Search results for: data analyses

Authors: Babak Rezaei, Arash Zargar Shoushtari

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Kangan anticline is located in the Coastal Fars area, southwest of Nar and west of west Assaluyeh anticlines and north of Kangan harbor in Boushehr province. The Kangan anticline is nearly asymmetric and with 55Km long and 6Km wide base on structural map of Kangan Formation. The youngest and the oldest Formations on surface are Bakhtiyari (Pliocene) and Sarvak (Cenomanian) respectively. The highest dip angles of 30 and 40 degree were observed in north and south flanks of Kangan anticline respectively and two reverse faults cut these flanks parallel to structure strike. Existence of sweet gas in Kangan Fm. and Upper Dalan in this structure is confirmed with probable Silurian shales origin. Main facies belts in these formations include super tidal and intertidal flat, lagoon, oolitic-bioclastic shoals and open marine sub environments that expand in a homoclinal and shallow water carbonate ramp under the arid climates. Digenetic processes studies, indicates the influence of all digenetic environments (marine, meteoric, burial) in the reservoir succession. These processes sometimes has led to reservoir quality improvement (such as dolomitization and dissolution) but in many instances reservoir units has been destroyed (such as compaction, anhydrite and calcite cementation). In this study, petrophysical evaluation is made in Kangan and upper Dalan formations by using well log data of five selected wells. Probabilistic method is used for petrophysical evaluation by applying appropriate soft wares. According to this evaluation the lithology of Kangan and upper Dalan Formations mainly consist of limestone and dolomite with thin beds of Shale and evaporates. In these formations 11 Zones with different reservoir characteristic have been identified. Based on wire line data analyses, in some part of these formations, high porosity can be observed. The range of porosity (PHIE) and water saturation (Sw) are estimated around 10-20% and 20-30%, respectively.

Keywords: microfacies, electrofacies, petrophysics, diagenese, gas fields

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Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

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Authors: James Hilton Williams

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Recent tsunami events, including the 2011 Tohoku Tsunami, Japan, and the 2015 Illapel Tsunami, Chile, have highlighted the potential for tsunami impacts on the built environment. International research in the tsunami impacts domain has been largely focused toward impacts on buildings and casualty estimations, while only limited attention has been placed on the impacts on infrastructure which is critical for the recovery of impacted communities. New Zealand, with 75% of the population within 10 km of the coast, has a large amount of coastal infrastructure exposed to local, regional and distant tsunami sources. To effectively manage tsunami risk for New Zealand critical infrastructure, including energy, transportation, and communications, the vulnerability of infrastructure networks and components must first be determined. This research develops infrastructure asset vulnerability, functionality and repair- cost functions based on international post-event tsunami impact assessment data from technologically similar countries, including Japan and Chile, and adapts these to New Zealand. These functions are then utilized within a New Zealand based impact framework, allowing for cost benefit analyses, effective tsunami risk management strategies and mitigation options for exposed critical infrastructure to be determined, which can also be applied internationally.

Keywords: impact assessment, infrastructure, tsunami impacts, vulnerability functions

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Authors: Ivana Hrebickova, Maria Sevcikova, Romana Slamberova

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The present study was aimed at evaluation of cognitive changes following scheduled neonatal methamphetamine exposure in combination with long-term exposure in adulthood of female Wistar rats. Pregnant mothers were divided into two groups: group with indirect exposure (methamphetamine in dose 5 mg/ml/kg, saline in dose 1 ml/kg) during early lactation period (postnatal day 1–11) - progeny of these mothers were exposed to the effects of methamphetamine or saline indirectly via the breast milk; and the second group with direct exposure – all mothers were left intact for the entire lactation period, while progeny was treated with methamphetamine (5 mg/ml/kg) by injection or the control group, which was received needle pick (shame, not saline) at the same time each day of period of application (postnatal day 1–11). Learning ability and memory consolidation were tested in the Morris Water Maze, which consisted of three types of tests: ‘Place Navigation Test ‘; ‘Probe Test ‘; and ‘Memory Recall Test ‘. Adult female progeny were injected daily, after completion last trial with saline or methamphetamine (1 mg/ml/kg). We compared the effects of indirect/direct neonatal methamphetamine exposure and adult methamphetamine treatment on cognitive function of female rats. Statistical analyses showed that neonatal methamphetamine exposure worsened spatial learning and ability to remember the position of the platform. The present study demonstrated that direct methamphetamine exposure has more significant impact on process of learning and memory than indirect exposure. Analyses of search strategies (thigmotaxis, scanning) used by females during the Place Navigation Test and Memory Recall Test confirm all these results.

