Estimation of Part Waiting Time and Fleet Sizing in AGV Systems

As manufacturing systems have grown in size and complexity, material flow control has become one of the key issues for system efficiency, and determination of the number of vehicles required is an important issue in the design of the AGV (automatic guided vehicle) systems for automated material flow control. In an AGV system, a part issues a delivery request for its transportation, and then an empty vehicle is assigned based on a pre-determined vehicle selection rule and provides delivery service.This research presents a fleet sizing procedure for an AGV system with multiple pickup and delivery stations. A queueing model is used to estimate part waiting times. The fleet sizing procedure estimates the minimum number of vehicles needed to ensure a predefined part waiting time limit. While most stochastic models assume first-come-first-served or random vehicle selection rules for the selection of an empty vehicle, this model considers such additional rules as the nearest vehicle selection rule, which is the most popular among all vehicle selection rules. The performance of the proposed model is examined through computational experiments.     

Controlling Test Size While Gaining the Benefits of an Internal Pilot Design

To compensate for a power analysis based on a poor estimate of variance, internal pilot designs use some fraction of the planned observations to reestimate error variance and modify the final sample size. Ignoring the randomness of the final sample size may bias the final variance estimate and inflate test size. We propose and evaluate three different tests that control test size for an internal pilot in a general linear univariate model with fixed predictors and Gaussian errors. Test 1 uses the first sample plus those observations guaranteed to be collected in the second sample for the final variance estimate. Test 2 depends mostly on the second sample for the final variance estimate. Test 3 uses the unadjusted variance estimate and modifies the critical value to bound test size. We also examine three sample-size modification rules. Only test 2 can control conditional test size, align with a modification rule, and provide simple power calculations. We recommend it if the minimum second (incremental) sample is at least moderate (perhaps 20). Otherwise, the bounding test appears to have the highest power in small samples. Reanalyzing published data highlights some advantages and disadvantages of the various tests.     

Exotoxin A-PLGA nanoconjugate vaccine against Pseudomonas aeruginosa infection: protectivity in murine model

Recombinase polymerase amplification (RPA) is an isothermal amplification technique. Because of its short detection cycle and high specificity, it has been applied in various fields. However, the design of probe on the efficiency of RPA is not well understood and the effect of sequence mismatches of oligonucleotides on the performance of RPA is rarely discussed. In this study, we found that different primers with the same probe have a slight effect on the efficiency of fluorescent RPA, and different probes with the same amplified region have a great influence on the efficiency of fluorescent RPA. We summarized the design rules of probes suitable for fluorescent RPA by analyzing the experimental data. The rule is that the best distance between fluorescent groups in the probe is 1–2 bases, and the G content should be reduced as far as possible. In addition, we verified this rule by designing a series of probes. Furthermore, we found the base mismatches of the probe had a significant effect on RPA, which can lead to false positives and can change the amplification efficiency. However, 1–3 mismatches covering the center of the primer sequence only affect the amplification efficiency of RPA, not its specificity. And with an increase in the number of primer mismatches, the efficiency of RPA will decrease accordingly. This study suggests that the efficiency of fluorescent RPA is closely related to the probe. We recommend that when designing a fluorescent probe, one must consider the presence of closely related non-targets and specific bases.

     

Hierarchical graphs for rule-based modeling of biochemical systems

Background  

In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal) of an edge represents a class of association (dissociation) reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system.     

