Original Rule Research Articles

CELLULAR AUTOMATA MODELLING APPROACH FOR URBAN GROWTH

Cellular Automaton (CA) consists of a regular grid cells of which states change according to simple repetitive rules regulated by their contiguous and adjacent cells, which often expresses an unexpected complexity. Thus, CA is one of the major techniques to imitate and/or assess complex behaviors of natural systems. CA can be applied to physical and biological phenomena, such as turbulence in fluid, patterns of biological growth, and wildfire, and also some human-induced phenomena such as urban growth that is the main target of this review. In 1970s, cellular approach was initially adopted in geography, showing the clue to the urban growth application. To overcome the limitation or constraints the conventional standard cell-space models inherently include, alternative formulations were theoretically proposed in 1980s. And the pioneering work applied to realistic cities was conducted in 1990s. Subsequently, numerous models have been presented by relaxing original rules to express reality and by introducing some additional techniques such as geographical information system and system dynamics, thus far. This paper reviews 87 published cellular automata studies on urban growth simulations, urban land use change assessments, urban planning and related information from 18 countries, and examines the characteristics of each relaxation method. In addition, the scale problems are frequently discussed in the validation of the CA model is addressed.

Analysis of the 13C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources.

Adult blow flies are one of the first necrophagous insects to colonize fresh carcasses. The eggs they lay hatch into larvae, which then feed on the decomposing body. Like all organisms, blow flies "are what they eat," meaning that the isotopic composition of their body tissues reflects their diet. This manuscript combines ecology witha forensic application by using isotope ratio mass spectrometry (IRMS) to understand the relationship between the δ13C of amino acids in different carrion sources and the blow fly that feed on them. We also measure the amino acid-level fractionation that occurs at each major life stage of the blow flies. Adult blow flies from a commercial strain of Calliphora vicina (Robineau-Desvoidy) (Diptera: Calliphoridae) oviposited on raw pork muscle, beef muscle, or chicken liver. Larvae, pupae, and adult blow flies from each carrion were selected for amino acid compound-specific isotope analysis. Canonical discriminant analysis showed that flies were correctly classified to specific carrion types in 100% (original rules) and 96.8% (leave-one-out cross-validation [LOOCV]) of cases. Regarding life stages, we obtained 100% and 71% of correct classification in original rules and LOOCV, respectively. The isotope ratios of most of the essential amino acids did not significantly change between life stages (at 95% CI). However, some non-essential amino acids(Ala, Ser, and Glu) and some conditionally essential amino acids (Gly and Pro) were isotopically depleted in the adult stage. Except for the essential amino acids, the amino acids in larvae and pupae were enriched in 13C, and adult blow flies were depleted in 13C relative to the carrion on which they fed. These results make it possible to exclude potential sources of carrion as larval food. Amino acid-specific IRMS could help inform entomologists whether a fly has just arrived from another location to feed on a corpse or has emerged from a pupa whose feedstock was the corpse. Such insight could enhance the significance of blow flies for post-mortem interval determinations. The analytical ability to link organisms from one trophic level to another through the use of compound-specific isotope analysis of amino acids could have wide-reaching consequences in a variety of disciplines. Graphical abstract ᅟ.

Exploring efficient grouping algorithms in regular expression matching.

