Sequential Rules Research Articles

E-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules

As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.

INVESTIGATION OF THE PEDAGOGICAL PRACTICES LITERATURE TEACHERS USE FOR TEACHING MODERN GREEK LANGUAGE IN SENIOR HIGH SCHOOL: A SOCIOLOGICAL APPROACH

<p>The aim of this paper, which makes use of Basil Bernstein’s theoretical framework, is to take a sociological approach to the pedagogical practices that are implemented by literature teachers for teaching Modern Greek Language in the first year of Senior High School, which is a transitional year in the second tier of Greek secondary education. We also aim to investigate the corrective ‘strategies’ that these teachers adopt in order to contribute to their pupils’ successful acquisition of school knowledge related to the Language lesson. The research was carried out using the tools of structured observation and semi-structured interview with a sample of 14 literature teachers, who were working in Greek secondary education. The most significant research findings revealed that the pedagogical practices that were implemented by the teachers in the sample are linked to the implementation of explicit regulative and instructional rules that originate in a visible pedagogical practice. What’s more, ‘corrective’ strategies were implemented with the pupils who weren’t able to satisfy the sequencing and pacing rules of the transmitted knowledge. It also emerged that an important site for the completion of the acquisition of transmitted school knowledge is related to the field of ‘shadow’ education.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0688/a.php" alt="Hit counter" /></p>

Hierarchia i relacje miast w województwie lubuskim

In socio-economic geography any regional settlement system makes one of its majorfactors of development. It is towns that play a specific role in this system, the meaning of which canbe approached from different perspectives. This paper focuses on the hierarchies and relationshipsbetween towns in the Province of Lubuskie seen from the spatial point of view. The volume and sequence rule of Zipf ’s law as well as the gravitation model have been used as the statistical tools. Thedata valid as in 2015 were accounted for in the analysis. Results from the empirical studies show thatdespite a similar urban density in Lubuskie Province and generally across Poland, the town system in theregion is visibly specific. Apart from two largest cities playing a regional and cross-regional function,i.e. Zielona Góra and Gorzów Wlkp., the issue of how small and middle-sized towns function in theprovince comes to the fore.

Sequenciamento da produção: uma proposta para o sistema make to order

Um dos objetivos do sequenciamento da produção é a movimentação sincronizada de insumos dentro de um processo produtivo de maneira rápida e ordenada nos recursos disponíveis, visando o processamento de pedidos para atender a demanda do mercado. Diante disso, a presente pesquisa pretende apresentar uma proposta de sequenciamento dos subprodutos que compõe o produto produzido por uma indústria de equipamentos médicos, ao passo que seu sistema de manufatura é classificado como job shop com capacidade finita em um ambiente make to order. A proposta será desenvolvida por meio da análise e caracterização dos processos produtivos; levantamento das restrições; identificação das prioridades da organização, realização da cronoanálise dos subprodutos; e apresentação dos resultados obtidos por uma regra de sequenciamento utilizando o método gráfico de Gantt. Em relação aos procedimentos metodológicos de pesquisa, a abordagem utilizada foi a combinada de triangulação, quanto à sua finalidade é caracterizada como pesquisa aplicada, em relação aos seus objetivos é considerada descritiva, segundo os procedimentos utilizados é classificada como bibliográfica e pesquisa-ação. O resultado obtido foi o sequenciamento dos subprodutos por meio da regra menor tempo de processamento (MTP). As limitações da pesquisa estão relacionadas a escassez de um histórico de informações sobre os resultados da empresa.

