Search results for: apache hama

Authors: Anocha Rojanapanich

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The purpose of this research is to develop and design an accounting management information system for convenient shop in Bangkok Thailand. The study applied the System Development Life Cycle (SDLC) for development which began with study and analysis of current data, including the existing system. Then, the system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Product diversity, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management and importance of cost information for decision making also as well as.

Keywords: accounting management information system, convenient shop, cost information for decision making system, development life cycle

Procedia PDF Downloads 421

Authors: Ahlem Abid, Farah Jemili

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Intrusion detection has been the subject of numerous studies in industry and academia, but cyber security analysts always want greater precision and global threat analysis to secure their systems in cyberspace. To improve intrusion detection system, the visualisation of the security events in form of graphs and diagrams is important to improve the accuracy of alerts. In this paper, we propose an approach of an IDS based on cloud computing, big data technique and using a machine learning graph algorithm which can detect in real time different attacks as early as possible. We use the MAWILab intrusion detection dataset . We choose Microsoft Azure as a unified cloud environment to load our dataset on. We implement the k2 algorithm which is a graphical machine learning algorithm to classify attacks. Our system showed a good performance due to the graphical machine learning algorithm and spark structured streaming engine.

Keywords: Apache Spark Streaming, Graph, Intrusion detection, k2 algorithm, Machine Learning, MAWILab, Microsoft Azure Cloud

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Authors: Michael Okeke, Andrew Blyth

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Supervisory Control And Data Acquisition (SCADA) as the state of the art Industrial Control Systems (ICS) are used in many different critical infrastructures, from smart home to energy systems and from locomotives train system to planes. Security of SCADA systems is vital since many lives depend on it for daily activities and deviation from normal operation could be disastrous to the environment as well as lives. This paper describes how No-Trust-Zone (NTZ) architecture could be incorporated into SCADA Systems in order to reduce the chances of malicious intent. The architecture is made up of two distinctive parts which are; the field devices such as; sensors, PLCs pumps, and actuators. The second part of the architecture is designed following lambda architecture, which is made up of a detection algorithm based on Particle Swarm Optimization (PSO) and Hadoop framework for data processing and storage. Apache Spark will be a part of the lambda architecture for real-time analysis of packets for anomalies detection.

Keywords: industrial control system (ics, no-trust-zone (ntz), particle swarm optimisation (pso), supervisory control and data acquisition (scada), swarm intelligence (SI)

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Authors: Barzan Hadi Hama Karim

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In recent years, an increasing number of faculties and colleges of basic education are established by the universities and ministry of Higher Education and Scientific Research of Iraqi Kurdistan to graduate English teachers to teach in the basic and high schools. One central consideration of this study is to what extent graduate teachers receive adequate preparation from these faculties and college of basic education. An important program which is offered in the department of English language in these colleges and faculties is Viewing and Practicing. The purpose of this research is to explore how students of basic education colleges and faculties are using the program of Viewing and Practicing to support the educational process. This study provides a general framework about educational uses of the program as a pedagogical tool to teach English Language in the basic schools and describes the different perceptions of the students at the final stage of their education. A survey is used to collect responses from a group of students to determine their expectations and perceptions about the program. The results display that the program has several aspects of strengths, such as improving English teaching and speaking proficiency, cultivating subject knowledge related to applied linguistics and promoting research engagement. The findings of the study address the following questions: Is Viewing and Practicing Program beneficial for students to experience English language for future career at schools? To what extent do the students prefer teaching English Language in the schools?

Keywords: teaching experience, viewing and practicing, perception, expectation

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

Procedia PDF Downloads 394

Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Khaled M. Hama, Mohammed Al Magableh, Zaid Al Kuri, Ahmad Qayam

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The study aimed to identify the role of academic leaders at Jerash University in crisis management from the faculty members' point of view, ‘the emerging Corona pandemic as a model’, as well as to identify the differences in the role of academic leaders at Jerash University in crisis management at the significance level (0.05 ≤ α) according to the study variables Gender Academic rank, years of experience, and identifying proposals that contribute to developing the performance of academic leaders at Jerash University in crisis management, ‘the Corona pandemic as a model’. The study was applied to a randomly selected sample of (72) faculty members at Jerash University, The researcher designed a tool for the study, which is the questionnaire, and it included two parts: the first part related to the personal data of the study sample members, and the second part was divided into five areas and (34) paragraphs to reveal the role of academic leaders at Jerash University in crisis management - the Corona pandemic as a model, it was confirmed From the validity and reliability of the tool, the study used the descriptive analytical method The study reached the following results: that the role of academic leaders at Jerash University in crisis management from the point of view of faculty members, ‘the emerging corona pandemic as a model’, came to a high degree, and there were no statistically significant differences at the level of statistical significance (α = 0.05) between the computational circles for the estimates of individuals The study sample for the role of academic leaders at Jerash University in crisis management is attributed to the study variables (gender, academic rank, and years of experience)

