Search results for: data loss

Authors: Rakesh Hajiyani, Chetan Chauhan, Harshkant Jethva, Mihir Joshi

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Metal bis-thiourea type organo-metallic crystals are popular as non-linear optical materials. Bis-thiourea nickel barium chloride was synthesized and crystals were grown by slow aqueous solvent evaporation technique. The transparent and colorless crystals having maximum dimensions of 13 mm x 8 mm x 2.2 mm were obtained. The EDAX was carried out to estimate the content of nickel and barium in the grown crystals. The powder XRD analysis suggested orthorhombic crystal structure with unit cell parameters as: a= 9.70 Å, b= 10.68 Å and c= 17.95 Å. The FTIR spectroscopy study confirmed the presence of various functional groups. The UV-vis spectroscopy study indicated that the crystals were transparent in the visible region with 90% transmittance level further optical parameters were studied. From the TGA it was found that the crystals remained stable up to 170 0C and then decomposed through two decomposition stages. The dielectric study was carried out in the frequency range of applied field from 500 Hz to 1 MHz. The variations of dielectric constant, dielectric loss were studied with frequency. It was found that the dielectric constant and the dielectric loss decreased as the frequency of applied field increased. The results are discussed.

Keywords: crystal growth, dielectric study, optical parameters, organo-metallic crystals, powder xrd, slow evaporation technique, TGA

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Authors: Asnawi Mohd Busrah, Au Mau Teng, Tan Chin Hooi, Lau Chee Chong

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This paper describes value based planning (VBP) methodology incorporating risk assessment as an enhanced and more practical approach to evaluate distribution network projects in Peninsular Malaysia. Assessment indicators associated with economics, performance and risks are formulated to evaluate distribution projects to quantify their benefits against investment. The developed methodology is implemented in a web-based software customized to capture investment and network data, compute assessment indicators and rank the proposed projects according to their benefits. Value based planning approach addresses economic factors in the power distribution planning assessment, so as to minimize cost solution to the power utility while at the same time provide maximum benefits to customers.

Keywords: value based planning, distribution network, value of loss load (VoLL), energy not served (ENS)

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Authors: Bharath Anantharamaiah, Tomas Bouda, Elke Deckers, Stijn Jonckheere, Wim Desmet, Juan J. Garcia

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Automotive tyres are gaining importance recently in terms of their noise emission, not only with respect to reduction in noise, but also their perception and detection. Tyres exhibit a mechanical noise generation mechanism up to 1 kHz. However, owing to the fact that tyre is a composite of several materials, it has been difficult to model it using finite elements to predict noise at high frequencies. The currently available FE models have a reliability of about 500 Hz, the limit which, however, is not enough to perceive the roughness or sharpness of noise from tyre. These noise components are important in order to alert pedestrians on the street about passing by slow, especially electric vehicles. In order to model tyre noise behaviour up to 1 kHz, its dynamic behaviour must be accurately developed up to a 1 kHz limit using finite elements. Materials play a vital role in modelling the dynamic tyre behaviour precisely. Since tyre is a composition of several components, their precise definition in finite element simulations is necessary. However, during the tyre manufacturing process, these components are subjected to various pressures and temperatures, due to which these properties could change. Hence, material definitions are better described based on the tyre responses. In this work, the hyperelasticity of tyre component rubbers is calibrated, using the design of experiments technique from the tyre characteristic responses that are measured on a stiffness measurement machine. The viscoelasticity of rubbers are defined by the Prony series for rubbers, which are determined from the loss factor relationship between the loss and storage moduli, assuming that the rubbers are excited within the linear viscoelasticity ranges. These values of loss factor are measured and theoretically expressed as a function of rubber shore hardness or hyperelasticities. From the results of the work, there exists a good correlation between test and simulation vibrational transfer function up to 1 kHz. The model also allows flexibility, i.e., the frequency limit can also be extended, if required, by calibrating the Prony parameters of rubbers corresponding to the frequency of interest. As future work, these tyre models are used for noise generation at high frequencies and thus for tyre noise perception.

