Search results for: health data

Authors: Agria Rhamdhan

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WhatsApp as the most popular mobile messaging app has been used as evidence in many criminal cases. As the use of mobile messages generates large amounts of data, forensic investigation faces the challenge of large data problems. The hardest part of finding this important evidence is because current practice utilizes tools and technique that require manual analysis to check all messages. That way, analyze large sets of mobile messaging data will take a lot of time and effort. Our work offers methodologies based on forensic triage to reduce large data to manageable sets resulting easier to do detailed reviews, then show the results through interactive visualization to show important term, entities and relationship through intelligent ranking using Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) Model. By implementing this methodology, investigators can improve investigation processing time and result's accuracy.

Keywords: forensics, triage, visualization, WhatsApp

Procedia PDF Downloads 173

Authors: Mohkammad Nur Cahyadi, Hepi Hapsari Handayani, Agus Budi Raharjo, Ronny Mardianto, Daud Wahyu Imani, Arizal Bawazir, Luki Adi Triawan

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PDAM (local water company) determines customer charges by considering the customer's building or house. Charges determination significantly affects PDAM income and customer costs because the PDAM applies a subsidy policy for customers classified as small households. Periodic updates are needed so that pricing is in line with the target. A thorough customer survey in Surabaya is needed to update customer building data. However, the survey that has been carried out so far has been by deploying officers to conduct one-by-one surveys for each PDAM customer. Surveys with this method require a lot of effort and cost. For this reason, this research offers a technology called moblie mapping, a mapping method that is more efficient in terms of time and cost. The use of this tool is also quite simple, where the device will be installed in the car so that it can record the surrounding buildings while the car is running. Mobile mapping technology generally uses lidar sensors equipped with GNSS, but this technology requires high costs. In overcoming this problem, this research develops low-cost mobile mapping technology using a webcam camera sensor added to the GNSS and IMU sensors. The camera used has specifications of 3MP with a resolution of 720 and a diagonal field of view of 78⁰. The principle of this invention is to integrate four camera sensors, a GNSS webcam, and GPS to acquire photo data, which is equipped with location data (latitude, longitude) and IMU (roll, pitch, yaw). This device is also equipped with a tripod and a vacuum cleaner to attach to the car's roof so it doesn't fall off while running. The output data from this technology will be analyzed with artificial intelligence to reduce similar data (Cosine Similarity) and then classify building types. Data reduction is used to eliminate similar data and maintain the image that displays the complete house so that it can be processed for later classification of buildings. The AI method used is transfer learning by utilizing a trained model named VGG-16. From the analysis of similarity data, it was found that the data reduction reached 50%. Then georeferencing is done using the Google Maps API to get address information according to the coordinates in the data. After that, geographic join is done to link survey data with customer data already owned by PDAM Surya Sembada Surabaya.

Keywords: mobile mapping, GNSS, IMU, similarity, classification

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Authors: Ram Sharan Mehta, Naveen Kumar Pandey, Binod Kumar Deo

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Quality of Life (QOL) is an important component in the evaluation of the well-being of People Living with AIDS (PLWA). This study assessed the effectiveness of education intervention programme in improving the QOL of PLWA on ART attaining the ART-clinics at B. P. Koirala Institute of Health Sciences (BPKIHS), Nepal. A pre-experimental research design was used to conduct the study among the PLWA on ART at BPKIHS from June to August 2013 involving 60 PLWA on pre-test randomly. The mean age of the respondents was 36.70 ± 9.92, and majority of them (80%) were of age group of 25-50 years and Male (56.7%). After education intervention programme there is significant change in the QOL in all the four domains i.e. Physical (p=0.008), Psychological (p=0.019), Social (p=0.046) and Environmental (p=0.032) using student t-test at 0.05 level of significance. There is significant (p= 0.016) difference in the mean QOL scores of pre-test and post-test. High QOL scores in post-test after education intervention programme may reflective of the effectiveness of planned education interventions programme.

Keywords: telephone, AIDS, health service, Nepal

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Authors: Abraham Motlhabane

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This research analysed the written views of science education students regarding the teaching of laboratory work using the online mode. The research adopted the qualitative methodology. The qualitative research was aimed at investigating small and distinct groups normally regarded as a single-site study. Qualitative research was used to describe and analyze the phenomena from the student’s perspective. This means the research began with assumptions of the world view that use theoretical lenses of research problems inquiring into the meaning of individual students. The research was conducted with three groups of students studying for Postgraduate Certificate in Education, Bachelor of Education and honors Bachelor of Education respectively. In each of the study programmes, the science education module is compulsory. Five science education students from each study programme were purposively selected to participate in this research. Therefore, 15 students participated in the research. In order to analysis the data, the data were first printed and hard copies were used in the analysis. The data was read several times and key concepts and ideas were highlighted. Themes and patterns were identified to describe the data. Coding as a process of organising and sorting data was used. The findings of the study are very diverse; some students are in favour of online laboratory whereas other students argue that science can only be learnt through hands-on experimentation.

