Search results for: data block

Authors: Ermelinda Bloise, Giuseppe Mele

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In recent years, Cashew Nut Shell Liquid (CNSL) has received great attention from researchers because it is an abundant waste material from the agri-food industry that fits perfectly into the idea of reusing waste from renewable resources for the production of new functional materials. The different components of this waste showed a certain chemical versatility and, above all, various biological activities. Take advantage of their surface-active capacity in particular conditions, various amphiphilic nanostructures have been prepared through sustainable chemical processes using cardanol (CA) and anacardic acid (AA) as two main components of the CNSL. In-batch solvent-free method has been developed to obtain new versatile green nanovesicles capable of effectively incorporating and stabilizing both hydrophobic and hydrophilic bioactive molecules. Furthermore, these nanosystems have shown antioxidant and cytotoxic properties and, in vitroinvestigations, established that they efficiently taken-up some human cells. With the idea of meeting the principles of green chemistry, even more, some improvements of the synthetic procedure have been implemented in terms of milder temperature and pH conditions, producing one-component nanovesicles, in which the AA and CA-derivatives are the sole building block of the green nanosystems. Finally, a new experimental approach has been carried out by a microfluidic route, with the advantage to operate at continuous flows, with a reduced amount of reagents, waste, and at lower temperatures, ensuring the achievement of size-monodisperse amphiphilic nanostructures that do not need further purification steps.

Keywords: bioactive nanosystems, bio-based renewables, cashew oil, green nanoformulations

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Authors: Shrey Sharma, Gunjan Kumar Verma

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High density electrical prospecting has been widely used in groundwater investigation, civil engineering and environmental survey. For efficient inversion, the forward modeling routine, sensitivity calculation, and inversion algorithm must be efficient. This paper attempts to provide a brief summary of the past and ongoing developments of the method. It includes reviews of the procedures used for data acquisition, processing and inversion of electrical resistivity data based on compilation of academic literature. In recent times there had been a significant evolution in field survey designs and data inversion techniques for the resistivity method. In general 2-D inversion for resistivity data is carried out using the linearized least-square method with the local optimization technique .Multi-electrode and multi-channel systems have made it possible to conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve complex geological structures that were not possible with traditional 1-D surveys. 3-D surveys play an increasingly important role in very complex areas where 2-D models suffer from artifacts due to off-line structures. Continued developments in computation technology, as well as fast data inversion techniques and software, have made it possible to use optimization techniques to obtain model parameters to a higher accuracy. A brief discussion on the limitations of the electrical resistivity method has also been presented.

Keywords: inversion, limitations, optimization, resistivity

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Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

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Authors: Estela Mayra de Moura Vianna, Thiago José Tavares Ávila, Bruno Morais Silva, Diego Henrique Bezerra, Paulo Henrique Gomes Silva, Alan Pedro da Silva

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The Brazilian Federal Constitution defines the access to information as a crucial right of the citizen and the Law on Access to Public Information, which regulates this right. Accordingly, the Fiscal Responsibility Act, 2000, amended in 2009 by the “Law of Transparency”, began demanding a wider disclosure of public accounts for the society, including electronic media for public access. Thus, public entities began to create "Transparency Portals," which aim to gather a diversity of data and information. However, this information, in general, is still published in formats that do not simplify understanding of the data by citizens and that could be better especially available for audit purposes. In this context, a proposal of ontology about Brazilian Transparency Portal can play a key role in how these data will be better available. This study aims to identify and implement in ontology, the data model about Transparency Portal ecosystem, with emphasis in activities that use these data for some applications, like audits, press activities, social government control, and others.

Keywords: audit, government transparency, ontology, public sector

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Authors: Grace Omowunmi Soyebi

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Data Mining is the extraction of information from a large database which helps in predicting a trend or behavior, thereby helping management make knowledge-driven decisions. One principal problem of most hospitals in rural areas is making use of the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This Data Mining application is to be designed using a Structured System Analysis and design method, which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the Design and Implementation of a Computerized medical record system. This Computerized system will replace the file management system and help to easily retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: data mining, medical record system, systems programming, computing

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Authors: Loh Fu Quan, Fong Zi Heng, Burra Venkata Durga Kumar

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Cloud computing has completely transformed the way many businesses operate. Traditionally, confidential data of a business is stored in computers located within the premise of the business. Therefore, a lot of business capital is put towards maintaining computing resources and hiring IT teams to manage them. The advent of cloud computing changes everything. Instead of purchasing and managing their infrastructure, many businesses have started to shift towards working with the cloud with the help of a cloud service provider (CSP), leading to cost savings. However, it also introduces security risks. This research paper focuses on the security risks that arise during data migration and user authentication in cloud computing. To overcome this problem, this paper provides a comprehensive framework that includes Transport Layer Security (TLS), user authentication, security tokens and multi-level data encryption. This framework aims to prevent authorized access to cloud resources and data leakage, ensuring the confidentiality of sensitive information. This framework can be used by cloud service providers to strengthen the security of their cloud and instil confidence in their users.