BackgroundRegular expression matching (REM) is widely employed as the major tool for deep packet inspection (DPI) applications. For automatic processing, the regular expression patterns need to be converted to a deterministic finite automata (DFA). However, with the ever-increasing scale and complexity of pattern sets, state explosion problem has brought a great challenge to the DFA based regular expression matching. Rule grouping is a direct method to solve the state explosion problem. The original rule set is divided into multiple disjoint groups, and each group is compiled to a separate DFA, thus to significantly restrain the severe state explosion problem when compiling all the rules to a single DFA.ObjectiveFor practical implementation, the total number of DFA states should be as few as possible, thus the data structures of these DFAs can be deployed on fast on-chip memories for rapid access. In addition, to support fast pattern update in some applications, the time cost for grouping should be as small as possible. In this study, we aimed to propose an efficient grouping method, which generates as few states as possible with as little time overhead as possible.MethodsWhen compiling multiple patterns into a single DFA, the number of DFA states is usually greater than the total number of states when compiling each pattern to a separate DFA. This is mainly caused by the semantic overlaps among different rules. By quantifying the interaction values for each pair of rules, the rule grouping problem can be reduced to the maximum k-cut graph partitioning problem. Then, we propose a heuristic algorithm called the one-step greedy (OSG) algorithm to solve this NP-hard problem. What’s more, a subroutine named the heuristic initialization (HI) algorithm is devised to further optimize the grouping algorithms.ResultsWe employed three practical rule sets for the experimental evaluation. Results show that the OSG algorithm outperforms the state-of-the-art grouping solutions regarding both the total number of DFA states and time cost for grouping. The HI subroutine also demonstrates its significant optimization effect on the grouping algorithms.ConclusionsThe DFA state explosion problem has became the most challenging issue in the regular expression matching applications. Rule grouping is a practical direction by dividing the original rule sets into multiple disjoint groups. In this paper, we investigate the current grouping solutions, and propose a compact and efficient grouping algorithm. Experiments conducted on practical rule sets demonstrate the superiority of our proposal.

Dynamic pricing for inventories with reference price effects

Abstract This article presents a dynamic pricing model of a retailer selling an inventory, accounting for consumer behavior. The authors propose an optimal control model, maximizing the intertemporal profit with consumers sensitive to the selling price and to a reference price. The optimal dynamic pricing policy is solved with Pontryagin’s maximum principle with a structural (general) demand function. The authors obtain an original pricing rule, which explicitly accounts for the impact of price and inventory on future profits. The dynamics of price do not have to imitate the dynamics of the reference price. Instead, the dynamics of price are tied to opposing effects linked to this reference price. The authors also discuss managerial implications with regards to behavioral pricing policies.

Self-Authentication Generally: Certifications Under The Confrontation Clause; Confrontation Clause Application Summary

Self-Authentication Generally: Certifications Under The Confrontation Clause; Confrontation Clause Application Summary

Dynamic Fuzzy Rule Interpolation and Its Application to Intrusion Detection

Fuzzy rule interpolation (FRI) offers an effective approach for making inference possible in sparse rule-based systems (and also for reducing the complexity of fuzzy models). However, requirements of fuzzy systems may change over time and hence, the use of a static rule base may affect the accuracy of FRI applications. Fortunately, an FRI system in action will produce interpolated rules in abundance during the interpolative reasoning process. While such interpolated results are discarded in existing FRI systems, they can be utilized to facilitate the development of a dynamic rule base in supporting subsequent inference. This is because the otherwise relinquished interpolated rules may contain possibly valuable information, covering regions that were uncovered by the original sparse rule base. This paper presents a dynamic fuzzy rule interpolation (D-FRI) approach by exploiting such interpolated rules in order to improve the overall system's coverage and efficacy. The resulting D-FRI system is able to select, combine, and generalize informative, frequently used interpolated rules for merging with the existing rule base while performing interpolative reasoning. Systematic experimental investigations demonstrate that D-FRI outperforms conventional FRI techniques, with increased accuracy and robustness. Furthermore, D-FRI is herein applied for network security analysis, in devising a dynamic intrusion detection system (IDS) through integration with the Snort software, one of the most popular open source IDSs. This integration, denoted as D-FRI-Snort hereafter, delivers an extra amount of intelligence to predict the level of potential threats. Experimental results show that with the inclusion of a dynamic rule base, by generalising newly interpolated rules based on the current network traffic conditions, D-FRI-Snort helps reduce both false positives and false negatives in intrusion detection.