Keywords: tyre dynamics, rubber materials, prony series, hyperelasticity

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Jian-You Wang, Feng Yi Hsu, Chieh-Hsi Wu

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Androgenetic alopecia (AGA) is an androgen-dependent disorder caused by excess testosterone in blood capillaries or excess enzyme activity of 5α- reductase in hair follicles. Plants, alone or in combination, have been widely used for hair growth promotion since ancient times in Asia. In this study, the efficacy of a traditional Chinese herbal formula, Shen-Ying-Yang-Zhen-Dan (SYYZD) with different kinds of extract solvents, facilitating hair regrowth in testosterone-induced hair loss have been determined. The study was performed by treating with either 95 % ethanol aqueous extracts, 50% ethanol aqueous extracts or deionized water extracts orally in four-week-old male S.D. rats that experienced hair regrowth interruption induced by testosterone treatment. The 50% ethanol aqueous extracts group showed better hair regrowth promotion activities than either 95% ethanol aqueous extracts or deionized water extracts groups in 14 days treatment. In conclusion, our results suggest that 50% ethanol aqueous SYYZD extracts have hair growth promoting potential and may be beneficial as an alternative medicine for androgenetic alopecia treatment.

Keywords: Shen-Ying-Yang-Zhen-Dan, androgenic alopecia, hair loss, hair growth promotion, hair regrowth effect

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Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

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Authors: Lourdes Hanna, Edward Poluyi, Chibuikem Ikwuegbuenyi, Eghosa Morgan, Grace Imaguezegie

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Background: Degeneration of the central nervous system (CNS), also known as neurodegeneration, describes an age-associated progressive loss of the structure and function of neuronal materials, leading to functional and mental impairments. Main body: Neuroinflammation contributes to the continuous worsening of neurodegenerative states which are characterised by functional and mental impairments due to the progressive loss of the structure and function of neu-ronal materials. Some of the most common neurodegenerative diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Whilst neuroinflammation is a key contributor to the progression of such disease states, it is not the single cause as there are multiple factors which contribute. Theoretically, non-steroidal anti-inflammatory drugs (NSAIDs) have potential to target neuroinflammation to reduce the severity of disease states. Whilst some animal models investigating the effects of NSAIDs on the risk of neurodegenerative diseases have shown a beneficial effect, this is not the same finding. Conclusion: Further investigation using more advanced research methods is required to better understand neuroinflammatory pathways and understand if there is still a potential window for NSAID efficacy.

Keywords: intervention, central nervous system, neurodegeneration, neuroinflammation

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Authors: Salisu Dahiru, Jibrin M. Kaura, Abubakar I. Jumare, Sulaiman M. Mahmood

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Reclaimed asphalt, used automobile tires and rice husk, were regarded as waste. These materials could be used in construction of new roads and for roads rehabilitation. Investigation into the production of a Green Hot Mixed Asphalt (GHMA) pavement using Reclaimed Asphalt Pavement (RAP) as partial replacement for coarse aggregate, Crumb Rubber (CR) from waste automobile tires as modifier for bitumen binder and Rice Husk Ash (RHA) as partial replacement of ordinary portland cement (OPC) filler, for roads construction and rehabilitation was presented. 30% Reclaimed asphalt of total aggregate, 15% Crumb Rubber of total binder content, 5% Rice Husk Ash of total mix, and 5.2% Crumb Rubber Modified Bitumen content were recommended for optimum performance. Loss of marshal stability was investigated on mix with the recommended optimum CRMB. The mix revealed good performance with only about 13% loss of stability after 24 hours of immersion in hot water bath, as against about 24% marshal stability lost reported in previous studies for conventional Hot Mixed Asphalt (HMA).

