Search results for: semantic correlation analysis

Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Ayuko Itsuki, Sachiyo Aburatani

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In recent years, pollutions of the environment by toxic substances become a serious problem. While there are many methods of environmental clean-up, the methods by microorganisms are considered to be reasonable and safety for environment. Compost is known that it catabolize the meladorous substancess in its production process, however the mechanism of its catabolizing system is not known yet. In the catabolization process, organic matters turn into inorganic by the released enzymes from lots of microorganisms which live in compost. In other words, the cooperative of activated enzymes in the compost decomposes malodorous substances. Thus, clarifying the interaction among enzymes is important for revealing the catabolizing system of meladorous substance in compost. In this study, we utilized statistical method to infer the interaction among enzymes. We developed a method which combined partial correlation with cross correlation to estimate the relevance between enzymes especially from time series data of few variables. Because of using cross correlation, we can estimate not only the associative structure but also the reaction pathway. We applied the developed method to the enzyme measured data and estimated an interaction among the enzymes in decomposition mechanism of toluene.

Keywords: enzyme activities, comparative analysis, compost, toluene

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Authors: Deepa Vijay, Chellammal Surianarayanan, Gopinath Ganapathy

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Requirements of a software project serve as a guideline for the entire project team which enable the team towards producing the right outcome. As requirements are the key in deciding the success of the project, it should be specified in an unambiguous manner. Also, the requirements should be complete and consistent. It should be interpreted in the same way by the entire software project team as the customer interprets. Specifying requirements in textual manner is common in software development. This leads to poor understanding of the requirements which results in more errors and degraded quality. There are some literatures which focus on semantic way of specifying functional requirement which ensure the consistency and completeness of requirements. Alternately in the work, a method is proposed to map the syntactic requirements with corresponding semantics in the form of ontologies. This improves the understanding of requirements, prevents errors and improves quality.

Keywords: functional requirement, ontology, requirements management, semantics

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Authors: Malay Bhattacharyya, Saparya Suresh

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The Correlation Structure associated with a portfolio is subjected to vary across time. Studying the structural breaks in the time-dependent Correlation matrix associated with a collection had been a subject of interest for a better understanding of the market movements, portfolio selection, etc. The current paper proposes a methodology for testing the change in the time-dependent correlation structure of a portfolio in the high dimensional data using the techniques of generalized inverse, singular valued decomposition and multivariate distribution theory which has not been addressed so far. The asymptotic properties of the proposed test are derived. Also, the performance and the validity of the method is tested on a real data set. The proposed test performs well for detecting the change in the dependence of global markets in the context of high dimensional data.

Keywords: correlation structure, high dimensional data, multivariate distribution theory, singular valued decomposition

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Authors: András Rövid, Takeshi Hashimoto

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The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Skiker Kaoutar, Mounir Maouene

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Neuroimaging studies have shown that animal and tool concepts rely on distinct networks of brain areas. Animal concepts depend predominantly on temporal areas while tool concepts rely on fronto-temporo-parietal areas. However, the origin of this neurofunctional distinction for processing animal and tool concepts remains still unclear. Here, we address this question from a network perspective suggesting that the neural distinction between animals and tools might reflect the differences in their structural semantic networks. We build semantic networks for animal and tool concepts derived from Mc Rae and colleagues’s behavioral study conducted on a large number of participants. These two networks are thus analyzed through a large number of graph theoretical measures for small-worldness: centrality, clustering coefficient, average shortest path length, as well as resistance to random and targeted attacks. The results indicate that both animal and tool networks have small-world properties. More importantly, the animal network is more vulnerable to targeted attacks compared to the tool network a result that correlates with brain lesions studies.