Keywords: online learning, laboratory work, views, perceptions

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Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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Modern experiments in high energy physics impose great demands on the reliability, the efficiency, and the data rate of Data Acquisition Systems (DAQ). This contribution focuses on the development and deployment of the new communication library DIALOG for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. The iFDAQ utilizing a hardware event builder is designed to be able to readout data at the maximum rate of the experiment. The DIALOG library is a communication system both for distributed and mixed environments, it provides a network transparent inter-process communication layer. Using the high-performance and modern C++ framework Qt and its Qt Network API, the DIALOG library presents an alternative to the previously used DIM library. The DIALOG library was fully incorporated to all processes in the iFDAQ during the run 2016. From the software point of view, it might be considered as a significant improvement of iFDAQ in comparison with the previous run. To extend the possibilities of debugging, the online monitoring of communication among processes via DIALOG GUI is a desirable feature. In the paper, we present the DIALOG library from several insights and discuss it in a detailed way. Moreover, the efficiency measurement and comparison with the DIM library with respect to the iFDAQ requirements is provided.

Keywords: data acquisition system, DIALOG library, DIM library, FPGA, Qt framework, TCP/IP

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Addisu Tadesse Sahile, Tennyson Mgutshini

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Background: Periodontal disease is a common, complex, inflammatory disease characterized by the destruction of tooth-supporting soft and hard tissues of the periodontium and a major public health problem across developed and developing countries. Objectives: The study was aimed at assessing the prevalence of periodontal disease and associated factors among diabetes patients in Addis Ababa, Ethiopia, 2018. Methods: Institutional based cross-sectional study was conducted on 388 diabetes patients selected by systematic random sampling method from March to May 2018. The study was conducted at two conveniently selected public hospitals in Addis Ababa. Data were collected with pre-tested, structured and translated questionnaire then entered to SPSS version 23 software for analysis. Descriptive statistics as a summary, in line with chi-square and binary logistics regression to identify factors associated with periodontal disease, were applied. A 95% CI with a p-value less than 5% was used as a level of significance. Results: Ninety-one percent (n=353) of participants had periodontal disease while oral examination was done in six regions. While only 9% (n=35) of participants were free of periodontal disease. The number of tooth brushings per day, correct techniques of brushing, malocclusion, and fillings that are defective were associated with periodontal disease at p < 0.05. Conclusion and recommendation: A higher prevalence of periodontal disease among diabetes patient was observed. The frequency of tooth brushing, correct techniques of brushing, malocclusion and defective fillings were associated with periodontal disease. Emphasis has to be given to oral health of diabetes patients by every concerned body so as to control the current higher burden of periodontal disease in diabetes.

Keywords: periodontal disease, risk factors, diabetes mellitus, Addis Ababa

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: M. M. Ali, Ahmed Al- Ani, Derek Eamus, Daniel K. Y. Tan

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In this glasshouse study, we developed the new image-based non-destructive technique for detecting leaf P status of different crops such as cotton, tomato and lettuce. Plants were allowed to grow on nutrient media containing different P concentrations, i.e. 0%, 50% and 100% of recommended P concentration (P0 = no P, L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P as NaH2PO4). After 10 weeks of growth, plants were harvested and data on leaf P contents were collected using the standard destructive laboratory method and at the same time leaf images were collected by a handheld crop image sensor. We calculated leaf area, leaf perimeter and RGB (red, green and blue) values of these images. This data was further used in the linear discriminant analysis (LDA) to estimate leaf P contents, which successfully classified these plants on the basis of leaf P contents. The data indicated that P deficiency in crop plants can be predicted using the image and morphological data. Our proposed non-destructive imaging method is precise in estimating P requirements of different crop species.

Keywords: image-based techniques, leaf area, leaf P contents, linear discriminant analysis

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Authors: Lucie Bielská, Lucia Škulcová, Martina Hvězdová, Jakub Hofman, Zdeněk Šimek

