Search results for: data mining techniques

Authors: Po-Chun Lee, Li-Lin Liang, Ching-Yuan Huang

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Purpose: Research on electronic word-of-mouth (eWOM)& online review has been widely used in service industry management research in recent years. The SERVQUAL scale is the most commonly used method to measure service quality. Therefore, the purpose of this research is to combine electronic word of mouth & online review with the SERVQUAL scale. To explore the comparative study of positive and negative electronic word-of-mouth reviews of a certain armed force general hospital in Taiwan. Data sources: This research obtained online word-of-mouth comment data on google maps from a military hospital in Taiwan in the past ten years through Internet data mining technology. Research methods: This study uses the semantic content analysis method to classify word-of-mouth reviews according to the revised PZB SERVQUAL scale. Then carry out statistical analysis. Results of data synthesis: The results of this study disclosed that the negative reviews of this military hospital in Taiwan have been increasing year by year. Under the COVID-19 epidemic, positive word-of-mouth has a downward trend. Among the five determiners of SERVQUAL of PZB, positive word-of-mouth reviews performed best in “Assurance,” with a positive review rate of 58.89%, Followed by 43.33% of “Responsiveness.” In negative word-of-mouth reviews, “Assurance” performed the worst, with a positive rate of 70.99%, followed by responsive 29.01%. Conclusions: The important conclusions of this study disclosed that the total number of electronic word-of-mouth reviews of the military hospital has revealed positive growth in recent years, and the positive word-of-mouth growth has revealed negative growth after the epidemic of COVID-19, while the negative word-of-mouth has grown substantially. Regardless of the positive and negative comments, what patients care most about is “Assurance” of the professional attitude and skills of the medical staff, which needs to be strengthened most urgently. In addition, good “Reliability” will help build positive word-of-mouth. However, poor “Responsiveness” can easily lead to the spread of negative word-of-mouth. This study suggests that the hospital should focus on these few service-oriented quality management and audits.

Keywords: quality of medical service, electronic word-of-mouth, armed forces general hospital

Procedia PDF Downloads 174

Authors: A. Laksanayothin, W. Ariyawong

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Mae Moh Lignite Mine is the largest open-pit mine in Thailand. The mine serves coal to the power plant about 16 million tons per year. This amount of coal can produce electricity accounting for about 10% of Nation’s electric power generation. The mining area of Mae Moh Mine is about 28 km2. At present, the deepest area of the pit is about 280 m from ground level (+40 m. MSL) and in the future the depth of the pit can reach 520 m from ground level (-200 m.MSL). As the size of the pit is quite large, the stability of the pit is seriously important. Furthermore, the preliminary drilling and extended drilling in year 1989-1996 had found high pressure aquifer under the pit. As a result, the pressure of the underground water has to be released in order to control mine pit stability. The study by the consulting experts later found that 3-5 million m3 per year of the underground water is needed to be de-watered for the safety of mining. However, the quality of this discharged water should meet the standard. Therefore, the ground water treatment facility has been implemented, aiming to reduce the amount of naturally contaminated Arsenic (As) in discharged water lower than the standard limit of 10 ppb. The treatment system consists of coagulation and filtration process. The main components include rapid mixing tanks, slow mixing tanks, sedimentation tank, thickener tank and sludge drying bed. The treatment process uses 40% FeCl3 as a coagulant. The FeCl3 will adsorb with As(V), forming floc particles and separating from the water as precipitate. After that, the sludge is dried in the sand bed and then be disposed in the secured land fill. Since 2011, the treatment plant of 12,000 m3/day has been efficiently operated. The average removal efficiency of the process is about 95%.

