Search results for: data mining techniques

Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

Abstract:

In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

Procedia PDF Downloads 134

Authors: Phomid Techi, L. N. Padmasini, Mohan Mathew

Abstract:

Objective: To evaluate the effectiveness of co-parent support on the duration of exclusive breastfeeding by a randomized control trial. Introduction: The current rates of exclusive breastfeeding for 6 months in India is 46% (NFHS3 2008.). The purpose of the study is to evaluate the effectiveness of co-parenting support on duration of exclusive breastfeeding in primi mothers. Design: RCT: Willing parents of healthy TAGA babies born in our hospital were explained about the study purpose and randomly assigned to either trial or control group. The control group was given the usual care. The intervention group received usual care and in addition the trial intervention. Follow-up data was collected at the end of 6 mon. Intervention: Face to face 30-minute discussion in post partum unit on breast feeding benefits, techniques, and problem-solving information followed up by phone calls to mother every 4 weeks to answer questions/concerns. Outcome measures: Duration of exclusive breastfeeding Baseline demographic variables were measured. Results: After obtaining IEC approval a total of 100 couples were recruited, 100 is each group. In the intervention group, the rate of exclusive breastfeeding was 97.2% while in the control group it was 64% (p-value 0.00). Conclusion: Co-parenting support has an important role in promoting exclusive breastfeeding.

Keywords: co-parenting, exclusive breastfeeding, developing nation, randomised control trial

Procedia PDF Downloads 231

Authors: Ketan N. Bajad, Manisha V. Waghmare

Abstract:

Earthquakes are the biggest threat to the civil engineering structures as every year it cost billions of dollars and thousands of deaths, around the world. There are various experimental techniques such as pseudo-dynamic tests – nonlinear structural dynamic technique, real time pseudo dynamic test and shaking table test method that can be employed to verify the seismic performance of structures. Shake table is a device that is used for shaking structural models or building components which are mounted on it. It is a device that simulates a seismic event using existing seismic data and nearly truly reproducing earthquake inputs. This paper deals with the use of shaking table test method to check the response of structure subjected to earthquake. The various types of shake table are vertical shake table, horizontal shake table, servo hydraulic shake table and servo electric shake table. The goal of this experiment is to perform seismic analysis of a civil engineering structure with the help of 3 degree of freedom (i.e. in X Y Z direction) shake table. Three (3) DOF shaking table is a useful experimental apparatus as it imitates a real time desired acceleration vibration signal for evaluating and assessing the seismic performance of structure. This study proceeds with the proper designing and erection of 3 DOF shake table by trial and error method. The table is designed to have a capacity up to 981 Newton. Further, to study the seismic response of a steel industrial building, a proportionately scaled down model is fabricated and tested on the shake table. The accelerometer is mounted on the model, which is used for recording the data. The experimental results obtained are further validated with the results obtained from software. It is found that model can be used to determine how the structure behaves in response to an applied earthquake motion, but the model cannot be used for direct numerical conclusions (such as of stiffness, deflection, etc.) as many uncertainties involved while scaling a small-scale model. The model shows modal forms and gives the rough deflection values. The experimental results demonstrate shake table as the most effective and the best of all methods available for seismic assessment of structure.

Keywords: accelerometer, three degree of freedom shake table, seismic analysis, steel industrial shed

Procedia PDF Downloads 132

Authors: Seah Siok Peh, Hashimah Mohd Yunus, Nor Hashimah Hashim, Mariam Mohamad

Abstract:

A country needs a workforce that encompasses knowledge, skilled labourers to generate innovation, productivity and being able to solve problems creatively via technology. Science education experts believe that the mastery of science skills help preschoolers to generate such knowledge on scientific concepts by providing constructive experiences. Science process skills are skills used by scientists to study or investigate a problem, issue, problem or phenomenon of science. In line with the skills used by scientists. The purpose of this study is to investigate the basic science process skill and interest in learning early science through mobile application. This study aimed to explore six spesific basic science process skills by the use of a mobile application as a learning support tool. The descriptive design also discusses on the extent of the use of mobile application in improving basic science process skill in young children. This study consists of six preschoolers and two preschool teachers from two different classes located in Perak, Malaysia. Techniques of data collection are inclusive of observations, interviews and document analysis. This study will be useful to provide information and give real phenomena to policy makers especially Ministry of education in Malaysia.