Keywords: methamphetamine, Morris water maze, neonatal exposure, strategies, Wistar rats

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Authors: Abdul Ghani Khan

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This paper focuses on the Buddhist rock carvings in the Diamer-Basha reservoir area, Gilgit-Baltistan, which is perhaps the largest rock art province of the world. The study region has thousands of rock carvings, particularly of the stupa carvings, engraved by artists, devotees or pilgrims, merchants have left their marks in the landscape or for the propagation of Buddhism. The Pak-German Archaeological Mission prepared, documented, and published the extensive catalogues of these carvings. Though, to date, very little systematic or statistically driven analysis was undertaken for in-depth understandings of the Buddhist rock carving tradition of the study region. This paper had made an attempt to examine stupa carvings and their constituent parts from the five selected sites, namely Oshibat, Shing Nala, Gichi Nala, Dadam Das, and Chilas Bridge. The statistical analyses and classification of the stupa carvings and their chronological contexts were carried out with the help of modern scientific tools such as STATA, FileMaker Pro, and MapSource softwares. The study had found that the tradition of stupa carvings on the surfaces of the rocks at the five selected sites continued for around 900 years, from the 1st century BCE to 8th century CE. There is a variation within the chronological settings of each of selected sites, possibly impacted by their utilization within particular landscapes, such as political (for example, change in political administrations or warfare) landscapes and geographical (for example, shifting of routes). The longer existence of the stupa carvings' tradition at these specific locations also indicates their central position on the trade and communication routes, and these were possibly also linked with religious ideologies within their particular times. The analyses of the different architectural elements of stupa carvings in the study area show that this tradition had structural similarities and differences in temporal and spatial contexts.

Keywords: rock carvings, stupa, stupa carvings, Buddhism, Pak-German archaeological mission

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Amy Halseth, Kevin Shan, Kye Gilder, John Buse

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The study assessed the effect of prolonged-release naltrexone 32 mg/bupropion 360 mg (NB) on cardiovascular (CV) events in overweight/obese participants at elevated CV risk. Participants must lose ≥ 2% body weight at 16 wks, without a sustained increase in blood pressure, to continue drug. The study was terminated early after second interim analysis with 50% of all CV events. Data on CV endpoints has been published. Current analyses focus on weight change. Intent-to-treat (ITT) population (placebo [PBO] N=4450, NB N=4455) was 54.5% female, 83.5% white, mean age 61 yrs, mean BMI 37.3 kg/m2; 85.2% had type 2 diabetes, 32.1% had CV disease, 17.4% had both. At 52 wks, ITT-LOCF analysis showed greater least squares mean percent change in weight (LSM%ΔBW) with NB (-3.1%; 95% CI -4.8, -1.4) vs PBO (-0.3%; 95% CI -1.9, 1.4). Both groups demonstrated greater weight loss while on-treatment (NB [-7.3%], PBO [-3.9%]). Odds ratios of 5% and 10% weight loss were 3.3 and 4.1 (ITT-LOCF), respectively, in NB over PBO. At 104 wks, on-treatment LSM%ΔBW was -6.3% with NB (n=1137) vs -3.5% with PBO (n=741). Major reasons for NB withdrawal were adverse events (AE, 29%) and patient decision (21%), with GI disorders being the most common. Weight loss with NB in this study, in an older population predominantly with diabetes and elevated CV risk, was somewhat lower than that observed in overweight/obese participants without diabetes and similar to participants with diabetes in Phase 3 studies.

