Replacement Rules Research Articles

Selecting Balls From Urns With Partial Replacement Rules

Consider an urn with an initial state of R red balls and W white balls. Draw a ball from the urn, uniformly at random, and note its color. If the ball is white, do not replace it; if the ball is red, do replace it. Define this sampling rule to be "Preferential". We study the random variable X denoting the number of white balls drawn under the Preferential sampling rule for a sample size n. It is known that the expected number of X is bounded below by 3nW/(4N), and bounded above by nW/N. In this paper we improve the lower bound, give a heuristic for the best possible lower bound, and we explore some properties of a generalization of this sampling rule, we call "Super-Preferential", where the probability of retaining a white ball is w and the probability of retaining a red ball is r.

Target localization image encryption of wind turbines based on DNA strand replacement rule

The continuous development of the industrial internet of things has raised increasingly high requirements for the secure storage and transmission of information. Ensuring that image information avoids malicious attacks and tampering during transmission has become an important aspect of information security. In response to the security requirements for drone-captured image transmission during wind turbine inspection, this paper proposes a new target localization encryption scheme based on DNA strand replacement rules. In traditional methods using DNA encryption, the DNA computing rules used include DNA addition, DNA subtraction, and DNA XOR operations. These operations are relatively simple and have weak biological relevance, making them vulnerable to attack and decryption. To address this issue, this article introduces the principle of DNA strand replacement reaction and proposes a DNA strand displacement reaction rule. The rule has stronger biological relevance and more complex operational rule than traditional DNA algorithms, thus providing stronger security for encrypted images. Experimental results indicate that this encryption algorithm effectively enhances the information security of ciphertext images and can withstand brute-force attacks and various statistical attacks.

Construction and Multi-Mode Motion Analysis of Single-Degree-of-Freedom Four-Bar Multi-Mode Planar Mechanisms Based on Singular Configuration

Abstract The traditional four-bar mechanism is renowned for its simple structure, dependable performance, and wide range of applications. The single-degree-of-freedom (DOF) four-bar multi-mode planar mechanism (MMPM) is a type of four-bar mechanism that not only has the structural characteristics of the traditional four-bar mechanisms but also can achieve multiple motion modes by changing its structure. It has the advantage of performing diverse functions while conserving resources, which opens up new possibilities for research and application of the four-bar mechanism. However, due to the lack of a systematic configuration construction method, the design and application of single-DOF four-bar MMPMs are seriously limited. This paper presents a systematic method to construct a set of single-DOF four-bar MMPMs based on the loop equations and the proposed multi-mode modules (MMMs). First, depending on the loop equations, the four-bar planar mechanism containing two branches is identified by the corresponding branch graphs. Then, three kinds of MMMs are systematically proposed for the first time, helping the identified mechanism realize multiple motion modes. Subsequently, single-DOF four-bar MMPMs are constructed by replacing the specific component of the planar mechanism with the MMMs. Furthermore, the replacement rules of MMMs and the corresponding construction steps are summarized. Finally, 14 kinds of single-DOF four-bar MMPMs are listed, and the corresponding multi-mode motion analysis is discussed at the end of this paper. The proposed method is a straightforward one, which will provide great convenience for the configuration design of single-DOF four-bar MMPMs and promote the development and application of MMPMs.

Model of preventive replacements of complex systems elements depending on the operation time and the number of failures

Modern conditions of economic activity of civil aviation operators make actual the problem of economically feasible measures for the organization of technical operation and maintenance of industry equipment, in particular, means of radio technical support for flights and aeronautical telecommunications. At the same time, it is obvious that these means need to be transferred to maintenance on a condition, which, in turn, causes the need to solve the problems related to determining the time for the preventive replacement of elements which diagnosable parameters can reach limit values. This paper presents an algorithm for estimating the optimal replacement of elements using the conditional probabilistic characteristic method for systems with a long period of operation and having a fixed number of failures. An assessment of the accuracy of determining the desired parameter is carried out, provided that its changes have deterministic and random components. The mathematical expectation and variance of the obtained estimate are found. Provided that the time of means operation between restorations (repairs) tends to decrease with an increase in the number of failures, the average number of final failures is obtained that satisfies the Volterra integral equation. To analyze the cost of restoration within the framework of the proposed model, an expression was found for the unit cost of work, depending on the accepted replacement rule and the duration of the expected cycles. Taking into account the mathematical expectation of the latter and associated costs, a two-dimensional optimal replacement rule is formed, and the expediency of using such a replacement period that minimizes the maximum average costs is shown. The obtained results are useful in organizing activities for preventive maintenance of radio technical support for flights and aeronautical telecommunications at various stages of their life cycle.