Keywords: Cloud computing, Cloud security, Cloud security issues, Cloud security framework

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Eyob Seife, Tesfalem Teshome, Bereket Seyoum, Behailu Getachew, Yohans Demis

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Developing countries continue to face preventable communicable diseases, such as vaccine-preventable diseases. The Expanded Programme on Immunization (EPI) was established by the World Health Organization in 1974 to control these diseases. Health data use is crucial in decision-making, but ensuring data quality remains challenging. The study aimed to assess the accuracy ratio, timeliness, and quality index of regular immunization programme data in the Birkod district of the Somali Region, Ethiopia. For poor data quality, technical, contextual, behavioral, and organizational factors are among contributors. The study used a quantitative cross-sectional design conducted in September 2022GC using WHO-recommended data quality self-assessment tools. The accuracy ratio and timeliness of reports on regular immunization programmes were assessed for two health centers and three health posts in the district for one fiscal year. Moreover, the quality index assessment was conducted at the district level and health facilities by trained assessors. The study found poor data quality in the accuracy ratio and timeliness of reports at all health units, which includes zeros. Overreporting was observed for most facilities, particularly at the health post level. Health centers showed a relatively better accuracy ratio than health posts. The quality index assessment revealed poor quality at all levels. The study recommends that responsible bodies at different levels improve data quality using various approaches, such as the capacitation of health professionals and strengthening the quality index components. The study highlighted the need for attention to data quality in general, specifically at the health post level, and improving the quality index at all levels, which is essential.

Keywords: Birkod District, data quality, quality index, regular immunization programme, Somali Region-Ethiopia

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Authors: Dina L. DiSantis

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Strengthening a sense of responsibility while relating global sustainability concepts such as water quality and pollution to a local water system can be achieved by teaching students to conduct and interpret water quality monitoring tests. When students conduct their own research, they become better stewards of the environment. Providing outdoor learning and place-based opportunities for students helps connect them to the natural world. By conducting stream studies and collecting data, students are able to better understand how the natural environment is a place where everything is connected. Students have been collecting physical, chemical and biological data along the West and East Branches of the Brandywine River, in Pennsylvania for over ten years. The stream studies are part of the advanced placement environmental science and aquatic science courses that are offered as electives to juniors and seniors at the Downingtown High School West Campus in Downingtown, Pennsylvania. Physical data collected includes: temperature, turbidity, width, depth, velocity, and volume of flow or discharge. The chemical tests conducted are: dissolved oxygen, carbon dioxide, pH, nitrates, alkalinity and phosphates. Macroinvertebrates are collected with a kick net, identified and then released. Students collect the data from several locations while traveling by canoe. In the classroom, students prepare a water quality data analysis and interpretation report based on their collected data. The summary of the results from longitudinal water quality data collection by students, as well as the strengths and weaknesses of student data collection will be presented.

Keywords: place-based, student data collection, sustainability, water quality monitoring

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Authors: Geetakshi Arora, Danish Malhotra

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Diabetic wounds are serious health issues and often fail to heal, leading to limb amputation that makes the life of the patient miserable. Delayed wound healing has been characterized by an increase in matrix metalloproteinase-9 (MMP-9). Thus research throughout the world has been going on to develop selective MMP-9 inhibitors for aiding diabetic wound healing. Bioactive constituents from natural sources always served as potential leads in drug development with high rates of success. Considering the need for novel selective MMP-9 inhibitors and the importance of natural bioactive compounds in drug development, we have screened a library of bioactive constituents from plant sources that were effective in diabetic wound healing on human MMP-9 (hMMP-9) using molecular docking studies. Screened constituents are ranked according to their dock score, ∆G value (binding affinity), and Ligand efficiency evaluated from FleXX docking and Hyde scoring modules available with drug designing platform LeadIT. Rhamnocitrin showed the highest correlation between dock score, ∆G value (binding affinity), and Ligand efficiency was further explored for binding interactions with hMMP-9. The overall study suggest that Rhamnocitrin is sufficiently decorated with both hydrophilic and hydrophobic substitutions that perfectly block hMMP-9 and act as a potential lead in the design and development of selective hMMP-9 inhibitors.

