Original Rule Research Articles

The effect of finite temperature and chemical potential on nucleon properties in the logarithmic quark sigma model

The logarithmic quark sigma model is applied to study the nucleon properties at finite temperature and chemical potential. The field equations have been solved numerically in the mean-field approximation by using the extended iteration method at finite temperature and baryon chemical potential. Baryon properties are investigated, such as the hedgehog mass, the magnetic moments of the proton and neutron, and the pion-nucleon coupling constant. We find that the hedgehog mass and the magnetic moments of the proton and neutron increase with increasing temperature and chemical potential, while the pion-nucleon coupling constant decreases. A comparison with the original sigma model and QCD sum rules is presented. We conclude that the logarithmic quark sigma model successfully describes baryon properties of a hot and dense medium.

Rule-based fuzzy classifier based on quantum ant optimization algorithm

Fuzzy rule-based classification systems have been used extensively in data mining. This paper proposes a fuzzy rule- based classification algorithm based on a quantum ant optimization algorithm. A method of generating the hierarchical rules with different granularity hybridization is used to generate the initial rule set. This method can obtain an original rule set with a smaller number of rules. The modified quantum ant optimization algorithm is used to generate the optimal individual. Compared to other similar algorithms, the algorithm proposed in this paper demonstrates higher classification accuracy and a higher convergence rate. The algorithm is proved to be convergent on theory. Some experiments have been conducted on the algorithm, and the results proved that the algorithm is feasible.

Gravitational fixed radius nearest neighbor for imbalanced problem

This paper proposes a novel learning model that introduces the calculation of the pairwise gravitation of the selected patterns into the classical fixed radius nearest neighbor method, in order to overcome the drawback of the original nearest neighbor rule when dealing with imbalanced data. The traditional k nearest neighbor rule is considered to lose power on imbalanced datasets because the final decision might be dominated by the patterns from negative classes in spite of the distance measurements. Differently from the existing modified nearest neighbor learning model, the proposed method named GFRNN has a simple structure and thus becomes easy to work. Moreover, all parameters of GFRNN do not need initializing or coordinating during the whole learning procedure. In practice, GFRNN first selects patterns as candidates out of the training set under the fixed radius nearest neighbor rule, and then introduces the metric based on the modified law of gravitation in the physical world to measure the distance between the query pattern and each candidate. Finally, GFRNN makes the decision based on the sum of all the corresponding gravitational forces from the candidates on the query pattern. The experimental comparison validates both the effectiveness and the efficiency of GFRNN on forty imbalanced datasets, comparing to nine typical methods. As a conclusion, the contribution of this paper is constructing a new simple nearest neighbor architecture to deal with imbalanced classification effectively without any manually parameter coordination, and further expanding the family of the nearest neighbor based rules.

Shifts in dynamic regime of an invasive lady beetle are linked to the invasion and insecticidal management of its prey.

The spread and impact of invasive species may vary over time in relation to changes in the species itself, the biological community of which it is part, or external controls on the system. We investigate whether there have been changes in dynamic regimes over the last 20 years of two invasive species in the midwestern United States, the multicolored Asian lady beetle Harmonia axyridis and the soybean aphid Aphis glycines. We show by model selection that after its 1993 invasion into the American Midwest, the year-to-year population dynamics of H. axyridis were initially governed by a logistic rule supporting gradual rise to a stable carrying capacity. After invasion of the soybean aphid in 2000, food resources at the landscape level became abundant, supporting a higher year-to-year growth rate and a higher but unstable carrying capacity, with two-year cycles in both aphid and lady beetle abundance as a consequence. During 2005-2007, farmers in the Midwest progressively increased their use of insecticides for managing A. glycines, combining prophylactic seed treatment with curative spraying based on thresholds. This human intervention dramatically reduced the soybean aphid as a major food resource for H. axyridis at landscape level and corresponded to a reverse shift towards the original logistic rule for year-to-year dynamics. Thus, we document a short episode of major predator-prey fluctuations in an important agricultural system resulting from two biological invasions that were apparently damped by widespread insecticide use. Recent advances in development of plant resistance to A. glycines in soybeans may mitigate the need for pesticidal control and achieve the same stabilization of pest and predator populations at lower cost and environmental burden.

