Cut Sets Research Articles

Elucidating the effect of intermediate resonances in the quark interaction kernel on the timelike electromagnetic pion form factor

An exploratory study of the timelike pion electromagnetic form factor in a Poincar\'e-covariant bound state formalism in the isospin symmetric limit is presented. Starting from a quark interaction kernel representing gluon-intermediated interactions for valence-type quarks, nonvalence effects are included by introducing pions as explicit degrees of freedom. The two most important qualitative aspects are, in view of the presented study, the opening of the dominant $\ensuremath{\rho}$-meson decay channel and the presence of a multiparticle branch cut setting in which the two-pion threshold is crossed. Based on a recent respective computation of the quark-photon vertex, the pion electromagnetic form factor for spacelike and timelike kinematics is calculated. The obtained results for its absolute value and its phase compare favorably to the available experimental data, and they are analyzed in detail by comparing them to the expectations based on an isospin-symmetric version of a vector-meson dominance model.

Searching for axionlike particles with low masses in pPb and PbPb collisions

The production of axionlike particles (ALPs) with small masses in ultraperipheral Pb–p and Pb–Pb collisions at the LHC is investigated. The cross section and kinematical distributions associated to the diphoton final state produced in the gamma gamma rightarrow a rightarrow gamma gamma subprocesses are estimated considering a realistic set of kinematical cuts. A detailed analysis of the backgrounds is performed and the expected sensitivity to the ALP production is derived. Our results demonstrate that a future experimental analysis of the exclusive diphoton production for the forward rapidities probed by the LHCb detector can improve the existing exclusion limits on the ALP–photon coupling in the mass range 2 GeV le m_a le 5 GeV.

In Vitro Comparison of Various High-Speed Vitrectomy Machines Using Dual Blade Cutters.

ObjectivesThe objective of this study is to compare various dual blade vitrectomy cutters for their efficiency in an in vitro setting.MethodsIn this in vitro experimental study, we compared various vitrectomy systems including EVA (Dutch Ophthalmic Research Center, Zuidland, The Netherlands), REVOLUTION (Optikon 2000, Inc., Rome, Italy), and OS4 (Oertli Instrumente AG, Berneck, Switzerland) in terms of efficiency in vitreous cutting and aspiration for various vitreous substitutes. These substitutes included water, chicken egg albumin, and goat vitreous. We only used 23-gauge dual blade cutters across all platforms to maintain uniformity. The cutting and aspiration efficiency was measured across various cut and vacuum settings of vitrectomy machines and measured as mass aspirated in a given time. Data analysis included comparing the amount of mass aspirated by these machines at preset cut and vacuum settings.ResultsScatter plots showed a comparable mass of water aspirated by the EVA and REVOLUTION at 1000 to 5000 cuts per minute at a constant vacuum of 500 mm Hg whereas OS4 aspirated lesser mass at similar settings. Same trends were noted for goat vitreous for EVA and REVOLUTION but aspirated mass of albumin fluctuated widely across various platforms. At peak machine settings, REVOLUTION showed superiority across all three vitreous models due to its higher peak settings. The area under curve (AUC) analysis showed no significant differences among machines for water and goat vitreous at comparable settings but results were fluctuating for egg albumin.ConclusionEmploying higher cut rates for dual blade cutters results in better efficiency of vitrectomy machines.

A Multi-Objective Reconfiguration Scheme for Reliability and Energy Usage Enhancement of Distribution Systems in the Presence of Wind Turbines Using the MOHSA Optimization Algorithm

The stochastic nature of the active power generated by wind turbines (WTs) has posed new challenges to the reliability assessment, load flow analysis, and reconfiguration process of distribution systems penetrated with WTs. In this paper, a new optimization approach has been presented to find the best configuration of the distribution system along with optimal locations of WTs. This is aimed at maximizing the reliability of the system, while energy losses are minimized. To this end, based on the graph theory and the system topology, a simple technique is proposed for finding minimal cut sets that could be used for calculating reliability indices. Furthermore, the multiple flow directions created in the presence of WTs cause difficulties in calculating reliability indices and in performing load flow analysis. Accordingly, a new concept called the independent reliability zone is introduced to overcome these problems. In this paper, the multi-objective harmony search algorithm, due to its suitability for multi-objective problems, is employed as a solution for solving the proposed optimization problem. The effect of the proposed approach has been evaluated on both test and real systems. The results show that using the proposed approach, a more practical solution is achieved for low-cost and reliable 24-hour planning of the system in the presence of uncertainties in DGs.