Keywords: rice husk, reclaimed asphalt, filler, crumb rubber, bitumen content green hot mix asphalt

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Authors: Mohamed Elkholy, Ahmed Elfatatry

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Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Technology map. However, cloud computing brought about a number of security challenges as a result of the physical distribution of computational resources and the limited control that users have over the physical storage. This situation raises many security challenges for data integrity and confidentiality as well as authentication and access control. This work proposes a security mechanism for data integrity that allows a data owner to be aware of any modification that takes place to his data. The data integrity mechanism is integrated with an extended Kerberos authentication that ensures authorized access control. The proposed mechanism protects data confidentiality even if data are stored on an untrusted storage. The proposed mechanism has been evaluated against different types of attacks and proved its efficiency to protect cloud data storage from different malicious attacks.

Keywords: access control, data integrity, data confidentiality, Kerberos authentication, cloud security

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Authors: Nazli Hardy

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Multidisciplinary application of computer science, interactive database-driven web application, the Internet of Things (IoT) represents a digital ecosystem that has pervasive technological, social, and economic, impact on the human population. It is a long-term technology, and its development is built around the connection of everyday objects, to the Internet. It is estimated that by 2020, with billions of people connected to the Internet, the number of connected devices will exceed 50 billion, and thus IoT represents a paradigm shift in in our current interconnected ecosystem, a communication shift that will unavoidably affect people, businesses, consumers, clients, employees. By nature, in order to provide a cohesive and integrated service, connected devices need to collect, aggregate, store, mine, process personal and personalized data on individuals and corporations in a variety of contexts and environments. A significant factor in this paradigm shift is the necessity for secure and appropriate transmission, processing and storage of the data. Thus, while benefits of the applications appear to be boundless, these same opportunities are bounded by concerns such as trust, privacy, security, loss of control, and related issues. This poster and presentation look at a multi-factor authentication (MFA) mechanisms that need to change from the login-password tuple to an Identity and Access Management (IAM) model, to the more cohesive to Identity Relationship Management (IRM) standard. It also compares and contrasts messaging protocols that are appropriate for the IoT ecosystem.

Keywords: Internet of Things (IoT), authentication, protocols, survey

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Authors: Sukhleen Bindra Narang, Dalveer Kaur, Kunal Pubby

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Dielectric ceramic samples in the BaO-Re2O3-TiO2 ternary system were synthesized with structural formula Ba2-xRe4+2x/3Ti8O24 where Re= rare earth metal and Re= Sm and La where x varies from 0.0 to 0.6 with step size 0.1. Polycrystalline samples were prepared by the conventional solid state reaction technique. The dielectric, electrical and impedance analysis of all the samples in the frequency range 1KHz- 1MHz at room temperature (25°C) have been done to get the understanding of electrical conduction and dielectric relaxation and their correlation. Dielectric response of the samples at lower frequencies shows dielectric dispersion while at higher frequencies it shows dielectric relaxation. The ac conductivity is well fitted by the Jonscher law (σac = σdc+Aωn). The spectroscopic data in the impedance plane confirms the existence of grain contribution to the relaxation. All the properties are found out to be function of frequency as well as the amount of substitution.

Keywords: dielectric ceramics, dielectric constant, loss tangent, AC conductivity, impedance spectroscopy

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Authors: Bourama Mane, Ibrahima Fall, Mamadou Samba Camara, Alassane Bah

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At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal.

Keywords: Semantic Web, linked open data, database, statistic

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Authors: Paul Buchana, Patrick E. Mc Sharry

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In 2017 there will be about 38 wind farms in the North Sea belonging to 5 different countries. The North Sea is ideal for offshore wind power generation and is thus attractive to offshore wind energy developers and investors. With concerns about the potential for offshore wind turbines to sustain substantial damage as a result of extreme weather conditions, particularly windstorms, this poses a unique challenge to insurers and reinsurers as to adequately quantify the risk and offer appropriate insurance cover for these assets. The need to manage this risk also concerns regulators, who provide the oversight needed to ensure that if a windstorm or a series of storms occur in this area over a one-year time frame, the insurers of these assets in the EU remain solvent even after meeting consequent damage costs. In this paper, using available European windstorm data for the past 33 years and actual wind farm locations together with information pertaining to each of the wind farms (number of turbines, total capacity and financial value), we present a Monte Carlo simulation approach to assess the number of turbines that would be buckled in each of the wind farms using maximum wind speeds reaching each of them. These wind speeds are drawn from historical windstorm data. From the number of turbines buckled, associated financial loss and output capacity can be deduced. The results presented in this paper are targeted towards offshore wind energy developers, insurance and reinsurance companies and regulators.