Keywords: arsenic, coagulant, ferric chloride, groundwater, lignite, coal mine

Procedia PDF Downloads 307

Authors: Muhammet Şahal, Oğuz Köklü

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As statistical thinking and problem-solving processes have become increasingly important, teachers need to be more rigorously prepared with statistical knowledge to teach their students effectively. This study examined preservice mathematics teachers' development of statistical investigation projects using data and exploratory data analysis tools, following a design-based research perspective and statistical investigation cycle. A total of 26 pre-service senior mathematics teachers from a public university in Turkiye participated in the study. They formed groups of 3-4 members voluntarily and worked on their statistical investigation projects for six weeks. The data sources were audio recordings of pre-service teachers' group discussions while working on their projects in class, whole-class video recordings, and each group’s weekly and final reports. As part of the study, we reviewed weekly reports, provided timely feedback specific to each group, and revised the following week's class work based on the groups’ needs and development in their project. We used content analysis to analyze groups’ audio and classroom video recordings. The participants encountered several difficulties, which included formulating a meaningful statistical question in the early phase of the investigation, securing the most suitable data collection strategy, and deciding on the data analysis method appropriate for their statistical questions. The data collection and organization processes were challenging for some groups and revealed the importance of comprehensive planning. Overall, preservice senior mathematics teachers were able to work on a statistical project that contained the formulation of a statistical question, planning, data collection, analysis, and reaching a conclusion holistically, even though they faced challenges because of their lack of experience. The study suggests that preservice senior mathematics teachers have the potential to apply statistical knowledge and techniques in a real-world context, and they could proceed with the project with the support of the researchers. We provided implications for the statistical education of teachers and future research.

Keywords: design-based study, pre-service mathematics teachers, statistical investigation projects, statistical model

Procedia PDF Downloads 74

Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

Procedia PDF Downloads 581

Authors: Delphine Mukaneza, Lin Qiao, Wang Pengxin, Li Yan, Chen Yingyi

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Human activities on land use make the land-cover gradually change or transit. In this study, we examined the use of Landsat TM data to detect the land use change of Kigali between 1987 and 2009 using remote sensing techniques and analysis of data using ENVI and ArcGIS, a GIS software. Six different categories of land use were distinguished: bare soil, built up land, wetland, water, vegetation, and others. With remote sensing techniques, we analyzed land use data in 1987, 1999 and 2009, changed areas were found and a dynamic situation of land use in Kigali city was found during the 22 years studied. According to relevant Landsat data, the research focused on land use change in accordance with the role of remote sensing in the process of urbanization. The result of the work has shown the rapid increase of built up land between 1987 and 1999 and a big decrease of vegetation caused by the rebuild of the city after the 1994 genocide, while in the period of 1999 to 2009 there was a reduction in built up land and vegetation, after the authority of Kigali city established, a Master Plan where all constructions which were not in the range of the master Plan were destroyed. Rwanda's capital, Kigali City, through the expansion of the urban area, it is increasing the internal employment rate and attracts business investors and the service sector to improve their economy, which will increase the population growth and provide a better life. The overall planning of the city of Kigali considers the environment, land use, infrastructure, cultural and socio-economic factors, the economic development and population forecast, urban development, and constraints specification. To achieve the above purpose, the Government has set for the overall planning of city Kigali, different stages of the detailed description of the design, strategy and action plan that would guide Kigali planners and members of the public in the future to have more detailed regional plans and practical measures. Thus, land use change is significantly the performance of Kigali active human area, which plays an important role for the country to take certain decisions. Another area to take into account is the natural situation of Kigali city. Agriculture in the region does not occupy a dominant position, and with the population growth and socio-economic development, the construction area will gradually rise and speed up the process of urbanization. Thus, as a developing country, Rwanda's population continues to grow and there is low rate of utilization of land, where urbanization remains low. As mentioned earlier, the 1994 genocide massacres, population growth and urbanization processes, have been the factors driving the dramatic changes in land use. The focus on further research would be on analysis of Rwanda’s natural resources, social and economic factors that could be, the driving force of land use change.

Keywords: land use change, urbanization, Kigali City, Landsat

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Authors: Abdelkader Hadjoudja, Hajar Bouazza

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The PLD/CPLD devices are widely used for logic synthesis since several decades. Based on sum of product terms (PTs) architecture, the PLD/CPLD offer a high degree of flexibility to support various application requirements. They are suitable for large combinational logic, finite state machines as well as intensive I/O designs. CPLDs offer very predictable timing characteristics and are therefore ideal for critical control applications. This paper describes how the logic synthesis techniques, such as 1) XOR detection, 2) logic doubling, 3) complement of a Boolean function are combined, applied and used to optimize the CPLDs devices architecture that is based on PAL-like macrocells. Our goal is to use these techniques for minimizing the number of macrocells required to implement a circuit and minimize the delay of mapped circuit.