Keywords: MMP-9, diabetic wound, molecular docking, phytoconstituents

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Authors: Ali Karrech, Alberto Puccini, Ben Galvin, Davide Galli

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This paper summarises the laboratory tests, numerical models and statistical approach developed to investigate the behaviour of concrete blocks loaded in shear through metallic anchors. This research is proposed to bridge a gap in the state of the art and practice related to anchors loaded against partially confined concrete surfaces. Eight concrete blocks (420 mm x 500 mm x 1000 mm) with 150 and/or 250 deep anchors were tested. The stainless-steel anchors of diameter 16 mm were bonded with HIT-RE 500 V4 injection epoxy resin and were subjected to shear loading against partially supported edges. In addition, finite element models were constructed to validate the laboratory tests and explore the influence of key parameters such as anchor depth, anchor distance from the edge, and compressive strength on the stability of the block. Upon their validation experimentally, the numerical results were used to populate, develop and interpret a systematic parametric study based on the Design of Experiment approach through the Box-Behnken design and Response Surface Methodology. An empirical model has been derived based on this approach, which predicts the load capacity with the desirable intervals of confidence.

Keywords: finite element modelling, design of experiment, response surface methodology, Box-Behnken design, empirical model, interval of confidence, load capacity

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Authors: Mariia V. Efremova, Iana O. Tcareva, Anastasia D. Blokhina, Ivan S. Grebennikov, Anastasia S. Garanina, Maxim A. Abakumov, Yury I. Golovin, Alexander G. Savchenko, Alexander G. Majouga, Natalya L. Klyachko

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During last decades magnetite nanoparticles (NPs) attract a deep interest of scientists due to their potential application in therapy and diagnostics. However, magnetite nanoparticles are toxic and non-stable in physiological conditions. To solve these problems, we decided to create two types of hybrid systems based on magnetite and gold which is inert and biocompatible: gold as a shell material (first type) and gold as separate NPs interfacially bond to magnetite NPs (second type). The synthesis of the first type hybrid nanoparticles was carried out as follows: Magnetite nanoparticles with an average diameter of 9±2 nm were obtained by co-precipitation of iron (II, III) chlorides then they were covered with gold shell by iterative reduction of hydrogen tetrachloroaurate with hydroxylamine hydrochloride. According to the TEM, ICP MS and EDX data, final nanoparticles had an average diameter of 31±4 nm and contained iron even after hydrochloric acid treatment. However, iron signals (K-line, 7,1 keV) were not localized so we can’t speak about one single magnetic core. Described nanoparticles covered with mercapto-PEG acid were non-toxic for human prostate cancer PC-3/ LNCaP cell lines (more than 90% survived cells as compared to control) and had high R2-relaxivity rates (>190 mМ-1s-1) that exceed the transverse relaxation rate of commercial MRI-contrasting agents. These nanoparticles were also used for chymotrypsin enzyme immobilization. The effect of alternating magnetic field on catalytic properties of chymotrypsin immobilized on magnetite nanoparticles, notably the slowdown of catalyzed reaction at the level of 35-40 % was found. The synthesis of the second type hybrid nanoparticles also involved two steps. Firstly, spherical gold nanoparticles with an average diameter of 9±2 nm were synthesized by the reduction of hydrogen tetrachloroaurate with oleylamine; secondly, they were used as seeds during magnetite synthesis by thermal decomposition of iron pentacarbonyl in octadecene. As a result, so-called dumbbell-like structures were obtained where magnetite (cubes with 25±6 nm diagonal) and gold nanoparticles were connected together pairwise. By HRTEM method (first time for this type of structure) an epitaxial growth of magnetite nanoparticles on gold surface with co-orientation of (111) planes was discovered. These nanoparticles were transferred into water by means of block-copolymer Pluronic F127 then loaded with anti-cancer drug doxorubicin and also PSMA-vector specific for LNCaP cell line. Obtained nanoparticles were found to have moderate toxicity for human prostate cancer cells and got into the intracellular space after 45 minutes of incubation (according to fluorescence microscopy data). These materials are also perspective from MRI point of view (R2-relaxivity rates >70 mМ-1s-1). Thereby, in this work magnetite-gold hybrid nanoparticles, which have a strong potential for biomedical application, particularly in targeted drug delivery and magnetic resonance imaging, were synthesized and characterized. That paves the way to the development of special medicine types – theranostics. The authors knowledge financial support from Ministry of Education and Science of the Russian Federation (14.607.21.0132, RFMEFI60715X0132). This work was also supported by Grant of Ministry of Education and Science of the Russian Federation К1-2014-022, Grant of Russian Scientific Foundation 14-13-00731 and MSU development program 5.13.

