Search results for: count data

Authors: Erez Kita, Gil Luria

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This study examines gender as a boundary condition for the relationship between the psychological variable of mindfulness and the social variable of income with regards to the use of smartphones by young drivers. The use of smartphones while driving increases the likelihood of a car accident, endangering young drivers and other road users. The study sample included 186 young drivers who were legally permitted to drive without supervision. The subjects were first asked to complete questionnaires on mindfulness and income. Next, their smartphone use while driving was monitored over a one-month period. This study is unique as it used an objective smartphone monitoring application (rather than self-reporting) to count the number of times the young participants actually touched their smartphones while driving. The findings show that gender moderates the effects of social and personal factors (i.e., income and mindfulness) on the use of smartphones while driving. The pattern of moderation was similar for both social and personal factors. For men, mindfulness and income are negatively associated with the use of smartphones while driving. These factors are not related to the use of smartphones by women drivers. Mindfulness and income can be used to identify male populations that are at risk of using smartphones while driving. Interventions that improve mindfulness can be used to reduce the use of smartphones by male drivers.

Keywords: mindfulness, using smartphones while driving, income, gender, young drivers

Procedia PDF Downloads 170

Authors: Mohammed Jibrin Ndejiko

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Modern and current models such as flow cell technology which enhances a non-destructive growth and inspection of the sessile microbial communities revealed a great understanding of biofilms. A continuous flow system was designed to evaluate possibility of biofilm formation by Escherichia coli DH5α on the stainless steel (type 304) under continuous nutrient supply. The result of the colony forming unit (CFU) count shows that bacterial attachment and subsequent biofilm formation on stainless steel coupons with average surface roughness of 1.5 ± 1.8 µm and 2.0 ± 0.09 µm were both significantly higher (p ≤ 0.05) than those of the stainless steel coupon with lower surface roughness of 0.38 ± 1.5 µm. These observations support the hypothesis that surface profile is one of the factors that influence biofilm formation on stainless steel surfaces. The SEM and FESEM micrographs of the stainless steel coupons also revealed the attached Escherichia coli DH5α biofilm and dehydrated extracellular polymeric substance on the stainless steel surfaces. Thus, the fabricated flow system represented a very useful tool to study biofilm formation under continuous nutrient supply.

Keywords: biofilm, flowcell, stainless steel, coupon

Procedia PDF Downloads 317

Authors: Ashiff Khan, A. Seetharaman, Abhijit Dasgupta

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The new era of big data (BD) is influencing chemical industries tremendously, providing several opportunities to reshape the way they operate and help them shift towards intelligent manufacturing. Given the availability of free software and the large amount of real-time data generated and stored in process plants, chemical industries are still in the early stages of big data adoption. The industry is just starting to realize the importance of the large amount of data it owns to make the right decisions and support its strategies. This article explores the importance of professional competencies and data science that influence BD in chemical industries to help it move towards intelligent manufacturing fast and reliable. This article utilizes a literature review and identifies potential applications in the chemical industry to move from conventional methods to a data-driven approach. The scope of this document is limited to the adoption of BD in chemical industries and the variables identified in this article. To achieve this objective, government, academia, and industry must work together to overcome all present and future challenges.

Keywords: chemical engineering, big data analytics, industrial revolution, professional competence, data science

Procedia PDF Downloads 85

Authors: M. Sumana, K. S. Hareesha

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Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.

Keywords: homomorphic property, secure product, secure mean and variance, secure dot product, vertically partitioned data

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Authors: Jully Handoko, B. Kuntoro, E. Saleh, Sadarman

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Most farmers in Bangkinang Seberang sub district raise Bali cattle in semi-intensive husbandry system. The farmers believe that raising such a way is economical and quite effective. The farmers do not need to provide forage and plant feed crops. Furthermore, the raising method is considered not to interfere with the main job. Screening for anemia in Bali cattle of Bangkinang Seberang subdistrict, Kampar regency, Riau, Indonesia, had been conducted. The aim of the study was to analyze hematological alterations in the anemic Bali cattle. A number of 75 Bali cattle were screened for anemia on the basis of Hemoglobin (Hb) concentration. The other hematological parameters that were measured including packed cell volume (PCV), total erythrocyte count (TEC), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH) and mean corpuscular hemoglobin concentration (MCHC). The screening showed that 18 (24.00%) of Bali cattle were anemic. Levels of Hb, PCV, TEC, MCV, MCH and MCHC in anemic Bali cattle were 7.15±1.61 g/dl, 21.15±4.16%, 3.72±1.10x106/µl, 52.75±4.13 fl, 17.31±1.86 pg and 32.77±1.69 g/dl respectively. Hematological values of Hb, PCV, TEC, MCV, MCH and MCHC were significantly (p < 0.05) lower in anemic Bali cattle compared to non-anemic Bali cattle.

