNIQUAC	Equations	Combinatorial contribution The combinatorial contribution accounts for shape differences between molecules and affects the entropy of the mixture and is based on the lattice theory. The Stavermann–Guggenheim equation is used to approximate this term from pure chemical parameters, using the relative Van der Waals volumes ri and surface areas qi of the pure chemicals: ln ln ⁡FiVi Differentiating yields the excess entropy γC, ln ln ln ⁡ViFi) with the volume fraction per mixture mole fraction, Vi, for the ith component given by: Vi=ri∑jxjrj The surface area fraction per mixture molar fraction, Fi, for the ith component is given by: Fi=qi∑jxjqj The first three terms on the right hand side of the combinatorial term form the Flory–Huggins contribution, while the remaining term, the Guggenhem–Staverman correction, reduce this because connecting segments cannot be placed in all direction in space. This spatial correction shifts the result of the Flory–Huggins term about 5% towards an ideal solution. The coordination number, z, i.e. the number of close interacting molecules around a central molecule, is frequently set to 10. It can be regarded as an average value that lies between cubic (z = 6) and hexagonal packing (z = 12) of molecules that are simplified by spheres.
UNIQUAC	Equations	In the case of infinite dilution for a binary mixture, the equations for the combinatorial contribution reduce to: ln ln ln ln ln ln ⁡r2q1r1q2) This pair of equations show that molecules of same shape, i.e. same r and q parameters, have γ1C,∞=γ2C,∞=1 Residual contribution The residual, enthalpic term contains an empirical parameter, τij , which is determined from the binary interaction energy parameters. The expression for the residual activity coefficient for molecule i is: ln ln ⁡∑jqjxjτji∑jqjxj−∑jqjxjτij∑kqkxkτkj) with τij=e−Δuij/RT Δuii [J/mol] is the binary interaction energy parameter. Theory defines Δuij=uij−uii , and Δuji=uji−ujj , where uij is the interaction energy between molecules i and j . The interaction energy parameters are usually determined from activity coefficients, vapor-liquid, liquid-liquid, or liquid-solid equilibrium data.
UNIQUAC	Equations	Usually Δuij≠Δuji , because the energies of evaporation (i.e. Δuii ), are in many cases different, while the energy of interaction between molecule i and j is symmetric, and therefore uij=uji . If the interactions between the j molecules and i molecules is the same as between molecules i and j, there is no excess energy of mixing, Δuij=Δuji=0 . And thus γiR=1 Alternatively, in some process simulation software τij can be expressed as follows : ln ln ⁡(T)+DijT+Eij/T2 .The C, D, and E coefficients are primarily used in fitting liquid–liquid equilibria data (with D and E rarely used at that). The C coefficient is useful for vapor-liquid equilibria data as well. The use of such an expression ignores the fact that on a molecular level the energy, Δuij , is temperature independent. It is a correction to repair the simplifications, which were applied in the derivation of the model.
UNIQUAC	Applications (phase equilibrium calculations)	Activity coefficients can be used to predict simple phase equilibria (vapour–liquid, liquid–liquid, solid–liquid), or to estimate other physical properties (e.g. viscosity of mixtures). Models such as UNIQUAC allow chemical engineers to predict the phase behavior of multicomponent chemical mixtures. They are commonly used in process simulation programs to calculate the mass balance in and around separation units.
UNIQUAC	Parameters determination	UNIQUAC requires two basic underlying parameters: relative surface and volume fractions are chemical constants, which must be known for all chemicals (qi and ri parameters, respectively). Empirical parameters between components that describes the intermolecular behaviour. These parameters must be known for all binary pairs in the mixture. In a quaternary mixture there are six such parameters (1–2,1–3,1–4,2–3,2–4,3–4) and the number rapidly increases with additional chemical components. The empirical parameters are obtained by a correlation process from experimental equilibrium compositions or activity coefficients, or from phase diagrams, from which the activity coefficients themselves can be calculated. An alternative is to obtain activity coefficients with a method such as UNIFAC, and the UNIFAC parameters can then be simplified by fitting to obtain the UNIQUAC parameters. This method allows for the more rapid calculation of activity coefficients, rather than direct usage of the more complex method.
