Search results for: temporal data

Authors: Balogun R. Ayodeji, Adefisan E. Adesanya, Adeyewa Z. Debo, E. C. Okogbue

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Deep convective systems during active periods of the West African monsoon season have not been properly investigated over better temporal and spatial resolution in West Africa. Deep convective systems are investigated over seven climatic zones of the West African sub-region, which are; west-coast rainforest, dry rainforest, Nigeria-Cameroon rainforest, Nigeria savannah, Central African and South Sudan (CASS) Savannah, Sudano-Sahel, and Sahel, using data from Tropical Rainfall Measurement Mission (TRMM) Precipitation Feature (PF) database. The vertical structure of the convective systems indicated by the presence of at least one 40 dBZ and reaching (attaining) at least 1km in the atmosphere showed strong core (highest frequency (%)) of reflectivity values around 2 km which is below the freezing level (4-5km) for all the zones. Echoes are detected above the 15km altitude much more frequently in the rainforest and Savannah zones than the Sudano and Sahel zones during active periods in March-May (MAM), whereas during active periods in June-September (JJAS) the savannahs, Sudano and Sahel zones convections tend to reach higher altitude more frequently than the rainforest zones. The percentage frequencies of deep convection indicated that the occurrences of the systems are within the range of 2.3-2.8% during both March-May (MAM) and June-September (JJAS) active periods in the rainforest and savannah zones. On the contrary, the percentage frequencies were found to be less than 2% in the Sudano and Sahel zones, except during the active-JJAS period in the Sudano zone.

Keywords: active periods, convective system, frequency, reflectivity

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Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Mahdi Eskandarlou, Mehrdad Taghipour

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Background: Partial-thickness skin graft is the cornerstone for scalp defect repair. Routine donor sites include abdomen, thighs, and buttocks. Given the potential side effects following harvesting from these sites and the potential advantages of harvesting from scalp (broad surface, rapid healing, and better cosmetics results), this study is trying to compare the outcomes of graft harvesting from scalp and lower limb. Methods: This clinical trial is conducted among a sample number of 40 partial thickness graft candidates (20 case and 20 control group) with scalp defect presenting to plastic surgery clinic at Besat Hospital during the time period between 2018 and 2019. Sampling was done by simple randomization using random digit table. Data gathering was performed using a designated checklist. The donor site in case group and control group was scalp and lower limb, respectively. The resultant data were analyzed using chi-squared and t-test and SPPS version 21 (SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp). Results: Of the total 40 patients participating in this study, 28 patients (70%) were male, and 12 (30%) were female with and mean age of 63.62 ± 09.73 years. Hypertension and diabetes mellitus were the most common comorbidities among patients with basal cell carcinoma (BCC) and trauma being the most common etiology for the defects. There was a statistically meaningful relationship between two groups regarding the etiology of defect (P=0.02). The most common anatomic location of defect for case and control groups was temporal and parietal, respectively. Most of the defects were deep to galea zone. The mean diameter of defect was 24.28 ± 45.37 mm for all of the patients. The difference between diameter of defect in both groups was statistically meaningful, while no such difference between graft diameter was seen. The graft 'Take' was completely successful in both groups according to evaluations. The level of postoperative pain was lower in the case group compared to the control according to VAS scale, and the satisfaction was higher in them per Likert scale. Conclusion: Scalp can safely be used as donor site for skin graft to be used for scalp defects, which is associated with better results and lower complication rates compared to other donor sites.

Keywords: donor site, leg, partial-thickness graft, scalp

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Authors: Yan Huang

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Event-related potentials (ERPs) technique helps researchers to make continuous measures on the whole process of language comprehension, with an excellent temporal resolution at the level of milliseconds. The research on sentence processing has developed from the behavioral level to the neuropsychological level, which brings about a variety of sentence processing theories and models. However, the applicability of these models to L2 learners is still under debate. Therefore, the present study aims to investigate the neural mechanisms underlying English subjunctive mood processing by Chinese ESL learners. To this end, English subject clauses with subjunctive moods are used as the stimuli, all of which follow the same syntactic structure, “It is + adjective + that … + (should) do + …” Besides, in order to examine the role that language proficiency plays on L2 processing, this research deals with two groups of Chinese ESL learners (18 males and 22 females, mean age=21.68), namely, high proficiency group (Group H) and low proficiency group (Group L). Finally, the behavioral and neurophysiological data analysis reveals the following findings: 1) Syntax and semantics interact with each other on the SECOND phase (300-500ms) of sentence processing, which is partially in line with the Three-phase Sentence Model; 2) Language proficiency does affect L2 processing. Specifically, for Group H, it is the syntactic processing that plays the dominant role in sentence processing while for Group L, semantic processing also affects the syntactic parsing during the THIRD phase of sentence processing (500-700ms). Besides, Group H, compared to Group L, demonstrates a richer native-like ERPs pattern, which further demonstrates the role of language proficiency in L2 processing. Based on the research findings, this paper also provides some enlightenment for the L2 pedagogy as well as the L2 proficiency assessment.