Keywords: animals, tools, network, semantics, small-world, resilience to damage

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Authors: Athdath Waduge Susantha Janaka Kumara, Xiefei Zhi, Zin Mie Mie Sein

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Understanding the air temperature variability is crucially important for disaster risk reduction and management. In this study, we used 15 synoptic meteorological stations to assess the spatiotemporal variability of air temperature over Sri Lanka during 1972–2021. The empirical orthogonal function (EOF), Principal component analysis (PCA), Mann-Kendall test, power spectrum analysis and correlation coefficient analysis were used to investigate the long-term trends of air temperature and their possible relation to sea surface temperature (SST) over the region. The results indicate that an increasing trend in air temperature was observed with the abrupt climate change noted in the year 1994. The spatial distribution of EOF1 (63.5%) shows the positive and negative loading dipole patterns from south to northeast, while EOF2 (23.4%) explains warmer (colder) in some parts of central (south and east) areas. The power spectrum of PC1 (PC2) indicates that there is a significant period of 3-4 years (quasi-2 years). Moreover, Indian Ocean Dipole (IOD) provides a strong positive correlation with the air temperature of Sri Lanka, while the EL Nino Southern Oscillation (ENSO) presents a weak negative correlation. Therefore, IOD events led to higher temperatures in the region. This study’s findings can help disaster risk reduction and management in the country.

Keywords: air temperature, interannaul variability, ENSO, IOD

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Hsien-Tsen Wang

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Business Intelligence (BI) has long helped organizations make informed decisions based on data-driven insights and gain competitive advantages in the marketplace. In the past two decades, businesses witnessed not only the dramatically increasing volume and heterogeneity of business data but also the emergence of new technologies, such as Artificial Intelligence (AI), Semantic Web (SW), Cloud Computing, and Big Data. It is plausible that the convergence of these technologies would bring more value out of business data by establishing linked data frameworks and connecting in ways that enable advanced analytics and improved data utilization. In this paper, we first review and summarize current BI applications and methodology. Emerging technologies that can be integrated into BI applications are then discussed. Finally, we conclude with a proposed synergy framework that aims at achieving a more flexible, scalable, and intelligent BI solution.

Keywords: business intelligence, artificial intelligence, semantic web, big data, cloud computing

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Authors: Fatimah Alhomaid

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The present study was planned to investigate the role of molecular changes and immunohistochemical in early detection of colon cancer in Saudi patients. Our results were carried out on 48 patients colon cancer. We obtained our data from laboratory in King Khalid university hospital. The specimens were taken (48) patients with colon cancer 34 male and 14 female and 2 control. The average age of varied from 37-85 years. The tumor was diagnosed as I in tow patients (male and female) and grade 2 in 42 patients (29 male and 13 female) while the grade 3 in 4 patients (all males). The specimens were processed for haematoxylin and eosin staining , immunohistochemical technique and flow cytometry analysis. Our study noted that most patients had adenocarcinoma which characterized by presence of signet-ring cells were very clear in advanced patients of adenocarcinoma. Our sections in adenocarcinoma in grade 2 and stage 3 had an increase in signet ring cells,an increase in the acini of glands and an increase in number of lymphocytes which spread to the muscularis layer. With advancing the disease, there were haemorge in blood and increase in lymphocytes and increase number of nuclei in the tubular glands. Our study was carried on 48 patients, immunohistochemical diagnosis (CK20,PCNA,P53) and the analysis of DNA content by flow cytometry technique. Our study indicated that the presence of correlation between the immunohistochemical analysis for P53 and the grades. The reaction of P53 appeared as strong in nucleus in grades &stage 3 and appeared in other sections as dark brown pigment. Our study indicated that the absence of correlation between the immunohistochemical analysis for pcan and the grades. In our sections, there were strong reactions in the more 80% of nuclei in grade 1& stage 2. Our study indicated that the presence of correlation between the immunohistochemical analysis for CK20 and the grades. Our results indicated the presence of positive reaction in cytoplasm varied from weak to moderate in grade 3 & stage 4. Concerning the Flow cytometry technique our results indicated that the presence of correlation between the DNA and different stages of colon cancer.