UNIQUAC	Parameters determination	Remark that the determination of parameters from LLE data can be difficult depending on the complexity of the studied system. For this reason it is necessary to confirm the consistency of the obtained parameters in the whole range of compositions (including binary subsystems, experimental and calculated lie-lines, Hessian matrix, etc.).
UNIQUAC	Parameters determination	Newer developments UNIQUAC has been extended by several research groups. Some selected derivatives are: UNIFAC, a method which permits the volume, surface and in particular, the binary interaction parameters to be estimated. This eliminates the use of experimental data to calculate the UNIQUAC parameters, extensions for the estimation of activity coefficients for electrolytic mixtures, extensions for better describing the temperature dependence of activity coefficients, and solutions for specific molecular arrangements.The DISQUAC model advances UNIFAC by replacing UNIFAC's semi-empirical group-contribution model with an extension of the consistent theory of Guggenheim's UNIQUAC. By adding a "dispersive" or "random-mixing physical" term, it better predicts mixtures of molecules with both polar and non-polar groups. However, separate calculation of the dispersive and quasi-chemical terms means the contact surfaces are not uniquely defined. The GEQUAC model advances DISQUAC slightly, by breaking polar groups into individual poles and merging the dispersive and quasi-chemical terms.
Panasonic Lumix DMC-TZ20	Panasonic Lumix DMC-TZ20	Panasonic Lumix DMC-TZ20, also known as Panasonic Lumix DMC-TZ22 or Panasonic Lumix DMC-ZS10, is a digital camera by Panasonic Lumix. The highest-resolution pictures it records is 14.1 megapixels, through its 24mm Ultra Wide-Angle Leica DC VARIO-ELMAR.
Panasonic Lumix DMC-TZ20	Property	24 mm LEICA DC 16x optical zoom Full HD movies 1.920 x 1.080 50i GPS integrated touch-screen LCD 3D photos iA (Intelligent Auto) mode with night shot free-hand
Hodge structure	Hodge structure	In mathematics, a Hodge structure, named after W. V. D. Hodge, is an algebraic structure at the level of linear algebra, similar to the one that Hodge theory gives to the cohomology groups of a smooth and compact Kähler manifold. Hodge structures have been generalized for all complex varieties (even if they are singular and non-complete) in the form of mixed Hodge structures, defined by Pierre Deligne (1970). A variation of Hodge structure is a family of Hodge structures parameterized by a manifold, first studied by Phillip Griffiths (1968). All these concepts were further generalized to mixed Hodge modules over complex varieties by Morihiko Saito (1989).
Hodge structure	Hodge structures	Definition of Hodge structures A pure Hodge structure of integer weight n consists of an abelian group HZ and a decomposition of its complexification H into a direct sum of complex subspaces Hp,q , where p+q=n , with the property that the complex conjugate of Hp,q is Hq,p := HZ⊗ZC=⨁p+q=nHp,q, Hp,q¯=Hq,p. An equivalent definition is obtained by replacing the direct sum decomposition of H by the Hodge filtration, a finite decreasing filtration of H by complex subspaces FpH(p∈Z), subject to the condition and FpH⊕FqH¯=H. The relation between these two descriptions is given as follows: Hp,q=FpH∩FqH¯, FpH=⨁i≥pHi,n−i.
Hodge structure	Hodge structures	For example, if X is a compact Kähler manifold, HZ=Hn(X,Z) is the n-th cohomology group of X with integer coefficients, then H=Hn(X,C) is its n-th cohomology group with complex coefficients and Hodge theory provides the decomposition of H into a direct sum as above, so that these data define a pure Hodge structure of weight n. On the other hand, the Hodge–de Rham spectral sequence supplies Hn with the decreasing filtration by FpH as in the second definition.For applications in algebraic geometry, namely, classification of complex projective varieties by their periods, the set of all Hodge structures of weight n on HZ is too big. Using the Riemann bilinear relations, in this case called Hodge Riemann bilinear relations, it can be substantially simplified. A polarized Hodge structure of weight n consists of a Hodge structure (HZ,Hp,q) and a non-degenerate integer bilinear form Q on HZ (polarization), which is extended to H by linearity, and satisfying the conditions: for for 0.