Tenant Portfolio Selection for Managing a Shopping Center

In managing shopping centers, Kariya et al. (2005) formulate an analytical framework to optimize the net present value (NPV) of profit from each individual tenant with two control measures of fixed and variable rent linked to tenant sales or tenant replacement rules. Here, the framework is extended to a two-tenant case, where the sales growth rates of the new and old tenants are correlated in replacement. Hence, like the portfolio theory in finance, the combined NPV of the correlated cashflows of two tenants with the two control measures is optimized. To select a new tenant for a specific space, the sales correlation data of leaving and remaining tenants in proximity to the space can be used for optimization. Our simulation results show that it is better to select a new tenant whose predicted sales growth rate is high and has a largely negative correlation with that of the local tenants.

A cell membrane model that reproduces cortical flow-driven cell migration and collective movement.

Many fundamental biological processes are dependent on cellular migration. Although the mechanical mechanisms of single-cell migration are relatively well understood, those underlying migration of multiple cells adhered to each other in a cluster, referred to as cluster migration, are poorly understood. A key reason for this knowledge gap is that many forces-including contraction forces from actomyosin networks, hydrostatic pressure from the cytosol, frictional forces from the substrate, and forces from adjacent cells-contribute to cell cluster movement, making it challenging to model, and ultimately elucidate, the final result of these forces. This paper describes a two-dimensional cell membrane model that represents cells on a substrate with polygons and expresses various mechanical forces on the cell surface, keeping these forces balanced at all times by neglecting cell inertia. The model is discrete but equivalent to a continuous model if appropriate replacement rules for cell surface segments are chosen. When cells are given a polarity, expressed by a direction-dependent surface tension reflecting the location dependence of contraction and adhesion on a cell boundary, the cell surface begins to flow from front to rear as a result of force balance. This flow produces unidirectional cell movement, not only for a single cell but also for multiple cells in a cluster, with migration speeds that coincide with analytical results from a continuous model. Further, if the direction of cell polarity is tilted with respect to the cluster center, surface flow induces cell cluster rotation. The reason why this model moves while keeping force balance on cell surface (i.e., under no net forces from outside) is because of the implicit inflow and outflow of cell surface components through the inside of the cell. An analytical formula connecting cell migration speed and turnover rate of cell surface components is presented.

Multiple-drawing dynamic Friedman urns with opposite-reinforcement

AbstractIn this study, we consider a class of multiple-drawing opposite-reinforcing urns with time-dependent replacement rules. The class has the symmetric property of a Friedman-type urn. We divide the class into a small-increment regime and a large-increment regime. For small-increment schemes, we prove almost-sure convergence and a central limit theorem for the proportion of white balls by stochastic approximation. For large-increment schemes, by assuming the affinity condition, we show almost-sure convergence of the proportion of white balls by martingale theory and present a way to identify the limit distribution of the proportion of white balls.

Multi-modal chemical information reconstruction from images and texts for exploring the near-drug space.