Keywords: anemia, Bali cattle, alterations, hematology

Procedia PDF Downloads 452

Authors: So Yae Koh, YeonWoo Song, Ji-Yeon Ryu, Jeong Yong Moon, Somi Kim Cho

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Blueberry, a perennial shrub, is one of the most popular fruits due to its flavor and strong free radical scavenging properties. In this study, the blueberry puree was fermented by Lactobacillus plantarum CK10 and the antioxidant activities of fermentation products were examined. Various conditions with different supplements (5% sucrose or 10% skim milk) were evaluated for fermentation efficiency and the effects on antioxidant properties. The viable cell count of lactic acid bacteria, pH, total phenolic compounds and flavonoids contents were measured after 7 days of fermentation. DPPH (1,1-diphenyl-2-picrylhydrazyl) and ABTS [2,2’-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid)] radical scavenging activities were highly enhanced compared to non-fermented blueberry puree after fermentation. Interestingly, the antioxidant activities were greatly increased in the fermentation of blueberry puree alone without supplements. The present results indicate that the blueberry puree fermented by Lactobacillus plantarum CK10 could be used as a potential source of natural antioxidants and these findings will facilitate the utilization of blueberry as a resource for food additive.

Keywords: antioxidant activity, blueberry, lactobacillus plantarum CK10, fermentation

Procedia PDF Downloads 349

Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

Procedia PDF Downloads 344

Authors: Vesna Kirandziska, Nevena Ackovska, Ana Madevska Bogdanova

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The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued.

Keywords: emotion recognition, facial recognition, signal processing, machine learning

Procedia PDF Downloads 315

Authors: Sartaj Singh, Amar Singh, Ashok Sharma, Sandeep Kaur

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With upcoming threats in a digital world, we need to work continuously in the area of security in all aspects, from hardware to software as well as data modelling. The rise in social media activities and hunger for data by various entities leads to cybercrime and more attack on the privacy and security of persons. Cryptography has always been employed to avoid access to important data by using many processes. Symmetric key and asymmetric key cryptography have been used for keeping data secrets at rest as well in transmission mode. Various cryptosystems have evolved from time to time to make the data more secure. In this research article, we are studying various cryptosystems in asymmetric cryptography and their application with usefulness, and much emphasis is given to Elliptic curve cryptography involving algebraic mathematics.

Keywords: cryptography, symmetric key cryptography, asymmetric key cryptography

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Authors: Rong-Terng Juang

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Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.

Keywords: autonomous vehicle, data compression, remote monitoring, controller area networks (CAN), Lidar

Procedia PDF Downloads 163

Authors: Isyaku Labaran Fagge

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This study investigated on appraisal of civil servants level of participation in recreational activities in North Western States of Nigeria. To achieve this purpose, a descriptive survey was employed for the designed questionnaire which were administered on 300 respondents, who served as subject for this study, in North Western States of Nigeria. Descriptive statistics of simple frequency count, percentage and Chi square (x2) statistical techniques at 0.05 alpha level were used for all statistical tests of significance. The findings of the study revealed that senior civil servants by (gender, status and location) do participate in recreational activities. On the knowledgeable personnels, all the recreational centres (by gender, status and location) had no knowledgeable personnels to handle the centres across North Western States. Many recreational centers should be create. Government should train and employ more knowledgeable personnel to handle the centres. Civil servants in urban areas do participate more than the civil servants in rural areas.