Hodge structure	Hodge structures	In terms of the Hodge filtration, these conditions imply that for φ≠0, where C is the Weil operator on H, given by C=ip−q on Hp,q Yet another definition of a Hodge structure is based on the equivalence between the Z -grading on a complex vector space and the action of the circle group U(1). In this definition, an action of the multiplicative group of complex numbers C∗ viewed as a two-dimensional real algebraic torus, is given on H. This action must have the property that a real number a acts by an. The subspace Hp,q is the subspace on which z∈C∗ acts as multiplication by zpz¯q.
Hodge structure	Hodge structures	A-Hodge structure In the theory of motives, it becomes important to allow more general coefficients for the cohomology. The definition of a Hodge structure is modified by fixing a Noetherian subring A of the field R of real numbers, for which A⊗ZR is a field. Then a pure Hodge A-structure of weight n is defined as before, replacing Z with A. There are natural functors of base change and restriction relating Hodge A-structures and B-structures for A a subring of B.
Hodge structure	Mixed Hodge structures	It was noticed by Jean-Pierre Serre in the 1960s based on the Weil conjectures that even singular (possibly reducible) and non-complete algebraic varieties should admit 'virtual Betti numbers'. More precisely, one should be able to assign to any algebraic variety X a polynomial PX(t), called its virtual Poincaré polynomial, with the properties If X is nonsingular and projective (or complete) If Y is closed algebraic subset of X and U = X \ Y The existence of such polynomials would follow from the existence of an analogue of Hodge structure in the cohomologies of a general (singular and non-complete) algebraic variety. The novel feature is that the nth cohomology of a general variety looks as if it contained pieces of different weights. This led Alexander Grothendieck to his conjectural theory of motives and motivated a search for an extension of Hodge theory, which culminated in the work of Pierre Deligne. He introduced the notion of a mixed Hodge structure, developed techniques for working with them, gave their construction (based on Heisuke Hironaka's resolution of singularities) and related them to the weights on l-adic cohomology, proving the last part of the Weil conjectures.
Hodge structure	Mixed Hodge structures	Example of curves To motivate the definition, consider the case of a reducible complex algebraic curve X consisting of two nonsingular components, X1 and X2 , which transversally intersect at the points Q1 and Q2 . Further, assume that the components are not compact, but can be compactified by adding the points P1,…,Pn . The first cohomology group of the curve X (with compact support) is dual to the first homology group, which is easier to visualize. There are three types of one-cycles in this group. First, there are elements αi representing small loops around the punctures Pi . Then there are elements βj that are coming from the first homology of the compactification of each of the components. The one-cycle in Xk⊂X (k=1,2 ) corresponding to a cycle in the compactification of this component, is not canonical: these elements are determined modulo the span of α1,…,αn . Finally, modulo the first two types, the group is generated by a combinatorial cycle γ which goes from Q1 to Q2 along a path in one component X1 and comes back along a path in the other component X2 . This suggests that H1(X) admits an increasing filtration 0⊂W0⊂W1⊂W2=H1(X), whose successive quotients Wn/Wn−1 originate from the cohomology of smooth complete varieties, hence admit (pure) Hodge structures, albeit of different weights. Further examples can be found in "A Naive Guide to Mixed Hodge Theory".