Identification of new chemical compounds with desired structural diversity and biological properties plays an essential role in drug discovery, yet the construction of such a potential space with elements of 'near-drug' properties is still a challenging task. In this work, we proposed a multimodal chemical information reconstruction system to automatically process, extract and align heterogeneous information from the text descriptions and structural images of chemical patents. Our key innovation lies in a heterogeneous data generator that produces cross-modality training data in the form of text descriptions and Markush structure images, from which a two-branch model with image- and text-processing units can then learn to both recognize heterogeneous chemical entities and simultaneously capture their correspondence. In particular, we have collected chemical structures from ChEMBL database and chemical patents from the European Patent Office and the US Patent and Trademark Office using keywords 'A61P, compound, structure' in the years from 2010 to 2020, and generated heterogeneous chemical information datasets with 210K structural images and 7818 annotated text snippets. Based on the reconstructed results and substituent replacement rules, structural libraries of a huge number of near-drug compounds can be generated automatically. In quantitative evaluations, our model can correctly reconstruct 97% of the molecular images into structured format and achieve an F1-score around 97-98% in the recognition of chemical entities, which demonstrated the effectiveness of our model in automatic information extraction from chemical patents, and hopefully transforming them to a user-friendly, structured molecular database enriching the near-drug space to realize the intelligent retrieval technology of chemical knowledge.

Research on multi-core scheduling method for Sporadic task by line tree mode

At present, most of the scheduling models for real-time periodic tasks are established based on independent fixed periodic tasks and few by considering the task model with period allowed to change and the processor model in the scheduling process. In this paper, a task model and processor model based on line tree(LT) for sporadic real-time periodic tasks are designed, and a transformation algorithm from task line tree(TLT) model to processor line tree(PLT) model is proposed. The algorithm takes the least common multiple of all real-time periodic tasks as the benchmark of layer number, and based on the complete job replacement rule, the minimum common multiple of all real-time periodic tasks is used as the reference of layer number. In order to achieve the optimal multiprocessor scheduling result, the method of replacing the empty node of non-migrating job with the node of migrating job and the condition of allowing job to publish tasks ahead of time with variable cycle are used. Experimental results show that comparing with PEDF, GEDF and RMFF, the present method not only has higher core utilization and lower time loss rate, but also reduces the number of context switching and migration.

Semantic Image Segmentation Using Scant Pixel Annotations

The success of deep networks for the semantic segmentation of images is limited by the availability of annotated training data. The manual annotation of images for segmentation is a tedious and time-consuming task that often requires sophisticated users with significant domain expertise to create high-quality annotations over hundreds of images. In this paper, we propose the segmentation with scant pixel annotations (SSPA) approach to generate high-performing segmentation models using a scant set of expert annotated images. The models are generated by training them on images with automatically generated pseudo-labels along with a scant set of expert annotated images selected using an entropy-based algorithm. For each chosen image, experts are directed to assign labels to a particular group of pixels, while a set of replacement rules that leverage the patterns learned by the model is used to automatically assign labels to the remaining pixels. The SSPA approach integrates active learning and semi-supervised learning with pseudo-labels, where expert annotations are not essential but generated on demand. Extensive experiments on bio-medical and biofilm datasets show that the SSPA approach achieves state-of-the-art performance with less than 5% cumulative annotation of the pixels of the training data by the experts.

Predictive maintenance system for membrane replacement time detection using AI-based functional profile monitoring: Application to a full-scale MBR plant

The significant operational cost of membrane maintenance is an important issue to overcome alongside the membrane replacement time. We propose a membrane lifetime estimation method via functional machine learning (FML) using 12 biological-chemical-physical functional parameters and a predictive maintenance system for membrane replacement based on AI-driven functional profile monitoring (FPM). Biological, chemical, and physical information regarding membrane aging were reflected in the 12 functional parameters extracted from transmembrane pressure (TMP) and the chemical dosage. Membrane aging was diagnosed using hidden patterns and trends in the functional parameters, then the membrane lifetime was estimated by interpreting their relationships using FML. Influential functional parameters regarding membrane lifetime were monitored using FPM, and membrane replacement time was suggested according to newly suggested 4 types of membrane replacement rule. The predictive maintenance system improved the performance of permeability, pumping energy, and NaOCl cost by 0.66%, 0.45%, and 3.55%, respectively while extending membrane lifetime by 18 days. The proposed predictive maintenance system was considered efficient in comparison with manual maintenance and an incompetent maintenance system. • Functional machine learning extracted functional parameters from membrane fouling. • Membrane replacement rules were suggested to detect proper membrane replacement time. • A predictive maintenance system saved energy by 0.45% and chemical dosage by 3.55%. • The membrane could be operated 18 days longer by the predictive maintenance system.