Keywords: recreation, civil servants, participation, recreational activities

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Authors: Jigg Pelayo, Ricardo Villar

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The Philippines is long considered as a valuable producer of high value crops globally. The country’s employment and economy have been dependent on agriculture, thus increasing its demand for the efficient agricultural mechanism. Remote sensing and geographic information technology have proven to effectively provide applications for precision agriculture through image-processing technique considering the development of the aerial scanning technology in the country. Accurate information concerning the spatial correlation within the field is very important for precision farming of high value crops, especially. The availability of height information and high spatial resolution images obtained from aerial scanning together with the development of new image analysis methods are offering relevant influence to precision agriculture techniques and applications. In this study, an algorithm was developed and implemented to detect and count high value crops simultaneously through adaptive scaling of support vector machine (SVM) algorithm subjected to object-oriented approach combining watershed transformation and local maxima filter in enhancing tree counting and detection. The methodology is compared to cutting-edge template matching algorithm procedures to demonstrate its effectiveness on a demanding tree is counting recognition and delineation problem. Since common data and image processing techniques are utilized, thus can be easily implemented in production processes to cover large agricultural areas. The algorithm is tested on high value crops like Palm, Mango and Coconut located in Misamis Oriental, Philippines - showing a good performance in particular for young adult and adult trees, significantly 90% above. The s inventories or database updating, allowing for the reduction of field work and manual interpretation tasks.

Keywords: high value crop, LiDAR, OBIA, precision agriculture

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Authors: Samar M. Alqhtani, Suhuai Luo, Brian Regan

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Data fusion technology can be the best way to extract useful information from multiple sources of data. It has been widely applied in various applications. This paper presents a data fusion approach in multimedia data for event detection in twitter by using Dempster-Shafer evidence theory. The methodology applies a mining algorithm to detect the event. There are two types of data in the fusion. The first is features extracted from text by using the bag-ofwords method which is calculated using the term frequency-inverse document frequency (TF-IDF). The second is the visual features extracted by applying scale-invariant feature transform (SIFT). The Dempster - Shafer theory of evidence is applied in order to fuse the information from these two sources. Our experiments have indicated that comparing to the approaches using individual data source, the proposed data fusion approach can increase the prediction accuracy for event detection. The experimental result showed that the proposed method achieved a high accuracy of 0.97, comparing with 0.93 with texts only, and 0.86 with images only.

Keywords: data fusion, Dempster-Shafer theory, data mining, event detection

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Authors: Ioannis Iglezakis, Theodoros D. Trokanas, Panagiota Kiortsi

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This paper aims to explore major legal issues arising from the collection and processing of Health Big Data in the light of the new European secondary legislation for the protection of personal data of natural persons, placing emphasis on the General Data Protection Regulation 679/2016. Whether Big Health Data can be characterised as ‘personal data’ or not is really the crux of the matter. The legal ambiguity is compounded by the fact that, even though the processing of Big Health Data is premised on the de-identification of the data subject, the possibility of a combination of Big Health Data with other data circulating freely on the web or from other data files cannot be excluded. Another key point is that the application of some provisions of GPDR to Big Health Data may both absolve the data controller of his legal obligations and deprive the data subject of his rights (e.g., the right to be informed), ultimately undermining the fundamental right to the protection of personal data of natural persons. Moreover, data subject’s rights (e.g., the right not to be subject to a decision based solely on automated processing) are heavily impacted by the use of AI, algorithms, and technologies that reclaim health data for further use, resulting in sometimes ambiguous results that have a substantial impact on individuals. On the other hand, as the COVID-19 pandemic has revealed, Big Data analytics can offer crucial sources of information. In this respect, this paper identifies and systematises the legal provisions concerned, offering interpretative solutions that tackle dangers concerning data subject’s rights while embracing the opportunities that Big Health Data has to offer. In addition, particular attention is attached to the scope of ‘consent’ as a legal basis in the collection and processing of Big Health Data, as the application of data analytics in Big Health Data signals the construction of new data and subject’s profiles. Finally, the paper addresses the knotty problem of role assignment (i.e., distinguishing between controller and processor/joint controllers and joint processors) in an era of extensive Big Health data sharing. The findings are the fruit of a current research project conducted by a three-member research team at the Faculty of Law of the Aristotle University of Thessaloniki and funded by the Greek Ministry of Education and Religious Affairs.