Clean-in-place disinfection of dual-species biofilm (Listeria and Pseudomonas) by a green antibacterial product made from citrus extract

Food industries, which must ensure safety and quality of manufactured products, require effective and regular cleaning and disinfection processes. One of the most difficult issues to overcome is to ensure the elimination of biofilms that can generate contamination during food processing. Despite chemical disinfection is the most used strategy, it also requires to be optimised, reducing energy costs, time, and the environmental impact of these operations. In this work, the effectiveness of an environmental-friendly commercial disinfectant (Mico E-PRO®) made from food-grade citrus extracts has been addressed by two methods (i) in-vitro tests for evaluation of antimicrobial efficacy and (ii) a lab-simulated CIP system, where the efficacy was compared with NaClO 1%. In both cases, Mico E-PRO® shows that it is an effective product for the killing of E. coli (MBC = 625 ppm), L. monocytogenes (MBC = 625 ppm), S. enterica (MBC = 625 ppm) and P. aeruginosa (MBC = 156.25 ppm), being P. aeruginosa the most sensible to the disinfectant. The product also shows good bactericidal effect against a mature biofilm formed by L. innocua and P. putida – similar efficacy to that obtained with NaClO 1%. Overall, results demonstrated that Mico E-PRO® is a good option as a natural sanitiser for CIP systems, running as an efficient and safe alternative to the traditional chemicals-based disinfectants.

Workload control in additive manufacturing shops where post-processing is a constraint: an assessment by simulation

Additive Manufacturing (AM) shops typically produce high variety, low volume products on a to-order basis. Products are first created in parallel batches at a single AM station before being subjected to several post-processing operations. While there exists an emerging literature on AM station scheduling and order book smoothing, this literature has largely neglected downstream post-processing operations, which also affect overall performance. Workload Control provides a unique production control solution for these post-processing operations, but the specific AM shop structure has been neglected in the literature. Using simulation, this study shows that load balancing via the use of workload norms, as is typical for Workload Control, becomes ineffective since the norm must allow for the operation throughput time at the AM station and for its variability. A sequencing rule for the jobs waiting to be released that inherently creates a mix of jobs that balances the workload is therefore identified as the best-performing rule. These findings reinforce the principle that load limiting should be used at upstream stations whereas sequencing should be applied at downstream stations. Finally, although the focus is on AM shops, the findings have implications for other shops with similar structures, e.g. in the steel and semi-conductor industries.

Pseudoasymmetry paradox: A suggestion to introduce the term pseudostereogenicity and address reflection issues

The confusion about the nomenclature of a tetrahedral carbon attached to a pair of enantiomorphic ligands (AR and AS) with the other two ligands different from each other as well as from A has been critically reexamined. Until now, this carbon has been known as a pseudoasymmetric center. However, according to Mislow, since asymmetry is the property of a molecule as a whole, it seems appropriate to replace ‘pseudoasymmetry’ with the term ‘pseudostereogenicity’. This is more relevant, particularly for molecules where different achiral ligands other than the enantiomorphic ones are attached to the pseudoasymmetric center. The molecule is not asymmetric as it has a S1 or σ plane. Another contentious issue is whether chiral molecules can be included in the domain of “pseudostereogenicity” and the related reflection invariance of the configuration (rectus remains rectus or sinister remains sinister). For chiral molecules containing such centers, the expectation is that the absolute configuration of the pseudoasymmetric center should invert upon reflection which is not the case as per the existing system. This article addresses all these issues like inclusion of chiral molecules bearing pseudoasymmetric center and making the center reflection variant by a simple introduction of a pilot atom based approach, which is already used in CIP system for planar chirality.

Robustness Analysis in Sequential Statistical Decisions

The sequential statistical decision making is considered. Performance characteristics (error probabilities and expected sample sizes) for the sequential statistical decision rules (tests) are analysed. Both cases of simple and composite hypotheses are considered. Asymptotic expansions under distortions are constructed for the performance characteristics enabling robust sequential test construction.