How to incorporate human failure event recovery into minimal cut set generation stage for efficient probabilistic safety assessments of nuclear power plants

Human failure event (HFE) dependency analysis is a part of human reliability analysis (HRA). For efficient HFE dependency analysis, a maximum number of minimal cut sets (MCSs) that have HFE combinations are generated from the fault trees for the probabilistic safety assessment (PSA) of nuclear power plants (NPPs). After collecting potential HFE combinations, dependency levels of subsequent HFEs on the preceding HFEs in each MCS are analyzed and assigned as conditional probabilities. Then, HFE recovery is performed to reflect these conditional probabilities in MCSs by modifying MCSs.Inappropriate HFE dependency analysis and HFE recovery might lead to an inaccurate core damage frequency (CDF). Using the above process, HFE recovery is performed on MCSs that are generated with a non-zero truncation limit, where many MCSs that have HFE combinations are truncated. As a result, the resultant CDF might be underestimated.In this paper, a new method is suggested to incorporate HFE recovery into the MCS generation stage. Compared to the current approach with a separate HFE recovery after MCS generation, this new method can (1) reduce the total time and burden for MCS generation and HFE recovery, (2) prevent the truncation of MCSs that have dependent HFEs, and (3) avoid CDF underestimation. This new method is a simple but very effective means of performing MCS generation and HFE recovery simultaneously and improving CDF accuracy. The effectiveness and strength of the new method are clearly demonstrated and discussed with fault trees and HFE combinations that have joint probabilities.

A Noisy-OR gate based fuzzy fault tree approach for micro-leakage evaluation of bolt-gasket-flange connection (BGFC)

In the natural gas gathering and transportation station, bolt-gasket-flange connection (BGFC) is the main connection mode, but due to its large number, the micro-leakage may result in serious consequences. For this reason, early BGFC micro-leakage evaluation can effectively assist decision-making and prevent the occurrence of accidents. To solve the problem of insufficient field data collection, we proposed a Noisy-OR gate based fuzzy fault tree approach, and calculated the corresponding minimum cut sets and the highest risk factors. Moreover, sensitivity analysis (SA) was used to determine that material problems (wear and corrosion of the sealing surface), man-made operation problems (bolts and flanges) and working conditions (pipe vibration and temperature fluctuations) are the key factors that contributing to the BGFC micro-leakage. In brief, the combination of the fuzzy fault tree analysis and Noisy-OR gate is an effective approach for BGFC micro-leakage evaluation, which is of great significance to the micro-leakage analysis, safe operation, and risk assessment of the gas station.

Sensitivity Analysis of Epistemic Uncertainty on Input Parameters and System Structure Using Dempster-Shafer Theory

Abstract In this article, a method is proposed to conduct a global sensitivity analysis of epistemic uncertainty on both system input and system structure, which is very common in early stage of system development, using Dempster-Shafer theory (DST). In system reliability assessment, the input corresponds to component reliability and system structure is given by system reliability function, cut sets, or truth table. A method to propagate real-number mass function through set-valued mappings is introduced and applied on system reliability calculation. Secondly, we propose a method to model uncertain system with multiple possible structures and how to obtain the mass function of system level reliability. Finally, we propose an indicator for global sensibility analysis. Our method is illustrated, and its efficacy is proved by numerical application on two case studies.

A quantification methodology of Seismic Probabilistic Safety Assessment for nuclear power plant

A Seismic Probabilistic Safety Assessment (SPSA), unlike other internal or external event analyses, has a very high probability of basic events when a strong earthquake occurs. Because of this high probability, the Core Damage Frequency (CDF) might be overestimated by the Rare Events Approximation (REA) and Minimal Cutset Upper Bound (MCUB) method for general quantitative analysis. To compensate for this overestimation, the exact CDF value can be derived with post-processing software such as an Advanced Cutset Upper Bound Estimator (ACUBE) or Fault Tree Evaluation using Monte Carlo simulation (FTeMC). However, the Minimal Cut Sets (MCSs), which has the same result as the calculated CDF, cannot be derived with both methods. In this paper, we propose a Monte Carlo Simulation Allocation Method (MCSAM) to derive the exact CDF and MCSs. We expect that the MCSAM will easily identify the plant's vulnerabilities to review the exact MCSs consistent with CDF results without additional post-processing or Monte Carlo Simulation.

