Kinds Of Patterns Research Articles

An indicator of urban morphology for landscape planning in Lombardy (Italy)

PurposeThe spatial development of urban areas affects the characteristics of landscape as well as people’s aesthetic perception of it. Specifically, sprawl results in an urban morphology which is diametrically opposed to the compact city model and which assumes several kinds of patterns: for example “striped”, “ribbon” or “leapfrogged” urban development. Assessing urban morphology in spatial terms is crucial to urban policy, while landscape metrics are the key to a comprehensive understanding of different urban development patterns. The purpose of this paper to design and test an urban morphology indicator (UMI) for the Lombardy Regional Landscape Plan.Design/methodology/approachThe paper describes an UMI that can be used to identify the heterogeneity of built-up patterns according to urban porosity, fragmentation and patch shape. This UMI is a result of Esri ArcGIS 10.3 “grouping analysis” which works by applying a spatial statistical metric for clustering geometries in a given geographical area.FindingsMorphological analysis was used in regional urban development policies with a view to minimising impact on surrounding ecosystems and preserving the natural environment and landscape. It defines 28 different urban morphology patterns in the region, which are divided into systems, polarities and urbanised units.Originality/valueThe proposed methodology differs from those traditionally used in qualitative/descriptive landscape planning and supports the identification of morphological features with quantitative statistical and spatial data, allowing a fine-scale assessment of complex metrics.

From wholes to fragments to wholes-what gets lost in translation?

The highly demanding and, in a certain sense, unique, working conditions of general practitioners (GPs) are characterized by two phenomena: First, they involve an increasing familiarity with individual patients over time, which promotes a deepening of insight. Second, they enable the GP to encounter all kinds of health problems, which in turn facilitates pattern recognition, at both individual and group levels, particularly the kind of patterns currently termed "multimorbidity." Whereas the term "comorbidity" is used to denote states of bad health in which 1 disease is considered to predate and evoke other ailments or diseases, the term multimorbidity is applied when finding several presumably separate diseases in a person who suffers from them either sequentially or simultaneously. Encounters with patients whose suffering fits the biomedical concept and terminology of multimorbidity are among the most common which GPs face, presenting them with some of their most demanding tasks. The term multimorbidity needs to be examined, however. As it alludes to a multiplicity of diseases, it rests on an assumption of separateness of states of bad health that might not be well founded. An adequate determination of what to deem a "separate" state of bad health would require that the biomedical concept of causation be scrutinized.

Application of Science‒Technology‒Society Studies in Information Security Research

The research question and problem statement I posed to answer simply has been: what kind of patterns and specific discourses can be identified around the keywords of “information security” and “social construction” in information systems and its related reference fields such as social sciences, management studies, decision sciences extended to psychology. We may start summarizing the conclusions by stating that “information security” and “social construction” in the SCOPUS domain offers a wide range of literature in the social sciences and related subject areas; the initial search resulted in 406 article hits, whose basic bibliographic parameters with keywords and abstracts were downloaded. I categorized this sample according to the journals H-index, the Scimago Journal & Country Rank (SJR) Q1‒Q4 ranking and the individual papers’ citations, into five—socalled structural—clusters. Three papers were identified as the highest referenced and most influential, and analysed separately. The other were clustered as follows: 30 papers were classified into CL1, a high impact cluster due to its high citation and H-index, 122 papers were grouped into CL2, a mature cluster, due to their publishing date and medium referencing, 71 papers fell into CL3, a high potential cluster, due to their high H-index and recent appearance and, finally 152 papers were clustered into CL4, the mainstream of the sample due to their medium impact and wide spread of publishing dates. These clusters were further analysed with basic text mining techniques: word counting, key word analysis, textual clusters and N-Gram analysis, and concordance analysis. I found that clusters are different as far as the academic discourse on information security evolves, and each contain unique added value to the social construction of information security amongst users, institutions, technology and public policy. Finally, conclusion and further research opportunities are presented.

Human dynamics in repurchase behavior based on comments mining

Hundreds of thousands of individual deals and comments are analyzed to ask: what kinds of patterns appear in their repurchase process? Our results suggest that, in the empirical description, the intervals between two consecutive purchases obey a power-law distribution. Notwithstanding a wide range of individual preferences, shoppers’ repurchase behaviors show some similar patterns, called long-scale quiet and short-scale emergence, and the alternating appearance of them form an endless chain in repurchase. In agreement with the empirical results, these short-scale and long-scale patterns suggest an adaptive model with alterable exponents complying with a power-law distribution. And it also implies that each user behaves his own intrinsic pattern such as unique repurchase intensity and silence-emergence cycle, which contributes to customer life-time value from the new view of dynamics and repurchase cycles.