Keywords: science education, basic science process skill, interest, early science, mobile application

Procedia PDF Downloads 242

Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

Abstract:

Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

Procedia PDF Downloads 11

Authors: Yusuf Ziya Ayık, Ferhat Kahveci

Abstract:

Currently, database management systems have various tools such as backup and maintenance, and also provide statistical information such as resource usage and security. In terms of query performance, this paper covers query optimization, views, indexed tables, pre-computation materialized view, query performance analysis in which query plan alternatives can be created and the least costly one selected to optimize a query. Indexes and views can be created for related table columns. The literature review of this study showed that, in the course of time, despite the growing capabilities of the database management system, only database administrators are aware of the need for dealing with archival and transactional data types differently. These data may be constantly changing data used in everyday life, and also may be from the completed questionnaire whose data input was completed. For both types of data, the database uses its capabilities; but as shown in the findings section, instead of repeating similar heavy calculations which are carrying out same results with the same query over a survey results, using materialized view results can be in a more simple way. In this study, this performance difference was observed quantitatively considering the cost of the query.

Keywords: cost of query, database management systems, materialized view, query performance

Procedia PDF Downloads 273

Authors: Sankalp Devanand, Prateek Agasimani, Shamith V. S., Rohith Neeraje

Abstract:

Machine translation has witnessed significant advancements in recent years, but the translation of languages with distinct linguistic characteristics, such as English and Sanskrit, remains a challenging task. This research presents the development of a dedicated English-to-Sanskrit machine translation model, aiming to bridge the linguistic and cultural gap between these two languages. Using a variety of natural language processing (NLP) approaches, including FastText embeddings, this research proposes a thorough method to improve word meaning retrieval. Data preparation, part-of-speech tagging, dictionary searches, and transliteration are all included in the methodology. The study also addresses the implementation of an interpreter pattern and uses a word similarity task to assess the quality of word embeddings. The experimental outcomes show how the suggested approach may be used to enhance word meaning retrieval tasks with greater efficacy, accuracy, and adaptability. Evaluation of the model's performance is conducted through rigorous testing, comparing its output against existing machine translation systems. The assessment includes quantitative metrics such as BLEU scores, METEOR scores, Jaccard Similarity, etc.

Keywords: machine translation, English to Sanskrit, natural language processing, word meaning retrieval, fastText embeddings

Procedia PDF Downloads 35

Authors: Chia-Hau Liu, Tai-Yue Wang

Abstract:

Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

Procedia PDF Downloads 417

Authors: Sumedha Ganjoo, Santosh Goswami

Abstract:

The rising reliance on technology places national security at the forefront of 21st-century issues. It complicates the efforts of emerging and developed countries to combat cyber threats and increases the inherent risk factors connected with technology. The inability to preserve data securely might have devastating repercussions on a massive scale. Consequently, it is vital to establish national, regional, and global data protection rules and regulations that penalise individuals who participate in immoral technology usage and exploit the inherent vulnerabilities of technology. This study paper seeks to analyse GDPR-inspired Bills in the South Asian Region and determine their suitability for the development of a worldwide data protection framework, considering that Asian countries are much more diversified than European ones. In light of this context, the objectives of this paper are to identify GDPR-inspired Bills in the South Asian Region, identify their similarities and differences, as well as the obstacles to developing a regional-level data protection mechanism, thereby satisfying the need to develop a global-level mechanism. Due to the qualitative character of this study, the researcher did a comprehensive literature review of prior research papers, journal articles, survey reports, and government publications on the aforementioned topics. Taking into consideration the survey results, the researcher conducted a critical analysis of the significant parameters highlighted in the literature study. Many nations in the South Asian area are in the process of revising their present data protection measures in accordance with GDPR, according to the primary results of this study. Consideration is given to the data protection laws of Thailand, Malaysia, China, and Japan. Significant parallels and differences in comparison to GDPR have been discussed in detail. The conclusion of the research analyses the development of various data protection legislation regimes in South Asia.

Keywords: data privacy, GDPR, Asia, data protection laws

Procedia PDF Downloads 78

Authors: Yu Hung Chiang, Hei Chia Wang

Abstract:

Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for tourists to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary for them. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the targets of opinion (here they are called the feature) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, the study proposes an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews. We expect this method can help customers search what they want information as well as make decisions efficiently.

Keywords: text mining, sentiment analysis, product feature extraction, multi-lexicons

Procedia PDF Downloads 326

Authors: Musab Isah

Abstract:

This paper presents a longitudinal analysis of Internet speed data in the Gulf Cooperation Council (GCC) region, focusing on the most populous cities of each of the six countries – Riyadh, Saudi Arabia; Dubai, UAE; Kuwait City, Kuwait; Doha, Qatar; Manama, Bahrain; and Muscat, Oman. The study utilizes data collected from the Measurement Lab (M-Lab) infrastructure over a five-year period from January 1, 2019, to December 31, 2023. The analysis includes downstream and upstream throughput data for the cities, covering significant events such as the launch of 5G networks in 2019, COVID-19-induced lockdowns in 2020 and 2021, and the subsequent recovery period and return to normalcy. The results showcase substantial increases in Internet speeds across the cities, highlighting improvements in both download and upload throughput over the years. All the GCC countries have achieved above-average Internet speeds that can conveniently support various online activities and applications with excellent user experience.