Incremental Rule Splitting in Generalized Evolving Fuzzy Systems for Autonomous Drift Compensation

Gradual drifts in data streams are usually hard to detect and often do not necessarily trigger the evolution of new fuzzy rules during model adaptation steps in order to represent the new, drifted data distribution(s) appropriately in the fuzzy model. Over time, they thus lead to oversized rules with untypically large local errors (typically also worsening the global model error), as representing joint local data distributions before and after a drift happened likewise. We therefore propose an incremental rule splitting concept for generalized fuzzy rules in order to autonomously compensate these negative effects of gradual drifts. Our splitting condition is based on the local error of rules measured in terms of a weighted contribution to the whole model error and on the size of the rules measured in terms of the volume of the associated clusters. We use the concept of statistical process control in order to omit an extra threshold parameter in our splitting condition. The splitting technique relies on the eigendecomposition of the rule covariance matrix to adequately manipulate the largest eigenvector and eigenvalues in order to retrieve the new centers and contours of the two split rules. Furthermore, we guarantee sufficient flexibility in adapting the shapes and consequents of the split rules to the new drifted situation in the stream by integrating a specific dynamic and smooth forgetting concept of older samples, which formed the original (nonsplit) rules. Robustness against outliers is guaranteed by the realization of a two-layer model building process , where one layer represents the cluster partition and the other layer the rule partition: Only clusters becoming significant over time are accepted as rules in the fuzzy model. The splitting concepts are integrated in the generalized smart evolving learning engine for fuzzy systems (termed as Gen-Smart-EFS ) and successfully tested on two real-world application scenarios, engine test benches and rolling mills, the latter including a real-occurring gradual drift (whose position in the data is known). Results show clearly improved error trend lines over time when splitting is applied, compared to the case when it is not applied: reduction of the mean absolute model error by about one third (rolling mills) and about one half (engine test benches).

Validation and refinement of a clinical decision rule for the use of computed tomography in children with minor head injury in the emergency department.

There is uncertainty about which children with minor head injury need to undergo computed tomography (CT). We sought to prospectively validate the accuracy and potential for refinement of a previously derived decision rule, Canadian Assessment of Tomography for Childhood Head injury (CATCH), to guide CT use in children with minor head injury. This multicentre cohort study in 9 Canadian pediatric emergency departments prospectively enrolled children with blunt head trauma presenting with a Glasgow Coma Scale score of 13-15 and loss of consciousness, amnesia, disorientation, persistent vomiting or irritability. Phys icians completed standardized assessment forms before CT, including clinical predictors of the rule. The primary outcome was neurosurgical intervention and the secondary outcome was brain injury on CT. We calculated test characteristics of the rule and used recursive partitioning to further refine the rule. Of 4060 enrolled patients, 23 (0.6%) underwent neurosurgical intervention, and 197 (4.9%) had brain injury on CT. The original 7-item rule (CATCH) had sensitivities of 91.3% (95% confidence interval [CI] 72.0%-98.9%) for neurosurgical intervention and 97.5% (95% CI 94.2%-99.2%) for predicting brain injury. Adding "≥ 4 episodes of vomiting" resulted in a refined 8-item rule (CATCH2) with 100% (95% CI 85.2%-100%) sensitivity for neurosurgical intervention and 99.5% (95% CI 97.2%-100%) sensitivity for brain injury. Among children presenting to the emergency department with minor head injury, the CATCH2 rule was highly sensitive for identifying those children requiring neurosurgical intervention and those with any brain injury on CT. The CATCH2 rule should be further validated in an implementation study designed to assess its clinical impact.

Quasisymmetric and noncommutative skew Pieri rules

In this note we derive skew Pieri rules in the spirit of Assaf–McNamara for skew quasisymmetric Schur functions using the Hopf algebraic techniques of Lam–Lauve–Sottile, and recover the original rules of Assaf–McNamara as a special case. We then apply these techniques a second time to obtain skew Pieri rules for skew noncommutative Schur functions.