Keywords: academic leaders, crisis management, corona pandemic, Jerash University

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Authors: Hassan S. Hussein, Rania A. Abul Seoud, Amr M. Refaat

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Scientific researchers face in the analysis of very large data sets that is increasing noticeable rate in today’s and tomorrow’s technologies. Hadoop and Spark are types of software that developed frameworks. Hadoop framework is suitable for many Different hardware platforms. In this research, a scientific Linux cluster for Big Data analysis (SLBD) is presented. SLBD runs open source software with large computational capacity and high performance cluster infrastructure. SLBD composed of one cluster contains identical, commodity-grade computers interconnected via a small LAN. SLBD consists of a fast switch and Gigabit-Ethernet card which connect four (nodes). Cloudera Manager is used to configure and manage an Apache Hadoop stack. Hadoop is a framework allows storing and processing big data across the cluster by using MapReduce algorithm. MapReduce algorithm divides the task into smaller tasks which to be assigned to the network nodes. Algorithm then collects the results and form the final result dataset. SLBD clustering system allows fast and efficient processing of large amount of data resulting from different applications. SLBD also provides high performance, high throughput, high availability, expandability and cluster scalability.

Keywords: big data platforms, cloudera manager, Hadoop, MapReduce

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Authors: A. Kamel Abd Elaziz Mohamed, B. Sameh Kamal el Maraghi

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Introduction: Adaptive support ventilation (ASV) is an improved closed-loop ventilation mode that provides both pressure-controlled ventilation and PSV according to the patient’s needs. Aim of the work: To compare the short-term effects of Adaptive support ventilation (ASV), with conventional Pressure support ventilation (PSV) in weaning of intubated COPD patients. Patients and methods: Fifty patients admitted in the intensive care with acute exacerbation of COPD and needing intubation were included in the study. All patients were initially ventilated with control/assist control mode, in a stepwise manner and were receiving standard medical therapy. Patients were randomized into two groups to receive either ASV or PSV. Results: Out of fifty patients included in the study forty one patients in both studied groups were weaned successfully according to their ABG data and weaning indices. APACHE II score showed no significant difference in both groups. There were statistically significant differences between the groups in term of, duration of mechanical ventilation, weaning hours and length of ICU stay being shorter in (group 1) weaned by ASV. Re-intubation and mortality rate were higher in (group 11) weaned by conventional PSV, however the differences were not significant. Conclusion: ASV can provide automated weaning and achieve shorter weaning time for COPD patients hence leading to reduction in the total duration of MV, length of stay, and hospital costs.

Keywords: COPD patients, ASV, PSV, mechanical ventilation (MV)

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Authors: P. Efstathiou, E. Kouskouni, S. Papanikolaou, K. Karageorgou, Z. Manolidou, Tseroni Maria, A. Efstathiou, V. Karyoti, I. Agrafa

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Aim: Aim of this study was to evaluate the reduction on Intensive Care Unit (ICU) microbial flora after the antimicrobial copper alloy (Cu+) implementation as well as the effect on financial-epidemiological operation parameters. Methods: Medical, epidemiological and financial data in two time periods, before and after the implementation of copper (Cu 63% - Zn 37%, low lead) were recorded and analyzed in a general ICU. The evaluated parameters were: the importance of patients' admission (Acute Physiology and Chronic Health Evaluation - APACHE II and Simplified Acute Physiology Score - SAPS), microbial flora's record in the ICU before and after the implementation of Cu+ as well as the impact on epidemiological and ICU's operation financial parameters. Results: During December 2010 and March 2011 and respectively during December 2011 and March 2012 comparative results showed statistically significant reduction on the microbial flora (CFU/ml) by 95% and the use of antimicrobial medicine (per day per patient) by 30% (p = 0,014) as well as patients hospitalization time and cost. Conclusions: The innovative implementation of antimicrobial copper in ICUs contributed to their microbial flora significant reduction and antimicrobial drugs use reduction with the apparent positive effect (decrease) in both patient’s hospitalization time and cost. Under the present circumstances of economic crisis, survey results are of highest importance and value.