An Improved FFIP Method Based on Mathematical Logic and SysML

In recent years, the model-based safety analysis (MBSA) has been developing continuously. The Functional Failure Identification and Propagation (FFIP) method is a graphics processing technology which supports the analysis of fault propagation paths before making costly design commitments. However, the traditional FFIP has some deficiencies. In this paper, we extend the functional failure logic (FFL) in the FFIP and introduce the concept of deviation. So, FFIP can be used to analyze the failure process of the systems and make the logical analysis of functional failure easier. Based on the extended FFL, we present a new overview of the FFIP. The FFIP is improved by using mathematical logic and Systems Modeling Language (SysML). The standard expression of FFL is realized, which is conducive to the subsequent modeling and modification. Additionally, we use the failure logic analysis in the FFIP to improve the state machine diagram (SMD) in SysML. Finally, the improved FFIP method is used to analyze the fault propagation paths of the system and Simulink is used for simulation. The fault tree is generated according to the simulation results, the minimum cut set is calculated, and the key failure parts of the system are obtained.

A Method to Avoid Underestimated Risks in Seismic SUPSA and MUPSA for Nuclear Power Plants Caused by Partitioning Events

Seismic probabilistic safety assessment (PSA) models for nuclear power plants (NPPs) have many non-rare events whose failure probabilities are proportional to the seismic ground acceleration. It has been widely accepted that minimal cut sets (MCSs) that are calculated from the seismic PSA fault tree should be converted into exact solutions, such as binary decision diagrams (BDDs), and that the accurate seismic core damage frequency (CDF) should be calculated from the exact solutions. If the seismic CDF is calculated directly from seismic MCSs, it is drastically overestimated. Seismic single-unit PSA (SUPSA) models have random failures of alternating operation systems that are combined with seismic failures of components and structures. Similarly, seismic multi-unit PSA (MUPSA) models have failures of NPPs that undergo alternating operations between full power and low power and shutdown (LPSD). Their failures for alternating operations are modeled using fraction or partitioning events in seismic SUPSA and MUPSA fault trees. Since partitioning events for one system are mutually exclusive, their combinations should be excluded in exact solutions. However, it is difficult to eliminate the combinations of mutually exclusive events without modifying PSA tools for generating MCSs from a fault tree and converting MCSs into exact solutions. If the combinations of mutually exclusive events are not deleted, seismic CDF is underestimated. To avoid CDF underestimation in seismic SUPSAs and MUPSAs, this paper introduces a process of converting partitioning events into conditional events, and conditional events are then inserted explicitly inside a fault tree. With this conversion, accurate CDF can be calculated without modifying PSA tools. That is, this process does not require any other special operations or tools. It is strongly recommended that the method in this paper be employed for avoiding CDF underestimation in seismic SUPSAs and MUPSAs.

Reliability tracking method of power system equipment based on Embedded Internet of things technology

Power equipment is one of the most important transmission and transformation equipment in power system. Improving the reliability of power equipment is of great significance to the safe and reliable operation of the whole power grid. A reliability tracking method of power system equipment based on Embedded Internet of things technology is proposed. The minimum cut set algorithm of reliability index based on Embedded Internet of things technology is given. In this paper, a fault tree analysis method for power system equipment unreliability index is proposed. According to the analysis of equipment structure, component function and failure mode, the fault tree model of power system equipment is established, and the reliability tracking calculation example is analyzed. Experiments show that the method can track the order stably, determine the key components that affect the reliability of the equipment, and identify the weak links of the equipment.

Nw–filters of lattice Wajsberg algebras

In this paper, we define the 𝔑𝓌 – filters of Lattice wajsberg algebras. We prove that every truth – favorite set of 𝔑𝓌 – filter is also 𝔑𝓌 – filter and falsity - favorite set need not to be a 𝔑𝓌 – filter. After that we prove that some properties of 𝔑𝓌 – filters. Further we show the cut set of Truth-value based neutrosophic set are also 𝔑𝓌 – filters. Finally we define the 𝔑𝓌 – lattice filters of Lattice wajsberg algebra and example is given. We prove that every 𝔑𝓌 – filter is a 𝔑𝓌 – lattice filter of Lattice wajsberg algebra and converse need not be true.