Keywords: CPLD, doubling, optimization, XOR

Procedia PDF Downloads 275

Authors: Cahya Wimar Wicaksono, Reynara Davin Chen, Alvian Kristianto Santoso

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Grasberg Cu-Au ore deposit as one of the biggest porphyry deposits located in Papua Province, Indonesia produced by several intrusion that restricted by Heavy Sulphide Zone (HSZ) in peripheral. HSZ is the rock that becomes the contact between Grassberg Igneous Complex (GIC) with sedimentary and igneous rock outside, which is rich in sulphide minerals such as pyrite ± pyrrhotite. This research is to obtain the characteristic of HSZ based on geotechnical, geochemical and mineralogy aspect and those implication for daily mining operational activities. Method used in this research are geological and alteration mapping, core logging, FAA (Fire Assay Analysis), AAS (Atomic absorption spectroscopy), RQD (Rock Quality Designation) and rock water content. Data generated from methods among RQD data, mineral composition and grade, lithological and structural geology distribution in research area. The mapping data show that HSZ material characteristics divided into three type based on rocks association, there are near igneous rocks, sedimentary rocks and on HSZ area. And also divided based on its location, north and south part of research area. HSZ material characteristic consist of rock which rich of pyrite ± pyrrhotite, and RQD range valued about 25%-100%. Pyrite ± pyrrhotite which outcropped will react with H₂O and O₂ resulting acid that generates corrosive effect on steel wire and rockbolt. Whereas, pyrite precipitation proses in HSZ forming combustible H₂S gas which is harmful during blasting activities. Furthermore, the impact of H₂S gas in blasting activities is forming poison gas SO₂. Although HSZ high grade Cu-Au, however those high grade Cu-Au rich in sulphide components which is affected in flotation milling process. Pyrite ± pyrrhotite in HSZ will chemically react with Cu-Au that will settle in milling process instead of floating.

Keywords: combustible, corrosive, heavy sulphide zone, pyrite ± pyrrhotite

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Authors: Abraham J. Rizkalla, Tristan D. Yan

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Purpose: Durable mitral valve repair is preferred to a replacement, avoiding the need for anticoagulation or re-intervention, with a reduced risk of endocarditis. Robotic mitral repair has been gaining favour globally as a safe, effective, and reproducible method of minimally invasive valve repair. In this work, we showcase the use of the Davinci© Xi robotic platform to perform several advanced techniques, working synergistically to achieve successful mitral repair in advanced mitral disease. Techniques: We present the case of a Barlow type mitral valve disease with a tall and redundant posterior leaflet resulting in severe mitral regurgitation and systolic anterior motion. Firstly, quadrangular resection of P2 is performed to remove the excess and redundant leaflet. Secondly, a sliding leaflet plasty of P1 and P3 is used to reconstruct the posterior leaflet. To anchor the newly formed posterior leaflet to the papillary muscle, CV-4 Goretex neochordae are fashioned using the innovative string, ruler, and bulldog technique. Finally, mitral valve annuloplasty and closure of a patent foramen ovale complete the repair. Results: There was no significant residual mitral regurgitation and complete resolution of the systolic anterior motion of the mitral valve on post operative transoesophageal echocardiography. Conclusion: This work highlights the robotic approach to complex repair techniques for advanced mitral valve disease. Familiarity with resection and sliding plasty, neochord implantation, and annuloplasty allows the modern cardiac surgeon to achieve a minimally-invasive and durable mitral valve repair when faced with complex mitral valve pathology.

Keywords: robotic mitral valve repair, Barlow's valve, sliding plasty, neochord, annuloplasty, quadrangular resection

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Authors: Hy Dang, Bo Mei

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The development of machine learning methods and techniques has opened doors for investigation in many areas such as medicines, economics, finance, etc. One active research area involving machine learning is stock market prediction. This research paper tries to consider multiple techniques and methods for stock movement prediction using historical price or price factors. The paper explores the effectiveness of some deep learning frameworks for forecasting stock. Moreover, an architecture (TimeStock) is proposed which takes the representation of time into account apart from the price information itself. Our model achieves a promising result that shows a potential approach for the stock movement prediction problem.

Keywords: classification, machine learning, time representation, stock prediction