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The currently used pesticides represent a broad group of chemicals with various physicochemical and environmental properties which input has reached 2×106 tons/year and is expected to even increases. From that amount, only 1% directly interacts with the target organism while the rest represents a potential risk to the environment and human health. Despite being authorized and approved for field applications, the effects of pesticides in the environment can differ from the model scenarios due to the various pesticide-soil interactions and resulting modified fate and behavior. As such, a direct monitoring of pesticide residues and evaluation of their impact on soil biota, aquatic environment, food contamination, and human health should be performed to prevent environmental and economic damages. The present project focuses on fluvisols as they are intensively used in the agriculture but face to several environmental stressors. Fluvisols develop in the vicinity of rivers by the periodic settling of alluvial sediments and periodic interruptions to pedogenesis by flooding. As a result, fluvisols exhibit very high yields per area unit, are intensively used and loaded by pesticides. Regarding the floods, their regular contacts with surface water arise from serious concerns about the surface water contamination. In order to monitor pesticide residues and assess their environmental and biological impact within this project, 70 fluvisols were sampled over the Czech Republic and analyzed for the total and bioaccessible amounts of 40 various pesticides. For that purpose, methodologies for the pesticide extraction and analysis with liquid chromatography-mass spectrometry technique were developed and optimized. To assess the biological risks, both the earthworm bioaccumulation tests and various types of passive sampling techniques (XAD resin, Chemcatcher, and silicon rubber) were optimized and applied. These data on chemical analysis and bioavailability were combined with the results of soil analysis, including the measurement of basic physicochemical soil properties as well detailed characterization of soil organic matter with the advanced method of diffuse reflectance infrared spectrometry. The results provide unique data on the residual levels of pesticides in the Czech Republic and on the factors responsible for increased pesticide residue levels that should be included in the modeling of pesticide fate and effects.

Keywords: currently used pesticides, fluvisoils, bioavailability, Quechers, liquid-chromatography-mass spectrometry, soil properties, DRIFT analysis, pesticides

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Authors: Mary Ann L. Halliday, Zoengpari Gohain

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The stigma associated with HIV-AIDS negatively affect mental health and ability to effectively manage the disease. While the number of People living with HIV/AIDS (PLHIV) has been increasing day by day in Mizoram (a small north-eastern state in India), research on HIV/AIDS stigma has so far been limited. Despite the potential significance of Internalized HIV Stigma (IHS) in the lives of PLHIV, there has been very limited research in this area. It was therefore, felt necessary to explore the internalized HIV stigma, mental health, coping and perceived social support of PLHIV in Aizawl District, Mizoram. The present study was designed with the objectives to determine the degree of IHS, to study the relationship between the socio-demographic characteristics and level of IHS, to highlight the mental health status, coping strategies and perceived social support of PLHIV and to elucidate the relationship between these psychosocial variables. In order to achieve the objectives of the study, six hypotheses were formulated and statistical analyses conducted accordingly. The sample consisted of 300 PLWHA from Aizawl District, 150 males and 150 females, of the age group 20 to 70 years. Two- way classification of “Gender” (male and female) and three-way classification of “Level of IHS” (High IHS, Moderate IHS, Low IHS) on the dependent variables was employed, to elucidate the relationship between Internalized HIV Stigma, mental health, coping and perceived social support of PLHIV. The overall analysis revealed moderate level of IHS (67.3%) among PLHIV in Aizawl District, with a small proportion of subjects reporting high level of IHS. IHS was found to be significantly different on the basis of disclosure status, with the disclosure status of PLHIV accounting for 9% variability in IHS.  Results also revealed more or less good mental health among the participants, which was assessed by minimal depression (50.3%) and minimal anxiety (45%), with females with high IHS scoring significantly higher in both depression and anxiety (p<.01). Examination of the coping strategies of PLHIV found that the most frequently used coping styles were Acceptance (91%), Religion (84.3%), Planning (74.7%), Active Coping (66%) and Emotional Support (52.7%). High perception of perceived social support (48%) was found in the present study. Correlation analysis revealed significant positive relationships between IHS and depression as well as anxiety (p<.01), thus revealing that IHS negatively affects the mental health of PLHIV. Results however revealed that this effect may be lessened by the use of various coping strategies by PLHIV as well as their perception of social support.

Keywords: Aizawl, anxiety, depression, internalized HIV stigma, HIV/AIDS, mental health, mizoram, perceived social support

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Authors: Rushiraj Heshi, Smriti Bhandari

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Master Data Management requires creation of Central repository, applying constraints on Repository and designing processes to manage data. Designing of Repository, constraints on repository and business processes is very tedious and time consuming task for large Enterprise. Hence Visual Repository, constraints and Process (Workflow) modeling is the most critical step in Master Data Management.In this paper, we realize a Visual Modeling tool for implementing Repositories, Constraints and Processes based on Eclipse Plugin using GMF/EMF which follows principles of Model Driven Engineering (MDE).