Keywords: big health data, data subject rights, GDPR, pandemic

Procedia PDF Downloads 129

Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

Procedia PDF Downloads 78

Authors: Iñaki Iturria, Pasquale Russo, Montserrat Nacher-Vázquez, Giuseppe Spano, Paloma López, Miguel Angel Pardo

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Introduction: It is known that some Lactic Acid Bacteria (LAB) present an interesting probiotic effect. Probiotic bacteria stimulate host resistance to microbial pathogens and thereby aid in immune response, and modulate the host's immune responses to antigens with a potential to down-regulate hypersensitivity reactions. Therefore, probiotic therapy is valuable against intestinal infections and may be beneficial in the treatment of Inflammatory Bowel Disease (IBD). Several in vitro tests are available to evaluate the probiotic potential of a LAB strain. However, an in vivo model is required to understand the interaction between the host immune system and the bacteria. During the last few years, zebrafish (Danio rerio) has gained interest as a promising vertebrate model in this field. This organism has been extensively used to study the interaction between the host and the microbiota, as well as the host immune response under several microbial infections. In this work, we report on the use of the zebrafish model to investigate in vivo the colonizing ability and the immunomodulatory effect of probiotic LAB. Methods: Lactobacillus strains belonging to different LAB species were fluorescently tagged and used to colonize germ-free zebrafish larvae gastrointestinal tract (GIT). Some of the strains had a well-documented probiotic effect (L. acidophilus LA5); while others presented an exopolysaccharide (EPS) producing phenotype, thus allowing evaluating the influence of EPS in the colonization and immunomodulatory effect. Bacteria colonization was monitored for 72 h by direct observation in real time using fluorescent microscopy. CFU count per larva was also evaluated at different times. The immunomodulatory effect was assessed analysing the differential expression of several innate immune system genes (MyD88, NF-κB, Tlr4, Il1β and Il10) by qRT- PCR. The anti-inflammatory effect was evaluated using a chemical enterocolitis zebrafish model. The protective effect against a pathogen was also studied. To that end, a challenge test was developed using a fluorescently tagged pathogen (Vibrio anguillarum-GFP+). The progression of the infection was monitored up to 3 days using a fluorescent stereomicroscope. Mortality rates and CFU counts were also registered. Results and conclusions: Larvae exposed to EPS-producing bacteria showed a higher fluorescence and CFU count than those colonized with no-EPS phenotype LAB. In the same way, qRT-PCR results revealed an immunomodulatory effect on the host after the administration of the strains with probiotic activity. A downregulation of proinflammatory cytoquines as well as other cellular mediators of inflammation was observed. The anti-inflammatory effect was found to be particularly marked following exposure to LA% strain, as well as EPS producing strains. Furthermore, the challenge test revealed a protective effect of probiotic administration. As a matter of fact, larvae fed with probiotics showed a decrease in the mortality rate ranging from 20 to 35%. Discussion: In this work, we developed a promising model, based on the use of gnotobiotic zebrafish coupled with a bacterial fluorescent tagging in order to evaluate the probiotic potential of different LAB strains. We have successfully used this system to monitor in real time the colonization and persistence of exogenous LAB within the gut of zebrafish larvae, to evaluate their immunomodulatory effect and for in vivo competition assays. This approach could bring further insights into the complex microbial-host interactions at intestinal level.

Keywords: gnotobiotic, immune system, lactic acid bacteria, probiotics, zebrafish

Procedia PDF Downloads 328

Authors: Yang Jian-Hong, Zhang Ren-Cheng, Huang Li

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Arc fault is one of the main inducements of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively. Arc fault detections and unhooking standards are the keys to AFDD practical application. First, an arc fault continuous production system was developed, which could count the arc half wave number. Then, Combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The combustion degree of arc fault could be expressed effectively by arc area. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. At last, Practical tests were carried out on the self-developed AFDD system. The result showed that actual AFDD unhooking time was the sum of arc half wave cycling number, Arc wave identification time and unhooking mechanical operation time And the first two shared shorter time. Unhooking time standard depended on the shortest mechanical operation time.

