Search results for: cost prediction

Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

Procedia PDF Downloads 359

Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

Procedia PDF Downloads 160

Authors: Fahd I. Alghunaimi, Hind S. Dossary, Norah W. Aljuryyed, Tawfik A. Saleh

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Produced water (PW) is one of the largest by-volume waste streams and one of the most challenging effluents in the oil and gas industry. This is due to the variation of contaminants that make up PW. Severalmaterialshavebeen developed, studied, and implemented to remove hydrocarbonsfrom PW. Adsorption is one of the most effective ways ofremoving oil fromPW. In this work, three new and cost-effective hydrophobic adsorbentmaterials based on 9-octadecenoic acid grafted graphene (POG) were synthesized for oil/water separation. Graphene derived from graphite was modified with 9-octadecenoic acid to yield 9-octadecenoic acid grafted graphene (OG). The newsynthesized materials which called POG25, POG50, and POG75 were characterized by using N₂-physisorption (BET) and Fourier transform infrared (FTIR). The BET surface area of POG75 was the highest with 288 m²/g, whereas POG50 was 225 m²/g and POG25 was lowest 79 m²/g. These three materials were also evaluated for their oil-water separation efficiency using a model mixture, whichdemonstrated that POG-75 has the highest oil removal efficiency and the faster rate of the adsorption (Figure-1). POG75 was regenerated, and its performance was verified again with a little reduced adsorption rate compared to the fresh material. The mixtures that used in the performance test were prepared by mixing nonpolar organic liquids such as heptane, dodecane, or hexadecane into the colored water. In general, the new materials showed fast uptake of the certain quantity of the oildue to the high hydrophobicity nature of the materials, which repel water as confirmed by the contact angle of approximately 150˚. Besides that, novel superhydrophobic material was also synthesized by introducing hydrophobic branches of laurate on the surface of the stainless steel mesh (SSM). This novel mesh could help to hold the novel adsorbent materials in a column to remove oil from PW. Both BOG-75 and the novel mesh have the potential to remove oil contaminants from produced water, which will help to provide an opportunity to recover useful components, in addition, to reduce the environmental impact and reuse produced water in several applications such as fracturing.

Keywords: graphite to graphene, oleophilic, produced water, separation

Procedia PDF Downloads 124

Authors: Shashank Kumar, Mansi Chandra, Ujjawal Singh, Parth Gupta, Rishi Ram, Arnab Sarkar

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Milk is one of the basic and primary sources of food and energy as we start consuming milk from birth. Hence, milk quality and purity and checking the concentration of its constituents become necessary steps. Considering the importance of the purity of milk for human health, the following study has been carried out to simultaneously detect and quantify the different adulterants like urea and starch in milk with the help of a paper-based microfluidic device integrated with a smartphone. The detection of the concentration of urea and starch is based on the principle of colorimetry. In contrast, the fluid flow in the device is based on the capillary action of porous media. The microfluidic channel proposed in the study is equipped with a specialized detection zone, and it employs a colorimetric indicator undergoing a visible color change when the milk gets in touch or reacts with a set of reagents which confirms the presence of different adulterants in the milk. In our proposed work, we have used iodine to detect the percentage of starch in the milk, whereas, in the case of urea, we have used the p-DMAB. A direct correlation has been found between the color change intensity and the concentration of adulterants. A calibration curve was constructed to find color intensity and subsequent starch and urea concentration. The device has low-cost production and easy disposability, which make it highly suitable for widespread adoption, especially in resource-constrained settings. Moreover, a smartphone application has been developed to detect, capture, and analyze the change in color intensity due to the presence of adulterants in the milk. The low-cost nature of the smartphone-integrated paper-based sensor, coupled with its integration with smartphones, makes it an attractive solution for widespread use. They are affordable, simple to use, and do not require specialized training, making them ideal tools for regulatory bodies and concerned consumers.