Deriving operating rules for a large-scale hydro-photovoltaic power system using implicit stochastic optimization

Abstract Complementary operation is a new effective way to simultaneously manage hydropower and photovoltaic (PV) power. With the integration of a PV power into an existing hydropower reservoir system, the original operating rules need to be re-designed to improve operational performance. This study focuses on exploring long-term operating rules for such an integrated system using implicit stochastic optimization, which can address uncertainties in reservoir inflow and PV power. First, a long-term multi-objective optimization model for this system is established by maximizing the total energy production and assurance rate (i.e., reliability) simultaneously. The model is then solved using a dynamic programming technique. Operating rules are derived from the obtained optimal trajectory using a linear fitting method. Finally, a simulation-based optimization framework is used to refine the parameters of these rules. Our case study based on China's Longyangxia hybrid power system shows that: (1) a significant linear correlation between available energy and reservoir storage at the end of each time period, with fitting correlation coefficients above 0.9 for each month. Therefore, the available energy and reservoir storage at the end of each time period are selected, respectively, as independent variables and decision variables in the operating rules; (2) an improved average annual energy production and assurance rate are obtained for the hybrid system when considering complementary operations; and (3) the derived operating rules are effective in improving system benefits when compared with conventional operation. These findings are helpful to offer guidelines for the effective operation of the hydro/PV hybrid power system.

PCV85 - The Impact of the Centers for Medicare and Medicaid Services (CMS) 2016 Update to the Two-Midnight Rule On Inpatient/Outpatient Status and Length of Stay for Cardiac Ablation Procedures

CMS released an update to the 2013 2-midnight rule, effective for 2016, expanding a physician’s ability to admit patients under Medicare Part A who are expected to require less than two midnights of hospital care. We sought to quantify the impact of this change on rates of inpatient admissions and lengths of stay (LOS) for patients having cardiac catheter ablation. These procedures had high rates of extended observational outpatient stays based on the original rule. Patient-level de-identified hospital records from 2013-2016 were extracted from the MedAssets hospital database. Visits for adults (age 18+) with a primary diagnosis of atrial fibrillation, atrial flutter, ventricular tachycardia, or supraventricular tachycardia and a procedure code for catheter ablation of the associated arrhythmia were included, provided that they did not have additional codes for AV node ablation, endoscopic or surgical ablation, surgical left atrial appendage exclusion, valve surgery, or cardiac implant. The primary outcomes were inpatient/outpatient visit status and LOS by insurance type (Medicare/commercial) and year. A total of 11,398 commercial and 15,430 Medicare ablation visits met all inclusion criteria. The percentage of inpatient visits remained constant from 2015 to 2016 for patients with commercial insurance (32.4% to 32.7%, p=0.8511) but increased for those with Medicare (45.0% to 50.0%, p=0.0002). Similarly, the percentage of patients with single night LOS remained constant from 2015 to 2016 for the commercially insured (30.1% and 30.1%, p=0.9714) while decreasing for Medicare patients (25.5% to 20.2%, p<0.0001). In cardiac catheter ablation patients, the change to the CMS 2-midnight rule increased the percentage of Medicare patients admitted as inpatients and staying two or more nights. This increase impacts hospital reimbursement rates and patient eligibility for Medicare payment of skilled nursing care upon discharge.

Development of knowledge­based control systems with built­in functions of rules verification and correction