Towards Elementary Algebra for On-Line Knowledge Processing of Pattern Cube

Pattern warehousing is the most significant new technology to model, store, retrieve and manipulate the patterns. Pattern cube and On-Line Knowledge Processing (OLKP) are introduced in this work to make pattern representation and processing more efficient. OLKP is the process of carrying out operations over the patterns which are stored in the pattern warehouse. The complexities of operations on semantic rich patterns make inappropriate the standard algebra database technology. Moreover, there is no qualified model for OLKP introduced till date. In this work, we address this issue by introducing Pattern Cube and Pattern Algebra with the underlying fundamental operators to support. For simplicity, association kinds of patterns are used to illustrate various operations and properties.

Predicting semi-regular patterns in morphologically complex words

Abstract We expect generative models of language to correctly predict surface forms from underlying forms, but morphologically complex words, especially compounds, can exhibit idiosyncratic outputs, which require an extra lexical listing. This results in (a) a poorer Minimum Description Length of our model and (b) failure of a grammar to capture patterning among exceptions. To solve an instance of this problem, we examine pitch-accent patterns of 2-mora-2-mora Japanese Yamato (native) noun-noun compounds, hitherto considered semi-predictable but which show gradient tendencies among constituents to trigger a particular accent pattern. In the framework of Gradient Symbolic Computation (Smolensky, Paul & Matthew Goldrick. 2015. Gradient symbolic computation. LSA Summer Institute Workshop. Chicago.), a type of harmonic grammar which allows partially activated feature values and weighted constraints, such gradient patterns can be captured through the additive combination of coalescing features on each conjunct, which results in a pitch accent when the summed activations surpass a threshold determined by the grammar. The ability of this framework to completely predict these semi-regular patterns holds promise that it can also explain similar kinds of patterns in other languages.

Exploratory study and application of the angular wavelet analysis for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt structures

The angular wavelet analysis is applied for assessing the spatial distribution of breakdown spots in Pt/HfO2/Pt capacitors with areas ranging from 104 to 105 μm2. The breakdown spot lateral sizes are in the range from 1 to 3 μm, and they appear distributed on the top metal electrode as a point pattern. The spots are generated by ramped and constant voltage stresses and are the consequence of microexplosions caused by the formation of shorts spanning the dielectric film. This kind of pattern was analyzed in the past using the conventional spatial analysis tools such as intensity plots, distance histograms, pair correlation function, and nearest neighbours. Here, we show that the wavelet analysis offers an alternative and complementary method for testing whether or not the failure site distribution departs from a complete spatial randomness process in the angular domain. The effect of using different wavelet functions, such as the Haar, Sine, French top hat, Mexican hat, and Morlet, as well as the roles played by the process intensity, the location of the voltage probe, and the aspect ratio of the device, are all discussed.

Wave processes in relation to the tectono-sedimentary of the Tarim basin: A new method for basin study

This study gives insights into the tectonic and sedimentary evolution of the Tarim basin, the largest petroleum-bearing basin in Western China, by analyzing sedimentary wave process using multi-scale decomposition of the wavelet transform method, and the mathematical defects of sliding-window method in the process of sedimentary wave analysis was discussed. The filtering analysis, in the time domain, was carried out on cores collected from 14 wells in the Tarim basin. The results show that the evolution of the Tarim basin was controlled by various wave processes. The periods of 212 ± Ma, 102 ± Ma and 33 ± Ma indicated a strong sedimentary rhythm, which displayed a spatial and temporal evolution of wave characteristics. The 33 ± Ma period represented a third-order sequence of the stratigraphic division, which identified with the unconformities of the Tarim basin; 102 ± Ma period corresponded to an important period of sedimentation and the plate collision around the Tarim basin, which marked a second-order period of the basin evolution. The centers of subsidence showed periodic shifts since the Cambrian, and the time for periodic migration (completing a circle) of the depocenters or the centers of subsidence is traced as 212 ± Ma, which corresponded to the first-order period of the basin evolution. The coupling relationships between the wave processes and the evolution and distribution of source, reservoir and cap rocks in the basin were given by three kinds of patterns, which were important in predicting the space-time distribution of source, reservoir and cap rocks. This study gives a new insight on predicting the formation and distribution of petroleum, which could be used for selecting petroleum exploration targets effectively.