Keywords: paper based microfluidic device, milk adulteration, urea detection, starch detection, smartphone application

Procedia PDF Downloads 74

Authors: Asis Kumar Khan, Rajeev Kumar Goel

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Recent infrastructure growth all around the world lead to increase in demand for concrete day by day. Cement being binding material for concrete the usage of cement also gone up significantly. Cement manufacturing utilizes abundant natural resources and causes environment pollution by releasing a huge quantity of CO₂ into the atmosphere. So, it is high time to look for alternates to reduce the cement consumption in concrete. Geopolymer concrete is one such material which utilizes the industrial waste such as fly ash, ground granulated blast furnace slag and low-cost alkaline liquids such as sodium hydroxide and sodium silicate to produce the concrete. On the other side, river sand is becoming very expensive due to its large-scale depletion at source and the high cost of transportation. In this view, river sand is replaced by crushed sand in this study. In this work, an attempt has been made to understand the durability parameters of geopolymer concrete by partially replacing fly ash with cement. Fly ash is replaced by cement at various levels e.g., from 0 to 50%. Concrete cubes of 100x100x100mm were used for investigating different durability parameters. The various parameters studied includes compressive strength, split tensile strength, drying shrinkage, sodium sulphate attack resistance, sulphuric acid attack resistance and chloride permeability. Highest compressive strength & highest split tensile strength is observed in 30% replacement level. Least drying is observed with 30% replacement level. Very good resistance for sulphuric acid & sodium sulphate is found with 30% replacement. However, it was not possible to find out the chloride permeability due to the high conductivity of geopolymer samples of all replacement levels.

Keywords: crushed sand, compressive strength, drying shrinkage, geopolymer concrete, split tensile strength, sodium sulphate attack resistance, sulphuric acid attack resistance

Procedia PDF Downloads 298

Authors: Taiheng Zhang, Hongbin Zhao

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Compressed air energy storage become increasingly vital for solving intermittency problem of some renewable energies. In this study, a new hybrid system on a combination of compressed air energy storage (CAES), solid oxide fuel cell (SOFC), gas turbine (GT), and organic Rankine cycle (ORC) is proposed. In the new system, excess electricity during off-peak time is utilized to compress air. Then, the compressed air is stored in compressed air storage tank. During peak time, the compressed air enters the cathode of SOFC directly instead of combustion chamber of traditional CAES. There is no air compressor consumption of SOFC-GT in peak demand, so SOFC- GT can generate power with high-efficiency. In addition, the waste heat of exhaust from GT is recovered by applying an ORC. Three different organic working fluid (R123, R601, R601a) of ORC are chosen to evaluate system performance. Based on Aspen plus and Engineering Equation Solver (EES) software, energy and exergoeconomic analysis are used to access the viability of the combined system. Besides, the effect of two parameters (fuel flow and ORC turbine inlet pressure) on energy efficiency is studied. The effect of low-price electricity at off-peak hours on thermodynamic criteria (total unit exergy cost of products and total cost rate) is also investigated. Furthermore, for three different organic working fluids, the results of round-trip efficiency, exergy efficiency, and exergoeconomic factors are calculated and compared. Based on thermodynamic performance and exergoeconomic performance of different organic working fluids, the best suitable working fluid will be chosen. In conclusion, this study can provide important guidance for system efficiency improvement and viability.

Keywords: CAES, SOFC, ORC, energy and exergoeconomic analysis, organic working fluids