Keywords: DNA-CK20, PCNA, P53, colon cancer

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Authors: Svetlana Missina, Anatoliy Shipilov, Alexandr Vavaev

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The purpose of the present study was to investigate the relationship between sprint and vertical jump performance in elite female football players. Twenty four professional female football players (age, 18.6±3.1 years; height, 168.3±6.3 cm, body mass 61.6±7.4 kg; mean±SD) were tested for 30-m sprint time, 10-m sprint time and vertical countermovement (CMJ) and squat (SJ) jumps height. Participants performed three countermovement jumps and three squat jumps for maximal height on a force platform. Mean values of three trials were used in statistical analysis. The displacement of center of mass (COM) during flight phase (e.g. jump height) was calculated using the vertical velocity of the COM at the moment of take-off. 30-m and 10-m sprint time were measured using OptoGait optical system. The best of three trials were used for analysis. A significant negative correlation was found between 30-m sprint time and CMJ, SJ height (r = -0.85, r = -0.79 respectively), between 10-m sprint time and CMJ, SJ height (r = -0.73, r = -0.8 respectively), and step frequency was significantly related to CMJ peak power (r = -0.57). Our study indicates that there is strong correlation between sprint and jump performance in elite female football players, thus vertical jump test can be considered as a good sprint and agility predictor in female football.

Keywords: agility, female football players, sprint performance, vertical jump height

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Authors: Mohita Singh, Sunil Sachdev, Amrita Singh

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Background and Aim: Physical aerobic exercises promote positive changes in one’s mental health, intelligence, and ability to cope with stressful encounters. The present study was designed to explore the correlation between intelligence and stress parameters and to assess the correlation between the same parameters after the practice of one month of moderate-intensity physical exercise. Method: The study was conducted on thirty-five healthy male volunteer students to assess the correlation between stress parameters in subjects with varying level of general intelligence (GI) and emotional intelligence (EI). Correlation studies were again conducted after one month between the same parameters to evaluate the effect of moderate-intensity physical exercise (MIPE). Baseline values were recorded using standard scales. Result: IQ and EQ correlated negatively with both acute and chronic stress parameters and positively with each other. A positive correlation was found between acute and chronic stress. With the practice of one month of moderate-intensity physical exercise, there was significant increment between the parameters under study and hence improved results. Conclusion: MIPE improved correlation between GI, EI, stress parameters, and thus reduced stress and improved intelligence.

Keywords: emotional intelligence, general intelligence, moderate intensity physical exercise, stress response

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Authors: Amaal Ali Al-Gamde

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The Syrian revolution is a major issue in the Middle East, which draws in world powers and receives a great focus in international mass media since 2011. The heavy global reliance on cyber news and digital sources plays a key role in conveying a sense of bias to a wide range of online readers. Thus, based on the assumption that media discourse possesses ideological implications, this study investigates the representation of Syrian revolution in online media. The paper explores the discursive constructions of anti and pro-government powers in Syrian revolution in 1000,000-word corpus of Fars online reports (an Iranian news agency), issued between 2013 and 2015. Taking a corpus assisted discourse analysis approach, the analysis investigates three types of lexicosemantic relations, the semantic macrostructures within which the two social actors are framed, the lexical collocations characterizing the news discourse and the discourse prosodies they tell about the two sides of the conflict. The study utilizes computer-based approaches, sketch engine and AntConc software to minimize the bias of the subjective analysis. The analysis moves from the insights of lexical frequencies and keyness scores to examine themes and the collocational patterns. The findings reveal the Fars agency’s ideological mode of representations in reporting events of Syrian revolution in two ways. The first is by stereotyping the opposition groups under the umbrella of terrorism, using words such as (law breakers, foreign-backed groups, militant groups, terrorists) to legitimize the atrocities of security forces against protesters and enhance horror among civilians. The second is through emphasizing the power of the government and depicting it as the defender of the Arab land by foregrounding the discourse of international conspiracy against Syria. The paper concludes discussing the potential importance of triangulating corpus linguistic tools with critical discourse analysis to elucidate more about discourses and reality.

Keywords: discourse prosody, ideology, keyness, semantic macrostructure

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Authors: Pooja Khanal, Huaming Zhang

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Labeling of issues/bugs, also known as bug classification, plays a vital role in software engineering. Some known labels/classes of bugs are 'User Interface', 'Security', and 'API'. Most of the time, when a reporter reports a bug, they try to assign some predefined label to it. Those issues are reported for a project, and each project is a repository in GitHub/GitLab, which contains multiple issues. There are many software project repositories -ranging from individual projects to commercial projects. The labels assigned for different repositories may be dependent on various factors like human instinct, generalization of labels, label assignment policy followed by the reporter, etc. While the reporter of the issue may instinctively give that issue a label, another person reporting the same issue may label it differently. This way, it is not known mathematically if a label in one repository is similar or different to the label in another repository. Hence, the primary goal of this research is to find the semantic differences between bug labeling of different repositories via machine learning. Independent optimal classifiers for individual repositories are built first using the text features from the reported issues. The optimal classifiers may include a combination of multiple classifiers stacked together. Then, those classifiers are used to cross-test other repositories which leads the result to be deduced mathematically. The produce of this ongoing research includes a formalized open-source GitHub issues database that is used to deduce the similarity of the labels pertaining to the different repositories.