Hodge structure	Mixed Hodge structures	Definition of mixed Hodge structure A mixed Hodge structure on an abelian group HZ consists of a finite decreasing filtration Fp on the complex vector space H (the complexification of HZ ), called the Hodge filtration and a finite increasing filtration Wi on the rational vector space HQ=HZ⊗ZQ (obtained by extending the scalars to rational numbers), called the weight filtration, subject to the requirement that the n-th associated graded quotient of HQ with respect to the weight filtration, together with the filtration induced by F on its complexification, is a pure Hodge structure of weight n, for all integer n. Here the induced filtration on gr nW⁡H=Wn⊗C/Wn−1⊗C is defined by gr nW⁡H=(Fp∩Wn⊗C+Wn−1⊗C)/Wn−1⊗C.
Hodge structure	Mixed Hodge structures	One can define a notion of a morphism of mixed Hodge structures, which has to be compatible with the filtrations F and W and prove the following: Theorem. Mixed Hodge structures form an abelian category. The kernels and cokernels in this category coincide with the usual kernels and cokernels in the category of vector spaces, with the induced filtrations.The total cohomology of a compact Kähler manifold has a mixed Hodge structure, where the nth space of the weight filtration Wn is the direct sum of the cohomology groups (with rational coefficients) of degree less than or equal to n. Therefore, one can think of classical Hodge theory in the compact, complex case as providing a double grading on the complex cohomology group, which defines an increasing filtration Fp and a decreasing filtration Wn that are compatible in certain way. In general, the total cohomology space still has these two filtrations, but they no longer come from a direct sum decomposition. In relation with the third definition of the pure Hodge structure, one can say that a mixed Hodge structure cannot be described using the action of the group C∗.
Hodge structure	Mixed Hodge structures	An important insight of Deligne is that in the mixed case there is a more complicated noncommutative proalgebraic group that can be used to the same effect using Tannakian formalism.
Hodge structure	Mixed Hodge structures	Moreover, the category of (mixed) Hodge structures admits a good notion of tensor product, corresponding to the product of varieties, as well as related concepts of inner Hom and dual object, making it into a Tannakian category. By Tannaka–Krein philosophy, this category is equivalent to the category of finite-dimensional representations of a certain group, which Deligne, Milne and et el. has explicitly described, see Deligne & Milne (1982) and Deligne (1994). The description of this group was recast in more geometrical terms by Kapranov (2012). The corresponding (much more involved) analysis for rational pure polarizable Hodge structures was done by Patrikis (2016).
Hodge structure	Mixed Hodge structures	Mixed Hodge structure in cohomology (Deligne's theorem) Deligne has proved that the nth cohomology group of an arbitrary algebraic variety has a canonical mixed Hodge structure. This structure is functorial, and compatible with the products of varieties (Künneth isomorphism) and the product in cohomology. For a complete nonsingular variety X this structure is pure of weight n, and the Hodge filtration can be defined through the hypercohomology of the truncated de Rham complex.
Hodge structure	Mixed Hodge structures	The proof roughly consists of two parts, taking care of noncompactness and singularities. Both parts use the resolution of singularities (due to Hironaka) in an essential way. In the singular case, varieties are replaced by simplicial schemes, leading to more complicated homological algebra, and a technical notion of a Hodge structure on complexes (as opposed to cohomology) is used. Using the theory of motives, it is possible to refine the weight filtration on the cohomology with rational coefficients to one with integral coefficients.
Hodge structure	Examples	The Tate–Hodge structure Z(1) is the Hodge structure with underlying Z module given by 2πiZ (a subgroup of C ), with Z(1)⊗C=H−1,−1. So it is pure of weight −2 by definition and it is the unique 1-dimensional pure Hodge structure of weight −2 up to isomorphisms. More generally, its nth tensor power is denoted by Z(n); it is 1-dimensional and pure of weight −2n. The cohomology of a compact Kähler manifold has a Hodge structure, and the nth cohomology group is pure of weight n. The cohomology of a complex variety (possibly singular or non-proper) has a mixed Hodge structure. This was shown for smooth varieties by Deligne (1971), Deligne (1971a) and in general by Deligne (1974). For a projective variety X with normal crossing singularities there is a spectral sequence with a degenerate E2-page which computes all of its mixed Hodge structures. The E1-page has explicit terms with a differential coming from a simplicial set. Any smooth variety X admits a smooth compactification with complement a normal crossing divisor. The corresponding logarithmic forms can be used to describe the mixed Hodge structure on the cohomology of X explicitly.
