Different graph types may differ in their suitability to support group comparisons, due to the underlying graph schemas. This study examined whether graph schemas are based on perceptual features (i.e., each graph type, e.g., bar or line graph, has its own graph schema) or common invariant structures (i.e., graph types share common schemas). Furthermore, it was of interest which graph type (bar, line, or pie) is optimal for comparing discrete groups. A switching paradigm was used in three experiments. Two graph types were examined at a time (Experiment 1: bar vs. line, Experiment 2: bar vs. pie, Experiment 3: line vs. pie). On each trial, participants received a data graph presenting the data from three groups and were to determine the numerical difference of group A and group B displayed in the graph. We scrutinized whether switching the type of graph from one trial to the next prolonged RTs. The slowing of RTs in switch trials in comparison to trials with only one graph type can indicate to what extent the graph schemas differ. As switch costs were observed in all pairings of graph types, none of the different pairs of graph types tested seems to fully share a common schema. Interestingly, there was tentative evidence for differences in switch costs among different pairings of graph types. Smaller switch costs in Experiment 1 suggested that the graph schemas of bar and line graphs overlap more strongly than those of bar graphs and pie graphs or line graphs and pie graphs. This implies that results were not in line with completely distinct schemas for different graph types either. Taken together, the pattern of results is consistent with a hierarchical view according to which a graph schema consists of parts shared for different graphs and parts that are specific for each graph type. Apart from investigating graph schemas, the study provided evidence for performance differences among graph types. We found that bar graphs yielded the fastest group comparisons compared to line graphs and pie graphs, suggesting that they are the most suitable when used to compare discrete groups.

This dataset contains selected results of rigorous numerical computations conducted in the framework of the research described in the paper “Topological-numerical analysis of a two-dimensional discrete neuron model” by Paweł Pilarczyk, Justyna Signerska-Rynkowska and Grzegorz Graff. A preprint of this paper is available at https://doi.org/10.48550/arXiv.2209.03443.

Summary of network visualisation tools commonly used for the analysis of biological data.

This thesis investigates the parameterized computational complexity of six classic graph problems lifted to a temporal setting. More specifically, we consider problems defined on temporal graphs, that is, a graph where the edge set may change over a discrete time interval, while the vertex set remains unchanged. Temporal graphs are well-suited to model dynamic data and hence they are naturally motivated in contexts where dynamic changes or time-dependent interactions play an important role, such as, for example, communication networks, social networks, or physical proximity networks. The most important selection criteria for our problems was that they are well-motivated in the context of dynamic data analysis. Since temporal graphs are mathematically more complex than static graphs, it is maybe not surprising that all problems we consider in this thesis are NP-hard. We focus on the development of exact algorithms, where our goal is to obtain fixed-parameter tractability results, and refined computational hardness reductions that either show NP-hardness for very restricted input instances or parameterized hardness with respect to “large” parameters. In the context of temporal graphs, we mostly consider structural parameters of the underlying graph, that is, the graph obtained by ignoring all time information. However, we also consider parameters of other types, such as ones trying to measure how fast the temporal graph changes over time. In the following we briefly discuss the problem setting and the main results. Restless Temporal Paths. A path in a temporal graph has to respect causality, or time, which means that the edges used by a temporal path have to appear at non-decreasing times. We investigate temporal paths that additionally have a maximum waiting time in every vertex of the temporal graph. Our main contributions are establishing NP-hardness for the problem of finding restless temporal paths even in very restricted cases, and showing W[1]-hardness with respect to the feedback vertex number of the underlying graph. Temporal Separators. A temporal separator is a vertex set that, when removed from the temporal graph, destroys all temporal paths between two dedicated vertices. Our contribution here is twofold: Firstly, we investigate the computational complexity of finding temporal separators in temporal unit interval graphs, a generalization of unit interval graphs to the temporal setting. We show that the problem is NP-hard on temporal unit interval graphs but we identify an additional restriction which makes the problem solvable in polynomial time. We use the latter result to develop a fixed-parameter algorithm with a “distance-to-triviality” parameterization. Secondly, we show that finding temporal separators that destroy all restless temporal paths is Σ-P-2-hard. Temporal Matchings. We introduce a model for matchings in temporal graphs, where, if two vertices are matched at some point in time, then they have to “recharge” afterwards, meaning that they cannot be matched again for a certain number of time steps. In our main result we employ temporal line graphs to show that finding matchings is NP-hard even on instances where the underlying graph is a path. Temporal Coloring. We lift the classic graph coloring problem to the temporal setting. In our model, every edge has to be colored properly (that is, the endpoints are colored differently) at least once in every time interval of a certain length. We show that this problem is NP-hard in very restricted cases, even if we only have two colors. We present simple exponential-time algorithms to solve this problem. As a main contribution, we show that these algorithms presumably cannot be improved significantly. Temporal Cliques and s-Plexes. We propose a model for temporal s-plexes that is a canonical generalization of an existing model for temporal cliques. Our main contribution is a fixed-parameter algorithm that enumerates all maximal temporal s-plexes in a given temporal graph, where we use a temporal adaptation of degeneracy as a parameter. Temporal Cluster Editing. We present a model for cluster editing in temporal graphs, where we want to edit all “layers” of a temporal graph into cluster graphs that are sufficiently similar. Our main contribution is a fixed-parameter algorithm with respect to the parameter “number of edge modifications” plus the “measure of similarity” of the resulting clusterings. We further show that there is an efficient preprocessing procedure that can provably reduce the size of the input instance to be independent of the number of vertices of the original input instance.