Keywords: EMF, GMF, GEF, repository, constraint, process

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Authors: Lily Ingsrisawang, Tasanee Nacharoen

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Introduction: The problems of unbalanced data sets generally appear in real world applications. Due to unequal class distribution, many research papers found that the performance of existing classifier tends to be biased towards the majority class. The k -nearest neighbors’ nonparametric discriminant analysis is one method that was proposed for classifying unbalanced classes with good performance. Hence, the methods of discriminant analysis are of interest to us in investigating misclassification error rates for class-imbalanced data of three diabetes risk groups. Objective: The purpose of this study was to compare the classification performance between parametric discriminant analysis and nonparametric discriminant analysis in a three-class classification application of class-imbalanced data of diabetes risk groups. Methods: Data from a healthy project for 599 staffs in a government hospital in Bangkok were obtained for the classification problem. The staffs were diagnosed into one of three diabetes risk groups: non-risk (90%), risk (5%), and diabetic (5%). The original data along with the variables; diabetes risk group, age, gender, cholesterol, and BMI was analyzed and bootstrapped up to 50 and 100 samples, 599 observations per sample, for additional estimation of misclassification error rate. Each data set was explored for the departure of multivariate normality and the equality of covariance matrices of the three risk groups. Both the original data and the bootstrap samples show non-normality and unequal covariance matrices. The parametric linear discriminant function, quadratic discriminant function, and the nonparametric k-nearest neighbors’ discriminant function were performed over 50 and 100 bootstrap samples and applied to the original data. In finding the optimal classification rule, the choices of prior probabilities were set up for both equal proportions (0.33: 0.33: 0.33) and unequal proportions with three choices of (0.90:0.05:0.05), (0.80: 0.10: 0.10) or (0.70, 0.15, 0.15). Results: The results from 50 and 100 bootstrap samples indicated that the k-nearest neighbors approach when k = 3 or k = 4 and the prior probabilities of {non-risk:risk:diabetic} as {0.90:0.05:0.05} or {0.80:0.10:0.10} gave the smallest error rate of misclassification. Conclusion: The k-nearest neighbors approach would be suggested for classifying a three-class-imbalanced data of diabetes risk groups.

Keywords: error rate, bootstrap, diabetes risk groups, k-nearest neighbors

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Authors: Abi Miller, Eleanor Wilson, Claire Diver

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Background: UK Head and Neck Cancer incidence and prevalence were rising related to better treatment outcomes and changed demographics. More people of working-age now survive Head and Neck Cancer. For individuals, work provides income, purpose, and social connection. For society, work increases economic productivity and reduces welfare spending. In the UK, a cancer diagnosis is classed as a disability and more disabled people leave the workplace than non-disabled people. Limited evidence exists on return-to-work after Head and Neck Cancer, with no UK qualitative studies. Head and Neck Cancer survivors appear to return to work less when compared to other cancer survivors. This study aimed to explore the effects of Head and Neck Cancer disability on survivors’ return-to-work experience. Methodologies: This was an exploratory qualitative study using a critical realist approach to carry out semi-structured one-off interviews with Head and Neck Cancer survivors who had returned to work. Interviews were informed by an interview guide and carried out remotely by Microsoft Teams or telephone. Interviews were transcribed verbatim, pseudonyms allocated, and transcripts anonymized. Data were interpreted using Reflexive Thematic Analysis. Findings: Thirteen Head and Neck Cancer survivors aged between 41 -63 years participated in interviews. Three major themes were derived from the data: changed identity and meaning of work after Head and Neck Cancer, challenging and supportive work experiences and impact of healthcare professionals on return-to-work. Participants described visible physical appearance changes, speech and eating challenges, mental health difficulties and psycho-social shifts following Head and Neck Cancer. These factors affected workplace re-integration, ability to carry out work duties, and work relationships. Most participants experienced challenging work experiences, including stigmatizing workplace interactions and poor communication from managers or colleagues, which further affected participant confidence and mental health. Many participants experienced job change or loss, related both to Head and Neck Cancer and living through a pandemic. A minority of participants experienced strategies like phased return, which supported workplace re-integration. All participants, bar one, wanted conversations with healthcare professionals about return-to-work but perceived these conversations as absent. Conclusion: All participants found returning to work after Head and Neck Cancer to be a challenging experience. This appears to be impacted by participant physical, psychological, and functional disability following Head and Neck Cancer, work interaction and work context.