Keywords: contrave, mysimba, obesity, pharmacotherapy, weight loss

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Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

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Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

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Authors: Zebider Asire Munyelet, Yibeltal Chanie Manie

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In the modern digital landscape, the increase of social media platforms has become identical to the evolution of online consumer behavior. This study investigates the complicated relationship between social media and the purchasing decisions of online buyers. Through an extensive review of existing literature and empirical research, the aim is to comprehensively analyze the multidimensional impact that social media exerts on the various stages of the online buyer's journey. The investigation encompasses the exploration of how social media platforms serve as influential channels for information dissemination, product discovery, and consumer engagement. Additionally, the study investigates the psychological aspects underlying the role of social media in shaping buyer preferences, perceptions, and trust in online transactions. The methodologies employed include both quantitative and qualitative analyses, incorporating surveys, interviews, and data analytics to derive meaningful insights. Statistical models are applied to distinguish patterns in online buyer behavior concerning product awareness, brand loyalty, and decision-making processes. The expected outcomes of this research contribute not only to the academic understanding of the dynamic interplay between social media and online buyer behavior but also offer practical implications for marketers, e-commerce platforms, and policymakers.

Keywords: social platforms, buyer behavior, consumer behavior, digital era

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Manal Sultan Alhussein, Xiang Michelle Liu

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Telehealth is an advanced technology using digital information and telecommunication facilities that provide access to health services from a distance. It slows the transmission factor of COVID-19, especially for elderly patients and patients with chronic diseases during the pandemic. Therefore, understanding patient perspectives on telehealth services and the factors impacting their option of telehealth service will shed light on the measures that healthcare providers can take to improve the quality of telehealth services. This study aimed to evaluate perceptions of telehealth services among different patient groups and explore various aspects of telehealth utilization in the United States during the COVID-19 pandemic. An online survey distributed via social media platforms was used to collect research data. In addition to the descriptive statistics, both correlation and regression analyses were conducted to test research hypotheses. The empirical results highlighted that the factors such as accessibility to telehealth services and the type of specialty clinics that the patients required play important roles in the effectiveness of telehealth services they received. However, the results found that patients’ waiting time to receive telehealth services and their annual income did not significantly influence their desire to select receiving healthcare services via telehealth. The limitations of the study and future research directions are discussed.

Keywords: telehealth, patient satisfaction, pandemic, healthcare, survey

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Authors: Ahmad Shah Irshad, Naqibullah Kargar, Wais Samadi

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Building an integrated photovoltaic (BIPV) system is a new and modern technique for solar energy production in Kandahar. Due to its location, Kandahar has abundant sources of solar energy. People use both monocrystalline and polycrystalline silicon solar PV modules for the grid-connected solar PV system, and they don’t know which technology performs better for the BIPV system. This paper analyses the parameters described by IEC61724, “Photovoltaic System Performance Monitoring Guidelines for Measurement, Data Exchange and Analysis,” to evaluate which technology shows better performance for the BIPV system. The monocrystalline silicon BIPV system has a 3.1% higher array yield than the polycrystalline silicon BIPV system. The final yield is 0.2%, somewhat higher for monocrystalline silicon than polycrystalline silicon. Monocrystalline silicon has 0.2% and 4.5% greater yearly yield factor and capacity factors than polycrystalline silicon, respectively. Monocrystalline silicon shows 0.3% better performance than polycrystalline silicon. With 1.7% reduction and 0.4% addition in collection losses and useful energy produced, respectively, monocrystalline silicon solar PV system shows good performance than polycrystalline silicon solar PV system. But system losses are the same for both technologies. The monocrystalline silicon BIPV system injects 0.2% more energy into the grid than the polycrystalline silicon BIPV system.

Keywords: photovoltaic technologies, performance analysis, solar energy, solar irradiance, performance ratio

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Lynn Ruggieri

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The worldwide pandemic has only increased sustainability awareness. The public is demanding that businesses be held accountable for their impact on the environment. While financial data enjoys uniformity in reporting requirements, there are no uniform reporting requirements for non-financial data. Europe is leading the way with some standards being implemented for reporting non-financial sustainability data; however, there is no uniformity globally. And without uniformity, there is not a clear understanding of what information to include and how to disclose it. Sustainability reporting will provide important information to stakeholders and will enable businesses to understand their impact on the environment. Therefore, there is a crucial need for this data. This paper looks at the history of sustainability reporting in the countries of the European Union and throughout the world and makes a case for worldwide reporting requirements for sustainability.