Using the User Manual included in the research paper, and the Graph Design Example file as a reference, the user enters or saves all the vertices and edges needed to specify the model of the system topography.

Current-Deferred-Revenue Time Series for Emerson Electric Company. Emerson Electric Co., a technology and software company, provides various solutions in the Americas, Asia, the Middle East, Africa, and Europe. It operates through Final Control, Measurement & Analytical, Discrete Automation, Safety & Productivity, Control Systems & Software, and Test & Measurement segments. The Final Control segment provides control valves, isolation valves, shutoff valves, pressure relief valves, pressure safety valves, actuators, and regulators for process and hybrid industries under Anderson Greenwood, Bettis, Crosby, Fisher, Keystone, KTM, and Vanessa brands. The Measurement & Analytical segment supplies intelligent instrumentation measuring the physical properties of liquids or gases, such as pressure, temperature, level, flow, acoustics, corrosion, pH, conductivity, water quality, toxic gases, and flame under the Flexim, Micro Motion, and Rosemount brands. The Discrete Automation segment includes solenoid valves, pneumatic valves, valve position indicators, pneumatic cylinders and actuators, air preparation equipment, pressure and temperature switches, electric linear motion solutions, programmable automation control systems and software, electrical distribution equipment, and materials joining solutions used primarily in discrete industries under the Afag, Appleton, ASCO, Aventics, Branson, Movicon, PACSystems, SolaHD, TESCOM, and TopWorx brands. The Safety & Productivity segment delivers tools for professionals and homeowners that support infrastructure, promote safety, and enhance productivity under the Greenlee, Klauke, ProTeam, and RIDGID brands. The Control Systems & Software segment provides control systems and software that control plant processes by collecting and analyzing information from measurement devices in the plant under the DeltaV and Ovation brands. The Test & Measurement offers software-connected automated test and measurement systems. The company was incorporated in 1890 and is headquartered in Saint Louis, Missouri.

This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).

Network of 46 papers and 69 citation links related to "Discrete time interval measurement system: fundamentals, resolution and errors in the measurement of angular vibrations".