Quantitative assessment of regulation in metabolic systems

We show how metabolic regulation as commonly understood in biochemistry can be described in terms of metabolic control analysis. The steady-state values of the variables of metabolic systems (fluxes and concentrations) are determined by a set of parameters. Some of these parameters are concentrations that are set by the environment of the system; they can act as external regulators by communicating changes in the environment to the metabolic system. How effectively a system is regulated depends both on the degree to which the activity of the regulatory enzyme with which a regulator interacts directly can be altered by the regulator (its regulability) and on the ability of the regulatory enzyme to transmit the changes to the rest of the system (its regulatory capacity). The regulatory response of a system also depends on its internal organisation around key variable metabolites that act as internal regulators. The regulatory performance of the system can be judged in terms of how sensitivity the fluxes respond to the external stimulus and to what degree homeostasis in the concentrations of the internal regulators is maintained. We show how, on the level of both external and internal regulation, regulability can be quantified in terms of an elasticity coefficient and regulatory capacity in terms of a control coefficient. Metabolic regulation can therefore be described in terms of metabolic control analysis. The combined response relationship of control analysis relates regulability and regulatory capacity and allows quantification of the regulatory importance of the various interactions of regulators with enzymes in the system. On this basis we propose a quantitative terminology and analysis of metabolic regulation that shows what we should measure experimentally and how we should interpret the results. Analysis and numerical simulation of a simple model system serves to demonstrate our treatment.     

The impact of work organisation on the work life of people on pasture-based dairy farms

The seasonal workload combined with increased dairy herd sizes and a declining workforce have created social sustainability challenges for pasture-based dairy farms. Effective work organisation can build productive capacity that may have a positive impact on this scenario. Our objective was to develop a framework to characterise and examine the effect of work organisation on the working situations of the people involved in a sample of 55 pasture-based dairy farms in Ireland. We conceptualised that effective work organisation on a dairy farm could be considered as a system that is efficient from a labour input perspective, resulting in a profitable farming system with outcomes of good operator well-being, health and safety, and quality of life. A literature review established efficiency & productivity, flexibility and standardisation as our three characteristics of work organisation. Using data from an existing labour time-use study completed from the 1st February to 30th June 2019, we aimed to test the veracity of these work organisation characteristics in the Irish pasture-based dairy system. Two proxy indicators were selected to represent each of the three work organisation characteristics, and each of the 55 farms were categorised into quartiles based on their ranking for these six indicators (1 = most effective quartile to 4 = least effective quartile). The most and least effective quartiles of farms for work organisation showed similar levels of farm labour input and labour efficiency. Farmers in the most effective work organisation quartile were working 51.2 h/week from February to June compared with 70.0 h/week for farmers in the least effective quartile, which was attributed to later start times, earlier finish times, and more time at non-farm activity. Farms achieving effective work organisation had a labour-efficient system with relatively low farmer working hours. Extension of the work organisation concept to other farms could improve their labour situation and aid in alleviating some of the key quality of life challenges faced by dairy farmers.     

Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process

The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., “If A then do B”. As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.     

Genetically based kin recognition systems

Genetically based recognition systems are considered in which alleles are expressed as identifiable labels (odors, morphological structures, etc.). It is assumed that each individual can perceive the number of labels that it shares with any other individual and that it can utilize this information to make a decision on the degree to which it is related to that individual. The concept of a kingram is introduced as a graphical technique that can be used to obtain the minimum error decision rule and corresponding maximum efficiency for a particular recognition system. The efficiency of genetically based recognition systems for individuals to discriminate vetween sisters and half sisters in haplodiploid (e.g. Hymenoptera) and regular diploid organisms is compared for the case of k loci (k = 1, 2, 3, 5, 10, 14, 20) and an unlimited number of alleles at each locus. It is shown that haplodiploid systems can be four to five times more efficient than regular diploid systems. The analysis is extended to the case in which there are a limited number of alleles at each locus (two to twenty) and the question is addressed of whether it is better to distribute fewer alleles at more loci or vice versa.     

Role of connectivity in immune and neural network models: memory development and aging.