Food purchase patterns: empirical identification and analysis of their association with diet quality, socio-economic factors, and attitudes

BackgroundEmpirically derived food purchase patterns provide information about which combinations of foods were purchased from households. The objective of this study was to identify what kinds of patterns exist, which level of diet quality they represent and which factors are associated with the patterns.MethodsThe study made use of representative German consumption data in which approximately 12 million food purchases from 13,125 households are recorded. In accordance with healthy diet criteria the food purchases were assigned to 18 food groups of the German Food Pyramid. Based on these groups a factor analysis with a principal component technique was applied to identify food patterns. For these patterns nutrient and energy densities were examined. Using regression analysis, associations between pattern scores and socio-economic as well as attitude variables, reflecting personal statements about healthy eating, were analyzed.ResultsIn total, three food purchase patterns could be identified: a natural, a processed and a traditional one. The first one was characterized by a higher purchasing of natural foods, the second by an increased purchasing of processed foods and the third by a meat-oriented diet. In each pattern there were specific diet quality criteria that could be improved whereas others were in line with actual dietary guidelines. In addition to socio-demographic factors, attitudes were significantly associated with the purchase patterns.ConclusionsThe findings of this study are interesting from a public health perspective, as it can be assumed that measures focusing on specific aspects of diet quality are more promising than general ones. However, it is a major challenge to identify the population groups with their specific needs of improvement. As the patterns were associated with both socio-economic and attitude variables these grouping criteria could be used to define target groups.

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers

Abstract When applying data-mining techniques to real-world data, we often find ourselves facing observations that have no value recorded for some attributes. This can be caused by several phenomena, such as a machine’s incapability to record certain characteristics or a person refusing to answer a question in a poll. Depending on that motivation, values gone missing may follow one kind of pattern or another, or describe no regularity at all. One approach to palliate the effect of missing data on machine learning tasks is to replace the missing observations. Imputation algorithms attempt to calculate a value for a missing gap, using information associated with it, i.e., the attribute and/or other values in the same observation. While several imputation methods have been proposed in the literature, few works have addressed the question of the relationship between the type of missing data, the choice of the imputation method, and the effectiveness of classification algorithms that used the imputed data. In this paper we address the relationship among these three factors. By constructing a benchmark of hundreds of databases containing different types of missing data, and applying several imputation methods and classification algorithms, we empirically show that an interaction between imputation methods and supervised classification can be deduced. Besides, differences in terms of classification performance for the same imputation method in different missing data patterns have been found. This points to the convenience of considering the combined choice of the imputation method and the classifier algorithm according to the missing data type.

Cross-Diffusion Induced Turing Instability and Amplitude Equation for a Toxic-Phytoplankton–Zooplankton Model with Nonmonotonic Functional Response

The spatiotemporal pattern induced by cross-diffusion of a toxic-phytoplankton–zooplankton model with nonmonotonic functional response is investigated in this paper. The linear stability analysis shows that cross-diffusion is the key mechanism for the formation of spatial patterns. By taking cross-diffusion rate as bifurcation parameter, we derive amplitude equations near the Turing bifurcation point for the excited modes in the framework of a weakly nonlinear theory, and the stability analysis of the amplitude equations interprets the structural transitions and stability of various forms of Turing patterns. Furthermore, we illustrate the theoretical results via numerical simulations. It is shown that the spatiotemporal distribution of the plankton is homogeneous in the absence of cross-diffusion. However, when the cross-diffusivity is greater than the critical value, the spatiotemporal distribution of all the plankton species becomes inhomogeneous in spaces and results in different kinds of patterns: spot, stripe, and the mixture of spot and stripe patterns depending on the cross-diffusivity. Simultaneously, the impact of toxin-producing rate of toxic-phytoplankton (TPP) species and natural death rate of zooplankton species on pattern selection is also explored.

Nanowelding and patterning of silver nanowires via mask-free atmospheric cold plasma-jet scanning

Silver nanowire (AgNW) thin film is a promising candidate to replace traditional indium tin oxide in optoelectronics applications. To date however, the widespread application of AgNW thin film is limited by the weak point contacts between individual AgNWs and the lack of facile patterning techniques. Here, we demonstrate a novel and facile method to not only nanoweld AgNW junctions but also pattern AgNW thin films via mask-free cold plasma-jet scanning in ambient conditions. After the plasma-jet nanowelding treatment, the morphology of AgNWs change substantially and the junctions are welded together. The nanowelded AgNWs-based thin film shows enhanced electrical and mechanical properties. On the other hand, after the plasma-jet patterning treatment, the AgNWs are etched and transformed into separated large particles. Different kinds of patterns are produced via this patterning technique. At last, a simple light emitting diode circuit is fabricated to demonstrate the suitability of the nanowelded and patterned AgNW electrodes for flexible electronic devices.