Keywords: internet data science, internet performance measurement, throughput analysis, internet speed, measurement lab, network diagnostic tool

Procedia PDF Downloads 52

Authors: Rose A. Yemson, Ping Jiang, Oyedeji L. Inumoh

Abstract:

The deployment of wireless sensor network has rapidly increased, however with the increased capacity and diversity of sensors, and applications ranging from biological, environmental, military etc. generates tremendous volume of data’s where more attention is placed on the distributed sensing and little on how to manage, analyze, retrieve and understand the data generated. This makes it more quite difficult to process live sensor data, run concurrent control and update because sensor data are either heavyweight, complex, and slow. This work will focus on developing a web service platform for automatic detection of sensors, acquisition of sensor data, storage of sensor data into a database, processing of sensor data using reconfigurable software components. This work will also create a web service based sensor data management system to monitor physical movement of an individual wearing wireless network sensor technology (SunSPOT). The sensor will detect movement of that individual by sensing the acceleration in the direction of X, Y and Z axes accordingly and then send the sensed reading to a database that will be interfaced with an internet platform. The collected sensed data will determine the posture of the person such as standing, sitting and lying down. The system is designed using the Unified Modeling Language (UML) and implemented using Java, JavaScript, html and MySQL. This system allows real time monitoring an individual closely and obtain their physical activity details without been physically presence for in-situ measurement which enables you to work remotely instead of the time consuming check of an individual. These details can help in evaluating an individual’s physical activity and generate feedback on medication. It can also help in keeping track of any mandatory physical activities required to be done by the individuals. These evaluations and feedback can help in maintaining a better health status of the individual and providing improved health care.

Keywords: HTML, java, javascript, MySQL, sunspot, UML, web-based, wireless network sensor

Procedia PDF Downloads 208

Authors: Hae-Won Seo, Yong-Gu Lee

Abstract:

Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: connected-car, data modeling, route planning, navigation system

Procedia PDF Downloads 371

Authors: Smita Mondal, Dinesh Kumar Pandey, Prakash Biswas

Abstract:

During past two decades, considerable attention has been given to the value addition of biodiesel derived glycerol (~10wt.%) to make the biodiesel industry economically viable. Among the various glycerol value-addition methods, hydrogenolysis of glycerol to 1,2-propanediol is one of the attractive and promising routes. In this study, highly active and selective γ-Al₂O₃ supported bimetallic Cu-Ni catalyst was developed for selective hydrogenolysis of glycerol to 1,2-propanediol in the liquid phase. The catalytic performance was evaluated in a high-pressure autoclave reactor. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. Experimental results demonstrated that bimetallic copper-nickel catalyst was more active and selective to 1,2-PDO as compared to monometallic catalysts due to bifunctional behavior. To verify the effect of calcination temperature on the formation of Cu-Ni mixed oxide phase, the calcination temperature of 20wt.% Cu:Ni(1:1)/Al₂O₃ catalyst was varied from 300°C-550°C. The physicochemical properties of the catalysts were characterized by various techniques such as specific surface area (BET), X-ray diffraction study (XRD), temperature programmed reduction (TPR), and temperature programmed desorption (TPD). The BET surface area and pore volume of the catalysts were in the range of 71-78 m²g⁻¹, and 0.12-0.15 cm³g⁻¹, respectively. The peaks at the 2θ range of 43.3°-45.5° and 50.4°-52°, was corresponded to the copper-nickel mixed oxidephase [JCPDS: 78-1602]. The formation of mixed oxide indicated the strong interaction of Cu, Ni with the alumina support. The crystallite size decreased with increasing the calcination temperature up to 450°C. Further, the crystallite size was increased due to agglomeration. Smaller crystallite size of 16.5 nm was obtained for the catalyst calcined at 400°C. Total acidic sites of the catalysts were determined by NH₃-TPD, and the maximum total acidic of 0.609 mmol NH₃ gcat⁻¹ was obtained over the catalyst calcined at 400°C. TPR data suggested the maximum of 75% degree of reduction of catalyst calcined at 400°C among all others. Further, 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst calcined at 400°C exhibited highest catalytic activity ( > 70%) and 1,2-PDO selectivity ( > 85%) at mild reaction condition due to highest acidity, highest degree of reduction, smallest crystallite size. Further, the modified Power law kinetic model was developed to understand the true kinetic behaviour of hydrogenolysis of glycerol over 20wt.%Cu:Ni(1:1)/γ-Al₂O₃ catalyst. Rate equations obtained from the model was solved by ode23 using MATLAB coupled with Genetic Algorithm. Results demonstrated that the model predicted data were very well fitted with the experimental data. The activation energy of the formation of 1,2-PDO was found to be 45 kJ mol⁻¹.