Keywords: financial reporting, non-financial data, sustainability, global financial reporting

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Authors: Hazel P. Atilano

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Contrastive rhetoricians working on L2 writing are often unfamiliar with the theories and research of scholars in translation studies. Publications on translation studies give little or no attention to describing the translation strategies of translators, with a focus on the influence of their L1 on the language they produce. This descriptive qualitative study anchored on Eugene Nida’s Translation Theory employed stylistic, lexico-semantic, and grammatical analyses of the stylistic devices employed by poets across nine language cultures to reveal the translation strategies employed by translators and to establish the type of equivalence manifested in the translated texts. The corpus consists of 27 poems written in Bahasa Indonesia, Hiligaynon, Tagalog (Malayo-Polynesian languages), French, Italian, Spanish (Romance languages), German, Icelandic, and Norwegian (Germanic Languages), translated into English. Stylistic analysis reveals that both original texts and English translations share the same stylistic devices, suggesting that stylistic devices do not get lost in translation. Lexico-semantic and grammatical analyses showed that translators of Malayo-Polynesian languages employed idiomatic translation as a compensatory strategy, producing English translations that manifest Dynamic Equivalence or transparency; translators of Romance languages resorted to synonymous substitution or literal translation, suggesting Formal Equivalence or fidelity; and translators of Germanic languages used a combination of idiomatic and literal translation strategies, with noticeable preference for Dynamic Equivalence, evidenced by the prevalence of metaphorical translations as compensatory strategy. Implications on the intricate relationship between culture and language in the translation process were drawn based on the findings.

Keywords: translation strategy, dynamic equivalence, formal equivalence, translation theory, transparency, fidelity

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

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Authors: Sahil Imtiyaz

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One of the biggest challenges has emerged from the ever-expanding, dynamic, and instantaneously changing space-Big Data; and to find a data point and inherit wisdom to this space is a hard task. In this paper, we reduce the space of big data in Hamiltonian formalism that is in concordance with Ising Model. For this formulation, we simulate the system using dye laser in FORTRAN and analyse the dynamics of the data point in energy well of rhodium atom. After mapping the photon intensity and pulse width with energy and potential we concluded that as we increase the energy there is also increase in probability of tunnelling up to some point and then it starts decreasing and then shows a randomizing behaviour. It is due to decoherence with the environment and hence there is a loss of ‘quantumness’. This interprets the efficiency parameter and the extent of quantum evolution. The results are strongly encouraging in favour of the use of ‘Topological Property’ as a source of information instead of the qubit.

Keywords: big data, optimization, quantum evolution, hamiltonian, dye laser, fermionic computations

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Authors: Muhammad Muhammad Suleiman

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Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: Asima Mehaboob Khan, Rizwan Taj

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Purpose: This study aims to estimate the knowledge, attitude and practices of medical ethics among young healthcare professionals. Method: A qualitative descriptive study was conducted among young healthcare professionals from both public and private sector medical institutions. Using the convenience sampling technique, 272 healthcare professionals participated in this study. A pre-structured modified questionnaire was used to collect the data. Descriptive analyses were executed for each variable. Result: About 76.47% of healthcare professional considers the importance of adequate knowledge of medical ethics, and 82.24% declared lecture, seminars and clinical discussion as the source of their medical knowledge of biomedical ethics. About 42.44% of healthcare professionals exhibited a negative attitude toward medical ethics, 57.72% showed a mildly positive attitude, whereas 1.10% and 0.74% indicated a moderately positive attitude and a highly positive attitude towards medical ethics. Similarly, the level of practice according to medical ethics is also very poor among young healthcare professionals. 34.56% of healthcare professionals deviated from medical ethics during their clinical practices, whereas 0.74% showed a good level of medical practice according to medical ethics. Conclusion: It is concluded in this research study that young healthcare professionals have adequate theoretical knowledge of medical ethics but are not properly trained to perform their clinical practices according to the guidelines of medical ethics. Furthermore, their professional attitude is poorly developed to maintain medical ethics during their clinical practices.