Keywords: bug classification, bug labels, GitHub issues, semantic differences

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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In this paper, quantitative structure-retention relationships (QSRR) analysis was applied in order to correlate in silico biological and lipophilicity molecular descriptors with retention values for the set of selected s-triazine herbicides. In silico generated biological and lipophilicity descriptors were discriminated using generalized pair correlation method (GPCM). According to this method, the significant difference between independent variables can be noticed regardless almost equal correlation with dependent variable. Using established multiple linear regression (MLR) models some biological characteristics could be predicted. Established MLR models were evaluated statistically and the most suitable models were selected and ranked using sum of ranking differences (SRD) method. In this method, as reference values, average experimentally obtained values are used. Additionally, using SRD method, similarities among investigated s-triazine herbicides can be noticed. These analysis were conducted in order to characterize selected s-triazine herbicides for future investigations regarding their biodegradability. This study is financially supported by COST action TD1305.

Keywords: descriptors, generalized pair correlation method, pesticides, sum of ranking differences

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Authors: Nazanin Naderi, Milton Smith

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Since worldwide demand for renewable energy is increasing rapidly because of the climate problem and the limitation of fossil fuels, technologies of alternative energy sources have been developed and the electric power network now includes renewable energy resources such as wind energy. Because of the huge advantages that wind energy has, like reduction in natural gas use, price pressure, emissions of greenhouse gases and other atmospheric pollutants, electric sector water consumption and many other contributions to the nation’s economy like job creation it has got too much attention these days from different parts of the world especially in the United States which is trying to provide 20% of the nation’s energy from wind by 2030. This study is trying to evaluate the effect of splitting wind farms on power output. We are trying to find if we can get more output by installing wind turbines in different sites rather than installing all wind turbines in one site. Five potential sites in Texas have been selected as a case study and two years wind data has been gathered for these sites. Wind data are analyzed and effect of correlation between sites on power output has been evaluated. Standard deviation and autocorrelation effect has also been considered for this study. The paper has been organized as follows: After the introduction the second section gives a brief overview of wind analysis. The third section addresses the case study and evaluates correlation between sites, auto correlation of sites and standard deviation of power output. In section four we describe the results.

Keywords: auto correlation, correlation between sites, splitting wind farms, power output, standard deviation

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Ammar Z. Alwrekiat, Sara Ojeda Gonzalez, Maria Jose Miranda Martel, Antonio Mihi-Ramirez

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This paper analyses the economic situation in Jordan, which has been the political asylum destination for Syrians since 2011. We analyze the effects of the Jordanian situation through the following indicators: international aid, gross domestic product, remittances, and unemployment. A correlation analysis has been used to identify the main connections of these parameters with the reception of refugees. Although the economic effects of Syrian refugees in Jordan are uncertain, it involves an important challenge in the development of migration policies. Jordan has a special economic situation and limited capacities, but the country has provided humanitarian assistance to Syrian refugees. In this case, the support of the international community is of particular importance, taking an important role in the negotiation of international agreements on refugees.

Keywords: correlation analysis, economic implications, migration, refugees

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Authors: Ghazi Bouaziz, Abderrahim Derouiche, Damien Brulin, Hélène Pigot, Eric Campo

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Automatic ADL classification is a crucial part of ambient assisted living technologies. It allows to monitor the daily life of the elderly and to detect any changes in their behavior that could be related to health problem. But detection of ADLs is a challenge, especially because each person has his/her own rhythm for performing them. Therefore, we used a correlation matrix to extract custom rules that enable to detect ADLs, including eating activity. Data collected from 3 different individuals between 35 and 105 days allows the extraction of personalized eating patterns. The comparison of the results of the process of eating activity extracted from the correlation matrices with the declarative data collected during the survey shows an accuracy of 90%.