Keywords: Chinese ESL learners, English subjunctive mood, ERPs, L2 processing

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Authors: K. Alafogiannis, E. E. Houssos, E. Anagnostou, G. Kouvarakis, N. Mihalopoulos, A. Fotiadi

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The Mediterranean basin is an area where different aerosol types coexist, including urban/industrial, desert dust, biomass burning and marine particles. Particularly, mineral dust aerosols, mostly originated from North African deserts, significantly contribute to high aerosol loads above the Mediterranean. Dust transport, controlled by the variation of the atmospheric circulation throughout the year, results in a strong spatial and temporal variability of aerosol properties. In this study, the synoptic conditions which favor dust transport over the Eastern Mediterranean are thoroughly investigated. For this reason, three datasets are employed. Firstly, ground-based daily data of aerosol properties, namely Aerosol Optical Thickness (AOT), Ångström exponent (α440-870) and fine fraction from the FORTH-AERONET (Aerosol Robotic Network) station along with measurements of PM10 concentrations from Finokalia station, for the period 2003-2011, are used to identify days with high coarse aerosol load (episodes) over Crete. Then, geopotential height at 1000, 850 and 700 hPa levels obtained from the NCEP/NCAR Reanalysis Project, are utilized to depict the atmospheric circulation during the identified episodes. Additionally, air-mass back trajectories, calculated by HYSPLIT, are used to verify the origin of aerosols from neighbouring deserts. For the 227 identified dust episodes, the statistical methods of Factor and Cluster Analysis are applied on the corresponding atmospheric circulation data to reveal the main types of the synoptic conditions favouring dust transport towards Crete (Eastern Mediterranean). The 227 cases are classified into 11 distinct types (clusters). Dust episodes in Eastern Mediterranean, are found to be more frequent (52%) in spring with a secondary maximum in autumn. The main characteristic of the atmospheric circulation associated with dust episodes, is the presence of a low-pressure system at surface, either in southwestern Europe or western/central Mediterranean, which induces a southerly air flow favouring dust transport from African deserts. The exact position and the intensity of the low-pressure system vary notably among clusters. More rarely dust may originate from deserts of Arabian Peninsula.

Keywords: aerosols, atmospheric circulation, dust particles, Eastern Mediterranean

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

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Authors: Christopher Muhawe, Masooda Bashir

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With increased Internet Communication Technology(ICT), the virtual world has become the new normal. At the same time, there is an unprecedented collection of massive amounts of data by both private and public entities. Unfortunately, this increase in data collection has been in tandem with an increase in data misuse and data breach. Regrettably, the majority of data breach and data misuse claims have been unsuccessful in the United States courts for the failure of proof of direct injury to physical or economic interests. The requirement to express data privacy harms from an economic or physical stance negates the fact that not all data harms are physical or economic in nature. The challenge is compounded by the fact that data breach harms and risks do not attach immediately. This research will use a descriptive and normative approach to show that not all data harms can be expressed in economic or physical terms. Expressing privacy harms purely from an economic or physical harm perspective negates the fact that data insecurity may result into harms which run counter the functions of privacy in our lives. The promotion of liberty, selfhood, autonomy, promotion of human social relations and the furtherance of the existence of a free society. There is no economic value that can be placed on these functions of privacy. The proposed approach addresses data harms from a psychological and social perspective.