Procedia PDF Downloads 140

Authors: Lixin Zhao, Genmao Zhou

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Traditional acid in-situ leaching (ISL) is not suitable for the sandstone uranium deposit with low permeability and high content of carbonate minerals, because of the blocking of calcium sulfate precipitates. Another factor influences the uranium acid in-situ leaching is that the pyrite in ore rocks will react with oxidation reagent and produce lots of sulfate ions which may speed up the precipitation process of calcium sulphate and consume lots of oxidation reagent. Due to the advantages such as less chemical reagent consumption and groundwater pollution, CO₂+O₂ in-situ leaching method has become one of the important research areas in uranium mining. China is the second country where CO₂+O₂ ISL has been adopted in industrial uranium production of the world. It is shown that the CO₂+O₂ ISL in China has been successfully developed. The reaction principle, technical process, well field design and drilling engineering, uranium-bearing solution processing, etc. have been fully studied. At current stage, several uranium mines use CO₂+O₂ ISL method to extract uranium from the ore-bearing aquifers. The industrial application and development potential of CO₂+O₂ ISL method in China are summarized. By using CO₂+O₂ neutral leaching technology, the problem of calcium carbonate and calcium sulfate precipitation have been solved during uranium mining. By reasonably regulating the amount of CO₂ and O₂, related ions and hydro-chemical conditions can be controlled within the limited extent for avoiding the occurrence of calcium sulfate and calcium carbonate precipitation. Based on this premise, the demand of CO₂+O₂ uranium leaching has been met to the maximum extent, which not only realizes the effective leaching of uranium, but also avoids the occurrence and precipitation of calcium carbonate and calcium sulfate, realizing the industrial development of the sandstone type uranium deposit.

Keywords: CO₂+O₂ ISL, industrial production, well field layout, uranium processing

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Authors: Kadrefi Athanasia, Fronimaki Evgenia, Mavri Maria

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3D Printing (3DP) is a distinct, disruptive technology that belongs to a wider group of manufacturing technologies, Additive Manufacturing (AM). In 3DP, a custom digital file turns into a solid object using a single computer and a 3D printer. Among multiple advantages, 3DP offers production with fewer steps compared to conventional manufacturing, lower production costs, and customizable designs. 3DP can be performed by several techniques, while the most common is Fused Deposition Modeling (FDM). FDM belongs to a wider group of AM techniques, material extrusion, where a digital file converts into a solid object using raw material (called filament) melted in high temperatures. As in most manufacturing procedures, environmental issues have been raised here, too. This study aims to review the literature on issues that determine technical and mechanical factors that affect the sustainability and resilience of a final 3D-printed object. The research focuses on the collection of papers that deal with 3D printing techniques and use keywords or phrases like ‘3D printed objects’, ‘factors of 3DP sustainability’, ‘waste materials,’ ‘infill patterns,’ and ‘support structures.’ After determining factors, a pilot survey will be conducted at the 3D Printing Lab in order to define the significance of each factor in the final 3D printed object.

Keywords: additive manufacturing, 3D printing, sustainable manufacturing, sustainable production

Procedia PDF Downloads 50

Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

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Authors: Azene Zenebe

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Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

Procedia PDF Downloads 146

Authors: Jaber Shurrab, Matloub Hussain

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The rapid pace of changes in the construction industry, technological advancements, and rising costs present tremendous challenges for project managers. Project managers are under severe pressure to minimize the waste, improve the efficiency of the entire operations and the philosophy of ‘lean thinking’ so that ‘more could be achieved with less’ is becoming very popular. Though, lean management has strong roots in manufacturing industry and over the last decade lean philosophy has started gaining attention in the service industry as well. However, little has been known in the context of waste minimization and lean implementation in the construction industry and this paper deals with this important issue. The primary objective of this paper is to propose a conceptual framework for the exploration of appropriate lean techniques applicable to medium and large construction companies and measure their impact on the competitiveness and economic performance of construction companies of United Arab Emirates (UAE). To this end, a comprehensive literature review and interviews with eight project managers of medium and large construction companies of UAE have been conducted. It has been found that competitive, reduce waste and costs are critical to the construction industry. This is an ongoing research in lean management, giving project managers a practical framework for improving the efficiency of their project through various lean techniques. Originality/value: Research significance emphasizes increasing the effectiveness of the construction industry, influences the development of lean construction framework which improves lean construction practices using the lean techniques. This contributes to the effort of applying lean techniques in the construction industry. Limited publications were done in the construction industry mainly in United Arab Emirates (UAE) compared to the lean manufacturing. This research will recommend a systematic approach for the implementing of the anticipated framework within a cyclical look-ahead period and emphasizes the practical implications of the proposed approach.