Defensive-Intrval-Ratio Time Series for Emerson Electric Company. Emerson Electric Co., a technology and software company, provides various solutions in the Americas, Asia, the Middle East, Africa, and Europe. It operates through segments: Final Control, Measurement & Analytical, Discrete Automation, Safety & Productivity, Control Systems & Software, and Test & Measurement. It provides control, isolation, shutoff, pressure relief, and pressure safety valves, actuators, and regulators for process and hybrid industries. The company also offers intelligent instrumentation measuring the physical properties of liquids or gases, such as pressure, temperature, level, flow, acoustics, corrosion, pH, conductivity, water quality, toxic gases, and flame. In addition, it offers solenoid and pneumatic valves, valve position indicators, pneumatic cylinders, actuators, air preparation equipment, pressure and temperature switches, electric linear motion solutions, programmable automation control systems, electrical distribution equipment, and materials joining solutions. Further, it provides tools for professionals and homeowners; pipe-working tools, including pipe wrenches, pipe cutters, pipe threading and roll grooving equipment, battery hydraulic tools; electrical tools; and other professional tools. Additionally, the company provides control systems, safety instrumented systems, SCADA systems, application software, digital twins, asset performance management, and cybersecurity; and provides software-connected automated test and measurement systems, as well as provides asset optimization software that enables industrial manufacturers to design, operate, and maintain operations for enhancing performance through a combination of decades of modeling, simulation, and optimization capabilities. The company was incorporated in 1890 and is based in Saint Louis, Missouri.

The present article introduces the notion of smoothness of rhythm and proposes a unified method that transforms an arbitrary rhythm into a smooth one. The method employs a self-map Rav, discrete average map, on the space of rhythms of arbitrary length with a fixed number of onsets. It is shown that, for any rhythm a in the space, the iterations Ravk(a) become eventually periodic, and that the final cycle consists only of smooth rhythms. The discrete average map leads naturally to a finite directed graph, which visualizes the realm of smooth rhythms in the whole world of rhythms. This article has an Online Supplement, in which we give detailed proof of the main result.

In the first quarter of 2022, Nvidia held a ** percent shipment share within the global PC discrete graphics processing unit (dGPU) market, whilst AMD held a share of ** percent. Intel recorded a share of **** percent of the dGPU market in the first quarter of 2022.

The current model for Okazaki fragment maturation in bacteria invokes RNA cleavage by RNase H, followed by strand displacement synthesis and 5′ RNA flap removal by DNA polymerase I (Pol I). RNA removal by Pol I is thought to occur through the 5′-3′ flap endo/exonuclease (FEN) domain, located in the N-terminus of the protein. In addition to Pol I, many bacteria encode a second, Pol I-independent FEN. The contribution of Pol I and Pol I-independent FENs to DNA replication and genome stability remains unclear. In this work we purified Bacillus subtilis Pol I and FEN, then assayed these proteins on a variety of RNA-DNA hybrid and DNA-only substrates. We found that FEN is far more active than Pol I on nicked double-flap, 5′ single flap, and nicked RNA-DNA hybrid substrates. We show that the 5′ nuclease activity of B. subtilis Pol I is feeble, even during DNA synthesis when a 5′ flapped substrate is formed modeling an Okazaki fragment intermediate. Examination of Pol I and FEN on DNA-only substrates shows that FEN is more active than Pol I on most substrates tested. Further experiments show that ΔpolA phenotypes are completely rescued by expressing the C-terminal polymerase domain while expression of the N-terminal 5′ nuclease domain fails to complement ΔpolA. Cells lacking FEN (ΔfenA) show a phenotype in conjunction with an RNase HIII defect, providing genetic evidence for the involvement of FEN in Okazaki fragment processing. With these results, we propose a model where cells remove RNA primers using FEN while upstream Okazaki fragments are extended through synthesis by Pol I. Our model resembles Okazaki fragment processing in eukaryotes, where Pol δ catalyzes strand displacement synthesis followed by 5′ flap cleavage using FEN-1. Together our work highlights the conservation of ordered steps for Okazaki fragment processing in cells ranging from bacteria to human.

This study addresses the problem of attack identification in discrete event systems modeled with Petri nets, focusing specifically on sensor attacks that mislead observers to making incorrect decisions. Insertion attacks are one of the sensor attacks that are considered in this work. First, we formulate a novel observation structure to systematically model insertion attacks within the Petri net framework. Second, by generating an extended reachability graph that incorporates the observation structure, we can find a special class of markings whose components can have negative markings. Third, an observation place is computed by formulating an integer linear programming problem, enabling precise detection of attack occurrences. The occurrence of an attack can be identified by the number of tokens in the designed observation place. Finally, examples are provided to verify the proposed approach. Comparative analysis with existing techniques demonstrates that the reported approach offers enhanced detection accuracy and robustness, making it a significant advancement in the field of secure discrete event systems.

High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.