Keywords: catastrophe modeling, North Sea wind farms, offshore wind power, risk analysis

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Authors: Imran Khan Swati, Mohammad Younas

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This work aims to explore the potential applications of polymeric hydrophobic membranes and green ionic liquids (ILs). Protic and aprotic ILs were synthesized in the lab., characterized, and tested for CO₂/N₂ and CO₂/CH₄ separation using hydrophobic polymeric membranes via supported ionic liquid membrane (SILM). ILs were verified by FTIR spectroscopy. The SILMs were stable at room temperature up to 0.5 MPa. For CO₂, [BSmim][tos] had the greatest coefficient of solubility and permeability, along with all ILs. At 0.5 MPa, IL [BSmim][tos] was found with a selectivity of 56.2 and 47.5 for pure CO₂/N₂ and CO₂/CH₄, respectively. The ILs synthesized for this study are rated as [BSmim][tos]>[BSmpy][tos]>[Bmim][Cl]>[Bpy][Cl] based on their SILM separation performance. Furthermore, high values of selectivity of [BSmim][tos] and [BSmpy][tos] support the use of ILs for CO₂ separation using SILMs. The study was extended to synthesize and test the ammonium-based ILs, [2-HEA][f] and [2-HEA][Hs]. These ILs achieved 50 % less selectivity for CO₂/N₂ as compared to [BSmim][tos] and [BSmpy][tos]. Nevertheless, the permeability of CO₂ achieved with [2-HEA][f] and [2-HEA][Hs] is more than 20 times higher than the [BSmim][tos] and [BSmpy][tos]. Later, the CO₂/N₂ permeability and selectivity study was extended using a flat sheet membrane contactor with recirculated IL. The contact angle effects, liquid entry pressure (LEP), initial CO₂ concentration, and type of solvents and membrane material on the CO₂ capture efficiency and membrane wetting in the post-combustion capture (PCC) process have been experimentally investigated and evaluated. Polytetrafluoroethylene (PTFE) has shown the most hydrophobic property with 6-170 loss in the contact angle. Furthermore, [Omim][BF4] and [Bmim][BF6] have exhibited only 5-8 % loss in LEP using PTFE membrane support. The CO₂ capture efficiency has been achieved as 80.8-99.8 % in different combinations of ILs and membrane support, keeping all other variables constant. While increasing CO₂ concentration from 15 to 45 % vol., an increase of nearly three folds in the CO₂ mass transfer flux was observed. The combination of [Omim][BF4] and PTFE membrane witnessed good long-term stability with only a 20 % loss in CO₂ capture efficiency in 480 min of continuous operation. A 3- D simulation model for non-dispersive solvent absorption in membrane contactors provides insight into the optimum design of a separation system for a specific application minimizing the overall cost and making the process environment-friendly.

Keywords: Post-combustion CO2 capture, membrane synthesis, process development, permeability and selectivity, ionic liquids

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Authors: Eashwari Dahoe, Jody Scheuer, Harm-Jan Vink

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Alzheimer's disease is a progressive brain condition and is the most common form of dementia. Dementia is a global concern. It is an increasing crisis due to the worldwide aging population. The disease alters the body in different stages leading to several issues. The most common issues result in memory loss, responsive decline, and social decline. During the various stages, the dementia patient must be supported more in performing daily tasks. Eventually, the patient will have to be cared for entirely. There are many efforts in various domains to support this brain condition. This study focuses on the connection between three generations of solutions in the domain of technology and the struggles they tackle. To gather information regarding the struggles seniors with dementia face data has been acknowledged through reading scientific articles. The struggles are extracted from these articles and classified into various category struggles. To gather information regarding the three generations of technology data has been acknowledged through reading scientific articles regarding the generations. After understanding the difference between the three generations, international technological solutions from the past 20 years are connected to the generation they fit. This info is mainly collected through research on companies that aim to improve the lives of senior citizens with early stages of dementia. Eventually, the technological solutions (divided by generations) are linked to the struggles they tackle. By connecting the struggles and the solutions , it is hoped that this paper contributes to an informative overview of the currently available technological solutions and the struggles they tackle.