Keywords: arc fault detection device, arc area, arc half wave, unhooking time, arc fault

Procedia PDF Downloads 507

Authors: Sašo Pečnik, Borut Žalik

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This paper presents a real-time visualization technique and filtering of classified LiDAR point clouds. The visualization is capable of displaying filtered information organized in layers by the classification attribute saved within LiDAR data sets. We explain the used data structure and data management, which enables real-time presentation of layered LiDAR data. Real-time visualization is achieved with LOD optimization based on the distance from the observer without loss of quality. The filtering process is done in two steps and is entirely executed on the GPU and implemented using programmable shaders.

Keywords: filtering, graphics, level-of-details, LiDAR, real-time visualization

Procedia PDF Downloads 308

Authors: I. Benmehdi, O. Hasnaoui, N. Hachemi, M. Bouazza

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The Understanding of the mechanisms structuring of plant diversity in the region of Tlemcen (western Algeria) is a related problem. The current floristic composition of different groups in Pistacia lentiscus L. resulting from the combination of human and climate action. This study is devoted to biodiversity inventory and phytogeographic characterization of Pistacia lentiscus groups in the Honaine coastal (western Algeria). The floristic inventory (150 levels) made in three stations of the study area allowed to count a 109 species belonging to 44 families of vascular plants. The biogeographical analysis of the Pistacia lentiscus groups reveals the most representative elements. The Mediterranean elements are numerically the most dominant with 39.45% represented by: Pistacia lentiscus, Cistus monspeliensis, Plantago lagopus, Linum strictum, Echium vulgare; followed by the western Mediterranean elements with 10.09% and are represented by: Chamaerops humilis, Lavandula dentata, Ampelodesma mauritanicum and Iris xyphium. However, this phytotaxonomic wealth is exposed to anthropogenic impact causing its disruption see its decline.

Keywords: Pistacia lentiscus L., phytodiversity, phytogeography, honaine, western Algeria

Procedia PDF Downloads 398

Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Rania Hanafi Mahmoud Said, Rasha Mohamed Taha

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Background: A recent study has explored the potential of an approach to treating infected root canals using a combination of Mineral Trioxide Aggregate (MTA) and Tetracycline-loaded Nanochitosan. Material and methods: Forty dogs were included in the study, with infected periapical areas induced by leaving access openings in their teeth for four months. Bacteriological samples from the infected root canals were collected and managed anaerobically to identify and count the different microorganisms present. The most common microorganisms detected were Prevotella oris, Fusobacterium nucleatum, Streptococcus viridans, Enterococcus faecalis, Clostridium subterminale, Porphyromonas gingivalis, and Peptostreptococcus anaerobius. The dogs were divided into four groups based on the sealant used to treat the infected periapical areas: Group I: Negative control (no treatment) Group II: Positive control (MTA only) Group III: MTA + tetracycline Group IV: MTA + tetracycline loaded on nanochitosan Results: Periapical areas in Group IV showed significantly more bone healing than those in Groups I, II, and III. The newly formed bone was evaluated radiographically, histologically, and immunohistochemically using Osteopontin (OSP) antibodies. Data collected was statistically analysed using SPSS software at a 0.05 significance level. Conclusion: The study concluded that the combined use of Tetracycline-loaded Nanochitosan and MTA presents a promising approach for the treatment of infected root canals. The potent antimicrobial activity of Tetracycline-loaded Nanochitosan, along with the biocompatibility and desirable properties of MTA, may synergistically contribute to improved clinical outcomes in endodontic therapy. This study has important implications for the clinical management of infected root canals. The combination of Tetracycline-loaded Nanochitosan and MTA could provide a more effective and efficient means of treating these challenging cases. Further research is needed to confirm these findings in humans and to optimize the treatment protocol.

Keywords: mineral trioxide aggregate, tetracycline-loaded nanochitosan, periapical infection, osteopontine

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Authors: Jae Won Shin

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We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

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Authors: Rashi Bhalla, Russel Pears, M. Asif Naeem

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In this 'Information Explosion Era' analyzing data 'a critical commodity' and mining knowledge from vertically distributed data stream incurs huge communication cost. However, an effort to decrease the communication in the distributed environment has an adverse influence on the classification accuracy; therefore, a research challenge lies in maintaining a balance between transmission cost and accuracy. This paper proposes a method based on Bayesian inference to reduce the communication volume in a heterogeneous distributed environment while retaining prediction accuracy. Our experimental evaluation reveals that a significant reduction in communication can be achieved across a diverse range of dataset types.