Keywords: antimicrobial copper, financial benefits, ICU, cost reduction

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Authors: Yu-Chuan Huang

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Introduction: There is a higher mortality rate for unplanned transfer to intensive care units. It also needs a longer length of stay and makes the intensive care unit beds cannot be effectively used. It affects the immediate medical treatment of critically ill patients, resulting in a drop in the quality of medical care. Purpose: The purpose of this study was using SOFA and APACHEII score to analyze the mortality rate of the cases transferred from ED to ICU. According to the score that should be provide an appropriate care as early as possible. Methods: This study was a descriptive experimental design. The sample size was estimated at 220 to reach a power of 0.8 for detecting a medium effect size of 0.30, with a 0.05 significance level, using G-power. Considering an estimated follow-up loss, the required sample size was estimated as 242 participants. Data were calculated by medical system of SOFA and APACHEII score that cases transferred from ED to ICU in 2016. Results: There were 233 participants meet the study. The medical records showed 33 participants’ mortality. Age and sex with QSOFA , SOFA and sex with APACHEII showed p>0.05. Age with APCHHII in ED and ICU showed r=0.150, 0,268 (p < 0.001**). The score with mortality risk showed: ED QSOFA is r=0.235 (p < 0.001**), exp(B)=1.685(p = 0.007); ICU SOFA 0.78 (p < 0.001**), exp(B)=1.205(p < 0.001). APACHII in ED and ICU showed r= 0.253, 0.286 (p < 0.001**), exp(B) = 1.041,1.073(p = 0.017,0.001). For SOFA, a cutoff score of above 15 points was identified as a predictor of the 95% mortality risk. Conclusions: The SOFA and APACHE II were calculated based on initial laboratory data in the Emergency Department, and during the first 24 hours of ICU admission. In conclusion, the SOFA and APACHII score is significantly associated with mortality and strongly predicting mortality. Early predictors of morbidity and mortality, which we can according the predicting score, and provide patients with a detail assessment and proper care, thereby reducing mortality and length of stay.

Keywords: SOFA, APACHEII, mortality, ICU

Procedia PDF Downloads 147

Authors: Kamel Abd Elaziz Mohamed, Ahmed Yehia Mousa, Ahmed Samir ElSawy, Adel Mohamed Saleem

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Introduction: Critically ill patients frequently require tracheostomy to simplify long term air way management. While tracheostomy indications have remained unchanged, the timing of elective tracheostomy for the ventilated patient has been questioned. Aim of the work: This study was performed to compare the differences between early and late percutaneous dilatational tracheostomy (PDT) regarding, mechanical ventilation duration (MVD), length of ICU stay, length of hospital stay, incidence of ventilator associated pneumonia and hospital outcome. Patients and methods: Forty patients who met the inclusion criteria were randomly divided into early PDT who had the tracheostomy within the first 10 days of mechanical ventilation (MV) and the late PDT who had the tracheostomy after 10 days of MV. On admission, demographic data and Acute Physiology and Chronic ill Health II and GCS were collected. The duration of mechanical ventilation, ICU length of stay (LOS) and hospital LOS were all calculated. Results: Total of 40 patients were randomized to either early PDT (n= 20) or late PDT (n= 20). There were no significant differences between both groups regarding demographic data or the scores: APACHE II (22.75± 7 vs 24.35 ± 8) and GCS (6.10 ±2 vs 7.10 ± 2.71). An early PDT showed fewer complications vs late procedure, however it was insignificant. There were significant differences between the two groups regarding mean (MVD) which was shorter in early PDT than the late PDT group (32.2± 10.5) vs (20.6 ± 13 days; p= 0.004). Mean ICU stay was shorter in early PDT than late PDT (21 .0± 513.4) vs (40.15 ±12.7 days; p 6 0.001). Mean hospital stay was shorter in early PDT than late PDT (34.60± 18.37) vs (55.60± 25.73 days; p=0.005). Patients with early PDT suffered less sepsis and VAP than late PDT, there was no difference regarding the mortality rate between the two groups. Conclusion: Early PDT is recommended for patients who require prolonged tracheal intubation in the ICU as outcomes like the duration of mechanical ventilation length of ICU stay and hospital stay were significantly shorter in early tracheostomy.