Keywords: data breach and misuse, economic harms, privacy harms, psychological harms

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Authors: Zuoji Huang, Jinyan Sun, Chunlin Wang, Haiming Qian, Nan Xu

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The spatial extent and area of central river bars can always vary due to the impact of water level, sediment supply and human activities. In 2016, a catastrophic flood disaster caused by sustained and heavy rainfall happened in the middle and lower Yangtze River. The flood led to the most serious economic and social loss since 1954, and strongly affected the central river bar. It is essential to continuously monitor the dynamics change of central bars because it can avoid frequent field measurements in central bars before and after the flood disaster and is helpful for flood warning. This paper focused on the dynamic change of central bars of Phoenix bar and Changsha bar in the Yangtze River in 2016. In this study, GF-1 (GaoFen-1) WFV(wide field view) data was employed owing to its high temporal frequency and high spatial resolution. A simple NDWI (Normalized Difference Water Index) method was utilized for river central bar mapping. Human-checking was then performed to ensure the mapping quality. The relationship between the area of central bars and the measured water level was estimated using four mathematical models. Furthermore, a risk assessment index was proposed to map the spatial pattern of inundation risk of central bars. The results indicate a good ability of the GF-1 WFV imagery with a 16-m spatial resolution to characterize the seasonal variation of central river bars and to capture the impact of a flood disaster on the area of central bars. This paper observed a significant negative but nonlinear relationship between the water level and the area of central bars, and found that the cubic function fits best among four models (R² = 0.9839, P < 0.000001, RMSE = 0.4395). The maximum of the inundated area of central bars appeared during the rainy season on July 8, 2016, and the minimum occurred during the dry season on December 28, 2016, which are consistent with the water level measured by the hydrological station. The results derived from GF-1 data could provide a useful reference for decision-making of real-time disaster early warning and post-disaster reconstruction.

Keywords: central bars, dynamic change, water level, the Yangtze river

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Authors: Abeer Rababa'h, Ragad Bsoul, Mohammad Alkhatatbeh, Karem Alzoubi

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Introduction: Tobacco use is one of the main risk factors to cardiovascular diseases (CVD) and atherosclerosis in particular. WPS contains several toxic materials such as: nicotine, carcinogens, tar, carbon monoxide and heavy metals. Thus, WPS is considered to be as one of the toxic environmental factors that should be investigated intensively. Therefore, the aim of this study is to investigate the effect of WPS on several cardiovascular biological markers that may cause atherosclerosis in mice. The study also conducted to study the temporal effects of WPS on the atherosclerotic biomarkers upon short (2 weeks) and long-term (8 weeks) exposures. Methods: mice were exposed to WPS and heart homogenates were analyzed to elucidate the effects of WPS on matrix metalloproteinase (MMPs), endothelin-1 (ET-1) and, myeloperoxidase (MPO). Following protein estimation, enzyme-linked immunosorbent assays were done to measure the levels of MMPs (isoforms 1, 3, and 9), MPO, and ET-1 protein expressions. Results: our data showed that acute exposure to WPS significantly enhances the levels of MMP-3, MMP- 9, and MPO expressions (p < 0.05) compared to their corresponding control. However, the body was capable to normalize the level of expressions for such parameters following continuous exposure for 8 weeks (p > 0.05). Additionally, we showed that the level of ET-1 expression was significantly higher upon chronic exposure to WPS compared to both control and acute exposure groups (p < 0.05). Conclusion: Waterpipe exposure has a significant negative effect on atherosclerosis and the enhancement of the atherosclerotic biomarkers expression (MMP-3 and 9, MPO, and ET-1) might represent an early scavenger of compensatory efforts to maintain cardiac function after WP exposure.

Keywords: atherosclerotic biomarkers, cardiovascular disease, matrix metalloproteinase, waterpipe

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Authors: Avyarthana Dey, Naren P. Rao, Arpitha Jacob, Chaitra V. Hiremath, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Dawn Bharath, Bangalore N. Gangadhar