Keywords: big data, bayesian inference, distributed data stream mining, heterogeneous-distributed data

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Authors: Benny Sand, Yotam Lurie, Shlomo Mark

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Medical Internet of Things (MIoT), AI, and data privacy are linked forever in a gordian knot. This paper explores the conflicts of interests between the stakeholders regarding data privacy in the MIoT arena. While patients are at home during healthcare hospitalization, MIoT can play a significant role in improving the health of large parts of the population by providing medical teams with tools for collecting data, monitoring patients’ health parameters, and even enabling remote treatment. While the amount of data handled by MIoT devices grows exponentially, different stakeholders have conflicting understandings and concerns regarding this data. The findings of the research indicate that medical teams are not concerned by the violation of data privacy rights of the patients' in-home healthcare, while patients are more troubled and, in many cases, are unaware that their data is being used without their consent. MIoT technology is in its early phases, and hence a mixed qualitative and quantitative research approach will be used, which will include case studies and questionnaires in order to explore this issue and provide alternative solutions.

Keywords: MIoT, data privacy, stakeholders, home healthcare, information privacy, AI

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Authors: Deepak Singh, Rail Kuliev

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The abstract highlights the critical importance of optimizing data integration and management strategies in the upstream oil and gas industry. With its complex and dynamic nature generating vast volumes of data, efficient data integration and management are essential for informed decision-making, cost reduction, and maximizing operational performance. Challenges such as data silos, heterogeneity, real-time data management, and data quality issues are addressed, prompting the proposal of several strategies. These strategies include implementing a centralized data repository, adopting industry-wide data standards, employing master data management (MDM), utilizing real-time data integration technologies, and ensuring data quality assurance. Training and developing the workforce, “reskilling and upskilling” the employees and establishing robust Data Management training programs play an essential role and integral part in this strategy. The article also emphasizes the significance of data governance and best practices, as well as the role of technological advancements such as big data analytics, cloud computing, Internet of Things (IoT), and artificial intelligence (AI) and machine learning (ML). To illustrate the practicality of these strategies, real-world case studies are presented, showcasing successful implementations that improve operational efficiency and decision-making. In present study, by embracing the proposed optimization strategies, leveraging technological advancements, and adhering to best practices, upstream oil and gas companies can harness the full potential of data-driven decision-making, ultimately achieving increased profitability and a competitive edge in the ever-evolving industry.

Keywords: master data management, IoT, AI&ML, cloud Computing, data optimization

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Norsuhana Omar, Amilia Aminuddina Zaiton Zakaria, Raifana Rosa Mohamad Sattar, Kalaivani Chellappan, Mohd Alauddin Mohd Ali, Norizam Salamt, Zanariyah Asmawi, Norliza Saari, Aini Farzana Zulkefli, Nor Anita Megat Mohd. Nordin

Abstract:

Inflammation plays an important role in the pathogenesis of vascular dysfunction leading to arterial stiffness. Pulse wave velocity (PWV) and augmentation index (AS), as tools for the assessment of vascular damages are widely used and have been shown to predict cardiovascular disease (CVD). C-reactive protein (CRP) is a marker of inflammation. Several studies noted that regular exercise is associated with reduced arterial stiffness. The lack of exercise among Malaysians and the increasing CVD morbidity and mortality among young men are of concern. In Malaysia data on the workplace exercise intervention is scarce. A programme was designed to enable subjects to increase their level of walking as part of their daily work routine and self-monitored by using pedometers. The aim of this study to evaluate the reducing of inflammation by measuring CRP and improvement arterial stiffness measured by carotid femoral PWV (PWVCF) and AI. A total of 70 young men (20 - 40 years) who were sedentary, achieving less than 5,000 steps/day in casual walking with 2 or more cardiovascular risk factors were recruited in Institute of Vocational Skills for Youth (IKBN Hulu Langat). Subjects were randomly assigned to a control (CG) (n=34; no change in walking) and pedometer group (PG) (n=36; minimum target: 8,000 steps/day). The CRP was measured by using immunological method while PWVCF and AI were measured using Vicorder. All parameters were measured at baseline and after 12 weeks. Data for analysis was conducted using Statistical Package of Social Sciences Version 22 (SPSS Inc., Chicago, IL, USA). At post intervention, the CG step counts were similar (4983 ± 366vs 5697 ± 407steps/day). The PG increased step count from 4996 ± 805 to 10,128 ±511 steps/day (P<0.001). The PG showed significant improvement in anthropometric variables and lipid (time and group effect p<0.001). For vascular assessment, the PG showed significantly decreased for time and effect (p<0.001) for PWV (7.21± 0.83 to 6.42 ± 0.89) m/s; AI (11.88± 6.25 to 8.83 ± 3.7) % and CRP (pre= 2.28 ± 3.09, post=1.08± 1.37mg/L). However, no changes were seen in CG. As a conclusion, a pedometer-based walking programme may be an effective strategy for promoting increased daily physical activity which reduces cardiovascular risk markers and thus improve cardiovascular health in terms of inflammation and arterial stiffness. The community intervention for health maintenance has potential to adopt walking as an exercise and adopting vascular fitness index as the performance measuring tools.