Keywords: drug delivery, magnetite-gold, MRI contrast agents, nanoparticles, toxicity

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Authors: Ye Wang, Ickjai Lee

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Due to the widespread of mobile sensing, there is a strong need to handle trails of moving objects, trajectories. This paper proposes three visual analytic approaches for higher order information of trajectory data sets based on the higher order Voronoi diagram data structure. Proposed approaches reveal geometrical information, topological, and directional information. Experimental results demonstrate the applicability and usefulness of proposed three approaches.

Keywords: visual analytics, higher order information, trajectory datasets, spatio-temporal data

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Authors: Sean Paulsen, Michael Casey

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In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training

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Authors: A. Armin, R. Fotouhi, W. Szyszkowski

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This paper is part of a study to develop robots for farming. As such power requirement to operate equipment attach to such robots become an important factor. Soil-tool interaction play major role in power consumption, thus predicting accurately the forces which act on the blade during the farming is prime importance for optimal designing of farm equipment. In this paper a finite element investigation for tillage tools and soil interaction is described by using an inelastic constitutive material law for agriculture application. A 3-dimentional (3D) nonlinear finite element analysis (FEA) is developed to examine behavior of a blade with different rake angles moving in a block of soil, and to estimate the blade force. The soil model considered is an elastic-plastic with non-associated Drucker-Prager material model. Special use of contact elements are employed to consider connection between soil-blade and soil-soil surfaces. The FEA results are compared with experiment ones, which show good agreement in accurately predicting draft forces developed on the blade when it moves through the soil. Also, a very good correlation was obtained between FEA results and analytical results from classical soil mechanics theories for straight blades. These comparisons verified the FEA model developed. For analyzing complicated soil-tool interactions and for optimum design of blades, this method will be useful.

Keywords: finite element analysis, soil-blade contact modeling, blade force, mechanical engineering

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Authors: Vitali Kremez

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The researcher monitored an organized ransomware campaign in order to gain significant visibility into the tactics, techniques, and procedures employed by a campaign boss operating a ransomware scheme out of Russia. As the Russian hacking community lowered the access requirements for unsophisticated Russian cybercriminals to engage in ransomware campaigns, corporations and individuals face a commensurately greater challenge of effectively protecting their data and operations from being held ransom. This report discusses two notorious ransomware campaigns. Though the loss of data can be devastating, the findings demonstrate that sending ransom payments does not always help obtain data. Key learnings: 1. From the ransomware affiliate perspective, such campaigns have significantly lowered the barriers for entry for low-tier cybercriminals. 2. Ransomware revenue amounts are not as glamorous and fruitful as they are often publicly reported. Average ransomware crime bosses make only $90K per year on average. 3. Data gathered indicates that sending ransom payments does not always help obtain data. 4. The talk provides the complete payout structure and Bitcoin laundering operation related to the ransomware-as-a-service campaign.

Keywords: bitcoin, cybercrime, ransomware, Russia

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Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

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Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: marginal gingivitis, cross-sectional, retrograde, prevalence

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Authors: Mustafa Aydın, Ali Murat Sunbul

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Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes.

Keywords: hierarchical linear modeling, nested data, hierarchical structure, data structure

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Authors: Ralf Gliniorz, Carolin Petzoldt, Andreas Ehrlich, Sandra Gelbrich, Lothar Kroll

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The building envelope is integral part of buildings, and renewable resources have a key role in energy consumption. So our aim was the development and implementation of a free forming facade system, consisting of fibre-reinforced polymer, which is built up of commercial biobased resin systems and natural fibre reinforcement. The field of application is aimed in modern architecture, like the office block 'Fachagentur Nachwachsende Rohstoffe e.V.' with its oak wood recyclate facade. The build-up of our elements is a classically sandwich-structured composite: face sheets as fibre-reinforced composite using polymer matrix, here a biobased epoxy, and natural fibres. The biobased core consists of stuck cardboard structure (BC-flute). Each element is manufactured from two shells in a counterpart, via hand lay-up laminate. These natural fibre skins and cardboard core have adhered 'wet-on-wet'. As a result, you get the effect of translucent face sheets with matrix illumination. Each created pixel can be controlled in RGB-colours and form together a screen at buildings. A 10 x 5 m² area 'NFP-BIO' with 25 elements is planned as a reference object in Chemnitz. The resolution is about 100 x 50 pixels. Specials are also the efficient technology of production and the possibility to extensively 3D-formed elements for buildings, replacing customary facade systems, which can give out information or advertising.