Keywords: elderly monitoring, ADL identification, matrix correlation, meal-taking activity

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Authors: Santosh Kumar, M. Muralidhar

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The term ‘ergonomics’ is derived from two Greek words: ‘ergon’, meaning work and ‘nomoi’, meaning natural laws. Ergonomics is the study of how working conditions, machines and equipment can be arranged in order that people can work with them more efficiently. In this research communication an attempt has been made to study the effect of hand-arm vibrational exposure on the workers of a gear manufacturing plant by comparison of potential Carpal Tunnel Syndrome (CTS) symptoms and effect of different exposure levels of vibration on occurrence of CTS in actual industrial environment. Chi square test and correlation analysis have been considered for statistical analysis. From Chi square test, it has been found that the potential CTS symptoms occurrence is significantly dependent on the level of vibrational exposure. Data analysis indicates that 40.51% workers having potential CTS symptoms are exposed to vibration. Correlation analysis reveals that potential CTS symptoms are significantly correlated with exposure to level of vibration from handheld tools and to repetitive wrist movements.

Keywords: CTS symptoms, hand-arm vibration, ergonomics, physical tests

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Authors: Baba Mbaye

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In collaborative filtering recommendation systems, a user receives suggested items based on the opinions and evaluations of a community of users. This type of recommendation system uses only the information (notes in numerical values) contained in a usage matrix as input data. This matrix can be constructed based on users' behaviors or by offering users to declare their opinions on the items they know. The cold start problem leads to very poor performance for new users. It is a phenomenon that occurs at the beginning of use, in the situation where the system lacks data to make recommendations. There are three types of cold start problems: cold start for a new item, a new system, and a new user. We are interested in this article at the cold start for a new user. When the system welcomes a new user, the profile exists but does not have enough data, and its communities with other users profiles are still unknown. This leads to recommendations not adapted to the profile of the new user. In this paper, we propose an approach that improves cold start by using the notions of similarity and semantic proximity between users profiles during cold start. We will use the cold-metadata available (metadata extracted from the new user's data) useful in positioning the new user within a community. The aim is to look for similarities and semantic proximities with the old and current user profiles of the system. Proximity is represented by close concepts considered to belong to the same group, while similarity groups together elements that appear similar. Similarity and proximity are two close but not similar concepts. This similarity leads us to the construction of similarity which is based on: a) the concepts (properties, terms, instances) independent of ontology structure and, b) the simultaneous representation of the two concepts (relations, presence of terms in a document, simultaneous presence of the authorities). We propose an ontology, OIVCSRS (Ontology of Improvement Visitor Cold Start in Recommender Systems), in order to structure the terms and concepts representing the meaning of an information field, whether by the metadata of a namespace, or the elements of a knowledge domain. This approach allows us to automatically attach the new user to a user community, partially compensate for the data that was not initially provided and ultimately to associate a better first profile with the cold start. Thus, the aim of this paper is to propose an approach to improving cold start using semantic technologies.

Keywords: visitor cold start, recommender systems, collaborative filtering, semantic filtering

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Authors: Allahyar Arabmomeni, Hojjatollah Alaei

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Background: The objectives of this study was to investigate effect of anthropometric variables and body composition on physical capabilities among male and female students. Materials and Methods: The study had a descriptive correlation method. The statistical population consisted of all students of Islamic Azad University, Khomeinishahr Branch, from 2011 to 2013, which was about 7000 students. The statistical sample included 300 male and 300 female students who were randomly selected from among university students in proportion to frequency of students in each faculty. Descriptive statistical methods, t-test and Pearson correlation coefficient were used for data analysis. Results: Results of this research showed that body size of male students in the studied variables was more than that of female students (p<0.05). Moreover, there was significant difference between all the variables based on significance level of the table. Also, the results taken from the Pearson correlation of this study's variables showed a positive relationship between height and leg and hand length and sit-up, full-ups bar and vertical jump tests (p<0/01). Besides, there was a positive correlation between hand length, sit-up, full-ups bar and vertical jump tests. As far as tests of length of legs and vertical jump were concerned, a highly positive correlation was observed between them. Additionally, results of this study indicated a significant correlation at alpha level of 0.05 between age and height of the students; but, there was a negative correlation between age, sit-up and 1600-m tests (p<0.05). Conclusion: The results of this study indicated a relationship between size of weight, height, length of hands and legs and some physical fitness tests. Therefore, it is required to consider anthropometric factors in addition to gender and age while preparing norms of physical fitness since variables of height and length of hands also affect physical fitness evaluation.