Procedia PDF Downloads 128

Authors: Helga Heylen

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With dogs saving wildlife in New Zealand since 1890 and governments in Africa, Australia and Canada trusting them to give the best results, Conservation Dogs Ireland want to introduce more detection dogs to protect Europe's native wildlife. Conservation detection dogs are fast, portable and endlessly trainable. They are a cost-effective, highly sensitive and non-invasive way to detect protected and invasive species and wildlife disease. Conservation dogs find targets up to 40 times faster than any other method. They give results instantly, with near-perfect accuracy. They can search for multiple targets simultaneously, with no reduction in efficacy The European Red List indicates the decline in biodiversity has been most rapid in the past 50 years, and the risk of extinction never higher. Just two examples of major threats dogs are trained to tackle are: (I)Japanese Knotweed (Fallopia Japonica), not only a serious threat to ecosystems, crops, structures like bridges and roads - it can wipe out the entire value of a house. The property industry and homeowners are only just waking up to the full extent of the nightmare. When those working in construction on the roads move topsoil with a trace of Japanese Knotweed, it suffices to start a new colony. Japanese Knotweed grows up to 7cm a day. It can stay dormant and resprout after 20 years. In the UK, the cost of removing Japanese Knotweed from the London Olympic site in 2012 was around £70m (€83m). UK banks already no longer lend on a house that has Japanese Knotweed on-site. Legally, landowners are now obliged to excavate Japanese Knotweed and have it removed to a landfill. More and more, we see Japanese Knotweed grow where a new house has been constructed, and topsoil has been brought in. Conservation dogs are trained to detect small fragments of any part of the plant on sites and in topsoil. (II)Zebra mussels (Dreissena Polymorpha) are a threat to many waterways in the world. They colonize rivers, canals, docks, lakes, reservoirs, water pipes and cooling systems. They live up to 3 years and will release up to one million eggs each year. Zebra mussels attach to surfaces like rocks, anchors, boat hulls, intake pipes and boat engines. They cause changes in nutrient cycles, reduction of plankton and increased plant growth around lake edges, leading to the decline of Europe's native mussel and fish populations. There is no solution, only costly measures to keep it at bay. With many interconnected networks of waterways, they have spread uncontrollably. Conservation detection dogs detect the Zebra mussel from its early larvae stage, which is still invisible to the human eye. Detection dogs are more thorough and cost-effective than any other conservation method, and will greatly complement and speed up the work of biologists, surveyors, developers, ecologists and researchers.

Keywords: native biodiversity, conservation detection dogs, invasive species, Japanese Knotweed, zebra mussel

Procedia PDF Downloads 199

Authors: Emily Kambalame

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Water distribution networks necessitate many investments for construction, prompting researchers to seek cost reduction and efficient design solutions. Optimization techniques are employed in this regard to address these challenges. In this context, the penalty-free multi-objective evolutionary algorithm (PFMOEA) coupled with pressure-dependent analysis (PDA) was utilized to develop a multi-objective evolutionary search for the optimization of water distribution systems (WDSs). The aim of this research was to find out if the computational efficiency of the PFMOEA for WDS optimization could be enhanced. This was done by applying real coding representation and retaining different percentages of feasible and infeasible solutions close to the Pareto front in the elitism step of the optimization. Two benchmark network problems, namely the Two-looped and Hanoi networks, were utilized in the study. A comparative analysis was then conducted to assess the performance of the real-coded PFMOEA in relation to other approaches described in the literature. The algorithm demonstrated competitive performance for the two benchmark networks by implementing real coding. The real-coded PFMOEA achieved the novel best-known solutions ($419,000 and $6.081 million) and a zero-pressure deficit for the two networks, requiring fewer function evaluations than the binary-coded PFMOEA. In previous PFMOEA studies, elitism applied a default retention of 30% of the least cost-feasible solutions while excluding all infeasible solutions. It was found in this study that by replacing 10% and 15% of the feasible solutions with infeasible ones that are close to the Pareto front with minimal pressure deficit violations, the computational efficiency of the PFMOEA was significantly enhanced. The configuration of 15% feasible and 15% infeasible solutions outperformed other retention allocations by identifying the optimal solution with the fewest function evaluation

Keywords: design optimization, multi-objective evolutionary, penalty-free, water distribution systems

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Authors: Sara Najiazarpour, Mahsa Najiazarpour