The Next Generation: Upgrading Proportionality for a New Paradigm

SOMETIMES, it takes while for an idea catch on, even really good one. The first versions of automobile ran on steam and then electricity, and it took long time for inventors determine that gasoline was superior fuel. (1) Then things really took off. Similarly, lightbulb wasn't always safe, bright, long-lasting light source we know today; it took lot of tweaking get things just right. (2) Like automobile and lightbulb, principle of proportionality is really good idea. But, also like automobile and lightbulb, it's been slow catch on and has required some changes along its way. The proposed amendments Federal Rules of Civil Procedure seek make those last few adjustments that will finally encourage widespread application of proportionality in discovery. If everything goes as planned, future of discovery is bright. The proposals come none too soon. In recent years, problems of discovery have reached critical tipping point and contribute greatly oft-repeated sentiment that [o]ur discovery system is broken (3) and that our civil justice system is in serious need of repair. (4) These problems find their source in diversity of circumstances, but are perhaps most affected by modern rise of technology and resulting explosion in volume and variety of electronic information in world today. A recent study of reports that [l]ike physical universe, digital universe is large--by 2020 containing nearly as many digital bits as there are stars in universe. It is doubling in size every two years, and by 2020 digital universe--the data we create and copy annually--will reach 44 zettabytes, or 44 trillion gigabytes. (5) The effects of this dramatic information growth are increasingly felt by parties, counsel, and courts alike and underlie inevitable conclusion that modern discovery is different and that traditional notions no longer apply. Despite its inevitability, there is no denying that the process of change ... is tortuous and contentious (6) and that effort shift discovery paradigm will therefore be substantial. There is good news, however; much of work has been completed already. Indeed, principle of proportionality--arguably present in rules since their development--has been re-designed and improved over many years. Unfortunately, practical application of principle of proportionality has continuously met with strong resistance where traditional notions of broad and liberal discovery remain entrenched in jurisprudence surrounding questions related scope. Accordingly, United States Judicial Conference has once again undertaken reincorporate proportionality into design of Federal Rules. This article will explain principle of proportionality and its purpose, discuss changes in proposed new design and their intended effects, and provide some advice on how put principle use. I. A Brief History Although relevance is widely recognized as keystone of discovery, principle of proportionality has long existed in rules as restraint overbreadth, abuse, and misuse. Even original rules drafters recognized need for limitations on discovery and, pursuant Rule 30(b), for example, allowed court dictate limitations on depositions to protect [a] party or witness from annoyance, embarrassment or oppression. Per original Committee Note, such limitations were available in recognition of need for a safeguard for protection of parties and deponents on account of unlimited right of discovery given by Rule 26. (7) In subsequent years, further limitations discovery were adopted and principle of proportionality, although never explicitly named, became an established component of discovery analysis. In 1983, for example, several of now-familiar factors of proportionality analysis were adopted for first time with intention of guarding against redundant or disproportionate discovery by giving court authority reduce amount of discovery that may be directed matters that are otherwise proper subjects of inquiry. …

Practical flow table aggregation in SDN

In OpenFlow-driven SDN, flow tables are TCAM-hungry; commodity switches suffer from limited concrete flow table size. One method for coping with the limitations is to use aggregation schemes to reduce the number of flow entries required to express the same forwarding semantics. Unfortunately, the aggregation of rules would retard table updates and lengthen the updating duration, during which, the data plane is inconsistent with the control plane. Forwarding errors such as Reachability Failures, Forwarding Loops, Traffic Isolation and Leakage are prone to occur. Since network updates take place frequently in practice, the aggregation scheme must be efficient and effective.In this paper, we proposed FFTA (Fast Flow Table Aggregation) and its online companion, iFFTA (incremental FFTA), to make practical flow table aggregation. FFTA is an offline solution performing snapshot aggregation of non-prefix rules by (1) splitting them into prefix-permutable partitions in an aggregation-aware manner, and (2) applying optimal prefix-based aggregation techniques, respectively. When some original rules are updated, iFFTA is triggered to incorporate the update immediately by leveraging the order-independence relationship and structure information of rules. To the best of our knowledge, iFFTA is the first online aggregation scheme for non-prefix rules. We employed public available prefix rules as well as synthetic non-prefix rules generated with real parameters to evaluate their performances. Extensive experiments demonstrated that FFTA significantly outperforms prior art on both efficiency and effectiveness, while iFFTA greatly simplifies the update of aggregated rules with an acceptable loss of compression ratio. Accordingly, users could make a combination use of FFTA and iFFTA in practice: call iFFTA usually and recall FFTA once the switch is running out of concrete flow table space.