Keywords: disability, experience, head and neck cancer, qualitative, return-to-work

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Authors: Jelena Filipović, Milenko Košutić, Vladimir Filipović

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In the last decades urban population is characterized by sedentary lifestyles, low physical activity and "fast food". These changes in diet and physical non activity have been associated with the increase of chronic non diseases. Bread is one of the most popularly wheat products consumed worldwide. Spelt wheat has shown potential in various food applications, including bread, pasta, breakfast cereal and other products of altered nutritional characteristics compared to conventional wheat products. It has very high protein content and even 30 to 60% higher concentration of mineral elements Fe, Zn, Cu, Mg and P compared to Triticum Aestivum. Spelt wheat is growing without the use of pesticides in harsh ecological conditions and it is an old cultivar. So it can be used for organic and health safe food. Changes in the formulation of bread with the aim to improve their nutritional and functional properties usually lead to changes in the dough properties which is related reflected to the quality of the finished product. The aim of this paper is researching the impact of adding yeast extract to bread on sensory characteristics and consumer acceptance of a new product as a key factor for successful marketing of a new product. The sensory analysis of bread with 5% yeast extract is as follows: the technological quality is very good (3.8) and the color of the product is excellent (4.85). Based on data consumers survey declared that they liked the taste of bread with 5% yeast extract (74%), consumers marked the product as likeable (70%), and 75% of the total number of respondents would buy this new product. This paper is promoting a new type of bread with 5% yeast extract (Z score 0.80) to improve diet and novel functional product which intended for consumers conscious about their health and diet.

Keywords: bread, yeast extract, sensory analysis, consumer survey, score analysis Z

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Authors: R. S. Fisca, Y. R. Elox, L. Umi, U. Z. Luttfia, Kun Harismah

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Petai (Parkia speciosa) is a plant indigenous to Southeast Asia. It is consumed either raw or cooked. It has been used in folk medicine to treat diabetes, hypertension, and kidney problems. It contains minerals and vitamins. Petai contains a lot of chemical compounds that are beneficial for health, including antioxidants, Vitamin B6 0,9mg, energy 142 g. cal, 10.4 g protein. 2 g fat, 22 g carbohydrates, 95 mg calcium, phosphorus 115 mg, 1 mg iron, 200 IU of vitamin A, vitamin B1 0.17 mg, 36 mg of vitamin C that can resolve various health problems. These chips are the result of innovation from petai packaged in such a way becomes a tasty snack chips and can be enjoyed by many people to relax and also nutritious for health. In the manufacture of petai chips require several steps of them start by boiling, flating, drying and the last frying. In introducing the products widely we sell petai chips with several methods. Some of these methods include direct sales, delivery order, online/social media, and open some booth at a few places and the car free day in Solo every sunday. Opportunity in selling petai chips is very wide because there is no competitors with similar business. With the innovation of petai chips become healthy snacks can be introduced to the public and can even be exported out of the country as one of the extraordinary snacks from Indonesia.

Keywords: antioxidants, chips, healty, petai

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Rahmad Wisnu Wardana, Eakachai Warinsiriruk, Sutep Joy-A-Ka

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Selecting the most suitable welding process usually depends on experiences or common application in similar companies. However, this approach generally ignores many criteria that can be affecting the suitable welding process selection. Therefore, knowledge automation through knowledge-based systems will significantly improve the decision-making process. The aims of this research propose integrated data envelopment analysis (DEA) and fuzzy credibility constrained programming approach for identifying the best welding process for stainless steel storage tank in the food and beverage industry. The proposed approach uses fuzzy concept and credibility measure to deal with uncertain data from experts' judgment. Furthermore, 12 parameters are used to determine the most appropriate welding processes among six competitive welding processes.

Keywords: welding process selection, data envelopment analysis, fuzzy credibility constrained programming, storage tank

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Authors: Ya L. V., Benjamin Ong Wei Lin, Wanli Niu, Benjamin Seah Chin Tat

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This article introduces a methodology that improves reliability and cyclability of 2nd-life Li-ion battery system repurposed as an energy storage system (ESS). Most of the 2nd-life retired battery systems in the market have module/pack-level state-of-health (SOH) indicator, which is utilized for guiding appropriate depth-of-discharge (DOD) in the application of ESS. Due to the lack of cell-level SOH indication, the different degrading behaviors among various cells cannot be identified upon reaching retired status; in the end, considering end-of-life (EOL) loss and pack-level DOD, the repurposed ESS has to be oversized by > 1.5 times to complement the application requirement of reliability and cyclability. This proposed battery grading algorithm, using non-invasive methodology, is able to detect outlier cells based on historical voltage data and calculate cell-level historical maximum temperature data using semi-analytic methodology. In this way, the individual battery cell in the 2nd-life battery system can be graded in terms of SOH on basis of the historical voltage fluctuation and estimated historical maximum temperature variation. These grades will have corresponding DOD grades in the application of the repurposed ESS to enhance system reliability and cyclability. In all, this introduced battery grading algorithm is non-invasive, compatible with all kinds of retired Li-ion battery systems which lack of cell-level SOH indication, as well as potentially being embedded into battery management software for preventive maintenance and real-time cyclability optimization.