In this paper we analyzed how connectivity (defined as number of connections between network elements) can affect the memory capacity of a network-based model of the Immune System (IS) and of a model of the Nervous System (NS) synaptic plasticity (BCM model). The key point is the concept of competition between the characteristic variables that represent the response of such systems to environmental stimuli: the clonal concentrations for the IS, and the neuron responses for the BCM model. The memory states of both systems are characterized by a high selectivity to specific input patterns, reflecting a similar behaviour of their development rules. This selectivity property of memory states can be controlled by changing the degree of the internal connectivity in each system. We can explain the changes occurring in IS memory states during lifespan as due to a reshaping of its internal connectivity. This assumption is in agreement with experimental observations, reporting an increase of IS memory cells during lifespan. The change of connectivity in the BCM model leads to the introduction of quasilocal variables governing the plasticity of groups of synaptic junctions. This could be interpreted as the result of a refinement of neuron internal mechanisms during development, or it could be seen as a different learning rule deriving from the original BCM theory. We argue that connectivity seems to play an important role in a large class of biological systems controlled by competition mechanisms. Moreover, changes in connectivity may lead to changes in memory properties during development and aging.     

雄安新区上游白洋淀流域水质净化提升目标下的生态恢复格局优化

白洋淀流域是雄安新区重要的淡水来源之一,流域内退耕政策在改善水质同时大幅降低了农田面积。如何通过退耕空间规则的优化权衡水质净化效率与农田面积是亟待解决的问题。通过设置基于不同退耕规则的情景,并与实际情景对比,提出了退耕规则的优化方案。结果表明：相同面积下,河岸带退耕的水质净化效率高于坡耕地退耕;相同退耕规则下,退耕还林驱动的水质净化效率高于还灌,还草;单位面积河岸带退耕的水质净化效率随缓冲区距离增加而降低;300m河岸带退耕还林情景下的水质净化率提升高于其他所有模拟情景,也比实际退耕情景（2015）高,同时农田面积降幅也低于实际情景。研究表明通过对退耕规则进行空间优化不仅能够满足区域水质净化提升的目标,还有利于保障区域农田面积,减少生态系统服务间的权衡。     

Designing boosting ensemble of relational fuzzy systems

A method frequently used in classification systems for improving classification accuracy is to combine outputs of several classifiers. Among various types of classifiers, fuzzy ones are tempting because of using intelligible fuzzy if-then rules. In the paper we build an AdaBoost ensemble of relational neuro-fuzzy classifiers. Relational fuzzy systems bond input and output fuzzy linguistic values by a binary relation; thus, fuzzy rules have additional, comparing to traditional fuzzy systems, weights - elements of a fuzzy relation matrix. Thanks to this the system is better adjustable to data during learning. In the paper an ensemble of relational fuzzy systems is proposed. The problem is that such an ensemble contains separate rule bases which cannot be directly merged. As systems are separate, we cannot treat fuzzy rules coming from different systems as rules from the same (single) system. In the paper, the problem is addressed by a novel design of fuzzy systems constituting the ensemble, resulting in normalization of individual rule bases during learning. The method described in the paper is tested on several known benchmarks and compared with other machine learning solutions from the literature.     

What is multispecies MSY? A worked example from the North Sea

The concept of an optimum yield at intermediate levels of fishing (the so called maximum sustainable yield or MSY) has been with us since the 1930s and is now enshrined in legislation as a key objective of fisheries management. The concept seems intuitively reasonable and is readily applicable to a single stock treated in isolation and assuming a constant environment. However, translating this concept into a mixed and multispecies fishery, where there are complex trade-offs between fleets and stocks and in general no simple optimum solution, has been problematic. Here I introduce a framework for thinking about multispecies MSY in terms of an integrated risk of stock depletion and expected long-term yield. Within this framework I consider the performance of a set of simple harvest control rules based upon a single-limit fishing mortality rate (F) which is common to all stocks and a target biomass which is a set fraction of a stock's virgin biomass. Using a multispecies management strategy evaluation, I compare expected outcomes for a set of these harvest control rules with alternative scenarios, in which each stock has its own F based on the assessment process. I find that the simple framework can produce outcomes that are similar to those from the more sophisticated estimates of F. I therefore conclude that achieving multispecies MSY may depend more upon setting reasonable biomass targets and faithfully applying a harvest control rule approach rather than determining the best possible Fs for each stock.     