Two improved models of control rules were proposed. A model in a form of AND/OR graph; in contrast to the known graphical model of general rules, is based on dividing the rules into groups based on the controlled object state. The graph contains special markup that allows to convert the graph paths corresponding to the rules into Boolean expressions including formulas for both and inverse rule sets. The basic formulas of the rules model in a form of Boolean expressions cannot be constructed for general rules and based on these formulas the three methods for verification of the rules were developed: ‒ the method for verifying the control rules premises for inconsistency based on the SAT problem for Boolean formulas; ‒ the method for verifying the control rules for completeness based on visualization of both direct and inverse rules with conclusions in “inverse” rules opposite to the conclusions of the original rules; ‒ the method for verifying reachability of the object state vertices from the control rules. The main advantage of these methods is that they allow to find errors in the rules at early stages when specialists in the field for which the knowledge-based system is used (experts and decision makers work with them). The specificity of the control tasks makes it possible to do this effectively from the point of view of analysis and verification of the rule quality. The developed procedure of the control rules verification and correction assists in to bringing together and placing in a correct order various types of verification and correct errors in an automated mode. Main components were proposed for knowledge-based control systems: the rule editor for knowledge engineers and experts and the control system itself which includes extraction of the controlled object parameters essential for analysis as well as analysis of these parameters and their transfer to a DM for making a decision. A rule editor has been developed and control systems for two domains: safe operation with electric installations and control of computer networks. The presented experimental results on the management of the training process using the developed systems have shown that the number of errors in the created rules was reduced. When verifying for reachability of the object states, errors in an average of 5.4 % control rules were found and removed. When verifying for inconsistency of the rule premises, errors were found and corrected on average in 11.5 % of rules. When verifying for completeness, the rules base was expanded by on average of 12.3 %. In addition, due to consulting, verification and correction of the rules, the time spent by trainees on execution of their work was reduced by an average of 8 %.

A Modified Mixture Rules for Mie-Grüneisen Mixture Model

The Mie-Grüneisen mixture model can be conveniently used in multicomponent flow. However, the mixture rules in the original model can’t distinguish components at the interface. That cause abnormal transition near the interface. To solve this problem, a modified mixture rules which based on mass fraction is used here. And three non-conservative equations are added here to keep the pressure equilibrium condition. Comparing with the original mixture rules, the transition form at the interface is revised and the curve of sound speed is optimized. With the help of the modified rules, the times steps in calculation are reduced. Moreover, for spherical coordinate system, the modified mixture rules not only save steps but also get more accurate solution.

On applicability of differential mixing rules for statistically homogeneous and isotropic dispersions

The classical differential mixing rules are assumed to be independent effective-medium approaches, applicable to certain classes of systems. In the present work, the inconsistency of differential models for macroscopically homogeneous and isotropic systems is illustrated with a model for the effective permittivity of simple dielectric systems of impenetrable balls. The analysis is carried out in terms of the compact group approach reformulated in a way that allows one to analyze the role of different contributions to the permittivity distribution in the system. It is shown that the asymmetrical Bruggeman model (ABM) is physically inconsistent since the electromagnetic interaction between previously added constituents and those being added is replaced by the interaction of the latter with recursively formed effective medium. The overall changes in the effective permittivity due to addition of one constituent include the contributions from both constituents and depend on the system structure before the addition. Ignoring the contribution from one of the constituents, we obtain generalized versions of the original ABM mixing rules. They still remain applicable only in a certain concentration ranges, as is shown with the Hashin-Shtrikman bounds. The results obtained can be generalized to macroscopically homogeneous and isotropic systems with complex permittivities of constituents.

Induction of accurate and interpretable fuzzy rules from preliminary crisp representation

This paper proposes a novel approach for building transparent knowledge-based systems by generating accurate and interpretable fuzzy rules. The learning mechanism reported here induces fuzzy rules via making use of only predefined fuzzy labels that reflect prescribed notations and domain expertise, thereby ensuring transparency in the knowledge model adopted for problem solving. It works by mapping every coarsely learned crisp production rule in the knowledge base onto a set of potentially useful fuzzy rules, which serves as an initial step towards an intuitive technique for similarity-based rule generalisation. This is followed by a procedure that locally selects a compact subset of the emerging fuzzy rules, so that the resulting subset collectively generalises the underlying original crisp rule. The outcome of this local procedure forms the input to a global genetic search process, which seeks for a trade-off between accuracy and complexity of the eventually induced fuzzy rule base while maintaining transparency. Systematic experimental results are provided to demonstrate that the induced fuzzy knowledge base is of high performance and interpretability.