Keywords: construction, lean, lean manufacturing, waste

Procedia PDF Downloads 276

Authors: Jeisson A. Estrella, Laura C. Herrera, Cristian A. Arenas

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The Colombian electricity sector has been transforming through the insertion of new energy sources to generate electricity, one of them being solar energy, which is being promoted by companies interested in photovoltaic technology. The study of this technology is important for electricity generation in general and for the planning of the sector from the perspective of energy complementarity. Precisely in this last approach is where the project is located; we are interested in answering the concerns about the reliability of the electrical system when climatic phenomena such as El Niño occur or in defining whether it is viable to replace or expand thermoelectric plants. Reliability of the electrical system when climatic phenomena such as El Niño occur, or to define whether it is viable to replace or expand thermoelectric plants with renewable electricity generation systems. In this regard, some difficulties related to the basic information on renewable energy sources from measured data must first be solved, as these come from automatic weather stations. Basic information on renewable energy sources from measured data, since these come from automatic weather stations administered by the Institute of Hydrology, Meteorology and Environmental Studies (IDEAM) and, in the range of study (2005-2019), have significant amounts of missing data. For this reason, the overall objective of the project is to complete the global solar irradiance datasets to obtain time series to develop energy complementarity analyses in a subsequent project. Global solar irradiance data sets to obtain time series that will allow the elaboration of energy complementarity analyses in the following project. The filling of the databases will be done through numerical and statistical methods, which are basic techniques for undergraduate students in technical areas who are starting out as researchers technical areas who are starting out as researchers.

Keywords: time series, global solar irradiance, imputed data, energy complementarity

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Authors: Md Tareq Ferdous Khan, Shrabanti Mazumder, MB Rao

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Background: Substance use during pregnancy is a longstanding public health problem that results in severe consequences for pregnant women and fetuses. Methods: Eight (2011-2018) datasets on pregnant women’s admissions are extracted from TEDS-A. Distributions of sociodemographic, substance abuse behaviors, and clinical characteristics are constructed and compared over the years for trends by the Cochran-Armitage test. Market basket analysis is used in mining the association among polysubstance abuse. Results: Over the years, pregnant woman admissions as the percentage of total and female admissions remain stable, where total annual admissions range from 1.54 to about 2 million with the female share of 33.30% to 35.61%. Pregnant women aged 21-29, 12 or more years of education, white race, unemployed, holding independent living status are among the most vulnerable. Concerns prevail on a significant number of polysubstance users, young age at first use, frequency of daily users, and records of prior admissions (60%). Trends of abused primary substances show a significant rise in heroin (66%) and methamphetamine (46%) over the years, although the latest year shows a considerable downturn. On the other hand, significant decreasing patterns are evident for alcohol (43%), marijuana or hashish (24%), cocaine or crack (23%), other opiates or synthetics (36%), and benzodiazepines (29%). Basket analysis reveals some patterns of co-occurrence of substances consistent over the years. Conclusions: This comprehensive study can work as a reference to identify the most vulnerable groups based on their characteristics and deal with the most hazardous substances from their evidence of co-occurrence.

Keywords: basket analysis, pregnant women, substance abuse, trend analysis

Procedia PDF Downloads 192

Authors: Fatai Olakunle Ogundele

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Road construction is increasingly common in today's world as human development expands and people increasingly rely on cars for transportation on a daily basis. The construction of a large network of roads has dramatically altered the landscape and impacted well-being in a number of deleterious ways. In addition, the road can also shift population demographics and be a source of pollution into the environment. Road construction activities normally result in changes in alteration of the soil's physical properties through soil compaction on the road itself and on adjacent areas and chemical and biological properties, among other effects. Understanding roadside soil properties that are influenced by road construction activities can serve as a basis for formulating conservation-based management strategies. Therefore, this study examined the effects of road construction on soil properties and soil organic carbon along Lagos Badagry Expressway, Lagos, Nigeria. The study adopted purposive sampling techniques and 40 soil samples were collected at a depth of 0 – 30cm from each of the identified road intersections and infrastructures using a soil auger. The soil samples collected were taken to the laboratory for soil properties and carbon stock analysis using standard methods. Both descriptive and inferential statistical techniques were applied to analyze the data obtained. The results revealed that soil compaction inhibits ecological succession on roadsides in that increased compaction suppresses plant growth as well as causes changes in soil quality.

Keywords: highway, soil properties, organic carbon, road construction, land degradation

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Authors: Mohamed El Hattab

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The Supercritical carbon dioxide (SFE) and the focused microwave-assisted hydrodistillation (FMAHD) were employed to isolate the volatile fraction of the brown alga Dictyopteris membranacea from the crude extract. The volatiles fractions obtained were analyzed by GC/MS. The major compounds in this case: dictyopterene A, 6-butylcyclohepta-1,4-diene, Undec-1-en-3-one, Undeca-1,4-dien-3-one, (3-oxoundec-4-enyl) sulphur, tetradecanoic acid, hexadecanoic acid, 3-hexyl-4,5-dithia-cycloheptanone and albicanol (this later is present only in the FMAHD oil) are identified by comparing their mass spectra with those reported on the commercial MS data base and also on our previously work. A kinetic study realized on both extraction processes and followed by an external standard quantification has allowed the study of the mass percent evolution of the major compounds in the two oils, an empirical mathematical modelling was used to describe their kinetic extraction.