Keywords: Alzheimer’s disease, technological solutions to support dementia, struggles of seniors with dementia, struggles of dementia

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Authors: Nazura Abdul Manap, Siti Nur Farah Atiqah Salleh

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For decades, the misuse of personal data has been a critical issue. Malaysia has accepted responsibility by implementing the Malaysian Personal Data Protection Act 2010 to secure personal data (PDPA 2010). After more than a decade, this legislation is set to be revised by the current PDPA 2023 Amendment Bill to align with the world's key personal data protection regulations, such as the European Union General Data Protection Regulations (GDPR). Among the other suggested adjustments is the Data User's appointment of a Data Protection Officer (DPO) to ensure the commercial entity's compliance with the PDPA 2010 criteria. The change is expected to be enacted in parliament fairly soon; nevertheless, based on the experience of the Personal Data Protection Department (PDPD) in implementing the Act, it is projected that there will be a slew of additional concerns associated with the DPO mandate. Consequently, the goal of this article is to highlight the issues that the DPO will encounter and how the Personal Data Protection Department should respond to this subject. The study result was produced using a qualitative technique based on an examination of the current literature. This research reveals that there are probable obstacles experienced by the DPO, and thus, there should be a definite, clear guideline in place to aid DPO in executing their tasks. It is argued that appointing a DPO is a wise measure in ensuring that the legal data security requirements are met.

Keywords: guideline, law, data protection officer, personal data

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Authors: Ladda Thiamwong, Renata Komalasari

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Falls are the leading cause of disability and hospitalization in low-income older adults. Fear of falling is present in 20% to 85 % of older adults and has been identified as an independent risk factor of fall risk, activity restriction, and loss of independence. About 12% of American older adults have subjective cognitive decline. Cognitive impairment is also an established factor of fall risk. However, it is unclear whether measures of fear of falling and subjective cognitive decline have the greatest association with fall risk in low-income older adults. The aim of this study was to evaluate the association between fear of falling, subjective cognitive decline-functional performance (SCD-FP), and fall risk using simple screening tools. In this cross-section study, we collected data from community-dwelling older adults 60 years or older in low-income settings in Central Florida, and 86 participants were included in the data analysis. Fear of falling was assessed by the Short Fall Efficacy Scale- International (Short FES-I) with seven items. Subjective cognitive decline-functional performance (SCD-FP) was assessed by a self-reported experience of worsening or more frequent confusion or memory loss in the past 12 months and its functional implications. Fall risk was evaluated by the Centers for Disease Control and Prevention (CDC)'s Stay Independent checklist with 12 items. The majority of participants were female, and more than half of the participants were African American. More than half of the participants had a higher school degree or higher, and less than 20% had no financial problems. Less than 30% of the participants perceived their general health as very good- excellent. More than half of the participants lived alone, and less than 15% lived with a partner or spouse. About 60% of the participants had hypertension, 40% had diabetes, 16% had cancer, and 50% had arthritis. About 30% of the participants had difficulty walking up ten steps without resting, more than 40% felt unsteady when walking, and 30% had been advised to use a cane or walker to get around safely. Regression analysis showed that fall risk was associated with fear of falling ( = .524, p <.001) and subjective cognitive decline-functional performance ( = .465, p =.027). The structure coefficient showed that fear of falling (rs2 = .922) was a stronger predictor of fall risk than subjective cognitive decline-functional performance (rs2= .200). Fear of falling and subjective cognitive decline-functional performance are growing public health issues, and addressing those issues is a public priority. Proactive screening for fear of falling and subjective cognitive decline-functional performance is critical in fall prevention. A combination of all three self-reported tools (Short FES-I, SCD-FP, and CDC's Stay Independent checklist) takes less than 5 minutes to complete. Primary care providers or public health professionals should consider including these tools to screen fear of falling and subjective cognitive decline-functional performance as part of fall risk assessment, especially in low-income settings. Thus, encouraging older adults and healthcare professionals to discuss fear of falling, subjective cognitive decline, and fall risk during routine medical office visits.