A Decision Logic Approach to Mill’s Eliminative Induction

Abstract The subject of the paper is a contemporary interpretation of J.S. Mill’s elimination method using selected concepts of Zdzisław Pawlak’s decision logic. The aim of the interpretation is to reformulate the original rules (canons) of Mill’s induction so that they correspond more precisely to his concept of cause as a complex sufficient condition. In the first part of the paper, we turn to Mill’s writings and justify the thesis that in his understanding the cause is an aggregation of circumstances, and not a single circumstance; next, we point out that Mill’s original canons (for example the canon of agreement and the canon of difference) do not allow causes-aggregations to be singled out from empirical data. In the second part of this paper, we present such aspects of Z. Pawlak’s decision logic that serve as the basis for the formalisation of the method of eliminative induction. We describe exhaustively the schema of induction that involves a gradual - divided into three stages - simplification of a set of implications corresponding to the observed dependencies [system of potential causes, effect]. The simplification is deductive because it maintains consistency within the set of implications. We show that such schema is ideal for isolating complex causes (aggregations of circumstances), ultimately described using complex conditional formulas of decision logic.

The Chaboche hardening rule: A re-evaluation of calibration procedures and a modified rule with an evolving material parameter

This study first re-examines key existing calibration procedures for the Chaboche decomposed nonlinear kinematic hardening rule. Special attention has been paid to the extent to which the predictions of calibrated models represent the physics of phenomena observed in strain- and stress-controlled cyclic tests. It has been shown and discussed that newer methods suffer from some weaknesses and may yield nonphysical results. Second, with the aim of improving the accuracy of its ratcheting strain predictions in unsymmetric stress-controlled tests, a modified calibration procedure and a new Chaboche hardening rule with an evolving material parameter is introduced. The former improves the accuracy of predicted ratcheting strains at the initial cycles, while the latter is responsible for the better agreement of the results at the higher cycles. The new model assumes there exists a linear relationship between one of the coefficient of the model and its corresponding effective/equivalent backstress. Finally, the predictions of the new model and those of the original Chaboche rule are compared with the available experimental results from the literature. It has been shown that, regardless of the level of mean stress of unsymmetric stress-controlled test, the ratcheting rates of the higher cycles are satisfactorily predicted by the proposed modified model.

Pushing the rules: effects and aftereffects of deliberate rule violations.

Most of our daily life is organized around rules and social norms. But what makes rules so special? And what if one were to break a rule intentionally? Can we simply free us from the present set of rules or do we automatically adhere to them? How do rule violations influence subsequent behavior? To investigate the effects and aftereffects of violating simple S-R rule, we conducted three experiments that investigated continuous finger-tracking responses on an iPad. Our experiments show that rule violations are distinct from rule-based actions in both response times and movement trajectories, they take longer to initiate and execute, and their movement trajectory is heavily contorted. Data not only show differences between the two types of response (rule-based vs. violation), but also yielded a characteristic pattern of aftereffects in case of rule violations: rule violations do not trigger adaptation effects that render further rule violations less difficult, but every rule violation poses repeated effort on the agent. The study represents a first step towards understanding the signature and underlying mechanisms of deliberate rule violations, they cannot be acted out by themselves, but require the activation of the original rule first. Consequently, they are best understood as reformulations of existing rules that are not accessible on their own, but need to be constantly derived from the original rule, with an add-on that might entail an active tendency to steer away from mental representations that reflect (socially) unwanted behavior.

Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

The intrusion detection system (IDS) is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS Â can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks. Â We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS. Â The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

Rule caching in SDN-enabled mobile access networks

An SDN enabled mobile access network is a future network with great potential to support scalable and flexible network applications. To support various network applications, the SDN-enabled mobile access network usually uses forwarding rules in SDN devices. With a limited rule space in existing SDN devices, a rule caching mechanism is an efficient way to improve network performance. In this article we propose SRCMN, which is a new caching structure to improve network performance with a limited rule space in the SDN-enabled mobile access network. We design a two-layer rule space in each SDN device, which is managed by the SDN controller. We also design a cache prefetching mechanism with the consideration of user mobility. By conducting extensive simulations, we demonstrate that our proposed structure and mechanism significantly outperform original rule space management under various network settings.

Risk analysis for seasonal flood-limited water level under uncertainties

For floodwater utilization, seasonal flood-limited water level (FLWL) plays a more and more role in compromising between flood control and beneficial use in reservoir operation during flood season. The prerequisite of determining a seasonal FLWL is that flood control risks should not be increased in reservoir operation as compared with the original operating rule using a fixed FLWL. In this paper, a risk analysis model for deriving seasonal FLWL that considers uncertainties of hydrology, hydraulic condition and reservoir volume is proposed and developed. The risk analysis model consists of three modules: the first is a hydrological uncertainty analysis module, the second is a hydraulic uncertainty analysis module, as well as the third is a reservoir volume uncertainty analysis module. The acceptable risk constraints are given, and the upper limitation of seasonal FLWL is estimated by using Monte Carlo simulation. The China’ Wanjiazhai reservoir (WR) is selected as a case study. The application results show that (1) the hydrological uncertainty and the reservoir volume uncertainty are major contribution factors to seasonal FLWL while the discharge capacity uncertainty is inapparent influence of seasonal FLWL, (2) the most reasonable upper limitations of seasonal FLWL in WR during main-flood and post-flood seasons are 972.3 and 974.1 m, respectively, which considers hydrological uncertainty, minimum hydraulic capability and minimum reservoir volume. The relative magnitudes of seasonal FLWL and the flood water utilization rates during main-flood and post-flood seasons are 0.65% and 61.05%, as well as 0.84% and 81.60%, respectively. Seasonal FLWL can effectively enhance flood water utilization rate without lowering the annual flood control standard compared with annual FLWL.

A Power Efficient Test Data Compression Method on Count Compatible PRL Coding

A realistic test sets compression method is proposed to effectively reduce test data volume and test application time during system-on-chip (SoC) scan testing, count compatible pattern run-length (CCPRL) coding method counts the consecutive number of the equal to or contrary to the retained patterns, it modifies the compatible code of variable-length pattern run-length (VPRL) coding rules and adds a count code block to replace original rules for increasing compression ratio. Next, the decoder architecture and the state diagram of finite state machine (FSM) are designed. In addition, the power model of test vectors is analyzed, and the power consumption of scanned-in vectors is roughly evaluated. The six largest ISCAS'89 benchmark circuits verify the proposed coding method has a shorter codeword. Experiment results shows that all compression ratios have been increased as much as possible, test data decompression is lossless, less test application time is consumed, yet the peak power and average power consumption of scanned-in test vector needs to be further improved for modern circuit scan testing.

A Formal Account of the Open Provenance Model

On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The open provenance model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present article extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The article concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs.

Truthful Mechanisms with Implicit Payment Computation

It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation rule. Our main result is a general procedure to take a monotone allocation rule for a single-parameter domain and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. The mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once. We also provide an extension of this result to multiparameter domains and cycle-monotone allocation rules, under mild star-convexity and nonnegativity hypotheses on the type space and allocation rule, respectively. Because our reduction is simple, versatile, and general, it has many applications to mechanism design problems in which re-evaluating the allocation rule is either burdensome or informationally impossible. Applying our result to the multiarmed bandit problem, we obtain truthful randomized mechanisms whose regret matches the information-theoretic lower bound up to logarithmic factors, even though prior work showed this is impossible for truthful deterministic mechanisms. We also present applications to offline mechanism design, showing that randomization can circumvent a communication complexity lower bound for deterministic payments computation, and that it can also be used to create truthful shortest path auctions that approximate the welfare of the VCG allocation arbitrarily well, while having the same running time complexity as Dijkstra's algorithm.