Keywords: anthropometric variables, physical fitness factors, students, body composition

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Authors: Nam-Uk Kim, Sung-Hwan Kim, Tai-Myoung Chung

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Typical security management systems are not suitable for detecting APT attack, because they cannot draw the big picture from trivial events of security solutions. Although SIEM solutions have security analysis engine for that, their security analysis mechanisms need to be verified in academic field. Although this paper proposes merely an architectural model for APT detection, we will keep studying on correlation analysis mechanism in the future.

Keywords: advanced persistent threat, anomaly detection, data mining

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Authors: Efnie Indrianie

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Potential Intelligence constitutes one essential factor in every individual. This intelligence can be a provision for the development of Performance Intelligence if it is supported by surrounding environment. Fingerprint analysis is a method in recognizing this Potential Intelligence. This method is grounded on pattern and number of finger print outlines that are assumed symmetrical with the number of nerves in our brain, in which these areas have their own function among another. These brain’s functions are later being transposed into intelligence components in accordance with the Multiple Intelligences theory. This research tested the correlation between Potential Intelligence and the components of its Performance Intelligence. Statistical test results that used Pearson correlation showed that five components of Potential Intelligence correlated with Performance Intelligence. Those five components are Logic-Math, Logic, Linguistic, Music, Kinesthetic, and Intrapersonal. Also, this research indicated that cultural factor had a big role in shaping intelligence.

Keywords: potential intelligence, performance intelligence, multiple intelligences, fingerprint, environment, brain

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Authors: Sungik Kang, Ja-Hoon Koo

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Globally, urban environments have become unequal, and cities have been segmented by income class. It is predicted that urban inequality has arisen by urban redevelopment and reconstruction projects that improve the urban environment and innovate cities. This study aims to analyze the occurrence and characteristics of urban inequality by using the housing price and sale price and demonstrating the correlation with the urban redevelopment project. This study measures 14 years of urban inequality index for 25 autonomous districts in Seoul and analyzes the correlation between urban inequality with urban redevelopment projects. As a conclusion of this study, first, the urban inequality index of Seoul has been continuously rising since 2015. Trends from 2006 to 2019 have been in U-curved shape in between 2015. In 2019, Seoul's urban inequality index was 0.420, a level similar to that of the 2007 financial crisis. Second, the correlation between urban redevelopment and urban inequality was not statistically significant. Therefore, we judged that urban redevelopment's scale or project structure has nothing with urban inequality. Third, while district designation of urban reconstruction temporarily alleviates urban inequality, the completion of the project increases urban inequality. When designating a district, urban inequality is likely to decrease due to decreased outdated housing transactions. However, the correlation with urban inequality increases as expensive houses has been placed after project completion.

Keywords: urban inequality, urban redevelopment projects, urban reconstruction projects, housing price inequality, panel analysis

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Authors: Yuyang Cheng, Marcos Escobar-Anel

Abstract:

This paper introduces a multivariate 4/2 stochastic covariance process generalizing the one-dimensional counterparts presented in Grasselli (2017). Our construction permits stochastic correlation not only among stocks but also among volatilities, also known as co-volatility movements, both driven by more convenient 4/2 stochastic structures. The parametrization is flexible enough to separate these types of correlation, permitting their individual study. Conditions for proper changes of measure and closed-form characteristic functions under risk-neutral and historical measures are provided, allowing for applications of the model to risk management and derivative pricing. We apply the model to an expected utility theory problem in incomplete markets. Our analysis leads to closed-form solutions for the optimal allocation and value function. Conditions are provided for well-defined solutions together with a verification theorem. Our numerical analysis highlights and separates the impact of key statistics on equity portfolio decisions, in particular, volatility, correlation, and co-volatility movements, with the latter being the least important in an incomplete market.