Hodge structure	Examples	The Hodge structure for a smooth projective hypersurface X⊂Pn+1 of degree d was worked out explicitly by Griffiths in his "Period Integrals of Algebraic Manifolds" paper. If f∈C[x0,…,xn+1] is the polynomial defining the hypersurface X then the graded Jacobian quotient ring contains all of the information of the middle cohomology of X . He shows that For example, consider the K3 surface given by g=x04+⋯+x34 , hence d=4 and n=2 . Then, the graded Jacobian ring is The isomorphism for the primitive cohomology groups then read hence Notice that R(g)4 is the vector space spanned by which is 19-dimensional. There is an extra vector in H1,1(X) given by the Lefschetz class [L] . From the Lefschetz hyperplane theorem and Hodge duality, the rest of the cohomology is in Hk,k(X) as is 1 -dimensional. Hence the Hodge diamond reads We can also use the previous isomorphism to verify the genus of a degree d plane curve. Since xd+yd+zd is a smooth curve and the Ehresmann fibration theorem guarantees that every other smooth curve of genus g is diffeomorphic, we have that the genus then the same. So, using the isomorphism of primitive cohomology with the graded part of the Jacobian ring, we see that This implies that the dimension is as desired.
Hodge structure	Examples	The Hodge numbers for a complete intersection are also readily computable: there is a combinatorial formula found by Friedrich Hirzebruch.
Hodge structure	Applications	The machinery based on the notions of Hodge structure and mixed Hodge structure forms a part of still largely conjectural theory of motives envisaged by Alexander Grothendieck. Arithmetic information for nonsingular algebraic variety X, encoded by eigenvalue of Frobenius elements acting on its l-adic cohomology, has something in common with the Hodge structure arising from X considered as a complex algebraic variety. Sergei Gelfand and Yuri Manin remarked around 1988 in their Methods of homological algebra, that unlike Galois symmetries acting on other cohomology groups, the origin of "Hodge symmetries" is very mysterious, although formally, they are expressed through the action of the fairly uncomplicated group RC/RC∗ on the de Rham cohomology. Since then, the mystery has deepened with the discovery and mathematical formulation of mirror symmetry.
Hodge structure	Variation of Hodge structure	A variation of Hodge structure (Griffiths (1968), Griffiths (1968a), Griffiths (1970)) is a family of Hodge structures parameterized by a complex manifold X. More precisely a variation of Hodge structure of weight n on a complex manifold X consists of a locally constant sheaf S of finitely generated abelian groups on X, together with a decreasing Hodge filtration F on S ⊗ OX, subject to the following two conditions: The filtration induces a Hodge structure of weight n on each stalk of the sheaf S (Griffiths transversality) The natural connection on S ⊗ OX maps Fn into Fn−1⊗ΩX1.
Hodge structure	Variation of Hodge structure	Here the natural (flat) connection on S ⊗ OX induced by the flat connection on S and the flat connection d on OX, and OX is the sheaf of holomorphic functions on X, and ΩX1 is the sheaf of 1-forms on X. This natural flat connection is a Gauss–Manin connection ∇ and can be described by the Picard–Fuchs equation. A variation of mixed Hodge structure can be defined in a similar way, by adding a grading or filtration W to S. Typical examples can be found from algebraic morphisms f:Cn→C . For example, {f:C2→Cf(x,y)=y6−x6 has fibers Xt=f−1({t})={(x,y)∈C2:y6−x6=t} which are smooth plane curves of genus 10 for t≠0 and degenerate to a singular curve at 0. Then, the cohomology sheaves Rf∗i(Q_C2) give variations of mixed hodge structures.