Keywords: biobased facade, cardboard core, natural fibre skins, sandwich element

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Authors: Lorna Goulden, Kai M. Hermsen, Jari Isohanni, Mirko Ross, Jef Vanbockryck

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This article introduces disposable identities, with reference use cases and explores possible technical approaches. The proposed approach, when fully developed as an open-source toolkit, enables developers of mobile or web apps to employ a self-sovereign identity and data privacy framework, in order to rebuild trust in digital services by providing greater transparency, decentralized control, and GDPR compliance. With a user interface for the management of self-sovereign identity, digital authorizations, and associated data-driven transactions, the advantage of Disposable Identities is that they may also contain verifiable data such as the owner’s photograph, official or even biometric identifiers for more proactive prevention of identity abuse. These Disposable Identities designed for decentralized privacy management can also be time, purpose and context-bound through a secure digital contract; with verification functionalities based on tamper-proof technology.

Keywords: dentity, trust, self-sovereign, disposable identity, privacy toolkit, decentralised identity, verifiable credential, cybersecurity, data driven business, PETs, GDPRdentity, trust, self-sovereign, disposable identity, privacy toolkit, decentralised identity, verifiable credential, cybersecurity, data driven business, PETs, GDPRI

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Authors: Hongbin Yang, Mingxue Liao, Changwen Zheng, Mengyao Kong, Chaohui Liu

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Microfacet-based reflection models are widely used to model light reflections for rough surfaces. Microfacet models have become the standard surface material building block for describing specular components with varying roughness; and yet, while they possess many desirable properties as well as produce convincing results, their design ignores important sources of scattering, which can cause a significant loss of energy. Specifically, they only simulate the single scattering on the microfacets and ignore the subsequent interactions. As the roughness increases, the interaction will become more and more important. So a multiple-scattering microfacet model based on specular V-cavity is presented for this important open problem. However, it spends much unnecessary rendering time because of setting the same number of scatterings for different roughness surfaces. In this paper, we design a geometric attenuation term G to compute the BRDF (Bidirectional reflection distribution function) of multiple scattering of rough surfaces. Moreover, we consider determining the number of scattering by deterministic heuristics for different roughness surfaces. As a result, our model produces a similar appearance of the objects with the state of the art model with significantly improved rendering efficiency. Finally, we derive a multiple scattering BRDF based on the original microfacet framework.

Keywords: bidirectional reflection distribution function, BRDF, geometric attenuation term, multiple scattering, V-cavity model

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Authors: Lethola Tshikose, Munyaradzi Katurura

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Information and Communication Technology (ICT) plays a critical role in improving daily healthcare processes. The South African healthcare organizations have adopted Information Systems to integrate their patient records. This has made it much easier for healthcare organizations because patient information can now be accessible at any time. The primary purpose of this research study was to investigate the best practices that can be applied to enhance patient security and confidentiality when using e-health systems in South Africa. Security and confidentiality are critical in healthcare organizations as they ensure safety in EHRs. The research study used an inductive research approach that included a thorough literature review; therefore, no data was collected. The research paper’s scope included patient data and possible security threats associated with healthcare systems. According to the study, South African healthcare organizations discovered various patient data security and confidentiality issues. The study also revealed that when it comes to handling patient data, health professionals sometimes make mistakes. Some may not be computer literate, which posed issues and caused data to be tempered with. The research paper recommends that healthcare organizations ensure that security measures are adequately supported and promoted by their IT department. This will ensure that adequate resources are distributed to keep patient data secure and confidential. Healthcare organizations must correctly use standards set up by IT specialists to solve patient data security and confidentiality issues. Healthcare organizations must make sure that their organizational structures are adaptable to improve security and confidentiality.