Hybrid electric vehicle modeling accuracy verification and global optimal control algorithm research

Vehicle modeling and simulation is a very important part in the design and development of electric and hybrid vehicles. To precisely simulate the powertrain running status, many commercial software products are applied into the development of control strategy. However, these models cost too much time in the simulation analysis, which would be difficult to apply in the control strategy optimization. Therefore, vehicle dynamic system model is built by the software of matlab/Simulink and this vehicle model is compared with the commercial vehicle simulation model. Simulation results show that this model has relatively high simulation accuracy, which lays the foundation for further vehicle performance optimization. In this study, dynamic programming global optimization algorithm is applied on the hybrid electric vehicle to search the optimal solution in the determinate speed cycle. Obviously, this method can obtain the minimum fuel economy while keep the balance of battery state of charge (SOC), but it is difficult to apply in the real-time control system. Therefore, the optimal gear-shifting rules, vehicle operation mode control rules and engine torque control rules under the different of battery SOC are extracted into the rule-based control algorithm. Simulation results show that rule-based optimized control based on the DP control rules can reduce about 13.20% of fuel consumption when compared to original logic rule control algorithm under the China's urban driving cycle. It could obtain better fuel economy under the Beijing driving cycle and easily be implemented in a vehicle prototype.

Novel magneto-electric multiferroics from first-principles calculations

Interest in first-principles calculations within the multiferroic community has been rapidly on the rise over the last decade. Initially considered as a powerful support to explain experimentally observed behaviours, the trend has evolved and, nowadays, density functional theory calculations has become also an essential predicting tool for identifying original rules to achieve multiferroism and design new magneto-electric compounds. This chapter aims to highlight the key advances in the field of multiferroics for which first-principles methods have contributed significantly. The essential theoretical developments that made this search possible are also briefly presented.

Research on the optimal power management strategy for a hybrid electric bus

The power management strategy for a hybrid electric vehicle uses expert knowledge and experience to determine the control rules and thresholds, but it is difficult to obtain good performance. This paper presents a dynamic programming algorithm based on a powertrain system to find the optimal solution in the specific speed cycle, but this method cannot be applied to a real-time control system. Therefore, the optimal control rules and thresholds are extracted for the rule-based strategy; the simulation results show that the refined strategy can reduce the fuel consumption by about 14.41% compared with that obtained by the original logic rule control algorithm in the Chinese urban driving cycle. An online neural network energy controller based on the dynamic programming results is provided by choosing a reasonable network structure and control parameters to improve the control precision; the numerical simulation results show that the neural network controller improves the vehicle fuel economy by 15.05%, which is closer to the dynamic programming algorithm simulation result. This control algorithm not only can be used in real-time control and reduces the computational complexity but also allows new design ideas to be proposed for the control algorithm and the vehicle fuel consumption to be reduced.

The Manchester Acute Coronary Syndromes (MACS) decision rule: validation with a new automated assay for heart-type fatty acid binding protein

ObjectiveThe Manchester Acute Coronary Syndromes (MACS) decision rule may enable acute coronary syndromes to be immediately ‘ruled in’ or ‘ruled out’ in the emergency department. The rule incorporates heart-type fatty...

Optimal Design for Flood Limit Water Level of Cascade Reservoirs

For cascade reservoirs in river basin, the optimization design for flood limit water level (ODFLL) will be more important at the end of flood season. In this paper, an optimal design model for flood limit water level (ODMFLL) of cascade reservoirs is proposed to simultaneously optimize the flood control risk and economic benefits of reservoir groups in flood season. Also, compensation relationship among reservoirs has been studied to maximize the utility of water resources. Moreover, a modified self-adaptive electromagnetism-like Mechanism (SEM) algorithm is presented to deal with ODFLL problem by considering more than two purposes. In order to analysis the risk of optimizing control schemes, the classical Monte Carlo method, is adopted in this paper. Finally, the case of well-known Cascade Reservoir in Jinsha River (two reservoirs) and Three Gorges Reservoir is implemented for solving ODFLL problem. The simulation results reveal that, compared with original rules of flood limit water level, the ODMFLL can provide better solutions to the problem by raising the flood limit water level of cascade reservoirs and not increasing the flood control risk. Therefore, ODMFLL can rational utilize the water resource of watershed, and reduce the abandoned water in flood season.