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Objective: Impaired facial emotion recognition is widely reported in schizophrenia. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. However, its effect on facial emotion recognition deficits seen in schizophrenia is not well explored. In this study, we examined the effect of intranasal OXT on processing facial emotions and its neural correlates in patients with schizophrenia. Method: 12 male patients (age= 31.08±7.61 years, education= 14.50±2.20 years) participated in this single-blind, counterbalanced functional magnetic resonance imaging (fMRI) study. All participants underwent three fMRI scans; one at baseline, one each after single dose 24IU intranasal OXT and intranasal placebo. The order of administration of OXT and placebo were counterbalanced and subject was blind to the drug administered. Participants performed a facial emotion recognition task presented in a block design with six alternating blocks of faces and shapes. The faces depicted happy, angry or fearful emotions. The images were preprocessed and analyzed using SPM 12. First level contrasts comparing recognition of emotions and shapes were modelled at individual subject level. A group level analysis was performed using the contrasts generated at the first level to compare the effects of intranasal OXT and placebo. The results were thresholded at uncorrected p < 0.001 with a cluster size of 6 voxels. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. Results: Compared to placebo, intranasal OXT attenuated activity in inferior temporal, fusiform and parahippocampal gyri (BA 20), premotor cortex (BA 6), middle frontal gyrus (BA 10) and anterior cingulate gyrus (BA 24) and enhanced activity in the middle occipital gyrus (BA 18), inferior occipital gyrus (BA 19), and superior temporal gyrus (BA 22). There were no significant differences between the conditions on the accuracy scores of emotion recognition between baseline (77.3±18.38), oxytocin (82.63 ± 10.92) or Placebo (76.62 ± 22.67). Conclusion: Our results provide further evidence to the modulatory effect of oxytocin in patients with schizophrenia. Single dose oxytocin resulted in significant changes in activity of brain regions involved in emotion processing. Future studies need to examine the effectiveness of long-term treatment with OXT for emotion recognition deficits in patients with schizophrenia.

Keywords: recognition, functional connectivity, oxytocin, schizophrenia, social cognition

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Tokuei Sako

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Laser-induced current in a quasi-one-dimensional nanostructure has been studied by a model of a few electrons confined in a 1D electrostatic potential coupled to electrodes at both ends and subjected to a pulsed laser field. The time-propagation of the one- and two-electron wave packets has been calculated by integrating the time-dependent Schrödinger equation directly by the symplectic integrator method with uniform Fourier grid. The temporal behavior of the resultant light-induced current in the studied systems has been discussed with respect to the lifetime of the quasi-bound states formed when the static bias voltage is applied.

Keywords: pulsed laser field, nanowire, electron wave packet, quantum dots, time-dependent Schrödinger equation

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Authors: Claire Stafford, Charles Golden, Daniel Amen, Kristen Willeumier

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The aim of this study is to investigate the effect of the administration of the Conners Continuous Performance Test (CPT-3) on cerebral blood flow (CBF) in adults with ADHD. The data for this study was derived from a large SPECT database. Participants in the ADHD group (n=81, Mage=37.97) were similar to those in the healthy control group (n=8503, Mage=41.86). All participants were assessed for cerebral blood flow levels before and after CPT-3 administration. Both age and gender were considered covariates. Multiple 2-by-2 ANCOVAs with repeated measures were conducted with sphericity assumed. The main effects of CPT-3 administration on CBF levels were significant in the left and right side of the frontal and occipital, and right temporal lobe. The main effects of ADHD diagnosis were significant in all brain areas assessed. The interaction between CPT-3 administration and ADHD diagnosis was significant in the left and right side of the limbic system, basal ganglia, the frontal lobe, and occipital lobe. Post hoc tests with a Bonferroni adjustment revealed that CBF levels increased following CPT-3 administration but less so in the ADHD group. Individuals had higher levels of CBF following the administration of CPT-3. Due to a significant interaction, we can infer that ADHD diagnosis changes the effect of CPT-3 administration on CBF levels. This is consistent with our hypothesis considering that CPT-3 is a test of sustained attention, a common challenge for children with ADHD. The aforementioned interaction was not found to be significant in the parietal lobe. This may be due to the nature of CPT- 3 which does not require an integration of sensory information.

Keywords: SPECT, ADHD, conners continuous performance test, cerebral blood flow

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Authors: Chuping Lo

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This paper presents a simple model demonstrating that ceteris paribus countries with lower barriers to accessing global data tend to earn higher incomes than other countries. Therefore, large countries that inherently have greater data resources tend to have higher incomes than smaller countries, such that the former may be more hesitant than the latter to liberalize cross-border data flows to maintain this advantage. Furthermore, countries with higher artificial intelligence (AI) intensity in production technologies tend to benefit more from economies of scale in data aggregation, leading to higher income and more trade as they are better able to utilize global data.