Keywords: battery grading algorithm, 2nd-life repurposing battery system, semi-analytic methodology, reliability and cyclability

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Mohammad Didar Hossain

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Background/Purpose: The purpose of this study was to examine the associations between productive engagements and the psychological well-being of older adults in the U.S by analyzing cross-sectional data from a secondary dataset. Specifically, this paper analyzed the associations of 4 different types of productive engagements, including current work status, caregiving to the family members, volunteering and religious strengths with the psychological well-being as an outcome variable. Methods: Data and sample: The study used the data from the Health and Retirement Study (HRS). The HRS is a nationally representative prospective longitudinal cohort study that has been conducting biennial surveys since 1992 to community-dwelling individuals 50 years of age or older on diverse issues. This analysis was based on the 2016 wave (cross-sectional) of the HRS dataset and the data collection period was April 2016 through August 2017. The samples were recruited from a multistage, national area-clustered probability sampling frame. Measures: Four different variables were considered as the predicting variables in this analysis. Firstly, current working status was a binary variable that measured by 0=Yes and 1= No. The second and third variables were respectively caregiving and volunteering, and both of them were measured by; 0=Regularly, 1= Irregularly. Finally, find in strength was measured by 0= Agree and 1= Disagree. Outcome (Wellbeing) variable was measured by 0= High level of well-being, 1= Low level of well-being. Control variables including age were measured in years, education in the categories of 0=Low level of education, 1= Higher level of education and sex r in the categories 0=male, 1= female. Analysis and Results: Besides the descriptive statistics, binary logistic regression analyses were applied to examine the association between independent and dependent variables. The results showed that among the four independent variables, three of them including working status (OR: .392, p<.001), volunteering (OR: .471, p<.003) and strengths in religion (OR .588, p<.003), were significantly associated with psychological well-being while controlling for age, gender and education factors. Also, no significant association was found between the caregiving engagement of older adults and their psychological well-being outcome. Conclusions and Implications: The findings of this study are mostly consistent with the previous studies except for the caregiving engagements and their impact on older adults’ well-being outcomes. Therefore, the findings support the proactive initiatives from different micro to macro levels to facilitate opportunities for productive engagements for the older adults, and all of these may ultimately benefit their psychological well-being and life satisfaction in later life.

Keywords: productive engagements, older adults, psychological wellbeing, productive aging

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Authors: Maryam M. Bashir, Luai A. Ahmed, Meera R. Alshamsi, Sara Almahrooqi, Taif Alyammahi, Shooq A. Alshehhi, Waad I. Alhammadi, Fatima H. Alhammadi, Hind A. Alhosani, Rami H. Al-Rifai, Fatma Al-Maskari

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Obesity in the Middle East and North Africa (MENA) region has exponentially increased over the past five decades due to rapid urbanization and unhealthy lifestyle changes. It has been well established that overweight and obesity increase the risk of non-communicable diseases (NCDs) and are the leading cause of mortality and economic burden locally, and globally. In the United Arab Emirates (UAE), there is a growing epidemic of obesity and other chronic diseases like type 2 diabetes mellitus and cardiovascular diseases. Prevalence of overweight and obesity in UAE range up to 70% depending on the group being studied. Hence, there is a need to explore their patterns in the country for more targeted and responsive interventions. Our study aimed to explore the patterns of overweight and obesity and some self-reported chronic diseases among university students in Abu Dhabi, the capital city of UAE. A validated online self-administered questionnaire was used to collect data from UAE University (UAEU) students, 18years and above, from August to September 2021. Students’ characteristics were summarized using appropriate descriptive statistics. Overweight, obesity and self-reported chronic diseases were described and compared between male and female students using chi-square and t tests. Other associated factors were also explored in relation to overweight and obesity. All analyses were conducted using STATA statistical software version 16.1 (StataCorp LLC, College Station, TX, USA). 902 students participated in the study. 79.8% were females and mean age was 21.90 ± 5.19 years. Majority of the respondents were undergraduate students (80.71%). The prevalence of self-reported chronic diseases was 22.95%. Obesity (BMI≥30kg/m2), Diabetes Mellitus, and Asthma/Allergies were the commonest diseases (12.48%, 4.21% & 3.22%, respectively). Approximately 5% of the students reported more than one chronic disease. Out of the 833 participating students who had complete weight and height data, prevalence of overweight and obesity was 34.81% (22.33% and 12.48%, respectively). More than half of the male students (54.36%) were overweight or obese. This is significantly higher than in female students (30.56%, p=0.001). Overweight/obesity when compared to normal weight is associated with increasing mean age [23.40 vs 21.01, respectively (p=0.001)]. In addition to gender and age, being married [57.63% vs 31.05% (p=0.001)], being a postgraduate student [51.59% vs 30.92% (p=0.001)] and having two or more chronic diseases [65.85% vs 33.21% (p=0.001)] were also significantly associated with overweight/obesity. Our study showed that almost a quarter of the participating university students reported at least one chronic disease. Obesity was the commonest and more than 1 in 3 students were either overweight or obese. This shows the need for intensive health promotion and screening programs on obesity and other chronic diseases to meet the health needs of these students. This study is also a basis for further research, especially qualitative, to explore the relevant risk factors and risk groups for more targeted interventions.