Irregular model sets and tame dynamics

We study the dynamical properties of irregular model sets and show that the translation action on their hull always admits an infinite independence set. The dynamics can therefore not be tame and the topological sequence entropy is strictly positive. Extending the proof to a more general setting, we further obtain that tame implies regular for almost automorphic group actions on compact spaces. In the converse direction, we show that even in the restrictive case of Euclidean cut and project schemes irregular model sets may be uniquely ergodic and have zero topological entropy. This provides negative answers to questions by Schlottmann and Moody in the Euclidean setting.

Integrated resource planning for a meshed distribution network under uncertainty

Planning of renewable energy based distributed generators (DG) is to be done with utmost care because of the uncertain nature of their outputs. In this paper, a multi-objective PSO based solution methodology for the DG planning problem is proposed taking into account the concerns of the grid operators, distribution network operators and the end consumers. Load shifting along with time of use pricing is the strategy adopted for performing demand side management, thereby reducing the discomfort level and the electricity bill of the customers. DG planning is done for solar and wind based sources along with demand side management in order to minimize the system real power loss, improve voltage profile, increase reliability and reduce the expenses of the distribution network operator. Reliability is evaluated using encoded Markov cut set algorithm considering the failure rates, adequacy transition rates, mechanical failures, and starting and switching probability of the DG. The test network considered is the 102 node meshed distribution network connected to Bus 4 of the Roy Billinton test system. Results prove that load shifting along with dynamic pricing can be used as a viable option to reduce the operational expenses of the network operator.

Model-based Safety Assessment of a Triple Modular Generator with xSAP

Abstract The system design process needs to cope with the increasing complexity and size of systems,motivating the replacement of labor intensivemanual techniques with automated and semi-automated approaches.Recently, formal methods techniques, such as model-based verification and safety assessment, have been increasingly used to model systems under fault and to analyze them, generating artifacts such as fault trees and FMEA tables. In this paper, we show how to apply model-based techniques to a realistic case study from the avionics domain: a high integrity power distribution system, the Triple Modular Generator (TMG). The TMG is composed of a redundant and reconfigurable plant and a controller that must guarantee a high level of reliability. The case study is a significant challenge, from the modeling perspective, since it implements a complex reconfiguration policy, specified via a number of requirements in natural language, including a set of mutually dependent and potentially conflicting priority constraints. Moreover, from the verification standpoint, the controller must be able to handle an exponential number of possible faulty configurations. Our contribution is twofold. First, we formalize and validate the requirements and, using a constraint-based modeling style, we synthesize a correct by construction controller, avoiding the enumeration of all possible fault configurations, as is currently done by manual approaches. Second, we describe a comprehensive methodology and process, supported by the xSAP safety analysis platform that targets the modeling and safety assessment of faulty systems. Using xSAP, we are able to automatically extract minimal cut sets for the TMG. We demonstrate the scalability of our approach by analyzing a parametric version of the TMG case study that contains more than 700 variables and 90 faults.

Graph Neural Networks for Maximum Constraint Satisfaction.

Many combinatorial optimization problems can be phrased in the language of constraint satisfaction problems. We introduce a graph neural network architecture for solving such optimization problems. The architecture is generic; it works for all binary constraint satisfaction problems. Training is unsupervised, and it is sufficient to train on relatively small instances; the resulting networks perform well on much larger instances (at least 10-times larger). We experimentally evaluate our approach for a variety of problems, including Maximum Cut and Maximum Independent Set. Despite being generic, we show that our approach matches or surpasses most greedy and semi-definite programming based algorithms and sometimes even outperforms state-of-the-art heuristics for the specific problems.

Wearable 3D Machine Knitting: Automatic Generation of Shaped Knit Sheets to Cover Real-World Objects.

Knitting can efficiently fabricate stretchable and durable soft surfaces. These surfaces are often designed to be worn on solid objects as covers, garments, and accessories. Given a 3D model, we consider a knit for it wearable if the knit not only reproduces the shape of the 3D model but also can be put on and taken off from the model without deforming the model. This "wearability" places additional constraints on surface design and fabrication, which existing machine knitting approaches do not take into account. We introduce the first practical automatic pipeline to generate knit designs that are both wearable and machine knittable. Our pipeline handles knittability and wearability with two separate modules that run in parallel. Specifically, given a 3D object and its corresponding 3D garment surface, our approach first converts the garment surface into a topological disc by introducing a set of cuts. The resulting cut surface is then fed into a physically-based unclothing simulation module to ensure the garment's wearability over the object. The unclothing simulation determines which of the previously introduced cuts could be sewn permanently without impacting wearability. Concurrently, the cut surface is converted into an anisotropic stitch mesh. Then, our novel, stochastic, any-time flat-knitting scheduler generates fabrication instructions for an industrial knitting machine. Finally, we fabricate the garment and manually assemble it into one complete covering worn by the target object. We demonstrate our method's robustness and knitting efficiency by fabricating models with various topological and geometric complexities. Further, we show that our method can be incorporated into a knitting design tool for creating knitted garments with customized patterns.