Keywords: glycerol, 1, 2-PDO, calcination, kinetic

Procedia PDF Downloads 139

Authors: Javier Navarro Garcia, Narciso Vazquez Carretero

Abstract:

Eurocode 1, part 1-4, wind actions, includes in its article 1.5 the possibility of using numerical calculation methods to obtain information on the loads acting on a building. On the other hand, the analysis using computational fluids dynamics (CFD) in aerospace, aeronautical, and industrial applications is already in widespread use. The application of techniques based on CFD analysis on the building to study its aerodynamic behavior now opens a whole alternative field of possibilities for civil engineering and architecture; optimization of the results with respect to those obtained by applying the regulations, the possibility of obtaining information on pressures, speeds at any point of the model for each moment, the analysis of turbulence and the possibility of modeling any geometry or configuration. The present work compares the results obtained on a building, with respect to its aerodynamic behavior, from a mathematical model based on the analysis by CFD with the results obtained by applying Eurocode1, part1-4, wind actions. It is verified that the results obtained by CFD techniques suppose an optimization of the wind action that acts on the building with respect to the wind action obtained by applying the Eurocode1, part 1-4, wind actions. In order to carry out this verification, a 45m high square base truncated pyramid building has been taken. The mathematical model on CFD, based on finite volumes, has been calculated using the FLUENT commercial computer application using a scale-resolving simulation (SRS) type large eddy simulation (LES) turbulence model for an atmospheric boundary layer wind with turbulent component in the direction of the flow.

Keywords: aerodynamic, CFD, computacional fluids dynamics, computational mechanics

Procedia PDF Downloads 135

Authors: Tsao Jui-Jung

Abstract:

Previous research findings have shown that most students do not fully utilize the written corrective feedback provided by teachers (Stone, 2014). This common phenomenon results in the ineffective utilization of teachers' written corrective feedback. As Ellis (2010) points out, the effectiveness of written corrective feedback depends on the level of student engagement with it. Therefore, it is crucial to understand how students utilize the written corrective feedback from their teachers. Previous studies have confirmed the positive impact of teacher-student writing conferences on students' engagement in the writing process and their writing abilities (Hum, 2021; Nosratinia & Nikpanjeh, 2019; Wong, 1996; Yeh, 2016, 2019). However, due to practical constraints such as time limitations, this instructional activity is not fully utilized in writing classrooms (Alfalagg, 2020). Therefore, to address this research gap, the purpose of this study was to explore several aspects of teacher-student writing conferences, including the frequency of meaning negotiation (i.e., comprehension checks, confirmation checks, and clarification checks) and teacher scaffolding techniques (i.e., feedback, prompts, guidance, explanations, and demonstrations) in teacher-student writing conferences, examining students’ self-assessment of their writing strengths and weaknesses in post-conference journals and their experiences with teacher-student writing conferences (i.e., interaction styles, communication levels, how teachers addressed errors, and overall perspectives on the conferences), and gathering insights from their responses to open-ended questions in the final stage of the study (i.e., their preferences and reasons for different written corrective feedback techniques used by teachers and their perspectives and suggestions on teacher-student writing conferences). Data collection methods included transcripts of audio recordings of teacher-student writing conferences, students’ post-conference journals, and open-ended questionnaires. The participants of this study were sophomore students enrolled in an English writing course for a duration of one school year. Key research findings are as follows: Firstly, in terms of meaning negotiation, students attempted to clearly understand the corrective feedback provided by the teacher-researcher twice as often as the teacher-researcher attempted to clearly understand the students' writing content. Secondly, the most commonly used scaffolding technique in the conferences was prompting (indirect feedback). Thirdly, the majority of participants believed that teacher-student writing conferences had a positive impact on their writing abilities. Fourthly, most students preferred direct feedback from the teacher-research as it directly pointed out their errors and saved them time in revision. However, some students still preferred indirect feedback, as they believed it encouraged them to think and self-correct. Based on the research findings, this study proposes effective teaching recommendations for English writing instruction aimed at optimizing teaching strategies and enhancing students' writing abilities.