A bivariate process-based mean residual lifetime model for maintenance and inspection planning

In this paper, we present a new performance metric for the reliability analysis of a deteriorating system composed of n parallel subsystems. The explored model outperforms the ordinary mean residual lifetime (MRL) and other state-dependent MRL models in the actual circumstance that it incorporates not only the age factor, but also two basic processes: the number of failed subsystems and the operating environment. Using the new model as a decision variable and the average cost rate as a measure of policy, we further examine a maintenance optimization problem for the joint determination of an optimal inspection interval and a replacement rule. The proposed model and, in particular, the behavior of the optimal solutions as the model parameters vary are illustrated through numerical examples.

Nominal tonology and spreading rules in Tagbana (Fròʔò dialect)

In this article, nominal tonology of Tagbana, a Senufo language of Côte d’Ivoire is investigated. The contribution of this article is twofold as it concerns the whole tonal system, including lexical tones, sandhi tone rules, and the organisation of the prosodic hierarchy. It is shown that Tagbana has three level tones (L, M, H) and two floating tones (H) and (L). A mid tone (M) at the end of a noun is always followed by a floating tone (either H or L), which might be a historic trace left by the tone of a Class Marker. Two clusters of sandhi tonal rules are shown to play a role, called ‘Mid Replacement rules’ (RepM) and ‘Spreading rules on H & L’ (SprH&L). The domains of the sandhi tonal rules are studied in some detail, from the Minimal Prosodic Word (root + class marker), the Intermediate and Maximal Prosodic Words (nominal and adjectival compounds), the Prosodic Phrases (particularly in object + verb constructions), to the Intonational Phrase. Considering the prosodic levels above the Minimal Prosodic Word, more tonal sandhi processes are found to apply in smaller prosodic domains than in larger ones.

The in vivo Tetrahymena thermophila extracellular glucose drop assay for characterization of mammalian insulin activity

Insulin activity is generally determined by an in vivo rabbit blood glucose drop assay in research and industriel laboratories. The humane experimental techniques imply the use of alternative invertebrate organisms in place of animals, known as replacement rule of the 3Rs. In this study, we report an alternative in vivo extracellular glucose drop assay using unicellular invertebrate Tetrahymena thermophila to replace the use of rabbit and mouse. This assay has four major steps; growing cells, starving cells, treatment of cells and measurement of glucose drop. In this assay, 0.2 mg/ml of human, porcine and bovine insulins dropped extracellular glucose level to 16%, 14% and 12%, respectively in ten minutes. In addition, mammalian insulins respectively increased the cell area about 19%, 15%, and 16% at 6th hour with statistically significant effect on the cell growth, but not in the cell viability. The results showed that the in vivo Tetrahymena thermophila extracellular glucose drop assay could be used as an alternative assay to replace the mouse or the rabbit insulin blood glucose drop assay.

Abstract Geometrical Computation 10

Signal machines form an abstract and idealized model of collision computing. Based on dimensionless signals moving on the real line, they model particle/signal dynamics in Cellular Automata. Each particle, or signal , moves at constant speed in continuous time and space. When signals meet, they get replaced by other signals. A signal machine defines the types of available signals, their speeds, and the rules for replacement in collision. A signal machine A simulates another one B if all the space-time diagrams of B can be generated from space-time diagrams of A by removing some signals and renaming other signals according to local information. Given any finite set of speeds S we construct a signal machine that is able to simulate any signal machine whose speeds belong to S . Each signal is simulated by a macro-signal , a ray of parallel signals. Each macro-signal has a main signal located exactly where the simulated signal would be, as well as auxiliary signals that encode its id and the collision rules of the simulated machine. The simulation of a collision, a macro-collision , consists of two phases. In the first phase, macro-signals are shrunk, and then the macro-signals involved in the collision are identified and it is ensured that no other macro-signal comes too close. If some do, the process is aborted and the macro-signals are shrunk, so that the correct macro-collision will eventually be restarted and successfully initiated. Otherwise, the second phase starts: the appropriate collision rule is found and new macro-signals are generated accordingly. Considering all finite sets of speeds S and their corresponding simulators provides an intrinsically universal family of signal machines.