Sensitivity to invisible scalar decays at CLIC

We studied the possibility of constraining production of new scalar particles at CLIC running at 380 GeV and 1.5 TeV, assuming the associated production of Higgs-like neutral scalar with mathrm{Z}{}{} boson and its invisible decays. The analysis is based on the Whizard event generation and fast simulation of the CLIC detector response with Delphes. We considered {mathrm{e}{}{}}^{+} {mathrm{e}{}{}}^{-} background processes but also relevant upgamma {}{} upgamma {}{} and upgamma {}{} mathrm{e}{}{} ^{pm } interactions. The approach consisting of a two-step analysis was used to optimise separation between signal and background processes. First, a set of preselection cuts was applied; then, multivariate analysis methods were employed to optimise the significance of observations. We first estimated the expected limits on the invisible decays of the 125 GeV Higgs boson, which were then extended to the cross section limits for production of an additional neutral scalar, assuming its invisible decays, as a function of its mass. Extracted model-independent branching ratio and cross section limits were then interpreted in the framework of the Higgs-portal models to set limits on the mixing angle between the SM-like Higgs boson and the new scalar of the “dark sector”.

Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features

The main aim of brain Magnetic Resonance Image (MRI) segmentation is to extractthe significant objects like tumors for better diagnosis and proper treatment. As the brain tumors are distinct in the sense of shapes, location, and intensity it is hard to define a general algorithm for the tumor segmentation. Accurate extraction of tumors from the brain MRIs is a challenging task due to the complex anatomical structure of brain tissues in addition to the existence of intensity inhomogeneity, partial volume effects, and noise. In this paper, a method of Sparse coding based on non-linear features is proposed for the tumor segmentation from MR images of the brain. Initially, first and second-order statistical eigenvectors of 3 × 3 size are extracted from the MRIs then the process of Sparse coding is applied to them. The kernel dictionary learning algorithm is employed to obtain the non-linear features from these processed vectors to build two individual adaptive dictionaries for healthy and pathological tissues. This work proposes dictionary learning based kernel clustering algorithm to code the pixels, and then the target pixelsare classified by utilizing the method of linear discrimination. The proposed technique is applied to several tumor MRIs, taken from the BRATS database. This technique overcomes the problem of linear inseparability produced by the high level intensity similarity between the normal and abnormal tissues of the given brain image. All the performance parameters are high for the proposed technique. Comparison of the results with some other existing methods such as Fuzzy – C- Means (FCM), Hybrid k-Mean Graph Cut (HKMGC) and Neutrosophic Set – Expert Maximum Fuzzy Sure Entropy (NS-EMFSE) demonstrates that the proposed sparse coding method is effective in segmenting the brain tumor regions.

The scenario modeling of regional competitiveness risks based on the Chapman-Kolmogorov equations

The paper is devoted to the development of analytical tools for assessing the dynamics of risks to the competitiveness of Russian regions within the conditions of incomplete information. The choice of analysis methods for assessing the risk dynamics was substantiated. A solution based on the implementation of homogeneous Markov models with a discrete set of states and continuous time was proposed, it allowed to assess the probability of risks affecting regional competitiveness. A hierarchical structure of factors that determine the risks of regional competitiveness was formed as a cause-and-effect graph. Corresponding systems of Kolmogorov – Chapman differential equations were obtained and solved. An example for analyzing a 3-element minimal cut set of a cause-effect graph was presented. A set of scenarios for the study of risks and their combinations in various conditions was proposed. The solution was obtained for three leading Russian regions. For a set of scenarios, numerical assessments of risk factors that significantly affect the decline in the competitiveness of regions have been fulfilled. The study results made it possible to rank the effects of critical event occurrence, to identify the most significant risk factors for the loss of sustainable development and competitiveness for the Russian Federation regions.