Keywords: E-health, EHR, security, confidentiality, healthcare

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Authors: Richard Byrne, Emma Mulliner

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Smart cities are a vision for the future that is increasingly becoming a reality. While a key concept of the smart city is the ability to capture, communicate, and process data that has long been produced through day-to-day activities of the city, much of the assessment models in place neglect this fact to focus on ‘smartness’ concepts. Although it is true technology often provides the opportunity to capture and communicate data in more effective ways, there are also human processes involved that are just as important. The growing importance with regards to the use and ownership of data in society can be seen by all with companies such as Facebook and Google increasingly coming under the microscope, however, why is the same scrutiny not applied to cities? The research area is therefore of great importance to the future of our cities here and now, while the findings will be of just as great importance to our children in the future. This research aims to understand the influence data is having on organisations operating throughout the smart cities sector and employs a mixed-method research approach in order to best answer the following question: Would a data-based evaluation model for smart cities be more appropriate than a smart-based model in assessing the development of the smart city? A fully comprehensive literature review concluded that there was a requirement for a data-driven assessment model for smart cities. This was followed by a documentary analysis to understand the root source of data integration to the smart city. A content analysis of city data platforms enquired as to the alternative approaches employed by cities throughout the UK and draws on best practice from New York to compare and contrast. Grounded in theory, the research findings to this point formulated a qualitative analysis framework comprised of: the changing environment influenced by data, the value of data in the smart city, the data ecosystem of the smart city and organisational response to the data orientated environment. The framework was applied to analyse primary data collected through the form of interviews with both public and private organisations operating throughout the smart cities sector. The work to date represents the first stage of data collection that will be built upon by a quantitative research investigation into the feasibility of data network effects in the smart city. An analysis into the benefits of data interoperability supporting services to the smart city in the areas of health and transport will conclude the research to achieve the aim of inductively forming a framework that can be applied to future smart city policy. To conclude, the research recognises the influence of technological perspectives in the development of smart cities to date and highlights this as a challenge to introduce theory applied with a planning dimension. The primary researcher has utilised their experience working in the public sector throughout the investigation to reflect upon what is perceived as a gap in practice of where we are today, to where we need to be tomorrow.

Keywords: data, planning, policy development, smart cities

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Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Vivian Isaac, A. T. Joteeshwaran, Craig McLachlan

Abstract:

The University of New South Wales (UNSW) Rural Clinical School has a strategic collaborative focus on chronic disease and public health. Our objectives are to understand rural environmental and biological interactions in vulnerable community populations. The UNSW Rural Clinical School translational model is a spoke and hub network. This spoke and hub model connects rural data and biological specimens with city based collaborative public health research networks. Similar spoke and hub models are prevalent across research centers in India. The Australia-India Council grant was awarded so we could establish sustainable public health and community research collaborations. As part of the collaborative network we are developing strategies around data and biological sharing platforms between Indian Institute of Public Health, Public Health Foundation of India (PHFI), Hyderabad and Rural Clinical School UNSW. The key objective is to understand how research collaborations are conducted in India and also how data can shared and tracked with external collaborators such as ourselves. A framework to improve data sharing for research collaborations, including DNA was proposed as a project outcome. The complexities of sharing biological data has been investigated via a visit to India. A flagship sustainable project between Rural Clinical School UNSW and PHFI would illustrate a model of data sharing platforms.

Keywords: data sharing, collaboration, public health research, chronic disease

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Authors: Iman Abu-Rub

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This research paper examines if Artificial Intelligence is, in fact, racist or not. Different studies from all around the world, and covering different communities were analyzed to further understand AI’s true implications over different communities. The black community, Asian community, and Muslim community were all analyzed and discussed in the paper to figure out if AI is biased or unbiased towards these specific communities. It was found that the biggest problem AI faces is the biased distribution of data collection. Most of the data inserted and coded into AI are of a white male, which significantly affects the other communities in terms of reliable cultural, political, or medical research. Nonetheless, there are various research was done that help increase awareness of this issue, but also solve it completely if done correctly. Governments and big corporations are able to implement different strategies into their AI inventions to avoid any racist results, which could cause hatred culturally but also unreliable data, medically, for example. Overall, Artificial Intelligence is not racist per se, but the data implementation and current racist culture online manipulate AI to become racist.

Keywords: social media, artificial intelligence, racism, discrimination

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Authors: Chico Horacio Jose Sambo

Abstract:

Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Usman Qamar, Younus Javed

Abstract:

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: Salihah Alghamdi, Surajit Ray

Abstract:

Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

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