Keywords: intensive care unit, early PDT, late PDT, intubation

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Authors: benabbess anja

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This research presents the design and implementation of a real-time course recommendation system for online learning platforms, leveraging user competencies and expertise levels. The system begins by extracting and classifying the complexity levels of courses from Udemy datasets using semantic enrichment techniques and resources such as WordNet and BERT. A predictive model assigns complexity levels to each course, adding columns that represent the course category, sub-category, and complexity level to the existing dataset. Simultaneously, user profiles are constructed through questionnaires capturing their skills, sub-skills, and proficiency levels. The recommendation process involves generating embeddings with BERT, followed by calculating cosine similarity between user profiles and courses. Courses are ranked based on their relevance, with the BERT model delivering the most accurate results. To enable real-time recommendations, Apache Kafka is integrated to track user interactions (clicks, comments, time spent, completed courses, feedback) and update user profiles. The embeddings are regenerated, and similarities with courses are recalculated to reflect users' evolving needs and behaviors, incorporating a progressive weighting of interactions for more personalized suggestions. This approach ensures dynamic and real-time course recommendations tailored to user progress and engagement, providing a more personalized and effective learning experience. This system aims to improve user engagement and optimize learning paths by offering courses that precisely match users' needs and current skill levels.

Keywords: recommendation system, online learning, real-time, user skills, expertise level, personalized recommendations, dynamic suggestions

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Authors: Ali Kamal M. Sami, Safa Almukhtar, Alaa Al-Krush, Ismael Hama-Amin Akha Weas, Ruqaya Ahmed Alqais

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Introduction and Objectives Lower urinary tract symptoms (LUTS) are common among men with benign prostatic hyperplasia (BPH). The combination of 5 alpha-reductase inhibitors and alpha-blockers has been used as a conservative treatment of male LUTS secondary to BPH. Nanotechnology structured water magnalife is a type of water that is produced by modulators and specific frequency and energy fields that transform ordinary water into this Nanowater. In this study, we evaluated the use of Nano-water with the conservative treatment and to see if it improves the outcome and gives better results in those patients with LUTS/BPH. Material and methods For a period of 3 months, 200 men with International Prostate Symptom Score (IPSS)≥13, maximum flow rate (Qmax)≤ 15ml/s, and prostate volume > 30 and <80 ccs were randomly divided into two groups. Group A 100 men were given Nano-water with the (tamsulosindutasteride) and group B 100 men were given ordinary bottled water with the (tamsulosindutasteride). The water bottles were unlabeled and were given in a daily dose of 20ml/kg body weight. Dutasteride 0.5mg and tamsulosin 0.4 mg daily doses. Both groups were evaluated for the IPSS, Qmax, Residual Urine (RU), International Index of Erectile Function–Erectile Function (IIEF-EF) domain at the beginning (baseline data), and at the end of the 3 months. Results Of the 200 men with LUTS who were included in this study, 193 men were followed, and 7 men dropped out of the study for different reasons. In group A which included 97 men with LUTS, IPSS decreased by 16.82 (from 20.47 to 6.65) (P<0.00001) and Qmax increased by 5.73 ml/s (from 11.71 to 17.44) (P<0.00001) and RU <50 ml in 88% of patients (P<0.00001) and IIEF-EF increased to 26.65 (from 16.85) (P<0.00001). While in group B, 96 men with LUTS, IPSS decreased by 8.74(from 19.59 to 10.85)(P<0.00001) and Qmax increased by 4.67 ml/s(from 10.74 to 15.41)(P<0.00001), RU<50 ml in 75% of patients (P<0.00001), and IIEF-EF increased to 21(from 15.87)(P<0.00001). Group A had better results than group B. IPSS in group A decreased to 6.65 vs 10.85 in group B(P<0.00001), also Qmax increased to 17.44 in group A vs 15.41 in group B(P<0.00001), group A had RU <50 ml in 88% of patients vs 75% of patients in group B(P<0.00001).Group A had better IIEF-EF which increased to 26.65 vs 21 in group B(P<0.00001). While the differences between the baseline data of both groups were statistically not significant. Conclusion The use of nanotechnology structured water magnalife gives a better result in terms of LUTS and scores in patients with BPH. This combination is showing improvements in IPSS and even in erectile function in those men after 3 months.