Hodge structure	Hodge modules	Hodge modules are a generalization of variation of Hodge structures on a complex manifold. They can be thought of informally as something like sheaves of Hodge structures on a manifold; the precise definition Saito (1989) is rather technical and complicated. There are generalizations to mixed Hodge modules, and to manifolds with singularities. For each smooth complex variety, there is an abelian category of mixed Hodge modules associated with it. These behave formally like the categories of sheaves over the manifolds; for example, morphisms f between manifolds induce functors f∗, f*, f!, f! between (derived categories of) mixed Hodge modules similar to the ones for sheaves.
Hodge structure	Introductory references	Debarre, Olivier, Periods and Moduli Arapura, Donu, Complex Algebraic Varieties and their Cohomology (PDF), pp. 120–123, archived from the original (PDF) on 2020-01-04 (Gives tools for computing hodge numbers using sheaf cohomology) A Naive Guide to Mixed Hodge Theory Dimca, Alexandru (1992). Singularities and Topology of Hypersurfaces. Universitext. New York: Springer-Verlag. pp. 240, 261. doi:10.1007/978-1-4612-4404-2. ISBN 0-387-97709-0. MR 1194180. S2CID 117095021. (Gives a formula and generators for mixed Hodge numbers of affine Milnor fiber of a weighted homogenous polynomial, and also a formula for complements of weighted homogeneous polynomials in a weighted projective space.)
Hodge structure	Survey articles	Arapura, Donu (2006), Mixed Hodge Structures Associated to Geometric Variations (PDF), arXiv:math/0611837, Bibcode:2006math.....11837A
FTH1P5	FTH1P5	Ferritin, heavy polypeptide 1 pseudogene 5 is a protein that in humans is encoded by the FTH1P5 gene.
ZYpp	ZYpp	ZYpp (or libzypp; "Zen / YaST Packages Patches Patterns Products") is a package manager engine that powers Linux applications like YaST, Zypper and the implementation of PackageKit for openSUSE and SUSE Linux Enterprise. Unlike some more basic package managers, it provides a satisfiability solver to compute package dependencies. It is a free and open-source software project sponsored by SUSE and licensed under the terms of the GNU General Public License v2 or later. ZYpp is implemented mostly in the programming language C++.
ZYpp	ZYpp	Zypper is the native command-line interface of the ZYpp package manager to install, remove, update and query software packages of local or remote (networked) media. Its graphical equivalent is the YaST package manager module. It has been used in openSUSE since version 10.2 beta1. In openSUSE 11.1, Zypper reached version 1.0. On June 2, 2009, Ark Linux announced that it has completed its review of dependency solvers and has chosen ZYpp and its tools to replace the aging APT-RPM, as the first distribution to do so. Zypper is also part of the mobile Linux distributions MeeGo, Sailfish OS, and Tizen.
ZYpp	History	Purpose Following its consecutive acquisitions of Ximian and SuSE GmbH in 2003, Novell decided to merge both package management systems, YaST package manager and Red Carpet, in a best of breed approach, as the two solutions so far were used at Novell. Looking at the extant open source tools and their maturity available back in 2005, none fulfilled the requirements, and were able to work smoothly with the extant Linux management infrastructure software developed by Ximian and SUSE, so it was decided to get the best ideas from extant pieces and to work on a new implementation. Libzypp, the resulting library, was planned to be the software management engine of the SUSE distributions and the Linux Management component of the Novell ZENworks Management suite.
ZYpp	History	Early days The Libzypp's solver was a port from the Red Carpet solver, which was written to update packages in installed systems. Using it for the full installing process brought it to its limits, and adding extensions such as support for weak dependencies and patches made it fragile and unpredictable. Although this first version of ZYpp's solver worked satisfactorily, on the company enterprise products with the coupled ZMD daemon, it led to an openSUSE 10.1 release which came out in May 2006 with a system package not working as expected. In December 2006, the openSUSE 10.2 release corrected some defects of the prior release, using the revisited ZYpp v2. ZMD was subsequently removed from the 10.3 release and reserved for only the company Enterprise products. While ZYpp v3 provided openSUSE with a relatively good package manager, equivalent to other existing package managers, it suffered from some flaws in its implementation which greatly limited its speed performance.