Keywords: written corrective feedback, student engagement, teacher-student writing conferences, action research

Procedia PDF Downloads 68

Authors: Mustafa K. Yilmaz, Ahmet Kaplan, Murat Guven, Vildan Kesici

Abstract:

Aviation industry influences the social and economic growth across the countries. Further, airline companies are highly affected by social, political, and financial crises and show a high degree of cyclicity in operational performance. Hence, this paper investigates the effects of available seat kilometers (ASK), revenue per kilometer (RPK), passenger load factor (PLF) as well as socio-political crisis on the number of passengers carried (PC) by Turkish Airlines company over the period of 2010M1-2018M12. To conduct the analysis, we employ fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) techniques using monthly data. We use ASK, RPK, PLF as independent variables to identify the determinants of the PC, as a dependent variable. We also test the effect of the socio-political crisis. The results reveal that there is a significant and negative relationship between ASK and PC, while the relationship between RPK and PC is positive and significant. We also find that there is an insignificant relationship between PLF and PC. Further, we also find a negative effect of the crisis on the PC. These findings show although the crisis had an immediate effect on the operational performance of Turkish Airlines, the company recovered from the crisis and cope with the situation very promptly. Thus, this proves the resilience and agile management ability of the company.

Keywords: airline industry, operational performance, air traffic, socio-political crisis

Procedia PDF Downloads 166

Authors: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

Abstract:

Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

Procedia PDF Downloads 159

Authors: Soojin Kim, Jiwon Yang, Sungzoon Cho

Abstract:

As a seasonal product is only in demand for a short time, inventory management is critical to profits. Both markdowns and stockouts decrease the return on perishable products; therefore, researchers have been interested in the distribution of seasonal products with the aim of maximizing profits. In this study, we propose a data-driven seasonal product sales pattern analysis method for individual retail outlets based on observed sales data clustering; the proposed method helps in determining distribution strategies.

Keywords: clustering, distribution, sales pattern, seasonal product

Procedia PDF Downloads 590

Authors: S. Tebibel, C. Mechati, S. Messaoudi

Abstract:

Algeria is currently experiencing the same rate of cancer progression as that registered these last years in the western countries. Colorectal cancer, constituting increasingly a major public health problem, is the most common form of cancer after breast and Neck-womb cancer at the woman and prostate cancer at the man. Our work is based on a retrospective study to determine the cases of colorectal cancer through eastern Algeria. Our goal is to carry out an epidemiological, histological and immune- histochemical study to investigate different techniques for the diagnosis of colorectal cancer and their interests and specific in detecting the disease. The study includes 110 patients (aged between 20 to 87 years) with colorectal cancer where the inclusions and exclusions criteria were established. In our study, colorectal cancer, expresses a male predominance, with a sex ratio of 1, 99 and the most affected age group is between 50 and 59 years. We noted that the colon cancer rate is higher than rectal cancer rate, whose frequencies are respectively 60,91 % and 39,09 %. In the series of colon cancer, the ADK lieberkunien is histological the most represented type, or 85,07 % of all cases. In contrast, the proportion of ADK mucinous (colloid mucous) is only 1,49% only. Well-differentiated ADKS, are very significant in our series, they represent 83,58 % of cases. Adenocarcinoma moderately and poorly differentiated, whose proportions are respectively 2,99 % and 0.05 %. For histological varieties of rectal ADK, we see in our workforce that ADK lieberkunien represent the most common histological form, or 76,74%, while the mucosal colloid is 13,95 %. Research of the mutation on the gene encoding K-ras, a major step in the targeted therapy of colorectal cancers, is underway in our study. Colorectal cancer is the subject of much promising research concern: the evaluation of new therapies (antiangiogenic monoclonal antibodies), the search for predictors of sensitivity to chemotherapy and new prognostic markers using techniques of molecular biology and proteomics.

Keywords: adenocarcinoma, age, colorectal cancer, epidemiology, histological section, sex

Procedia PDF Downloads 339

Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

Abstract:

Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

Procedia PDF Downloads 488

Authors: B. Philip, J. Littlewood, R. Radford, N. Evans, T. Whyman, D. P. Jones

Abstract:

UK buildings and energy infrastructures are heavily dependent on natural gas, a large proportion of which is used for domestic space heating. However, approximately half of the gas consumed in the UK is imported. Improving energy security and reducing carbon emissions are major government drivers for reducing gas dependency. In order to do so there needs to be a wholesale shift in the energy provision to householders without impacting on thermal comfort levels, convenience or cost of supply to the end user. Heat pumps are seen as a potential alternative in modern well insulated homes, however, can the same be said of older homes? A large proportion of housing stock in Britain was built prior to 1919. The age of the buildings bears testimony to the quality of construction; however, their thermal performance falls far below the minimum currently set by UK building standards. In recent years significant sums of money have been invested to improve energy efficiency and combat fuel poverty in some of the most deprived areas of Wales. Increasing energy efficiency of older properties remains a significant challenge, which cannot be achieved through insulation and air-tightness interventions alone, particularly when alterations to historically important architectural features of the building are not permitted. This paper investigates the energy demand of pre-1919 dwellings in a former Welsh mining village, the feasibility of meeting that demand using water from the disused mine workings to supply a district heat network and potential barriers to success of the scheme. The use of renewable solar energy generation and storage technologies, both thermal and electrical, to reduce the load and offset increased electricity demand, are considered. A wholistic surveying approach to provide a more accurate assessment of total household heat demand is proposed. Several surveying techniques, including condition surveys, air permeability, heat loss calculations, and thermography were employed to provide a clear picture of energy demand. Additional insulation can bring unforeseen consequences which are detrimental to the fabric of the building, potentially leading to accelerated dilapidation of the asset being ‘protected’. Increasing ventilation should be considered in parallel, to compensate for the associated reduction in uncontrolled infiltration. The effectiveness of thermal performance improvements are demonstrated and the detrimental effects of incorrect material choice and poor installation are highlighted. The findings show estimated heat demand to be in close correlation to household energy bills. Major areas of heat loss were identified such that improvements to building thermal performance could be targeted. The findings demonstrate that the use of heat pumps in older buildings is viable, provided sufficient improvement to thermal performance is possible. Addition of passive solar thermal and photovoltaic generation can help reduce the load and running cost for the householder. The results were used to predict future heat demand following energy efficiency improvements, thereby informing the size of heat pumps required.

Keywords: heat demand, heat pump, renewable energy, retrofit

Procedia PDF Downloads 90

Authors: Chamika Chiran Perera, Dananjaya Perera, Chirath Dasanayake, Banuka Athuraliya

Abstract:

Male infertility is a major problem in the world, and it is a neglected and sensitive health issue in Sri Lanka. It can be determined by analyzing human semen samples. Sperm motility is one of many factors that can evaluate male’s fertility potential. In Sri Lanka, this analysis is performed manually. Manual methods are time consuming and depend on the person, but they are reliable and it can depend on the expert. Machine learning and deep learning technologies are currently being investigated to automate the spermatozoa motility analysis, and these methods are unreliable. These automatic methods tend to produce false positive results and false detection. Current automatic methods support different techniques, and some of them are very expensive. Due to the geographical variance in spermatozoa characteristics, current automatic methods are not reliable for motility analysis in Sri Lanka. The suggested system, DeepMotile, is to explore a method to analyze motility of human spermatozoa automatically and present it to the andrology laboratories to overcome current issues. DeepMotile is a novel deep learning method for analyzing spermatozoa motility parameters in the Sri Lankan population. To implement the current approach, Sri Lanka patient data were collected anonymously as a dataset, and glass slides were used as a low-cost technique to analyze semen samples. Current problem was identified as microscopic object detection and tackling the problem. YOLOv5 was customized and used as the object detector, and it achieved 94 % mAP (mean average precision), 86% Precision, and 90% Recall with the gathered dataset. StrongSORT was used as the object tracker, and it was validated with andrology experts due to the unavailability of annotated ground truth data. Furthermore, this research has identified many potential ways for further investigation, and andrology experts can use this system to analyze motility parameters with realistic accuracy.

Keywords: computer vision, deep learning, convolutional neural networks, multi-target tracking, microscopic object detection and tracking, male infertility detection, motility analysis of human spermatozoa

Procedia PDF Downloads 102

Authors: Jalil ur Rehman, Ramesh C. Tailor, Muhammad Isa Khan, Jahnzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

Abstract:

Purpose: The purpose of this analysis was to investigate dose distribution of different techniques (3D-CRT, IMRT and VMAT) of head and neck cancer using 3-dimensional dosimeter called PRESAGETM Dosimeter. Materials and Methods: Computer tomography (CT) scans of radiological physics center (RPC) head and neck anthropomorphic phantom with both RPC standard insert and PRESAGETM insert were acquired separated with Philipp’s CT scanner and both CT scans were exported via DICOM to the Pinnacle version 9.4 treatment planning system (TPS). Each plan was delivered twice to the RPC phantom first containing the RPC standard insert having TLD and film dosimeters and then again containing the Presage insert having 3-D dosimeter (PRESAGETM) by using a Varian True Beam linear accelerator. After irradiation, the standard insert including point dose measurements (TLD) and planar Gafchromic® EBT film measurement were read using RPC standard procedure. The 3D dose distribution from PRESAGETM was read out with the Duke Midsized optical scanner dedicated to RPC (DMOS-RPC). Dose volume histogram (DVH), mean and maximal doses for organs at risk were calculated and compared among each head and neck technique. The prescription dose was same for all head and neck radiotherapy techniques which was 6.60 Gy/friction. Beam profile comparison and gamma analysis were used to quantify agreements among film measurement, PRESAGETM measurement and calculated dose distribution. Quality assurances of all plans were performed by using ArcCHECK method. Results: VMAT delivered the lowest mean and maximum doses to organ at risk (spinal cord, parotid) than IMRT and 3DCRT. Such dose distribution was verified by absolute dose distribution using thermoluminescent dosimeter (TLD) system. The central axial, sagittal and coronal planes were evaluated using 2D gamma map criteria(± 5%/3 mm) and results were 99.82% (axial), 99.78% (sagital), 98.38% (coronal) for VMAT plan and found the agreement between PRESAGE and pinnacle was better than IMRT and 3D-CRT plan excludes a 7 mm rim at the edge of the dosimeter. Profile showed good agreement for all plans between film, PRESAGE and pinnacle and 3D gamma was performed for PTV and OARs, VMAT and 3DCRT endow with better agreement than IMRT. Conclusion: VMAT delivered lowered mean and maximal doses to organs at risk and better PTV coverage during head and neck radiotherapy. TLD, EBT film and PRESAGETM dosimeters suggest that VMAT was better for the treatment of head and neck cancer than IMRT and 3D-CRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD, PRESAGETM

Procedia PDF Downloads 382

Authors: A. S. V Polgampala, F. Huang

Abstract:

This is an overview of what is entailed in an evaluation and issues to be aware of when class observation is being done. This study examined the effects of evaluating teaching practice of a 7-day ‘block teaching’ session in a pre -service science teacher training program at a reputed National College of Education in Sri Lanka. Effects were assessed in three areas: evaluation of the training process, evaluation of the training impact, and evaluation of the training procedure. Data for this study were collected by class observation of 18 teachers during 9th February to 16th of 2017. Prospective teachers of science teaching, the participants of the study were evaluated based on newly introduced format by the NIE. The data collected was analyzed qualitatively using the Miles and Huberman procedure for analyzing qualitative data: data reduction, data display and conclusion drawing/verification. It was observed that the trainees showed their confidence in teaching those competencies and skills. Teacher educators’ dissatisfaction has been a great impact on evaluation process.

Keywords: evaluation, perceptions & perspectives, pre-service, science teachering

Procedia PDF Downloads 310

Authors: Abhijith Asok

Abstract:

This paper presents a generalized approach for the simple linear data transformation, Y=bX, through an integration of multidimensional coordinate geometry, vector space theory and polygonal geometry. The scaling is performed by adding an additional ’Dummy Dimension’ to the n-dimensional data, which helps plot two dimensional component-wise straight lines on pairs of dimensions. The end result is a set of scaled extensions of observations in any of the 2n spatial divisions, where n is the total number of applicable dimensions/dataset variables, created by shifting the n-dimensional plane along the ’Dummy Axis’. The derived scaling factor was found to be dependent on the coordinates of the common point of origin for diverging straight lines and the plane of extension, chosen on and perpendicular to the ’Dummy Axis’, respectively. This result indicates the geometrical interpretation of a linear data transformation and hence, opportunities for a more informed choice of the factor ’b’, based on a better choice of these coordinate values. The paper follows on to identify the effect of this transformation on certain popular distance metrics, wherein for many, the distance metric retained the same scaling factor as that of the features.

Keywords: data transformation, dummy dimension, linear transformation, scaling

Procedia PDF Downloads 294

Authors: Minna Pikkarainen, Yueqiang Xu

Abstract:

The integration of digital technology with existing healthcare processes has been painfully slow, a huge gap exists between the fields of strictly regulated official medical care and the quickly moving field of health and wellness technology. We claim that the promises of preventive healthcare can only be fulfilled when this gap is closed – health care and self-care becomes seamless continuum “correct information, in the correct hands, at the correct time allowing individuals and professionals to make better decisions” what we call connected health approach. Currently, the issues related to security, privacy, consumer consent and data sharing are hindering the implementation of this new paradigm of healthcare. This could be solved by following MyData principles stating that: Individuals should have the right and practical means to manage their data and privacy. MyData infrastructure enables decentralized management of personal data, improves interoperability, makes it easier for companies to comply with tightening data protection regulations, and allows individuals to change service providers without proprietary data lock-ins. This paper tackles today’s unprecedented challenges of enabling and stimulating multiple healthcare data providers and stakeholders to have more active participation in the digital health ecosystem. First, the paper systematically proposes the MyData approach for healthcare and preventive health data ecosystem. In this research, the work is targeted for health and wellness ecosystems. Each ecosystem consists of key actors, such as 1) individual (citizen or professional controlling/using the services) i.e. data subject, 2) services providing personal data (e.g. startups providing data collection apps or data collection devices), 3) health and wellness services utilizing aforementioned data and 4) services authorizing the access to this data under individual’s provided explicit consent. Second, the research extends the existing four archetypes of orchestrator-driven healthcare data business models for the healthcare industry and proposes the fifth type of healthcare data model, the MyData Blockchain Platform. This new architecture is developed by the Action Design Research approach, which is a prominent research methodology in the information system domain. The key novelty of the paper is to expand the health data value chain architecture and design from centralization and pseudo-decentralization to full decentralization, enabled by blockchain, thus the MyData blockchain platform. The study not only broadens the healthcare informatics literature but also contributes to the theoretical development of digital healthcare and blockchain research domains with a systemic approach.