Keywords: falls, fall risk, fear of falling, cognition, subjective cognitive decline, low-income, older adults, community, screening, nursing, primary care

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Authors: Yan Lu, Song Hong, Janakiraman Udaiyappan, Aarti Nagayach, Quoc-Viet A. Duong, Masao Morita, Shun Saito, Yuichi Kobayashi, Yuhai, Zhao, Hongying Peng, Nicholas B. Pham, Walter J Lukiw, Christopher A. Vuong, Nicolas G. Bazan

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Aims: Neurodegeneration and behavior dysfunction occurs in patients with Alzheimer's Disease (AD), and as the disease progresses many patients develop cognitive impairment. 5XFAD mouse model of AD is widely used to study AD pathogenesis and treatment. This study aimed to investigate the effect of maresin like 1 (MaR-L1) treatment in AD pathology using 5XFAD mice. Methods: We tested 12-month-old male 5XFAD mice and wild type control mice treated with MaR-L1 in a battery of behavioral tasks. We performed open field test, beam walking test, clasping test, inverted grid test, acetone test, marble burring test, elevated plus maze test, cross maze test and novel object recognition test. We also studied neuronal loss, amyloid β burden, and inflammation in the brains of 5XFAD mice using immunohistology and Western blotting. Results: MaR-L1 treatment to the 5XFAD mice showed improved cognitive function of 5XFAD mice. MaR-L1 showed decreased anxiety behavior in open field test and marble burring test, increased muscular strength in the beam walking test, clasping test and inverted grid test. Cognitive function was improved in MaR-L1 treated 5XFAD mice in the novel object recognition test. MaR-L1 prevented neuronal loss and aberrant inflammation. Conclusion: Our finding suggests that behavioral abnormalities were normalized by the administration of MaR-L1 and the neuroprotective role of MaR-L1 in the AD. It also indicates that MaR-L1 treatment is able to prevent and or ameliorate neuronal loss and aberrant inflammation. Further experiments to validate the results are warranted using other AD models in the future.

Keywords: Alzheimer's disease, motor and cognitive behavior, 5XFAD mice, Maresin Like 1, microglial cell, astrocyte, neurodegeneration, inflammation, resolution of inflammation

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Authors: Nouha Mhimdi, Wahiba Ben Abdessalem Karaa, Henda Ben Ghezala

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The methods identified in data collection are diverse: electronic media, focus group interviews and short-answer questionnaires [1]. The collection of poor-quality data resulting, for example, from poorly designed questionnaires, the absence of good translators or interpreters, and the incorrect recording of data allow conclusions to be drawn that are not supported by the data or to focus only on the average effect of the program or policy. There are several solutions to avoid or minimize the most frequent errors, including obtaining expert advice on the design or adaptation of data collection instruments; or use technologies allowing better "anonymity" in the responses [2]. In this context, we opted to collect good quality data by doing a sizeable questionnaire-based survey on hospital emergencies to improve emergency services and alleviate the problems encountered. At the level of this paper, we will present our study, and we will detail the steps followed to achieve the collection of relevant, consistent and practical data.

Keywords: data collection, survey, questionnaire, database, data analysis, hospital emergencies

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Authors: Ayse Demirbas, Bruce A. Welt, Yavuz Yagiz

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Oxidation of polyunsaturated fatty acids (PUFA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in fish causes loss of product quality. Oxidative rancidity causes loss of nutritional value and undesirable color changes. Therefore, powerful antioxidant extracts may provide a relatively low cost and natural means to reduce oxidation, resulting in longer, higher quality and higher value shelf life of foods. In this study, we measured effects of red cabbage antioxidant on lipid oxidation in fresh tilapia filets using thiobarbituric acid reactive substances (TBARS) assay, peroxide value (PV) and color assesment analysis. Extraction of red cabbage was performed using an efficient microwave method. Fresh tilapia filets were dipped in or sprayed with solutions containing different concentrations of extract. Samples were stored for up to 9 days at 4°C and analyzed every other day for color and lipid oxidation. Results showed that treated samples had lower oxidation than controls. Lipid peroxide values on treated samples showed benefits through day-7. Only slight differences were observed between spraying and dipping methods. This work shows that red cabbage antioxidant extracts may represent an inexpensive and all natural method for reducing oxidative spoilage of fresh fish.