Paradox Aesthetics Blangkon In Yogyakarta And Surakarta

<p>Mataram kingdom became part of the history of Kraton Yogyakarta, starting from the Giyanti agreement in 1755. Then Mataram kingdom was divided into two regions Solo and Yogyakarta. Each region was headed by one king. The title for the king's Solo palace is Pakubuwono, while the term for the king's palace in Yogyakarta is Hamengkubowono. One of the relics from the palace, which is still preserved until now is <em>blangkon</em>. <em>Blangkon</em> is used as a headgear by the people in the palace. Yogyakarta Blangkon style is different from Solo blangkon style, although this palace used to be one area. Blangkon have a specific pattern that is related to the lifestyle of Javanese community, especially people of the palace. Blangkon is considered as one of artifacts as it has a philosophy of characteristics and identity of Javanese community. This research used qualitative research in the form of literature study. The method used by the authors is by collecting data source: primary data and secondary data. Blangkon will be examined through the used patterns which is connected in the aesthetic paradox. Basically there are kinds of pattern which are called pattern two, three, four, and five. Eventually it came to a conclusion that Blangkon is the development of aesthetic paradox patterns of patterns three to five patterns.</p><p> </p><p><strong>Keywords: </strong><em>Paradox aesthetics, Blangkon<strong></strong></em></p>

Measurement of Line-to-Ground Capacitance in Distribution Network Considering Magnetizing Impedance’s Frequency Characteristic

Signal injection method (SIM) is widely applied to the insulation parameters’ measurement in distribution network for its convenience and safety. It can be divided into two kinds of patterns: injecting a specific frequency signal or several frequencies’ groups, and scanning frequency in a scheduled frequency scope. In order to avoid the disadvantages in related researches, improved signal injection method (ISIM), in which the frequency characteristic of the transformer magnetizing impedance is taken into consideration, is proposed. In addition, optimization for signal injection position has been accomplished, and the corresponding three calculation methods of line-to-ground capacitance has been derived. Calculations are carried out through the vector information (vector calculation method), the amplitude information (amplitude calculation method), the phase information (phase calculation method) of voltage and current in signal injecting port, respectively. The line-to-ground capacitance is represented by lumped parameter capacitances in high-voltage simulation test. Eight different sinusoidal signals are injected into zero-sequence circuit, and then line-to-ground capacitance is calculated with the above-mentioned vector calculation method based on the voltage and the current data of the injecting port. The results obtained by the vector calculation method show that ISIM has a wider application frequency range compared with signal injection method with rated parameters (RSIM) and SIM. The RSIM is calculated with the rated transformer parameters of magnetizing impedance, and the SIM based on the ideal transformer model, and the relative errors of calculation results of ISIM are smaller than that for other methods in general. The six groups of two-frequency set are chosen in a specific scope which is recommended by vector calculation results. Based on ISIM, the line-to-ground capacitance calculations through the amplitude calculation method and phase calculation method are compared, and then its application frequency range, which can work as a guidance for line-to-ground capacitance measurement, is concluded.

Modulation in Translation Process

Many aspects have to be observed by translators in order to make a good and equivalent translation. One of these is meaning, because it is the main goal translators must reach in their translation activity. The object of this preliminary research was on the modulation or the shifting of meaning in the process of translation. This preliminary research was qualitative naturalistic research. It used the samples of translation, both from Indonesian to English and from English to Indonesian. The words that needed modulation were classified, the patterns of the words were analyzed and finally conclusion was drawn. The source text were about social and religious affairs, i.e. the training module for companionship for victims of violence against women (VAW) and Section III Chapter 14 and Glossary of Teaching and Researching Translation (Hatim, 2001).The aims of this preliminary research were to find out (1) what kinds of pattern that are said to be modulated, and (2) whether each pattern, either from TSu or TSa carries the same or different meaning. The result from this preliminary research showed that each pattern, even though different between TSu and TSa carries the same meaning and the patterns that are modulated were the changing of passive in TSu to active in TSa and both passive in TSu and TSa.

Seaweed eutectic-dendritic solidification pattern in a CoCrFeNiMnPd eutectic high-entropy alloy

A CoCrFeNiMnPd eutectic high-entropy alloy was designed and prepared. A unique eutectic-dendritic solidification pattern was observed in the as-cast alloy. Heretofore, this kind of pattern was rarely reported no matter in binary or multi-component alloys. Through a series of experimental characterization and theoretical analysis, it could be concluded that the sluggish diffusion effect of high-entropy alloys makes the interface deviate considerably from the local non-equilibrium condition and the alternate arrangement of CoCrFeNiPd-rich FCC and Tetragonal Mn7Pd9 lamellae results in weak interface energy anisotropy, thus forming the seaweed eutectic-dendrite. Application of the designing rules of high-entropy alloys to the current work indicates that mere consideration of the weighted average physical properties of constituent elements is not sufficient to the design of high-entropy alloys with a single solid-solution phase.