Keywords: knowledge, attitude, practices, medical ethics

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Authors: Azizeh Chalak, Firouzeh Baktash

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Reticence is a prominent and complex phenomenon which occurs in foreign language classrooms and influences students’ oral passivity. The present study investigated the extent in which students experience reticence in the EFL classrooms and explored the underlying factors triggering reticence. The participants were 104 Iranian freshmen undergraduate male and female EFL students, who enrolled in listening and speaking courses, all majoring in English studying at Islamic Azad University Isfahan (Khorasgan) Branch and University of Isfahan, Isfahan, Iran. To collect the data, the Reticence Scale-12 (RS-12) questionnaire which measures the level of reticence consisting of six dimensions (anxiety, knowledge, timing, organization, skills, and memory) was administered to the participants. The statistical analyses showed that the reticent level was high among the Iranian EFL undergraduate students, and their major problems were feelings of anxiety and delivery skills. Moreover, the results revealed that factors such as low English proficiency, the teaching method, and lack of confidence contributed to the students’ reticence in Iranian EFL classrooms. It can be implied that language teachers’ awareness of learners’ reticence can help them choose more appropriate activities and provide a friendly environment enhancing hopefully more effective participation of EFL learners. The findings can have implications for EFL teachers, learners and policy makers.

Keywords: anxiety, Iranian EFL learners, reticence, reticence scale-12

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Authors: İbrahi̇m Kara, Seher Arslankaya

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Frequently preferred in the field of engineering in particular, data mining has now begun to be used in the field of health as well since the data in the health sector have reached great dimensions. With data mining, it is aimed to reveal models from the great amounts of raw data in agreement with the purpose and to search for the rules and relationships which will enable one to make predictions about the future from the large amount of data set. It helps the decision-maker to find the relationships among the data which form at the stage of decision-making. In this study, it is aimed to determine the risk of heart attack at the first stage, to control it, and to make its resource planning with the method of data mining. Through the early and correct diagnosis of heart attacks, it is aimed to reveal the factors which affect the diseases, to protect health and choose the right treatment methods, to reduce the costs in health expenditures, and to shorten the durations of patients’ stay at hospitals. In this way, the diagnosis and treatment costs of a heart attack will be scrutinized, which will be useful to determine the risk of the disease at the first stage, to control it, and to make its resource planning.

Keywords: data mining, decision support systems, heart attack, health sector

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: John Gaber

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Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

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Authors: Cigdem Gul, Murat Tosunoglu, Nurcihan Hacioglu

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The contamination or desiccation of fresh waters also has a negative effect on freshwater turtles like other fresh water-dependent species. In order to conserve those species, which are confronted with such negative conditions, it is necessary to know beforehand the biology and the physiology of species. In this study, a comprehensive health assessment was conducted on a total of 20 clinically normal individuals free living Western Caspian Turtle (Mauremys rivulata) captured from two different freshwater localities in the Biga stream (Çanakkale-Turkey). When comparing our findings with the Turkish legislation (Water pollution control regulation), the 1. Locality of the Biga stream in terms of total coliform classified as "high quality water" (Coliform: 866.66 MPN/100 mL), while the 2. Locality was a “contaminated water" (Coliform: 53266.66 MPN/100 mL). Blood samples for hematological and biochemical analyses were obtained from the dorsal coccygeal vein. A total of 1-2 mL of blood was collected from each of the specimens via needle. After the required procedures had been performed, the turtles were put back in the same localities. Hematological and biochemical analyses based on high quality water and contaminated water, respectively, are as follows: Red blood cell count (512600-582666.66 per cubic millimeter of blood), white blood cell count (5920-5980 per cubic millimeter of blood), hematocrit value (24-24.66 %), hemoglobin concentration (6.52-6.35 g/dl), mean corpuscular volume (466.20-468.98 fl), mean corpuscular hemoglobin (125.77-113.84 pg), mean corpuscular hemoglobin concentration (28.25-26.49 %), glucose (94.43-87.43 mg/dl), creatinine (0.23-0.3241 mg/dl), uric acid (12.59-10.48 mg/L), albumin (1.46-1.25 g/dl), calcium (8.67-9.59 mg/dl), triglyceride (95.55-75.21 mg/dl), and total protein (4.85-3.45 g/dl). When an examination was made depending on the water quality of freshwater, variations were detected in hematology and biochemistry values, but not found significant difference.

Keywords: biochemistry, freshwater quality, hematological parameters, Mauremys rivulata.