Keywords: arterial stiffness, exercise, inflammation, pedometer

Procedia PDF Downloads 353

Authors: F. Amin, S. Feizi

Abstract:

Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

Procedia PDF Downloads 360

Authors: Simon Nyarko

Abstract:

This study examined the microbial flora contamination of the Ghanaian paper currency notes and antibiotic resistance in Ejura Municipal, Ashanti Region, Ghana. This is a descriptive cross-sectional study designed to assess the profile of microflora contamination of the Ghanaian paper currency notes and antibiotic-resistant in the Ejura Municipality. The research was conducted in Ejura, a town in the Ejura Sekyeredumase Municipal of the Ashanti region of Ghana. 70 paper currency notes which were freshly collected from the bank, consisting of 15 pieces of GH ¢1, GH ¢2, and GH ¢5, 10 pieces of GH ¢10 and GH ¢20, and 5 pieces of GH ¢50, were randomly sampled from people by exchanging their money in usage with those freshly secured from the bank. The surfaces of each GH¢ note were gently swabbed and sent to the lab immediately in sterile Zip Bags and sealed, and tenfold serial dilution was inoculated on plate count agar (PCA), MacConkey agar (MCA), mannitol salt agar (MSA), and deoxycholate citrate agar (DCA). For bacterial identification, the study used appropriate laboratory and biochemical tests. The data was analyzed using SPSS-IBM version 20.0. It was found that 95.2 % of the 70 GH¢ notes tested positive for one or more bacterial isolates. On each GH¢ note, mean counts on PCA ranged from 3.0 cfu/ml ×105 to 4.8 cfu/ml ×105. Of 124 bacteria isolated. 36 (29.03 %), 32 (25.81%), 16 (12.90 %), 20 (16.13%), 13 (10.48 %), and 7 (5.66 %) were from GH¢1, GH¢2, GH¢10, GH¢5, GH¢20, and GH¢50, respectively. Bacterial isolates were Escherichia coli (25.81%), Staphylococcus aureus (18.55%), coagulase-negative Staphylococcus (15.32%), Klebsiella species (12.10%), Salmonella species (9.68%), Shigella species (8.06%), Pseudomonas aeruginosa (7.26%), and Proteus species (3.23%). Meat shops, commercial drivers, canteens, grocery stores, and vegetable shops contributed 25.81 %, 20.16 %, 19.35 %, 17.74 %, and 16.94 % of GH¢ notes, respectively. There was 100% resistance of the isolates to Erythromycin (ERY), and Cotrimoxazole (COT). Amikacin (AMK) was the most effective among the antibiotics as 75% of the isolates were susceptible to it. This study has demonstrated that the Ghanaian paper currency notes are heavily contaminated with potentially pathogenic bacteria that are highly resistant to the most widely used antibiotics and are a threat to public health.

Keywords: microflora, antibiotic resistance, staphylococcus aureus, culture media, multi-drug resistance

Procedia PDF Downloads 107

Authors: Apkar Salatian

Abstract:

To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

Procedia PDF Downloads 212