ZYpp	History	SAT solver integration An area where libzypp needed improvement was the speed of the dependency solver. libsolv is being written and released under the revised BSD license.Projects like Optimal Package Install/Uninstall Manager (OPIUM) and MANCOOSI were trying to fix dependency solving issues with a SAT solver. Traditional solvers like Advanced Packaging Tool (APT) sometimes show unacceptable deficiencies. It was decided to integrate SAT algorithms into the ZYpp stack; the solver algorithms used were based on the popular minisat solver.The SAT solver implementation as it appears in openSUSE 11.0 is based on two major, but independent, blocks: Using a data dictionary approach to store and retrieve package and dependency information. A new solv format was created, which stores a repository as a string dictionary, a relation dictionary and then all package dependencies. Reading and merging multiple solv repositories takes only milliseconds.
ZYpp	History	Using satisfiability for computing package dependencies. The Boolean satisfiability problem is a well-researched problem with many exemplar solvers available. It is very fast, as package solving complexity is very low compared to other areas where SAT solvers are used. Also, it does not need complex algorithms and can provide understandable suggestions by calculating proof of why a problem is unsolvable.After several months of work, the benchmark results of this fourth ZYpp version integrated with the SAT solver are more than encouraging, moving YaST and Zypper ahead of other RPM-based package managers in speed and size.
Lead hydrogen arsenate	Lead hydrogen arsenate	Lead hydrogen arsenate, also called lead arsenate, acid lead arsenate or LA, chemical formula PbHAsO4, is an inorganic insecticide used primarily against the potato beetle. Lead arsenate was the most extensively used arsenical insecticide. Two principal formulations of lead arsenate were marketed: basic lead arsenate (Pb5OH(AsO4)3, CASN: 1327-31-7) and acid lead arsenate (PbHAsO4).
Lead hydrogen arsenate	Production and structure	It is usually produced using the following reaction, which leads to formation of the desired product as a solid precipitate: Pb(NO3)2 + H3AsO4 → PbHAsO4 +2 HNO3It has the same structure as the hydrogen phosphate PbHPO4. Like lead sulfate PbSO4, these salts are poorly soluble.
Lead hydrogen arsenate	Uses	As an insecticide, it was introduced in 1898 used against the gypsy moth in Massachusetts. It represented a less soluble and less toxic alternative to then-used Paris Green, which is about 10x more toxic. It also adhered better to the surface of the plants, further enhancing and prolonging its insecticidal effect.
Lead hydrogen arsenate	Uses	Lead arsenate was widely used in Australia, Canada, New Zealand, US, England, France, North Africa, and many other areas, principally against the codling moth and snow-white linden moth. It was used mainly on apples, but also on other fruit trees, garden crops, turfgrasses, and against mosquitoes. In combination with ammonium sulfate, it was used in southern California as a winter treatment on lawns to kill crab grass seed.The search for a substitute was commenced in 1919, when it was found that its residues remain in the products despite washing their surfaces. Alternatives were found to be less effective or more toxic to plants and animals, until 1947 when DDT was found. US EPA banned use of lead arsenate on food crops in 1988.
Lead hydrogen arsenate	Safety	LD50 is 1050 mg/kg (rat, oral).Morel mushrooms growing in old apple orchards that had been treated with lead arsenate may accumulate levels of toxic lead and arsenic that are unhealthy for human consumption.Lead arsenate was used as an insecticide in deciduous fruit trees from 1892 until around 1947 in Washington. Peryea et al. studied the distribution of Pb and As in these soils, concluding that these levels were above maximum tolerance levels. This indicates that these levels could be of environmental concern and potentially could be contaminating the groundwater in the area.
Ice XII	Ice XII	Ice XII is a metastable, dense, crystalline phase of solid water, a type of ice. Ice XII was first reported in 1996 by C. Lobban, J.L. Finney and W.F. Kuhs and, after initial caution, was properly identified in 1998.

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