Keywords: dictyopteris membranacea, extraction techniques, mathematical modeling, volatile oils

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Authors: Selin Küçük

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Pre-industrial architecture is integration of natural and subsequent properties by intelligence and experience. Since various settlements relatively industrialized or non-industrialized at any time, ‘pre-industrial’ term does not refer to a definite time. Natural properties, which are existent conditions and materials in natural local environment, are climate, geomorphology and local materials. Subsequent properties, which are all anthropological comparatives, are culture of societies, requirements of people and construction techniques that people use. Yet, after industrialization, technology took technique’s place, cultural effects are manipulated, requirements are changed and local/natural properties are almost disappeared in architecture. Technology is universal, global and expands simply; conversely technique is time and experience dependent and should has a considerable cultural background. This research is about construction techniques according to natural properties of a region and classification of these techniques. Understanding local architecture is only possible by searching its background which is hard to reach. There are always changes in positive and negative in architectural techniques through the time. Archaeological layers of a region sometimes give more accurate information about transformation of architecture. However, natural properties of any region are the most helpful elements to perceive construction techniques. Many international sources from different cultures are interested in local architecture by mentioning natural properties separately. Unfortunately, there is no literature deals with this subject as far as systematically in the correct way. This research aims to improve a clear perspective of local architecture existence by categorizing archetypes according to natural properties. The ultimate goal of this research is generating a clear classification of local architecture independent from subsequent (anthropological) properties over the world such like a handbook. Since local architecture is the most sustainable architecture with refer to its economic, ecologic and sociological properties, there should be an excessive information about construction techniques to be learned from. Constructing the same buildings in all over the world is one of the main criticism of modern architectural system. While this critics going on, the same buildings without identity increase incrementally. In post-industrial term, technology widely took technique’s place, yet cultural effects are manipulated, requirements are changed and natural local properties are almost disappeared in architecture. These study does not offer architects to use local techniques, but it indicates the progress of pre-industrial architectural evolution which is healthier, cheaper and natural. Immigration from rural areas to developing/developed cities should be prohibited, thus culture and construction techniques can be preserved. Since big cities have psychological, sensational and sociological impact on people, rural settlers can be convinced to not to immigrate by providing new buildings designed according to natural properties and maintaining their settlements. Improving rural conditions would remove the economical and sociological gulf between cities and rural. What result desired to arrived in, is if there is no deformation (adaptation process of another traditional buildings because of immigration) or assimilation in a climatic region, there should be very similar solutions in the same climatic regions of the world even if there is no relationship (trade, communication etc.) among them.

Keywords: climate zones, geomorphology, local architecture, local materials

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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: R. M. Kalayappan, N. Kathiravan

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In automobile industries, Controller Area Network (CAN) is widely used to reduce the system complexity and inter-task communication. Therefore, this paper proposes the hardware implementation of data frame communication between one controller to other. The CAN data frames and protocols will be explained deeply, here. The data frames are transferred without any collision or corruption. The simulation is made in the KEIL vision software to display the data transfer between transmitter and receiver in CAN. ARM7 micro-controller is used to transfer data’s between the controllers in real time. Data transfer is verified using the CRO.

Keywords: control area network (CAN), automotive electronic control unit, CAN 2.0, industry

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Authors: Tomasz Smolinski, Irena Herdzik-Koniecko, Marta Pyszynska, M. Rogowski

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Old electronic components can be easily found nowadays. Significant quantities of valuable metals such as gold, silver or copper are used for the production of advanced electronic devices. Old useless electronic device slowly became a new source of precious metals, very often more efficient than natural. For example, it is possible to recover more gold from 1-ton personal computers than seventeen tons of gold ore. It makes urban mining industry very profitable and necessary for sustainable development. For the recovery of metals from waste of electronic equipment, various treatment options based on conventional physical, hydrometallurgical and pyrometallurgical processes are available. In this group hydrometallurgy processes with their relatively low capital cost, low environmental impact, potential for high metal recoveries and suitability for small scale applications, are very promising options. Institute of Nuclear Chemistry and Technology has great experience in hydrometallurgy processes especially focused on recovery metals from industrial and agricultural wastes. At the moment, urban mining project is carried out. The method of effective recovery of valuable metals from central processing units (CPU) components has been developed. The principal processes such as acidic leaching and solvent extraction were used for precious metals recovery from old processors and graphic cards. Electronic components were treated by acidic solution at various conditions. Optimal acid concentration, time of the process and temperature were selected. Precious metals have been extracted to the aqueous phase. At the next step, metals were selectively extracted by organic solvents such as oximes or tributyl phosphate (TBP) etc. Multistage mixer-settler equipment was used. The process was optimized.