Intelligent vehicle knowledge representation and anomaly detection using neural knowledge DNA

The knowledge expression and anomaly detection of the intelligent vehicle are studied. Firstly, an Intelligent Vehicle Knowledge Management Framework (IVKMF) is proposed to describe the whole knowledge of Intelligent vehicle in a unified structure. Then, the detection rules are divided into simple rules based on features or statistical characteristics and complex sequence rules based on neural networks. The former can effectively detect the specific CAN command and flooding, replay attacks, the latter uses neural networks to learn the characteristics of CAN commands, which can effectively detect the complex attacks. The simple detection rules are standardized by SOEKS knowledge expression and the complex detection rules storage in Neural Knowledge DNA framework. With this unified knowledge expression, the detection rules can be shared and inherited easily. A secure gateway for intelligent vehicle is also designed. The gateway is placed between the external network and the vehicle bus network and it prevent all suspicious external data. The simulations of real car data prove the feasibility of the methods.

A Web-Based Stereochemistry Tool to Improve Students’ Ability to Draw Newman Projections and Chair Conformations and Assign R/S Labels

A set of web-based stereochemistry tools which help students use virtual 3D models to translate Newman projections and chair conformations and assign R/S labels to stereocenters from 2D dashed–wedged structures is described. These tools are unique in the way they link 2D dashed–wedged structures to 3D models to help students develop visual/spatial skills. They also provide feedback on the individual parts of a student’s attempt to switch between structural representations and apply the Cahn–Ingold–Prelog rules. The incorporation of these stereochemistry resources into an organic chemistry course led to improvements in students’ ability to draw accurate Newman projections and chair conformations and assign R and S labels to stereocenters.

Integrated management for sustainable cropping systems: Looking beyond the greenhouse balance at the field scale.

Cover crops (CC) promote the accumulation of soil organic carbon (SOC), which provides multiple benefits to agro-ecosystems. However, additional nitrogen (N) inputs into the soil could offset the CO2 mitigation potential due to increasing N2 O emissions. Integrated management approaches use organic and synthetic fertilizers to maximize yields while minimizing impacts by crop sequencing adapted to local conditions. The goal of this work was to test whether integrated management, centered on CC adoption, has the potential to maximize SOC stocks without increasing the soil greenhouse gas (GHG) net flux and other agro-environmental impacts such as nitrate leaching. To this purpose, we ran the DayCent bio-geochemistry model on 8,554 soil sampling locations across the European Union. We found that soil N2 O emissions could be limited with simple crop sequencing rules, such as switching from leguminous to grass CC when the GHG flux was positive (source). Additional reductions of synthetic fertilizers applications are possible through better accounting for N available in green manures and from mineralization of soil reservoirs while maintaining cash crop yields. Therefore, our results suggest that a CC integrated management approach can maximize the agro-environmental performance of cropping systems while reducing environmental trade-offs.

Reducing uncertainties of reservoir properties in an automatized process coupled with geological modeling considering scalar and spatial uncertain attributes

The current work introduces an automatized process coupled with geological modeling to reduce uncertainty in reservoir properties. The method assimilates dynamic data of wells interactively, and applies the deviations to constrain uncertainties. The methodology deals with scalar (e.g. rock compressibility) and spatial (e.g. porosity) attributes at same time, employing specialized uncertainty reduction procedures. The procedure reduces the uncertainty of scalar attributes through Iterative Discrete Latin HyperCube method (IDLHC). To reduce uncertainties of spatial attributes, we worked on an extension of a regionalized co-simulation (co-DSS) method. The main contributions regard the proposition of update, at same time, both kinds of uncertainties (spatial and scalar); the definition of sequential rules, that simplify the process execution and avoiding subjectivities on the coupling of the geological modeling on data assimilation, as well as the automation of the process. The procedure was validated under a siliciclastic black-oil benchmark field (UNISIM-I-M), established based on Namorado Field, Campos Basin, Brazil. The procedure reduced the range of uncertainty of the scalar attributes, centralizing final PDFs with the values presented in the reference model, without collapse to a particular level, as well as preserved the geological consistency throughout data assimilation, obtaining porosity responses in agreement with the reference porosity distribution. The potential of the procedure is supported by the consistent production forecast observed in the outcomes.