Keywords: digital intensity, digital divide, international trade, scale of economics

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Authors: G. R. Navaneesh, E. Nagarajan, C. H. Rajam Raju

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Nowadays the multimedia data are used to store some secure information. All previous methods allocate a space in image for data embedding purpose after encryption. In this paper, we propose a novel method by reserving space in image with a boundary surrounded before encryption with a traditional RDH algorithm, which makes it easy for the data hider to reversibly embed data in the encrypted images. The proposed method can achieve real time performance, that is, data extraction and image recovery are free of any error. A secure transmission process is also discussed in this paper, which improves the efficiency by ten times compared to other processes as discussed.

Keywords: secure communication, reserving room before encryption, least significant bits, image encryption, reversible data hiding

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Authors: Fuad M. Alkoot

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DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN). 

Keywords: biometrics, genetic data, identity verification, k nearest neighbor

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Authors: R Palson Kennedy, P.Kiran Sai

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This article introduces machine learning (ML) researchers to the hurdles that geoscience problems present, as well as the opportunities for improvement in both ML and geosciences. This article presents a review from the data life cycle perspective to meet that need. Numerous facets of geosciences present unique difficulties for the study of intelligent systems. Geosciences data is notoriously difficult to analyze since it is frequently unpredictable, intermittent, sparse, multi-resolution, and multi-scale. The first half addresses data science’s essential concepts and theoretical underpinnings, while the second section contains key themes and sharing experiences from current publications focused on each stage of the data life cycle. Finally, themes such as open science, smart data, and team science are considered.

Keywords: Data science, intelligent system, machine learning, big data, data life cycle, recent development, geo science

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: Ji Eun Shin, Byung Ho Jung, Dong Hoon Lim

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It is almost impossible to store or analyze big data increasing exponentially with traditional technologies. Hadoop is a new technology to make that possible. R programming language is by far the most popular statistical tool for big data analysis based on distributed processing with Hadoop technology. With RHadoop that integrates R and Hadoop environment, we implemented parallel multiple regression analysis with different sizes of actual data. Experimental results showed our RHadoop system was much faster as the number of data nodes increases. We also compared the performance of our RHadoop with lm function and big lm packages available on big memory. The results showed that our RHadoop was faster than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases.

Keywords: big data, Hadoop, parallel regression analysis, R, RHadoop

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Hamidoddin Yousefi, Ahmad Nikbakht

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The Sistan Plain, situated in the Balochistan region of southeastern Iran, is renowned for its arid climatic conditions and prevailing winds that persist for approximately 120 days annually. The region faces multiple challenges, including drought susceptibility, exacerbated by wind erosion, temperature fluctuations, and the influence of policies implemented by neighboring Afghanistan and Iran. This study focuses on investigating the characteristics of jet streams within the Sistan Plain and their implications for global climate change. Various models are employed to analyze convective mass fluxes, horizontal moisture transport, temporal variance, and the calculation of radiation convective equilibrium within the atmosphere. Key considerations encompass the distribution of relative humidity, dry air, and absolute humidity. Moreover, the research aims to predict the interplay between jet streams and human activities, particularly regarding their environmental impacts and water scarcity. The investigation encompasses both local and global environmental consequences, drawing upon historical climate change data and comprehensive field research. The anticipated outcomes of this study hold substantial potential for mitigating global climate change and its associated environmental ramifications. By comprehending the dynamics of jet streams and their interconnections with human activities, effective strategies can be formulated to address water scarcity and minimize environmental degradation.

Keywords: Sistani plain, Baluchistan, Hamoun lake, climate change, jet streams, environmental impact, water scarcity, mitigation

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Paula Simões, Isabel Natário

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This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Sergei Aleinik, Mikhail Stolbov

Abstract:

In this work, a method of time delay estimation for dual-channel acoustic signals (speech, music, etc.) recorded under reverberant conditions is investigated. Standard methods based on cross-correlation of the signals show poor results in cases involving strong reverberation, large distances between microphones and asynchronous recordings. Under similar conditions, a method based on cross-correlation of temporal envelopes of the signals delivers a delay estimation of acceptable quality. This method and its properties are described and investigated in detail, including its limits of applicability. The method’s optimal parameter estimation and a comparison with other known methods of time delay estimation are also provided.