Keywords: antioxidant, shelf life, fish, red cabbage, lipid oxidation

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Noor Shafifiyaz Mohd Yazid, Najihah Mohd Hashim, Hapipah Mohd Ali, Syam Mohan, Rosea Go

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Artocarpus kemando is a plant species from Moraceae family. This plant is used as household utensil by the local and the fruits are edible. The plants’ bark was used for the extraction process and yielded the chloroform crude extract which was used to screen for anticancer potential. The cytotoxic effect of the extract on CAOV-3 and WRL 68 cell lines were determined using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide or MTT assays. Qualitative AO/PI assay was performed to confirm the apoptosis and necrosis process. Meanwhile, the measurement of cell loss, nuclear morphology, DNA content, cell membrane permeability, mitochondrial membrane potential changes and cytochrome c release from mitochondria were detected through cytotoxicity 3 assay. In MTT assay, A. kemando inhibited 50% growth of CAOV-3 cells at 27.9 ± 0:03, 20.1± 0:03, 18.21± 0:04 µg/mL after 24, 48 and 72 hour, respectively. The morphology changes can be seen on CAOV-3 with a production of cell membrane blebbing, cromatin condensation and apoptotic bodies. Evaluation of cytotoxicity 3 on CAOV-3 cells after treated with extract resulting loss of mitochondrial membrane potential and release of cytochrome c from mitochondria. The results demonstrated A. kemando has potentially anticancer agent, particularly on human ovarian cancer.

Keywords: anticancer, Artocarpus kemando, ovarian cancer, cytotoxicity

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Authors: Francis Chinweuba Eboh, Tobias Richards

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In this study, a waste-to-energy combined heat and power plant under construction was modelled and simulated with the Aspen Plus software. The base case process plant was evaluated and compared when integrated with flue gas condensation (FGC) in order to find out the impact of the exergy efficiency and economic feasibility as well as the effect of overall system exergy losses and revenue generated in the investigated plant. The economic evaluations were carried out using the vendor cost data from Aspen process economic analyser. The results indicate that 4 % increase in the exergy efficiency and 29 % reduction in the exergy loss in the flue gas were obtained when the flue gas condensation was incorporated. Furthermore, with the integrated FGC, the net present values (NPV) and income generated in the base process plant were increased by 29 % and 10 % respectively after 20 years of operation.

Keywords: economic feasibility, exergy efficiency, exergy losses, flue gas condensation, waste-to-energy

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Authors: Daniel Brian Thompson, Subarmaniam Kannan

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The huge weightage of knowledge in this world and within the repository of organizations has already reached immense capacity and is constantly increasing as time goes by. To accommodate these constraints, Big Data implementation and algorithms are utilized to obtain new or enhanced knowledge for decision-making. With the transition from data to knowledge provides the transformational changes which will provide tangible benefits to the individual implementing these practices. Today, various organization would derive knowledge from observations and intuitions where this information or data will be translated into best practices for knowledge acquisition, generation and sharing. Through the widespread usage of Big Data, the main intention is to provide information that has been cleaned and analyzed to nurture tangible insights for an organization to apply to their knowledge-creation practices based on facts and figures. The translation of data into knowledge will generate value for an organization to make decisive decisions to proceed with the transition of best practices. Without a strong foundation of knowledge and Big Data, businesses are not able to grow and be enhanced within the competitive environment.