Keywords: electronic waste, leaching, hydrometallurgy, metal recovery, solvent extraction

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Authors: Klaus Peter Scherer, Daniel Knöll, Constantin Rieder

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Problem and Purpose: Intelligent systems are available and helpful to support the human being decision process, especially when complex surgical eye interventions are necessary and must be performed. Normally, such a decision support system consists of a knowledge-based module, which is responsible for the real assistance power, given by an explanation and logical reasoning processes. The interview based acquisition and generation of the complex knowledge itself is very crucial, because there are different correlations between the complex parameters. So, in this project (semi)automated self-learning methods are researched and developed for an enhancement of the quality of such a decision support system. Methods: For ophthalmic data sets of real patients in a hospital, advanced data mining procedures seem to be very helpful. Especially subgroup analysis methods are developed, extended and used to analyze and find out the correlations and conditional dependencies between the structured patient data. After finding causal dependencies, a ranking must be performed for the generation of rule-based representations. For this, anonymous patient data are transformed into a special machine language format. The imported data are used as input for algorithms of conditioned probability methods to calculate the parameter distributions concerning a special given goal parameter. Results: In the field of knowledge discovery advanced methods and applications could be performed to produce operation and patient related correlations. So, new knowledge was generated by finding causal relations between the operational equipment, the medical instances and patient specific history by a dependency ranking process. After transformation in association rules logically based representations were available for the clinical experts to evaluate the new knowledge. The structured data sets take account of about 80 parameters as special characteristic features per patient. For different extended patient groups (100, 300, 500), as well one target value as well multi-target values were set for the subgroup analysis. So the newly generated hypotheses could be interpreted regarding the dependency or independency of patient number. Conclusions: The aim and the advantage of such a semi-automatically self-learning process are the extensions of the knowledge base by finding new parameter correlations. The discovered knowledge is transformed into association rules and serves as rule-based representation of the knowledge in the knowledge base. Even more, than one goal parameter of interest can be considered by the semi-automated learning process. With ranking procedures, the most strong premises and also conjunctive associated conditions can be found to conclude the interested goal parameter. So the knowledge, hidden in structured tables or lists can be extracted as rule-based representation. This is a real assistance power for the communication with the clinical experts.

Keywords: an expert system, knowledge-based support, ophthalmic decision support, self-learning methods

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Authors: Ramadan M. El-Kahawy, M. A. El-Shafeiy, Mohamed Abd El-Wahab, S. A. Helal, Nabil Aboul-Ela

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Due to the economic importance of Safaga Bay, Quseir harbor and Ras Gharib harbor , a multidisciplinary approach was adopted to invistigate 27 surfecial sediment samples from the three sites and 9 samples for each in order to use the benthic foraminifera as bio-indicators for characterization of the environmental variations. Grain size analyses indicate that the bottom facies in the inner part of quseir is muddy while the inner part of Ras Gharib and Safaga is silty sand and those close to the entrance of Safaga bay and Ras Gharib is sandy facies while quseir still also muddy facies. geochemical data show high concentration of heavy-metals mainly in Ras Gharib due to oil leakage from the hydrocarbon oil field and Safaga bay due to the phosphate mining while quseir is medium concentration due to anthropocentric effect.micropaelontological analyses indicate the boundaries of the highest concentration of heavy metals and those of low concentration as well.the dominant benthic foraminifera in these three sites are Ammonia beccarii, Amphistigina and sorites. the study highlights the worsening of environmental conditions and also show that the areas in need of a priority recovery.