Keywords: chronic disease, obesity, overweight, students, United Arab Emirates

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Authors: Kayode Balogun, Femi Ayoola

Abstract:

Crime is at alarming rate in this part of world and there are many factors that are contributing to this antisocietal behaviour both among the youths and old. In this work, principal component analysis (PCA) was used as a tool to reduce the dimensionality and to really know those variables that were crime prone in the study region. Data were collected on twenty-eight crime variables from National Bureau of Statistics (NBS) databank for a period of fifteen years, while retaining as much of the information as possible. We use PCA in this study to know the number of major variables and contributors to the crime in the Southwest Nigeria. The results of our analysis revealed that there were eight principal variables have been retained using the Scree plot and Loading plot which implies an eight-equation solution will be appropriate for the data. The eight components explained 93.81% of the total variation in the data set. We also found that the highest and commonly committed crimes in the Southwestern Nigeria were: Assault, Grievous Harm and Wounding, theft/stealing, burglary, house breaking, false pretence, unlawful arms possession and breach of public peace.

Keywords: crime rates, data, Southwest Nigeria, principal component analysis, variables

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Authors: Hyun-Woo Cho

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Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

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Babesiosis is a haemoparasitic disease due to the multiplication of protozoan’s parasite, Babesia ovis in the red blood cells of the host, and contributes numerous economical losses, including sheep and goat ruminants. The early identification and successful treatment of Babesia Ovis spp. belong to the key steps of control and health management of livestock resources. The objective of this study was to construct a polymerase chain reaction (PCR) based method for the detection of Babesia spp. in small ruminants and to determine the risk factors involved in the spreading of babesiosis infections. A total of 100 blood samples were collected from 50 sheep and 50 goats along with different areas of Muzaffargarh, Pakistan, from randomly selected herds. Data on the characteristics of sheep and goats were collected through questionnaires. Of 100 blood samples examined, 18 were positive for Babesia ovis upon microscopic studies, whereas 11 were positive for the presence of Babesia spp. by PCR assay. For the recognition of parasitic DNA, a set of 500bp oligonucleotide was designed by PCR amplification with sequence 18S rRNA gene for B. ovis. The prevalence of babesiosis in small ruminant’s sheep and goat detected by PCR was significantly higher in female animals (28%) than male herds (08%). PCR analysis of the reference samples showed that the detection limit of the PCR assay was 0.01%. Taken together, all data indicated that this PCR assay was a simple, fast, specific detection method for Babesia ovis species in small ruminants compared to other available methods.

Keywords: Babesia ovis, PCR amplification, 18S rRNA, sheep and goat

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Authors: Rupali Bhardwaj

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Cryptography is the science of using mathematics to encrypt and decrypt data; the data are converted into some other gibberish form, and then the encrypted data are transmitted. The primary purpose of this paper is to provide two levels of security through a two-step process, rather than transmitted the message bits directly, first encrypted it using 2D cellular automata and then scrambled with Arnold Cat Map transformation; it provides an additional layer of protection and reduces the chance of the transmitted message being detected. A comparative analysis on effectiveness of scrambling technique is provided by scrambling degree measurement parameters i.e. Gray Difference Degree (GDD) and Correlation Coefficient.

Keywords: scrambling, cellular automata, Arnold cat map, game of life, gray difference degree, correlation coefficient

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Authors: Naveed Ghani, Samreen Javed

Abstract:

In today’s heterogeneous network environment, there is a growing demand for distrust clients to jointly execute secure network to prevent from malicious attacks as the defining task of propagating malicious code is to locate new targets to attack. Residual risk is always there no matter what solutions are implemented or whet so ever security methodology or standards being adapted. Security is the first and crucial phase in the field of Computer Science. The main aim of the Computer Security is gathering of information with secure network. No one need wonder what all that malware is trying to do: It's trying to steal money through data theft, bank transfers, stolen passwords, or swiped identities. From there, with the help of our survey we learn about the importance of white listing, antimalware programs, security patches, log files, honey pots, and more used in banks for financial data protection but there’s also a need of implementing the IPV6 tunneling with Crypto data transformation according to the requirements of new technology to prevent the organization from new Malware attacks and crafting of its own messages and sending them to the target. In this paper the writer has given the idea of implementing IPV6 Tunneling Secessions on private data transmission from financial organizations whose secrecy needed to be safeguarded.