Keywords: big data, knowledge management, data driven, knowledge creation

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Authors: Masese Chuma Benard, Martin Onsiro Ronald

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Businesses have been using cloud computing more frequently recently because they wish to take advantage of its advantages. However, employing cloud computing also introduces new security concerns, particularly with regard to data security, potential risks and weaknesses that could be exploited by attackers, and various tactics and strategies that could be used to lessen these risks. This study examines data security issues on cloud computing amongst sme’s in Nairobi county, Kenya. The study used the sample size of 48, the research approach was mixed methods, The findings show that data owner has no control over the cloud merchant's data management procedures, there is no way to ensure that data is handled legally. This implies that you will lose control over the data stored in the cloud. Data and information stored in the cloud may face a range of availability issues due to internet outages; this can represent a significant risk to data kept in shared clouds. Integrity, availability, and secrecy are all mentioned.

Keywords: data security, cloud computing, information, information security, small and medium-sized firms (SMEs)

Procedia PDF Downloads 89

Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás

Abstract:

Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.

Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization

Procedia PDF Downloads 355

Authors: Glenda Marie D. Balitaan

Abstract:

The branch of business management that deals with inventory planning and control is known as inventory management. It comprises keeping track of supply levels and forecasting demand, as well as scheduling when and how to plan. Keeping excess inventory results in a loss of money, takes up physical space, and raises the risk of damage, spoilage, and loss. On the other hand, too little inventory frequently causes operations to be disrupted and raises the possibility of low customer satisfaction, both of which can be detrimental to a company's reputation. The United Victorious Feed mill Corporation's present inventory management practices were assessed in terms of inventory level, warehouse allocation, ordering frequency, shelf life, and production requirement. To help the company achieve their optimal level of inventory, a mathematical model was created using Integer Linear Programming. Due to the season, the goal function was to reduce the cost of purchasing US Soya and Yellow Corn. Warehouse space, annual production requirements, and shelf life were all considered. To ensure that the user only uses one application to record all relevant information, like production output and delivery, the researcher built a Visual Basic system. Additionally, the technology allows management to change the model's parameters.

Keywords: inventory management, integer linear programming, inventory management system, feed mill

Procedia PDF Downloads 86

Authors: Amitha Puranik, V. S. Binu, Seena Biju

Abstract:

Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

Procedia PDF Downloads 386

Authors: Urooj Saeed, Sajid Rashid Ahmad, Iqra Khalid, Sahar Mirza, Imtiaz Younas

Abstract:

Pakistan, a flood-prone country, has experienced worst floods in the recent past which have caused extensive damage to the urban and rural areas by loss of lives, damage to infrastructure and agricultural fields. Poor flood management system in the country has projected the risks of damages as the increasing frequency and magnitude of floods are felt as a consequence of climate change; affecting national economy directly or indirectly. To combat the needs of flood emergency, this paper focuses on remotely sensed data based approach for rapid mapping and monitoring of flood extent and its damages so that fast dissemination of information can be done, from local to national level. In this research study, spatial extent of the flooding caused by heavy rains of 2014 has been mapped by using space borne data to assess the crop damages and affected population in sixteen districts of Punjab. For this purpose, moderate resolution imaging spectroradiometer (MODIS) was used to daily mark the flood extent by using Normalised Difference Water Index (NDWI). The highest flood value data was integrated with the LandScan 2014, 1km x 1km grid based population, to calculate the affected population in flood hazard zone. It was estimated that the floods covered an area of 16,870 square kilometers, with 3.0 million population affected. Moreover, to assess the flood damages, Object Based Image Analysis (OBIA) aided with spectral signatures was applied on Landsat image to attain the thematic layers of healthy (0.54 million acre) and damaged crops (0.43 million acre). The study yields that the population of Jhang district (28% of 2.5 million population) was affected the most. Whereas, in terms of crops, Jhang and Muzzafargarh are the ‘highest damaged’ ranked district of floods 2014 in Punjab. This study was completed within 24 hours of the peak flood time, and proves to be an effective methodology for rapid assessment of damages due to flood hazard

Keywords: flood hazard, space borne data, object based image analysis, rapid damage assessment

Procedia PDF Downloads 331

Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

Abstract:

Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

Procedia PDF Downloads 166