Keywords: benthic foraminifera, Ras Gharib, Safaga, Quseir, Red Sea, Egypt

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

Procedia PDF Downloads 135

Authors: Nevila Bardhi, Joanna Magda, Kolapo Alex-Oni, Slavenka Sedlar, Paromita Hore

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The New York City Department of Health and Mental Hygiene (NYC DOHMH) receives blood lead test results for NYC residents and conducts lead poisoning case investigations for individuals with elevated blood lead levels exposed to lead occupationally and non-occupationally. To (1) improve participant engagement, (2) aid the identification of potential lead sources, and (3) better tailor recommendations to reduce lead exposure, Motivational Interviewing (MI) techniques were incorporated during risk assessment interviews of non-pregnant adults by DOHMH’s Adult Lead Poisoning Prevention (ALP) Program. MI is an evidence-based counselling method used in clinical settings that have been effective in promoting behavior change by resolving ambivalence and enhancing motivation in treating both physiological and psychological health conditions. The incorporation of MI techniques in the ALP risk assessment interview was effective in improving the identification of lead sources for non-pregnant adult cases, thus, allowing for the opportunity to better tailor lead poisoning prevention recommendations. The embedding of MI cues in the ALP risk assessment interview also significantly increased engagement in the interview process, resulting in approximately 50 more interviews conducted per year and a decrease in interview refusals during case investigations. Additionally, the pre-MI interview completion rate was 57%, while the post-MI Interview completion rate was 68%. We recommend MI techniques to be used by other lead poisoning prevention programs during lead poisoning investigations in similar diverse populations.

Keywords: lead poisoning prevention, motivational interviewing, behavior change, lead poisoning risk factors, self-efficacy

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Authors: Xiaocong He

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Self-pierce riveting (SPR) is a new high-speed mechanical fastening technique which is suitable for point joining dissimilar sheet materials, as well as coated and pre-painted sheet materials. Mechanical structures assembled by SPR are expected to possess a high damping capacity. In this study, experimental measurement techniques were proposed for the prediction of vibration behavior of single-lap cantilevered SPR beams. The dynamic test software and the data acquisition hardware were used in the experimental measurement of the dynamic response of the single-lap cantilevered SPR beams. Free and forced vibration behavior of the single-lap cantilevered SPR beams was measured using the LMS CADA-X experimental modal analysis software and the LMS-DIFA Scadas II data acquisition hardware. The frequency response functions of the SPR beams of different rivet number were compared. The main goal of the paper is to provide a basic measuring method for further research on vibration based non-destructive damage detection in single-lap cantilevered SPR beams.

Keywords: self-piercing riveting, dynamic response, experimental measurement, frequency response functions

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Authors: Divya Sisodiya, Deepika Sipal

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Optical wireless communication (OWC) is a relatively new technology in optical communication systems that allows for high-speed wireless optical communication. This research focuses on developing a cost-effective OWC system using a hybrid configuration of optical amplifiers. In addition to using EDFA amplifiers, a comparison study was conducted to determine which modulation technique is more effective for communication. This research examines the performance of an OWC system based on ASK and PSK modulation techniques by varying OWC parameters under various atmospheric conditions such as rain, mist, haze, and snow. Finally, the simulation results are discussed and analyzed.

Keywords: OWC, bit error rate, amplitude shift keying, phase shift keying, attenuation, amplifiers

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Authors: Vinicius Marzall, Jussanã Milograna

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The urban drainwatering remains a major challenge for most of the Brazilian cities. Not so different of the most part, Goiania, a state capital located in Midwest of the country has few legislations about the subject matter and only one registered solution of compensative techniques for drainwater. This paper clam to show some solutions which are adopted in other Brazilian cities with consolidated legislation, suggesting technics about detention tanks in a building sit. This study analyzed and compared the legislation of Curitiba, Porto Alegre e Sao Paulo, with the actual legislation and politics of Goiania. After this, were created models with adopted data for dimensioning the size of detention tanks using the envelope curve method considering synthetic series for intense precipitations and building sits between 250 m² and 600 m², with an impermeabilization tax of 50%. The results showed great differences between the legislation of Goiania and the documentation of the others cities analyzed, like the number of techniques for drainwatering applied to the reality of the cities, educational actions to awareness the population about care the water courses and political management by having a specified funds for drainwater subjects, for example. Besides, the use of detention tank showed itself practicable, have seen that the occupation of the tank is minor than 3% of the building sit, whatever the size of the terrain, granting the exit flow to pre-occupational taxes in extreme rainfall events. Also, was developed a linear equation to measure the detention tank based in the size of the building sit in Goiania, making simpler the calculation and implementation for non-specialized people.

Keywords: clean technology, legislation, rainwater management, urban drainwater

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Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

Procedia PDF Downloads 77