Keywords: nano water, lower urinary tract symptoms, benign prostatic hypertrophy, erectile dysfunction

Procedia PDF Downloads 73

Authors: Ahmad Alsheikh Kaddour

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Herbal medicine has been a long-standing phenomenon in Arab countries since ancient times because of its breadth and moderate temperament. Therefore, it possesses a vast natural and economic wealth of medicinal and aromatic herbs. This prompted ancient Egyptians and Arabs to discover and exploit them. The economic importance of the plant is not only from medicinal uses; it is a plant of high economic value for its various uses, especially in food, cosmetic and aromatic industries. It is also an ornamental plant and soil stabilization. The main objective of this research is to study the chemical changes that occur in the plant during the growth period, as well as the production of plant buds, which were previously considered unwanted plants. The research was carried out in the period 2021-2022 in the valley of Al-Shaflah (common caper), located in Qumhana village, 7 km north of Hama Governorate, Syria. The results of the research showed a change in the percentage of chemical components in the plant parts. The ratio of protein content and the percentage of fatty substances in fruits and the ratio of oil in the seeds until the period of harvesting of these plant parts improved, but the percentage of essential oils decreased with the progress of the plant growth, while the Glycosides content where improved with the plant aging. The production of buds is small, with dimensions as 0.5×0.5 cm, which is preferred for commercial markets, harvested every 2-3 days in quantities ranging from 0.4 to 0.5 kg in one cut/shrubs with 3 years’ age as average for the years 2021-2022. The monthly production of a shrub is between 4-5 kg per month. The productive period is 4 months approximately. This means that the seasonal production of one plant is 16-20 kg and the production of 16-20 tons per year with a plant density of 1,000 shrubs per hectare, which is the optimum rate of cultivation in the unit of mass, given the price of a kg of these buds is equivalent to 1 US $; however, this means that the annual output value of the locally produced hectare ranges from 16,000 US $ to 20,000 US $ for farmers. The results showed that it is possible to transform the cultivation of this plant from traditional random to typical areas cultivation, with a plant density of 1,000-1,100 plants per hectare according to the type of soil to obtain production of medicinal and nutritious buds, as well as, the need to pay attention to this national wealth and invest in the optimal manner, which leads to the acquisition of hard currency through export to support the national income.

Keywords: common caper, medicinal plants, propagation, medical, economic importance

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Authors: Sarkawt M. Hama, Paul S. Monks, Rebecca L. Cordell

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Within the Joaquin project, ultrafine particles (UFP) are continuously measured at one urban background site in Leicester. The main aims are to examine the temporal and seasonal variations in UFP number concentration and size distribution in an urban environment, and to try to assess the added value of continuous UFP measurements. In addition, relations of UFP with more commonly monitored pollutants such as black carbon (BC), nitrogen oxides (NOX), particulate matter (PM2.5), and the lung deposited surface area(LDSA) were evaluated. The effects of meteorological conditions, particularly wind speed and direction, and also temperature on the observed distribution of ultrafine particles will be detailed. The study presents the results from an experimental investigation into the particle number concentration size distribution of UFP, BC, and NOX with measurements taken at the Automatic Urban and Rural Network (AURN) monitoring site in Leicester. The monitoring was performed as part of the EU project JOAQUIN (Joint Air Quality Initiative) supported by the INTERREG IVB NWE program. The total number concentrations (TNC) were measured by a water-based condensation particle counter (W-CPC) (TSI model 3783), the particle number concentrations (PNC) and size distributions were measured by an ultrafine particle monitor (UFP TSI model 3031), the BC by MAAP (Thermo-5012), the NOX by NO-NO2-NOx monitor (Thermos Scientific 42i), and a Nanoparticle Surface Area Monitor (NSAM, TSI 3550) was used to measure the LDSA (reported as μm2 cm−3) corresponding to the alveolar region of the lung between November 2013 and November 2015. The average concentrations of particle number concentrations were observed in summer with lower absolute values of PNC than in winter might be related mainly to particles directly emitted by traffic and to the more favorable conditions of atmospheric dispersion. Results showed a traffic-related diurnal variation of UFP, BC, NOX and LDSA with clear morning and evening rush hour peaks on weekdays, only an evening peak at the weekends. Correlation coefficients were calculated between UFP and other pollutants (BC and NOX). The highest correlation between them was found in winter months. Overall, the results support the notion that local traffic emissions were a major contributor of the atmospheric particles pollution and a clear seasonal pattern was found, with higher values during the cold season.