Keywords: blockchain, health data, platform, action design

Procedia PDF Downloads 96

Authors: Adewale O. Ogunde, Emmanuel O. Ajibade

Abstract:

The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counselors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counselors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest neighbor algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real students’ results dataset was used to build and test the recommendation model. The developed system will not only improve the academic performance of students, but it will also help reduce the workload on the level advisers and school counselors.

Keywords: collaborative filtering, elective courses, k-nearest neighbor algorithm, recommender systems

Procedia PDF Downloads 159

Authors: Ghadeer A.Al-Jabaree, Husam Aldeen Al-Khadash, M. Nassar

Abstract:

This study aimed at investigating the effect of well-designed corporate governance system on the financial stability of insurance companies listed in ASE. Further, this study provides a comprehensive model for evaluating and analyzing insurance companies' financial position and prospective for comparing the degree of corporate governance application provisions among Jordanian insurance companies. In order to achieve the goals of the study, a whole population that consist of (27) listed insurance companies was introduced through the variables of (board of director, audit committee, internal and external auditor, board and management ownership and block holder's identities). Statistical methods were used with alternative techniques by (SPSS); where descriptive statistical techniques such as means, standard deviations were used to describe the variables, while (F) test and ANOVA analysis of variance were used to test the hypotheses of the study. The study revealed the existence of significant effect of corporate governance variables except local companies that are not listed in ASE on financial stability within control variables especially debt ratio (leverage),where it's also showed that concentration in motor third party doesn't have significant effect on insurance companies' financial stability during study period. Moreover, the study concludes that Global financial crisis affect the investment side of insurance companies with insignificant effect on the technical side. Finally, some recommendations were presented such as enhancing the laws and regulation that help the appropriate application of corporate governance, and work on activating the transparency in the disclosures of the financial statements and focusing on supporting the technical provisions for the companies, rather than focusing only on profit side.

Keywords: corporate governance, financial stability and solvency margin, insurance companies, Jordan

Procedia PDF Downloads 485

Authors: Gabriel Sunday Ayayia

Abstract:

Food waste remains a critical global challenge with significant environmental, economic, and ethical implications. In an era where food waste and food insecurity coexist, innovative technology-driven solutions have emerged, aiming to bridge the gap between surplus food and those in need. Simultaneously, disparities in food access persist, exacerbating issues of hunger and malnutrition. Emerging food-sharing apps offer a promising avenue to mitigate these problems but require further examination within the context of the Ubuntu sharing economy. This study seeks to understand the impact of food-sharing apps, guided by the principles of Ubuntu, on reducing food waste and enhancing food access. The study examines how specific food-sharing apps within the Ubuntu sharing economy could contribute to fostering community resilience and reducing food waste. Ubuntu underscores the idea that we are all responsible for the well-being of our community members. In the context of food waste, this means that individuals and businesses have a collective responsibility to ensure that surplus food is shared rather than wasted. Food-sharing apps align with this principle by facilitating the sharing of excess food with those in need, transforming waste into a communal resource. This research employs a mixed-methods approach of both quantitative analysis and qualitative inquiry. Large-scale surveys will be conducted to assess user behavior, attitudes, and experiences with food-sharing apps, focusing on the frequency of use, motivations, and perceived impacts. Qualitative interviews with app users, community organizers, and stakeholders will explore the Ubuntu-inspired aspects of food-sharing apps and their influence on reducing food waste and improving food access. Quantitative data will be analyzed using statistical techniques, while qualitative data will undergo thematic analysis to identify key patterns and insights. This research addresses a critical gap in the literature by examining the role of food-sharing apps in reducing food waste and enhancing food access, particularly within the Ubuntu sharing economy framework. Findings will offer valuable insights for policymakers, technology developers, and communities seeking to leverage technology to create a more just and sustainable food system.

Keywords: sharing economy, food waste reduction, technology, community- based approach

Procedia PDF Downloads 64