Multisource Model-Driven Digital Twin System of Robotic Assembly

The digital twin technology effectively fuels improvements in the quality and efficiency of robotic assembly, especially for sophisticated processes. High-quality digitalization requires a comprehensive description and real-time rendering of system, which are challenging. This article aims to demonstrate a novel multisource model-driven digital twin system, which is based on the geometric, physics, and sequential rule descriptions for producing a precise and real-time simulation of a robotic assembly system. In our system, the digital counterpart rendered with virtual reality technology is created and updated by the real environment information. A 3-D graphic model is reconstructed first using the geometric information of surroundings captured by the depth sensor. During interaction, a fast and efficient approach is proposed to generate the contact force and render the object deformation in the virtual environment. The virtual space also provides the sequential rule that is based on the danger field, which helps to constrain the operation of digital twin. At last, the experimental platform is established in which the virtual space is at an update rate of 100 Hz, while the automatic sorting task is performed and verified; the results show the effectiveness and applicability of the method to digital twin system.

Modeling nonlinear fatigue damage accumulation in a welded runway girder

Due to its inherent linear nature, Miner’s damage accumulation rule is widely known to produce unsatisfactory results for lifetime prediction under variable amplitude loading. This can be attributed to its incapability of accounting for load sequence and load interaction effects. In this study, one piece-wise linear and three non-linear damage accumulation models have been implemented in a numerical framework for fatigue lifetime assessment of welded details of industrial assets. Lifetime predictions obtained by these models are first compared to lifetime predictions using Miner’s rule and to experimental data of two-level loading sequences. The results reveal that the studied damage models perform better than Miner’s rule for two-level load sequences within the scope of the studied dataset. Finally the different models are used to estimate the fatigue life of a weld detail that is part of a crane runway girder. A much larger (relative) discrepancy is observed between the lifetime predictions of the different models for a realistic load case than for simple two-level block loads.

Detecting intrusive transactions in databases using partially-ordered sequential rule mining and fractional-distance based anomaly detection

Detecting intrusive transactions in databases using partially-ordered sequential rule mining and fractional-distance based anomaly detection

Detecting intrusive transactions in databases using partially-ordered sequential rule mining and fractional-distance based anomaly detection

Illegitimate access to databases may compromise their integrity and confidentiality, resulting in legal and financial ramifications for organisations. We propose a database intrusion detection system (DIDS) called fractional distance based anomaly detection with partially-ordered dependency analysis (FADPDA) to identify malicious transactions issued to databases. To weed out such transactions, our DIDS combines data dependency analysis using security sensitive partially-ordered sequential rules (POSRs) with fractional distance based anomaly detection. Unlike most prior work, FADPDA can seamlessly run on both RBAC administered and non-RBAC databases. Detailed experiments on two databases- a TPC-C benchmark and a synthetic database, revealed that POSRs effectively and efficiently represent data dependencies. Furthermore, combining data dependency analysis and anomaly detection reduces our system's reliance on hyper-parameters such as support and confidence thresholds, and enhances its intrusion detection capabilities. We also show that our approach FADPDA outperforms major existing DIDS in terms of precision and recall values.

Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology

This article presents a novel methodology to detect insurance claim related frauds in the healthcare system using concepts of sequence mining and sequence prediction. Fraud detection in healthcare is a non-trivial task due to the heterogeneous nature of healthcare records. Fraudsters behave as normal patients and with the passage of time keep on changing their way of planting frauds; hence, there is a need to develop fraud detection models. The sequence generation is not the part of previous researches which mostly focus on amount based analysis or medication versus diseases sequential analysis. The proposed methodology is able to generate sequences of services availed or prescribed by each specialty and analyse via two cascaded checks for the detection of insurance claim related frauds. The methodology addresses these challenges and self learns from historical medical records. It is based on two modules namely “Sequence rule engine and Prediction based engine”. The sequence rule engine generates frequent sequences and probabilities of rare sequences for each specialty of the hospital. The comparison of such sequences with the actual patient sequences leads to the identification of anomalies as both sequences are not compliant to the sequences of the rule engine. The system performs further in detail analysis on all non-compliant sequences in the prediction based engine. The proposed methodology is validated by generating patient sequences from last five years transactional data of a local hospital and identifies patterns of service procedures administered to patients using Prefixspan algorithm and Compact prediction tree. Various experiments have been performed to validate the applicability of the developed methodology and the results demonstrate that the methodology is pertinent to detect healthcare frauds and provides on average 85% of accuracy. Thus can help in preventing fraudulent claims and provides better insight into how to improve patient management and treatment procedures.

A Sequence Rules Analysis on Active and Passive LCL filter for Three-Phase Inverter-Grid Connection for Damping Stability Consideration

This paper delivers steps on sequence analysis of the internal damping stability, which complies with a systematic design methodology for the LCL -type filter for the inverter-grid system. The use of the power inverter is significantly vital to transfer the energy from renewable energy sources to the existing electrical grid. Therefore, it is essential to model a non-response LCL -filter parameters value that not compromise the filter effectiveness and provides pure current waveform for a harmonic reduction before the current been injected to the grid. At the same time, LCL filter also must have stable damping performance that able to attenuate the high resonance peak and simultaneously offer better high-frequencies attenuation ability. To verify the selected inductor and capacitor in LCL , a passive and actively damping analysis methods are comparatively studied. In the end, the simulation results show significant evidence that by following each sequence steps provided in this paper for modelling and selection of vulnerable coefficient for LCL has been verified through a MATLAB/Simulink software platform which given a better signal current output, maintaining the cutoff frequency signal and reducing the Total Harmonics Distortions (THD) below the IEEE Standard

Backlog-Sequencing: A Comparison between Workload Control and ConWIP using a simulation approach

Order review and release research has brought several different release methods forward to improve the performance of a manufacturing system. Two of the most recognized methods are Workload Control (WLC) and Constant Work-in-Progress (ConWIP). Both depend on a backlog of not yet released orders which is organized in a pre-shop pool. Lately, the sequencing of the backlog has received increasing attention for both methods. Existing research shows the performance-increasing potential of capacity slack-based sequencing rules. This paper analyzes this potential by further developing some of the known rules and applying them to both WLC and ConWIP using a simulating approach.

Brain networks underlying aesthetic appreciation as modulated by interaction of the spectral and temporal organisations of music

Music is organised both spectrally and temporally, determining musical structures such as musical scale, harmony, and sequential rules in chord progressions. A number of human neuroimaging studies investigated neural processes associated with emotional responses to music investigating the influence of musical valence (pleasantness/unpleasantness) comparing the response to music and unpleasantly manipulated counterparts where harmony and sequential rules were varied. Interactions between the previously applied alterations to harmony and sequential rules of the music in terms of emotional experience and corresponding neural activities have not been systematically studied although such interactions are at the core of how music affects the listener. The current study investigates the interaction between such alterations in harmony and sequential rules by using data sets from two functional magnetic resonance imaging (fMRI) experiments. While replicating the previous findings, we found a significant interaction between the spectral and temporal alterations in the fronto-limbic system, including the ventromedial prefrontal cortex (vmPFC), nucleus accumbens, caudate nucleus, and putamen. We further revealed that the functional connectivity between the vmPFC and the right inferior frontal gyrus (IFG) was reduced when listening to excerpts with alterations in both domains compared to the original music. As it has been suggested that the vmPFC operates as a pivotal point that mediates between the limbic system and the frontal cortex in reward-related processing, we propose that this fronto-limbic interaction might be related to the involvement of cognitive processes in the emotional appreciation of music.