Keywords: size distribution, traffic emissions, UFP, urban area

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

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Authors: Rebin Mzhda Mohammed, Hoshmand Ali Hama Agha

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Excess skin, often consequential to substantial weight loss or the aging process, introduces physical discomfort, obstructs daily activities, and undermines an individual's self-esteem. As these challenges become increasingly prevalent, the need to explore viable solutions grows in significance. Extra skin removal surgery, colloquially known as body contouring surgery, has emerged as a compelling intervention to ameliorate the physical and psychological burdens of excess skin. This study undertakes a comprehensive review to illuminate the intricacies of extra skin removal surgery, encompassing its diverse procedures, associated risks, benefits, and psychological implications on patients. The methodological approach adopted involves a systematic and exhaustive review of pertinent scholarly literature sourced from reputable databases, including PubMed, Google Scholar, and specialized cosmetic surgery journals. Articles are meticulously curated based on their relevance, credibility, and recency. Subsequently, data from these sources are synthesized and categorized, facilitating a comprehensive understanding of the subject matter. Qualitative analysis serves to unravel the nuanced psychological effects, while quantitative data, where available, are harnessed to underpin the study's conclusions. In terms of major findings, the research underscores the manifold advantages of extra skin removal surgery. Patients experience a notable improvement in physical comfort, amplified mobility, enhanced self-confidence, and a newfound ability to don clothing comfortably. Nonetheless, the benefits are juxtaposed with potential risks, encompassing infection, scarring, hematoma, delayed healing, and the challenge of achieving symmetry. A salient discovery is the profound psychological impact of the surgery, as patients consistently report elevated body image satisfaction, heightened self-esteem, and a substantial enhancement in overall quality of life. In summation, this research accentuates the pivotal role of extra skin removal surgery in ameliorating the intricate interplay of physical and psychological difficulties posed by excess skin. By elucidating the diverse procedures, associated risks, and psychological outcomes, the study contributes to a comprehensive and informed comprehension of the surgery's multifaceted effects. Therefore, individuals contemplating this transformative surgical option are equipped with comprehensive insights, ultimately fostering informed decision-making, guided by the expertise of medical professionals.

Keywords: extra skin removal surgery, body contouring, abdominoplasty, brachioplasty, thigh lift, body lift, benefits, risks, psychological effects

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Authors: Chiyoung Kim, Jaegeol Yim

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Bus service is often either main or the only public transportation available in cities. In metropolitan areas, both subways and buses are available whereas in the medium sized cities buses are usually the only type of public transportation available. Bus Information Systems (BIS) provide current locations of running buses, efficient routes to travel from one place to another, points of interests around a given bus stop, a series of bus stops consisting of a given bus route, and so on to users. Thanks to BIS, people do not have to waste time at a bus stop waiting for a bus because BIS provides exact information on bus arrival times at a given bus stop. Therefore, BIS does a lot to promote the use of buses contributing to pollution reduction and saving natural resources. BIS implementation costs a huge amount of budget as it requires a lot of special equipment such as road side equipment, automatic vehicle identification and location systems, trunked radio systems, and so on. Consequently, medium and small sized cities with a low budget cannot afford to install BIS even though people in these cities need BIS service more desperately than people in metropolitan areas. It is possible to provide BIS service at virtually no cost under the assumption that everybody carries a smartphone and there is at least one person with a smartphone in a running bus who is willing to reveal his/her location details while he/she is sitting in a bus. This assumption is usually true in the real world. The smartphone penetration rate is greater than 100% in the developed countries and there is no reason for a bus driver to refuse to reveal his/her location details while driving. We have developed a mobile app that periodically reads values of sensors including GPS and sends GPS data to the server when the bus stops or when the elapsed time from the last send attempt is greater than a threshold. This app detects the bus stop state by investigating the sensor values. The server that receives GPS data from this app has also been developed. Under the assumption that the current locations of all running buses collected by the mobile app are recorded in a database, we have also developed a web site that provides all kinds of information that most BISs provide to users through the Internet. The development environment is: OS: Windows 7 64bit, IDE: Eclipse Luna 4.4.1, Spring IDE 3.7.0, Database: MySQL 5.1.7, Web Server: Apache Tomcat 7.0, Programming Language: Java 1.7.0_79. Given a start and a destination bus stop, it finds a shortest path from the start to the destination using the Dijkstra algorithm. Then, it finds a convenient route considering number of transits. For the user interface, we use the Google map. Template classes that are used by the Controller, DAO, Service and Utils classes include BUS, BusStop, BusListInfo, BusStopOrder, RouteResult, WalkingDist, Location, and so on. We are now integrating the mobile app system and the web app system.