Keywords: cross-correlation, delay estimation, signal envelope, signal processing

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Authors: C. B. Le, V. N. Pham

Abstract:

In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

Procedia PDF Downloads 189

Authors: S. Belgherras Bekkouche, T. Benouaz, S. M. A. Bekkouche

Abstract:

Tokamak plasma is far from having a stable background. The study of turbulent transport is an important part of the current research and advanced scenarios were devised to minimize it. To do this, we used a three-wave interaction model which allows to investigate the occurrence drift-wave turbulence driven by pressure gradients in the edge plasma of a tokamak. In order to simulate the energy redistribution among different modes, the growth/decay rates for the three waves was added. After a numerical simulation, we can determine certain aspects of the temporal dynamics exhibited by the model. Indeed for a wide range of the wave decay rate, an intermittent transition from periodic behavior to chaos is observed. Then, a control strategy of chaos was introduced with the aim of reducing or eliminating the weak turbulence.

Keywords: wave interaction, plasma drift waves, wave turbulence, tokamak, edge plasma, chaos

Procedia PDF Downloads 552

Authors: Neha Pranav Kolhe, Harithapriya Vijaye, Arushi Kamle

Abstract:

The economic growth engines are urban regions. As the economy expands, so does the need for peace and quiet, and noise pollution is one of the important social and environmental issue. Health and wellbeing are at risk from environmental noise pollution. Because of urbanisation, population growth, and the consequent rise in the usage of increasingly potent, diverse, and highly mobile sources of noise, it is now more severe and pervasive than ever before, and it will only become worse. Additionally, it will expand as long as there is an increase in air, train, and highway traffic, which continue to be the main contributors of noise pollution. The current study will be conducted in two zones of class I city of central India (population range: 1 million–4 million). Total 56 measuring points were chosen to assess noise pollution. The first objective evaluates the noise pollution in various urban habitats determined as formal and informal settlement. It identifies the comparison of noise pollution within the settlements using T- Test analysis. The second objective assess the noise pollution in silent zones (as stated in Central Pollution Control Board) in a hierarchical way. It also assesses the noise pollution in the settlements and compares with prescribed permissible limits using class I sound level equipment. As appropriate indices, equivalent noise level on the (A) frequency weighting network, minimum sound pressure level and maximum sound pressure level were computed. The survey is conducted for a period of 1 week. Arc GIS is used to plot and map the temporal and spatial variability in urban settings. It is discovered that noise levels at most stations, particularly at heavily trafficked crossroads and subway stations, were significantly different and higher than acceptable limits and squares. The study highlights the vulnerable areas that should be considered while city planning. The study demands area level planning while preparing a development plan. It also demands attention to noise pollution from the perspective of residential and silent zones. The city planning in urban areas neglects the noise pollution assessment at city level. This contributes to that, irrespective of noise pollution guidelines, the ground reality is far away from its applicability. The result produces incompatible land use on a neighbourhood scale with respect to noise pollution. The study's final results will be useful to policymakers, architects and administrators in developing countries. This will be useful for noise pollution in urban habitat governance by efficient decision making and policy formulation to increase the profitability of these systems.

Keywords: noise pollution, formal settlements, informal settlements, built environment, silent zone, residential area

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Authors: Gebremedhin Kiros, Amba Shetty, Lakshman Nandagiri

Abstract:

Soil erosion is a major threat to the sustainability of land and water resources in the catchment and there is a need to identify critical areas of erosion so that suitable conservation measures may be adopted. The present study was taken up to understand the temporal and spatial distribution of soil erosion and daily sediment yield in Geba catchment (5137 km2) located in the Northern Highlands of Ethiopia. Soil and Water Assessment Tool (SWAT) was applied to the Geba catchment using data pertaining to rainfall, climate, soils, topography and land use/land cover (LU/LC) for the historical period 2000-2013. LU/LC distribution in the catchment was characterized using LANDSAT satellite imagery and the GIS-based ArcSWAT version of the model. The model was calibrated and validated using sediment concentration measurements made at the catchment outlet. The catchment was divided into 13 sub-basins and based on estimated soil erosion, these were prioritized on the basis of susceptibility to soil erosion. Model results indicated that the average sediment yield estimated of the catchment was 12.23 tons/ha/yr. The generated soil loss map indicated that a large portion of the catchment has high erosion rates resulting in significantly large sediment yield at the outlet. Steep and unstable terrain, the occurrence of highly erodible soils and low vegetation cover appeared to favor high soil erosion. Results obtained from this study prove useful in adopting in targeted soil and water conservation measures and promote sustainable management of natural resources in the Geba and similar catchments in the region.

Keywords: Ethiopia, Geba catchment, MUSLE, sediment yield, SWAT Model

Procedia PDF Downloads 313