Keywords: network worms, malware infection propagating malicious code, virus, security, VPN

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Authors: N. Liu, N. Tuah, D. Ying

Abstract:

This research aims to identify and test whether serious games can help children learn and pick up healthy eating habits. The practical component takes the form of digitalizing an already existing educational board game called “All you can eat” (AYCE), designed with the nutritious subject matter in mind. This time with the added features of online playability, which will widen its availability and accessibility to reach more players compared to the physical iteration. The game will be deployed alongside the conducting of theoretical research, which also involves teachers leading children to play said digital version. The research methodology utilizes two experiments, such as handing out surveys to gather feedback from both the partners and students. The research was carried out in several countries, namely Brunei, Malaysia, and Taiwan. The results are to be used for validating the concept of “serious games,” particularly when tied to the health aspect of the players, which in this case were children. As for the research outcomes, they can be applied to a variety of serious games that are related to health topics more broadly and not simply limited to healthy eating habits alone, adopting a balanced combination of practical and theoretical considerations. The study will also help other researchers in the correlated fields of serious game development and pediatrics to better comprehend the needs of children. On the theoretical side, these findings can enable further technological advancements to be made possible, a case in point being more serious games, to provide the appropriate social support precisely on the matter of health-related issues. Not just individuals but rather communities could benefit from improved health and well-being as a result of the project, which, when done right, will potentially improve their quality of life and have fun while doing it. AYCE will be demonstrated to support a wide range of health issues as a result of this research case.

Keywords: culture heritage, digital games, digitalization, traditional religious culture

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Authors: Abdul-Rahman Al-Ali

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As the number of mobile devices is growing exponentially, it is estimated to connect about 50 million devices to the Internet by the year 2020. At the end of this decade, it is expected that an average of eight connected devices per person worldwide. The 50 billion devices are not mobile phones and data browsing gadgets only, but machine-to-machine and man-to-machine devices. With such growing numbers of devices the Internet of Things (I.o.T) concept is one of the emerging technologies as of recently. Within the smart grid technologies, smart home appliances, Intelligent Electronic Devices (IED) and Distributed Energy Resources (DER) are major I.o.T objects that can be addressable using the IPV6. These objects are called the smart grid internet of things (SG-I.o.T). The SG-I.o.T generates big data that requires high-speed computing infrastructure, widespread computer networks, big data storage, software, and platforms services. A company’s utility control and data centers cannot handle such a large number of devices, high-speed processing, and massive data storage. Building large data center’s infrastructure takes a long time, it also requires widespread communication networks and huge capital investment. To maintain and upgrade control and data centers’ infrastructure and communication networks as well as updating and renewing software licenses which collectively, requires additional cost. This can be overcome by utilizing the emerging computing paradigms such as cloud computing. This can be used as a smart grid enabler to replace the legacy of utilities data centers. The talk will highlight the role of I.o.T, cloud computing services and their development models within the smart grid technologies.

Keywords: intelligent electronic devices (IED), distributed energy resources (DER), internet, smart home appliances

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Authors: Oljana Hoxhaj

Abstract:

The integration process of Albania into the European family carries many difficulties. In this context, the Albanian legislator is inclined to implement in the domestic legal framework models which have been successful in other countries. Our paper aims to present an analytical and comparative approach to the health system in Albania and France, mainly focusing on citizen’s access to these services. Different standards and cultures between states, in the context of an approximate model, will be the first challenge of our paper. Over the last few years, the Albanian government has undertaken concrete reforms in this sector, aiming to transform the vision on which the previous health system was structured. In this perspective, the state fulfills not only an obligation to its citizens, but also consolidates progressive steps toward alignment with European Union standards. The necessity to undertake a genuine reform in this area has come as an exigency of society, which has permanently identified problems within this sector, considering it ineffective, out of standards, and corrupt. The inclusion of health services on the Albanian government agenda reflects its will in the function of good governance, transparency, and broadening access to the provision of quality health services in the public and private sectors. The success of any initiative in the health system consists of giving priority to patient needs. Another objective that should be in the state's consideration is to create the premise to provide a comprehensive process on whose foundations partnership and broader co-operation with beneficiary entities are established in any decision-making that is directly related to their interests. Some other important and widespread impacts on the effective realization of citizens' access to the healthcare system coincide with the construction of appropriate infrastructure, increasing the professionalism and qualification of medical staff, and the allocation of a higher budget. France has one of the most effective healthcare models in Europe. That is why we have chosen to analyze this country, aiming to highlight the advantages of this system, as well as the commitment of the French state to drafting effective health policies. In the framework of the process of harmonization of the Albanian legislation with that of the European Union, through our work, we aim to identify the space to implement the whole of these legislative innovations in the Albanian legislation.

Keywords: effective service, harmonization level, innovation, reform

Procedia PDF Downloads 116