Study on UX Design of Omni Channel

Objectives: Invigoration of mobile environment with developed technology of IoT is forming new consumption channel abolishing each independent border. The purpose of this study lies in inducement of implication by analyzing stages of user’s experience within each service and suggesting strategic direction of Omni channel. Methods/Statistical Analysis: This study carried out case analysis of Omni Channel in depth, on the basis of Yin’ s theory saying like development of UX- oriented O2O platform, research area which is in a activated phase but lacks typical cases can be studied effectively through qualitative case-study method. Explanation was applied to interviews with relevant people, and case study was applied to analyze the interview contents and associated reports. 3 kinds of patterns were classified into single channel, multi-channel and Omni-channel based on preceding study data by analyzing content of interview and relevant reports applying technique of case study. Findings: Omni channel with diversified service, keyword emerged called “promptness” and “customized”. This is also a characteristic generally appearing in services based on big data at connected era. Eventually, factor for success of new form of marketing called Omni channel is also only limited to tool connecting of customer with online and offline, but shows implication of key for predominance in competition that goes ahead for creating continued customer’s experience. This contains things of making development as an important measure for continuous formation of relationship with customers. This study can find its meaning as a basic material for exploring channel service by analyzing feature of Omni channel focused on views of customer’s experience getting out of view point of prime territories of on and offline. Follow-up studies seems to be needed for developing practical working guideline by developing methodology for materializing continuity of continuous experience beyond the analysis by factor for special feature of Omni channel in the future. Improvements/Applications: Element of characteristics of user’s experience in Omni channel was induced with 1. Acceleration of consuming experience and 2. Extension of platform service experience.

A New Displaying Technology for Information Hiding Using Temporally Brightness Modulated Pattern

We propose a new display system technology that can hide secret information behind a displayed image, while simultaneously satisfying both high invisibility and readability requirements. The hidden information is a kind of binary image including characters, and various kinds of patterns, i.e., quadratic residue (QR) codes. This technique uses a temporally bright modulated invisible pattern in a moving image, or video. Frame images over some periods are summed up when read out, enhancing the contrast of the invisible pattern to make it visible. We also propose a new method to solve an issue that occurs due to asynchronous operations of the display and video camera, which is a technique that was achieved by using time-shift sampling. The hidden binary image could be read out according to experiments that we conducted to confirm the results. Moreover, the patterns used in this technique were decidedly invisible when laid behind the main images, which suggested the proposed technique was highly feasible in practical applications according to this confirmation.

A Robust and Versatile Multi-View Learning Framework for the Detection of Deviant Business Process Instances

Increasing attention has been paid to the detection and analysis of “deviant” instances of a business process that are connected with some kind of “hidden” undesired behavior (e.g. frauds and faults). In particular, several recent works faced the problem of inducing a binary classification model (here named deviance detection model ) that can discriminate between deviant traces and normal ones, based on a set of historical log traces (labeled as either deviant or normal). Current solutions rely on applying standard classifier-induction methods to a feature-based representation of the given traces, where the features include sequence-based patterns extracted from the corresponding sequences of activities. However, there is no consensus on which kinds of patterns are the most suitable for such a task. On the other hand, mixing multiple pattern families together may produce a heterogenous, redundant and sparse representation of the traces that likely leads to poor deviance detection models. In this paper, we propose an ensemble-learning method for solving this problem, where multiple base classifiers are trained on different feature-based views of the log (each obtained by mapping the traces onto a distinguished collection of patterns). A stacking procedure is used to combine the discovered base models into an overall probabilistic model that associates any new trace with an estimate of the probability that it reflects a deviant process instance. This helps the analyst prioritize the inspection of the cases that are more likely to be deviant. The method also takes advantage of all nonstructural data available in the log, and employs a resampling mechanism to deal with the rarity of deviances in the training log. It has been conceived as the core of a comprehensive framework for detecting and analyzing business process deviances. The framework supports the analyst to investigate suspect deviances, and provides some feedback to the learning method for improving the accuracy of the discovered deviance detection models. Tests on several real-life datasets proved the validity of the approach, as concerns its capability to discover an accurate deviance detection model, and to effectively exploit new (originally unlabeled) traces via active learning and self-training mechanisms.