Keywords: bus information system, GPS, mobile app, web site

Procedia PDF Downloads 218

Authors: S. Moustafa El Shanawany, I. Labib Salem, F. Mohamed Magdy Badr El Dine, H. Tag El Deen Abd Allah

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Thermal injuries remain a global health problem and a common issue encountered in forensic pathology. They are a devastating cause of morbidity and mortality in children and adults especially in developing countries, causing permanent disfigurement, scarring and grievous hurt. Burns have always been a matter of legal concern in cases of suicidal burns, self-inflicted burns for false accusation and homicidal attempts. Assessment of burn injuries as well as rating permanent disabilities and disfigurement following thermal injuries for the benefit of compensation claims represents a challenging problem. This necessitates the development of reliable scoring systems to yield an expected likelihood of permanent disability or fatal outcome following burn injuries. The study was designed to identify the risk factors of mortality in acute burn patients and to evaluate the applicability of FLAMES (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex) and BOBI (Belgian Outcome in Burn Injury) model scores in predicting the outcome. The study was conducted on 100 adult patients with acute burn injuries admitted to the Burn Unit of Alexandria Main University Hospital, Egypt from October 2014 to October 2015. Victims were examined after obtaining informed consent and the data were collected in specially designed sheets including demographic data, burn details and any associated inhalation injury. Each burn patient was assessed using both BOBI and FLAMES scoring systems. The results of the study show the mean age of patients was 35.54±12.32 years. Males outnumbered females (55% and 45%, respectively). Most patients were accidently burnt (95%), whereas suicidal burns accounted for the remaining 5%. Flame burn was recorded in 82% of cases. As well, 8% of patients sustained more than 60% of total burn surface area (TBSA) burns, 19% of patients needed mechanical ventilation, and 19% of burnt patients died either from wound sepsis, multi-organ failure or pulmonary embolism. The mean length of hospital stay was 24.91±25.08 days. The mean BOBI score was 1.07±1.27 and that of the FLAMES score was -4.76±2.92. The FLAMES score demonstrated an area under the receiver operating characteristic (ROC) curve of 0.95 which was significantly higher than that of the BOBI score (0.883). A statistically significant association was revealed between both predictive models and the outcome. The study concluded that both scoring systems were beneficial in predicting mortality in acutely burnt patients. However, the FLAMES score could be applied with a higher level of accuracy.

Keywords: BOBI, burns, FLAMES, scoring systems, outcome

Procedia PDF Downloads 336

Authors: Gordana Branković, Vesna Dragičević, Dejan Dodig, Desimir Knežević, Srbislav Denčić, Gordana Šurlan-Momirović

Abstract:

Phytic acid is a major pool in the flux of phosphorus through agroecosystems and represents a sum equivalent to > 50% of all phosphorus fertilizer used annually. Nutrition rich in phytic acid can substantially decrease micronutrients apsorption as calcium, zink, iron, manganese, copper due to phytate salts excretion by human and non-ruminant animals as poultry, swine and fish, having in common very scarce phytase activity, and consequently the ability to digest and utilize phytic acid, thus phytic acid derived phosphorus in animal waste contributes to water pollution. The tested accessions consisted of 15 genotypes of bread wheat (Triticum aestivum L. ssp. vulgare) and of 15 genotypes of durum wheat (Triticum durum Desf.). The trials were sown at the three test sites in Serbia: Rimski Šančevi (RS) (45º19´51´´N; 19º50´59´´E), Zemun Polje (ZP) (44º52´N; 20º19´E) and Padinska Skela (PS) (44º57´N 20º26´E) during two vegetation seasons 2010-2011 and 2011-2012. The experimental design was randomized complete block design with four replications. The elementary plot consisted of 3 internal rows of 0.6 m2 area (3 × 0.2 m × 1 m). Grains were grinded with Laboratory Mill 120 Perten (“Perten”, Sweden) (particles size < 500 μm) and flour was used for the analysis. Phytic acid grain content was determined spectrophotometrically with the Shimadzu UV-1601 spectrophotometer (Shimadzu Corporation, Japan). Objectives of this study were to determine: i) variability and stability of the phytic acid content among selected genotypes of bread and durum wheat, ii) predominant source of variation regarding genotype (G), environment (E) and genotype × environment interaction (GEI) from the multi-environment trial, iii) influence of climatic variables on the GEI for the phytic acid content. Based on the analysis of variance it had been determined that the variation of phytic acid content was predominantly influenced by environment in durum wheat, while the GEI prevailed for the variation of the phytic acid content in bread wheat. Phytic acid content expressed on the dry mass basis was in the range 14.21-17.86 mg g-1 with the average of 16.05 mg g-1 for bread wheat and 14.63-16.78 mg g-1 with the average of 15.91 mg g-1 for durum wheat. Average-environment coordination view of the genotype by environment (GGE) biplot was used for the selection of the most desirable genotypes for breeding for low phytic acid content in the sense of good stability and lower level of phytic acid content. The most desirable genotypes of bread and durum wheat for breeding for phytic acid were Apache and 37EDUYT /07 No. 7849. Models of climatic factors in the highest percentage (> 91%) were useful in interpreting GEI for phytic acid content, and included relative humidity in June, sunshine hours in April, mean temperature in April and winter moisture reserves for genotypes of bread wheat, as well as precipitation in June and April, maximum temperature in April and mean temperature in June for genotypes of durum wheat.