Методологічні аспекти створення геоінформаційної системи лісових автодоріг

Здійснено аналіз тенденцій розвитку геоінформаційних систем (ГІС) в лісовій галузі та міжнародних вимог стосовно запровадження природозберігаючих технологій на базі оптимальної транспортної мережі як передумови для реального запровадження принципів сталого ведення лісового господарства. Обґрунтовано необхідність наявності об’єктивних даних про транспортну мережу лісових підприємств. Подано методичні підходи до створення ГІС лісових автодоріг, які передбачають подання інформації про лісові автодороги як лінійні об'єкти з відомими координатами вздовж дороги та наявністю певної кількості каліброваних точок, тобто коректно розташованої лінії, що відображає дорогу, і відомостей про неї у вигляді таблиць та фото (креслень), які відображають її технічні та експлуатаційні характеристики. Також висвітлено особливості структури і порядку роботи та операційні можливості дослідної ГІС лісових автодоріг, розробленої для ДП «Осмолодське лісове господарство».

Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation

The speckle noise of sonar images affects the human interpretation and automatic recognition of images seriously. It is important and difficult to realize the precision segmentation of sonar image with speckle noise in the field of image processing. Full convolution neural network (FCN) has the advantage of accepting arbitrary size image and preserving spatial information of original input image. In this paper, the image features are obtained by autonomic learning of convolutional neural network, the original learning rules based on the mean square error loss function is improved. Taking the pixel as the processing unit, the segmentation method based on FCN model with relative loss function(FCN-RLF) for small submarine sonar image is proposed, sonar image pixel-level segmentation is achievied. Experimental results show that the improved algorithm can improve the segmentation accuracy and keep the edge and detail of sonar image better. The proposed model has better ability to reject sonar image speckle noise.

Mnemonics of Basic Differentiation and Integration for Trigonometric Functions

Calculus has always been issue of underachievement performance for public university students. Insufficient background of SPM Additional Mathematics affected the students’ performance in Mathematics at university level. One of the main reasons that contribute to this problem is the lack of understanding of the basic concept in differentiation and integration. Two innovative techniques of basic differentiation and integration for trigonometric functions by using mnemonic chart are developed in this paper. Many students face difficulties to memorize the formulas which unable for them to solve the given problems. These proposed techniques minimize students’ problem in memorizing formulas as well as improve teaching method for educators. In addition, these new technique is not only can be advantageous for university students in Calculus course, but also for Matriculation and STPM students in Mathematics subject. This technique emphasizes square and triangle shape to be used for Original Rule and Chain Rule. All formulas can be derived from clockwise and anticlockwise direction. In the future, further research need to be done whether mnemonics are effective and useful to academic programmes such as Science- based programmes and also for secondary high schools.

Cooperative Abnormality Detection via Diffusive Molecular Communications.

In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects a number of molecules into the medium which is proportional to its sensing output. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, each sensor releases a different type of molecule (DTM), whereas in the second scheme, all sensors release the same type of molecule (STM). The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the tradeoff between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

A modified brain emotional learning model for earthquake magnitude and fear prediction

Brain emotional learning (BEL) model has been used frequently for predicting a quantity or modeling complex and nonlinear systems in recent years. In this research, two methods proposed for improving the efficiency of original BEL model using fuzzy rules, learning automata concepts and optimization algorithms. In the first proposed method, different optimization algorithms and continuous action-set learning automata (CALA) were used for finding the weights of BEL model, while in the second proposed model, the weights obtained using original rules of BEL model. In fact, in the second model finite action-set learning automata, CALA and different optimization algorithms were used for calibrating the learning parameters of the model. Also in the both proposed methods after extracting frequency features in thalamus, deep belief network is used in the sensory cortex for reducing the size of features. In addition, ANFIS is used for making fuzzy rules in the amygdala. The proposed models were used for magnitude and consequently fear prediction of the earthquakes. The results show that although both proposed methods are more accurate than original BEL model and could be used successfully, the second proposed model is more precise and reliable than the first proposed model.