Classical gravitational self-energy from double copy

We apply the classical double copy to the calculation of self-energy of composite systems with multipolar coupling to gravitational field, obtaining next-to-leading order results in the gravitational coupling GN by generalizing color to kinematics replacement rules known in literature. When applied to the multipolar description of the two-body system, the self-energy diagrams studied in this work correspond to tail processes, whose physical interpretation is of radiation being emitted by the non-relativistic source, scattered by the curvature generated by the binary system and then re-absorbed by the same source. These processes contribute to the conservative two-body dynamics and the present work represents a decisive step towards the systematic use of double copy within the multipolar post-Minkowskian expansion.

Concussion among soccer players in the 2017 Brazilian championship -the gap between protocol and medical practice.

Background:The present study aims to report traumatic brain injury (TBI) among soccer players in the 2017 Brazilian Soccer Championship and discuss the protocols for concussion evaluation.Materials & methods:This is an observational study utilizing video analysis of 380 matches. TBI was considered as any event in which one or more soccer player(s) had a head trauma. For potential concussion diagnosis, we analyzed players with one of the following signs: slowness to get up, disorientation, motor incoordination, loss of consciousness, head clutching and impact seizure.Results:There were 374 TBIs in total. The average time for medical assessment was 1′35”. 13 players had concussion with an average time of 3′19″ for medical evaluation. Four players were replaced after having a concussion.Conclusion:There is a gap between concussion protocols and medical practices in Brazilian elite soccer. Further discussion about soccer replacement rules are imperative.

Agent-Based and System Dynamics Modeling of Water Field Services

This paper explores the applicability of the agent-based (AB) and system dynamics (SD) methods to model a case study of the management of water field services. Water borehole sites are distributed over an area and serve the water needs of a population. The equipment at all borehole sites is managed by a single water utility that has adopted specific repair, replacement, and maintenance rules and policies. The water utility employs several service crews initially stationed at a single central location. The crews respond to specific operation and maintenance requests. Two software modeling tools (AnyLogic and STELLA) are used to explore the benefits and limitations of the AB and SD methods to simulate the dynamic being considered. The strength of the AB method resides in its ability to capture in a disaggregated way the mobility of the individual service crews and the performance of the equipment (working, repaired, replaced, or maintained) at each borehole site. The SD method cannot capture the service crew dynamics explicitly and can only model the average state of the equipment at the borehole sites. Their differences aside, both methods offer policymakers the opportunity to make strategic, tactical, and logistical decisions supported by integrated computational models.

A NOTE ON THE SEQUENT CALCULI

AbstractWe show that the replacement rule of the sequent calculi ${\bf G3[mic]}^= $ in [8] can be replaced by the simpler rule in which one of the principal formulae is not repeated in the premiss.

Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal

In the process of milling, tool wear directly affects the quality and accuracy of workpieces. Online recognition of milling cutter wear state has been and remains a growing interest in intelligent manufacturing to increase the machining efficiency and control the unqualified rate of workpieces. The effective value of spindle current can effectively characterize the wear state of milling cutter, but it will change along with machining process parameters, which are not suitable for the wear state recognition of milling cutter (WSRMC) under complex working conditions. We present LeNet-WSRMC network, a novel approach to recognize wear state of milling cutter based on the clutter signal of spindle current. The cutting vibration and tool wear are the main reasons for exciting the dynamic cutting force and the clutter signal of spindle current. In order to fully describe the generation mechanism of the clutter signal, we divide the wear state of milling cutter into four categories (i.e., normal wear, severe wear, abnormal vibration caused by tool wear, and abnormal vibration caused by improper selection of cutting parameter when the tool is sharp). LeNet-WSRMC network uses the deep convolutional neural network (DCNN) model to extract features from the spindle current clutter signal (SCCS) as the wear state of milling cutter classification index. A series of experiments with different cutting parameters and conditions are implemented to validate the effectiveness and generalization of our proposed methodology. The experimental results show that this method can realize the online accurate recognition of the wear state of milling cutter under the condition of complex working condition. This study lays a foundation for the prediction of the remaining life of the milling cutter under complex working conditions and the reasonable formulation of the replacement rules of the milling cutter.