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: S. Ravindran, Y.Liu, I. Gratchev, D.Jeng

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Rainfall-induced shallow landslides are more common in the northern New South Wales (NSW), Australia. From 2009 to 2017, around 105 rainfall-induced landslides occurred along the road corridors and caused temporary road closures in the northern NSW. Rainfall causing shallow landslides has different distributions of rainfall varying from uniform, normal, decreasing to increasing rainfall intensity. The duration of rainfall varied from one day to 18 days according to historical data. The objective of this research is to analyse slope instability of some of the sites in the northern NSW by varying cumulative rainfall using SLOPE/W and SEEP/W and compare with field data of rainfall causing shallow landslides. The rainfall data and topographical data from public authorities and soil data obtained from laboratory tests will be used for this modelling. There is a likelihood of shallow landslides if the cumulative rainfall is between 100 mm to 400 mm in accordance with field data.

Keywords: landslides, modelling, rainfall, suction

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

Abstract:

Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

Procedia PDF Downloads 138

Authors: Melba Sheila D'Souza, Anandhi Amirtharaj, Shreedevi Balachandran

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Background: Type 2 diabetes has a significant impact on individuals’ health and well-being. Glycemic control may influence self-efficacy and self-care behaviors, and reduce the risk of complications among adults with type 2 diabetes. Type 2 diabetes has substantial morbidity and mortality and 60% of adults’ poor self-care. Glycemic control is associated with reported self-efficacy and self-care behavior. Adults with type 2 diabetes with less information were less likely to take diabetes self-care. Aim: To examine the relationship between glycemic control, demographic factors, clinical factors on self-efficacy, self-care behaviors among Omani adults with type 2 diabetes. Methods: A correlational, descriptive study was used. Omani adults with type 2 diabetes (n=140) were recruited from a public hospital in Oman. The data were collected during January-March 2015. Ethical approval was given by the college research and ethics committee, College of Nursing, and the Hospital, Sultan Qaboos University Data was collected on self-efficacy, self-care behaviors and glycemic control. The study was approved by the Institution Ethics and Research Committee. Bivariate and multivariate analyses were conducted. Results: Most adults had a fasting blood glucose >7.2mmol/L (90.7%), with the majority demonstrating ‘uncontrolled or poor HbA1c of > 8%’ (65%). Variance of self-care behavior (20.6%) and 31.3% of the variance of the self-efficacy was explained by the age, duration of diabetes, medication, HbA1c and prevention of activities of living. Adults with type 2 diabetes with poor glycemic control were more likely to have poor self-efficacy and poor self-care behaviors. Conclusion: This study confirms that self-efficacy model on outcome predicts self-efficacy and self-care behavior. Higher understanding of diabetes, prevention of normal daily activities, higher ability to fit diabetes life in a positive manner and high patient-physician communication were significant with self-efficacy and self-care behaviors. Hence, glycemic control has a high effect on improving self-care behaviors like diet, exercise, medication, foot care and self-efficacy among type 2 diabetes. Implications: Using these findings to improve self-efficacy, individualized self-care management is recommended for better self-efficacy and self-care behaviors among adults with type 2 diabetes.

Keywords: self-efficacy, self-care behaviors, self-care management, glycemic control, type 2 diabetes, nurse

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Authors: Zebider Asire Munyelet, Yibeltal Chanie Manie

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In the modern digital landscape, the increase of social media platforms has become identical to the evolution of online consumer behavior. This study investigates the complicated relationship between social media and the purchasing decisions of online buyers. Through an extensive review of existing literature and empirical research, the aim is to comprehensively analyze the multidimensional impact that social media exerts on the various stages of the online buyer's journey. The investigation encompasses the exploration of how social media platforms serve as influential channels for information dissemination, product discovery, and consumer engagement. Additionally, the study investigates into the psychological aspects underlying the role of social media in shaping buyer preferences, perceptions, and trust in online transactions. The methodologies employed include both quantitative and qualitative analyses, incorporating surveys, interviews, and data analytics to derive meaningful insights. Statistical models are applied to distinguish patterns in online buyer behavior concerning product awareness, brand loyalty, and decision-making processes. The expected outcomes of this research contribute not only to the academic understanding of the dynamic interplay between social media and online buyer behavior but also offer practical implications for marketers, e-commerce platforms, and policymakers.

Keywords: consumer Behavior, social media, online purchasing, online transaction

Procedia PDF Downloads 62