Keywords: stochastic covariance process, 4/2 stochastic volatility model, stochastic co-volatility movements, characteristic function, expected utility theory, verication theorem

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Authors: Shreya Borthakur, Aastha Vartak

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This behavioral experiment aimed to investigate the global precedence phenomenon in a South Asian sample and its correlation with mobile screen time. The global precedence effect refers to the tendency to process overall structure before attending to specific details. Participants completed attention tasks involving global and local stimuli with varying consistencies. The results showed a tendency towards local precedence, but no significant differences in reaction times were found between consistency levels or attention conditions. However, the correlation analysis revealed that participants with higher screen time exhibited a stronger negative correlation with local attention, suggesting that excessive screen usage may impact perceptual organization. Further research is needed to explore this relationship and understand the influence of screen time on cognitive processing.

Keywords: global precedence, visual attention, perceptual organization, screen time, cognition

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Authors: Dian Ariyawati, Romi Sukoco, Sinung Agung Joko

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Background: Postural Orthostatic Tachycardia Syndrome (POTS) is a form of orthostatic intolerance caused by autonomic dysfunction. POTS predominantly occurs in young women. Regular exercise has proven to improve the organ system functions, including autonomous systems. The aim of this research was to determine the correlation between exercise frequency and POTS in young women. Method: 510 young women (16-23 years of age) were screened. They were obtained by interview and physical examination. The diagnosis of POTS was performed with Active Stand Test (AST) and heart rate measurement using a pulsemeter. There were 29 young women who suffered from POTS. The exercise frequency was obtained by interview. Data was statistically analyzed using Spearman Correlation test. Result: The subjects’, who tested positive for POTS didn’t perform regular exercise. The Spearman correlation test showed there was a moderate negative correlation between exercise frequency and POTS in young women (r = -0.487, p < 0.00). Conclusion: There is a moderate reverse correlation between exercise frequency and POTS in young women. Further studies are suggested to develop an exercise program for young who suffered from POTS.

Keywords: POTS, autonomic dysfunction, exercise frequency, young woman

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Authors: Andi Syahputra, R. H. Jatmiko, D. R. Hizbaron

Abstract:

Land surface temperature (LST) in an area is influenced by conditions of vegetation density, building density, and the number of inhabitants who live in the area. Medan City is one of the largest cities in Indonesia, with a high rate of change from vegetation to developed land. This study aims to identify the relationship between the percentage of building density and land surface temperature in Medan City. Pixel image analysis method is carried out to obtain the value of building density in pixel images of Landsat 8 images with the help of WorldView-2 satellite imagery. The results showed the highest land surface temperature in 2018 of 35, 4°C was found in Medan Perjuangan District, and the lowest was 22.5°C in Medan Belawan District. Building density samples with a density level of 889.17 m were also found in Medan Perjuangan District, while the lowest building density sample was found in Medan Timur District. Linear regression analysis of the effect of building density with land surface temperature obtained a correlation (R) was 0.64, and a coefficient of determination (R²) was 0.411 and modeling of building density based on the LST has a correlation (R), and a coefficient of determination (R²) was 0.72 with The RMSE obtained 0.853.

Keywords: land surface temperature, Landsat, imagery, building density, vegetation, density

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Authors: Feroz A. Shah, M. H. Balkhi

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The study was aimed at to record the distribution and prevalence of various metazoan parasites of fish from hill stream/coldwater fishes of various water bodies of northwest Himalayan region of India. Snow trout (Schizoth oracids) from eutrophic lakes and fresh water streams were collected from January to December 2012, to study the impact of environmental factors on the dynamics and distribution of parasitic infection. The prevalence of helminth parasites was correlated with available physico-chemical parameters including water temperature, pH and dissolved oxygen (DO). The most abundant parasitic infection recorded during this study was Adenoscolex sp. (Cestode parasite) which showed positive correlation with pH (significant p≤0.05) negative correlation with temperature. The Bothriocephalus was having positive correlation with water temperature while as negative correlation was observed with pH and DO. The correlation between Diplozoon sp. and Clinostomum sp. with the physiochemical parameters were non-significant.

Keywords: hill stream fishes, parasites, Western Himalayas, prevelance

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