The spatiotemporal learning rule and its efficiency in separating spatiotemporal patterns

The hippocampus plays an important role in the course of establishing long-term memory, i.e., to make short-term memory of spatially and temporally associated input information. In 1996 (Tsukada et al. 1996), the spatiotemporal learning rule was proposed based on differences observed in hippocampal long-term potentiation (LTP) induced by various spatiotemporal pattern stimuli. One essential point of this learning rule is that the change of synaptic weight depends on both spatial coincidence and the temporal summation of input pulses. We applied this rule to a single-layered neural network and compared its ability to separate spatiotemporal patterns with that of other rules, including the Hebbian learning rule and its extended rules. The simulated results showed that the spatiotemporal learning rule had the highest efficiency in discriminating spatiotemporal pattern sequences, while the Hebbian learning rule (including its extended rules) was sensitive to differences in spatial patterns.     

Computerised order entry systems and pathology services--a synthesis of the evidence

Computerised Physician Order Entry (CPOE) systems have been promoted in Australia and internationally for their potential to improve the quality of care. The existing research of the effect of CPOE on pathology laboratories has been variable, pointing to the potential to increase efficiency and effectiveness and contribute to enhancing the quality of patient care on the one hand, while leading to significant disruptions in work organisation with a negative impact on departmental relations on the other hand. In this paper we provide an overview of the research evidence about the impact of CPOE on four areas associated with pathology services; a) efficiency of the ordering process, e.g. test turnaround times, b) effectiveness as measured by test ordering volumes and test order appropriateness, c) quality of care, particularly its effects on patient care and d) work organisation patterns, which can be severely disrupted by CPOE. We discuss the possible ramifications of CPOE and offer three broad, but important recommendations for pathology laboratories, based on our own research experience investigating CPOE implementations over three years. Firstly, pathology laboratories need to be active participants in planning the implementation of CPOE. Secondly, the importance of building a firm organisational foundation for the introduction of the new system that includes openness and responsiveness to feedback. And thirdly, the implementation process needs to be underpinned by a strong commitment to a multi-method evaluation at every stage of the process to be able to measure the impact of the system on work practices and outcomes.     

Modelling the branching growth fractal pattern of the maize root system

Using the technique of L-systems, a growth model of the maize root system is developed. From the observation of the root systems developed under various soil density in eight root boxes, a spatial hierarchy of growth rules was extracted. The rules were divided into three categories: a meta-rule for describing features of an entire root system, a branching growth rule and a tip elongation rule. Some variations in the entire features of the root system, such as the outline and the root distribution, were confirmed by observation, and then the respective meta-rules were re-defined. The branching properties of first- and second-order lateral roots were statistically almost equal in the observations, and this lead us to set up a single stochastic branching growth rule. Tip elongation movement was not observed here; its rule had to be assumed by reference to data in the literature. A single set of branching growth and tip elongation rules were coupled with the respective meta-rules corresponding to the root samples observed, where a small scale rule was loosely governed by a large scale rule. Computer simulations offered optimized drawings of the observed root systems, and they also reproduced a typical anisotropic power distribution of roots similar to those observed.     

Mate choice when males are in patches: optimal strategies and good rules of thumb

In standard mate-choice models, females encounter males sequentially and decide whether to inspect the quality of another male or to accept a male already inspected. What changes when males are clumped in patches and there is a significant cost to travel between patches? We use stochastic dynamic programming to derive optimum strategies under various assumptions. With zero costs to returning to a male in the current patch, the optimal strategy accepts males above a quality threshold which is constant whenever one or more males in the patch remain uninspected; this threshold drops when inspecting the last male in the patch, so returns may occur only then and are never to a male in a previously inspected patch. With non-zero within-patch return costs, such a two-threshold rule still performs extremely well, but a more gradual decline in acceptance threshold is optimal. Inability to return at all need not decrease performance by much. The acceptance threshold should also decline if it gets harder to discover the last males in a patch. Optimal strategies become more complex when mean male quality varies systematically between patches or years, and females estimate this in a Bayesian manner through inspecting male qualities. It can then be optimal to switch patch before inspecting all males on a patch, or, exceptionally, to return to an earlier patch. We compare performance of various rules of thumb in these environments and in ones without a patch structure. A two-threshold rule performs excellently, as do various simplifications of it. The best-of-N rule outperforms threshold rules only in non-patchy environments with between-year quality variation. The cutoff rule performs poorly.     

Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata

The simulation of the dynamics of a cellular systems based on cellular automata (CA) can be computationally expensive. This is particularly true when such simulation is part of a procedure of rule induction to find suitable transition rules for the CA. Several efforts have been described in the literature to make this problem more treatable. This work presents a study about the efficiency of dynamic behavior forecasting parameters (DBFPs) used for the induction of transition rules of CA for a specific problem: the classification by the majority rule. A total of 8 DBFPs were analyzed for the 31 best-performing rules found in the literature. Some of these DBFPs were highly correlated each other, meaning they yield the same information. Also, most rules presented values of the DBFPs very close each other. An evolutionary algorithm, based on gene expression programming, was developed for finding transition rules according a given preestablished behavior. The simulation of the dynamic behavior of the CA is not used to evaluate candidate transition rules. Instead, the average values for the DBFPs were used as reference. Experiments were done using the DBFPs separately and together. In both cases, the best induced transition rules were not acceptable solutions for the desired behavior of the CA. We conclude that, although the DBFPs represent interesting aspects of the dynamic behavior of CAs, the transition rule induction process still requires the simulation of the dynamics and cannot rely only on the DBFPs.     

Sequential spiking neural P systems with exhaustive use of rules

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, we consider SN P systems with the restriction that at most one neuron can fire at each step, and each neuron works in an exhaustive manner (a kind of local parallelism - an applicable rule in a neuron is used as many times as possible). Such SN P systems are called sequential SN P systems with exhaustive use of rules. The computation power of sequential SN P systems with exhaustive use of rules is investigated. Specifically, characterizations of Turing computability and of semilinear sets of numbers are obtained, as well as a strict superclass of semilinear sets is generated. The results show that the computation power of sequential SN P systems with exhaustive use of rules is closely related with the types of spiking rules in neurons.     

Status and foraging in New Zealand honeyeaters

Abstract

Aggression is a common feature of sociality among nectar-feeding birds, and these have been used to consider many aspects of ecological theory, for example community structure, foraging patterns, social organisation and plant-pollinator systems.

Knowledge of aggression among and within New Zealand's honeyeater species is reviewed. Additional information on the importance of various asymmetric cues as learned predictors of status is considered. High-status individuals appear to gain access to nectar in all situations.

Honeyeaters are monogamous and the relationship between the sexes is examined. Even when mates are not formally associated, both appear to benefit from the presence of the other. Examples of the effect of status on pollination efficiency are also considered.

Workers are urged to consider status and individual differences if we are to increase our understanding of community structure, foraging and plant-pollinator interaction.     

Flexible implementations of group sequential stopping rules using constrained boundaries

Group sequential stopping rules are often used during the conduct of clinical trials in order to attain more ethical treatment of patients and to better address efficiency concerns. Because the use of such stopping rules materially affects the frequentist operating characteristics of the hypothesis test, it is necessary to choose an appropriate stopping rule during the planning of the study. It is often the case, however, that the number and timing of interim analyses are not precisely known at the time of trial design, and thus the implementation of a particular stopping rule must allow for flexible determination of the schedule of interim analyses. In this article, we consider the use of constrained stopping boundaries in the implementation of stopping rules. We compare this approach when used on various scales for the test statistic. When implemented on the scale of boundary crossing probabilities, this approach is identical to the error spending function approach of Lan and DeMets (1983).