Keywords: genotype × environment interaction, phytic acid, stability, variability

Procedia PDF Downloads 395

Authors: Anna Höfer, Johannes Herrmann, Patrick Meybohm, Christopher Lotz

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Mitochondria are pivotal for energy supply and regulation of cellular functions. Deficiencies of mitochondrial metabolism have been implicated in diverse stressful conditions including infections. Platelets are key mediators for thrombo-inflammation during development and resolution of acute respiratory distress syndrome (ARDS). Previous data point to an exhausted platelet phenotype in critically-ill patients with coronavirus 19 disease (COVID-19) impacting the course of disease. The objective of this work was to characterize platelet mitochondrial metabolism in patients suffering from COVID-19 ARDSA longitudinal analysis of platelet mitochondrial metabolism in 24 patients with COVID-19 induced ARDS compared to 35 healthy controls (ctrl) was performed. Blood samples were analyzed at two time points (t1=day 1; t2=day 5-7 after study inclusion). The activity of mitochondrial citrate synthase was photometrically measured. The impact of oxidative stress on mitochondrial permeability was assessed by a photometric calcium-induced swelling assay and the activity of superoxide dismutase (SOD) by a SOD assay kit. The amount of protein carbonylation and the activity of mitochondria complexes I-IV were photometrically determined. Levels of interleukins (IL)-1α, IL-1β and tumor necrosis factor (TNF-) α were measured by a Multiplex assay kit. Median age was 54 years, 63 % were male and BMI was 29.8 kg/m2. SOFA (12; IQR: 10-15) and APACHE II (27; IQR: 24-30) indicated critical illness. Median Murray Score was 3.4 (IQR: 2.8-3.4), 21/24 (88%) required mechanical ventilation and V-V ECMO support in 14/24 (58%). Platelet counts in ARDS did not change during ICU stay (t1: 212 vs. t2: 209 x109/L). However, mean platelet volume (MPV) significantly increased (t1: 10.6 vs. t2: 11.9 fL; p<0.0001). Citrate synthase activity showed no significant differences between ctrl and ARDS patients. Calcium induced swelling was more pronounced in patients at t1 compared to t2 and to ctrl (50µM; t1: 0.006 vs. ctrl: 0.016 ΔOD; p=0.001). The amount of protein carbonylation as marker for irreversible proteomic modification constantly increased during ICU stay and compared to ctrl., without reaching significance. In parallel, superoxid dismutase activity gradually declined during ICU treatment vs. ctrl (t2: - 29 vs. ctrl.: - 17 %; p=0.0464). Complex I analysis revealed significantly stronger activity in ARDS vs. ctrl. (t1: 0.633 vs. ctrl.: 0.415 ΔOD; p=0.0086). There were no significant differences in complex II, III or IV activity in platelets from ARDS patients compared to ctrl. IL-18 constantly increased during the observation period without reaching significance. IL-1α and TNF-α did not differ from ctrl. However, IL-1β levels were significantly elevated in ARDS (t1: 16.8; t2: 16.6 vs. ctrl.: 12.4 pg/mL; p1=0.0335, p2=0.0032). This study reveals new insights in platelet mitochondrial metabolism during COVID-19 caused ARDS. it data point towards enhanced platelet activity with a pronounced turnover rate. We found increased activity of mitochondria complex I and evidence for enhanced oxidative stress. In parallel, protective mechanisms against oxidative stress were narrowed with elevated levels of IL-1β likely causing a pro-apoptotic environment. These mechanisms may contribute to platelet exhaustion in ARDS.

Keywords: acute respiratory distress syndrome (ARDS), coronavirus 19 disease (COVID-19), oxidative stress, platelet mitochondrial metabolism

Procedia PDF Downloads 62

Authors: Pei Yi Lin

Abstract:

Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

Procedia PDF Downloads 79

Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

Procedia PDF Downloads 251