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There are many problems in contracting projects and their performance. At each project stage and due to different reasons, these problems affect cost, time and overall project quality. Hence, in order to increase the efficiency and performance in all levels of the chain and with supply chain management approach, there will be a coordination from the beginning of a project (contractor selection) to the end of project (handover of project). Contractor selection is the foremost part of construction projects which in this multi-criteria decision-making, the best contractor is determined by expert judgment, different variables and their priorities. In this paper for selecting the best contractor, numerous criteria were collected by asking from adept experts and then among them, 16 criteria with highest frequency were considered for questionnaire. This questionnaire was distributed between experts. Cronbach's alpha coefficient was obtained as 72%. Then based on Borda's function 12 important criteria was selected which was categorized in four main criteria and related sub-criteria as follow: Environmental factors and physical equipment: procurement and materials (supplier), company's machines, contractor’s proposed cost estimate - financial capacity: bank turnover and company's assets, the income of tax declaration in last year, Ability to compensate for losses or delays - past performance- records and technical expertise: experts and key personnel, the past technical backgrounds and experiences, employer satisfaction of previous contracts, the number of similar projects was done - standards: rank and field of expertise which company is qualified for and its validity, availability and number of permitted projects done. Then with PROMTHEE method, the criteria were normalized and monitored, finally the best alternative was selected. In this research, qualitative criteria of each company is became a quantitative criteria. Finally, information of some companies was evaluated and the best contractor was selected based on all criteria and their priorities.

Keywords: contractor evaluation and selection, project development, supply chain management, PROMTHEE method

Procedia PDF Downloads 76

Authors: Laura C. Loaiza, Elodie Salager, Nicolas Louvain, Athmane Boulaoued, Antonella Iadecola, Patrik Johansson, Lorenzo Stievano, Vincent Seznec, Laure Monconduit

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Lithium-ion batteries (LIBs) have an important place among energy storage devices due to their high capacity and good cyclability. However, the advancements in portable and transportation applications have extended the research towards new horizons, and today the development is hampered, e.g., by the capacity of the electrodes employed. Silicon and germanium are among the considered modern anode materials as they can undergo alloying reactions with lithium while delivering high capacities. It has been demonstrated that silicon in its highest lithiated state can deliver up to ten times more capacity than graphite (372 mAh/g): 4200 mAh/g for Li₂₂Si₅ and 3579 mAh/g for Li₁₅Si₄, respectively. On the other hand, germanium presents a capacity of 1384 mAh/g for Li₁₅Ge₄, and a better electronic conductivity and Li ion diffusivity as compared to Si. Nonetheless, the commercialization potential of Ge is limited by its cost. The synergetic effect of Si₁₋ₓGeₓ alloys has been proven, the capacity is increased compared to Ge-rich electrodes and the capacity retention is increased compared to Si-rich electrodes, but the exact performance of this type of electrodes will depend on factors like specific capacity, C-rates, cost, etc. There are several reports on various formulations of Si₁₋ₓGeₓ alloys with promising LIB anode performance with most work performed on complex nanostructures resulting from synthesis efforts implying high cost. In the present work, we studied the electrochemical mechanism of the Si₀.₅Ge₀.₅ alloy as a realistic micron-sized electrode formulation using carboxymethyl cellulose (CMC) as the binder. A combination of a large set of in situ and operando techniques were employed to investigate the structural evolution of Si₀.₅Ge₀.₅ during lithiation and delithiation processes: powder X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS), Raman spectroscopy, and 7Li solid state nuclear magnetic resonance spectroscopy (NMR). The results have presented a whole view of the structural modifications induced by the lithiation/delithiation processes. The Si₀.₅Ge₀.₅ amorphization was observed at the beginning of discharge. Further lithiation induces the formation of a-Liₓ(Si/Ge) intermediates and the crystallization of Li₁₅(Si₀.₅Ge₀.₅)₄ at the end of the discharge. At really low voltages a reversible process of overlithiation and formation of Li₁₅₊δ(Si₀.₅Ge₀.₅)₄ was identified and related with a structural evolution of Li₁₅(Si₀.₅Ge₀.₅)₄. Upon charge, the c-Li₁₅(Si₀.₅Ge₀.₅)₄ was transformed into a-Liₓ(Si/Ge) intermediates. At the end of the process an amorphous phase assigned to a-SiₓGey was recovered. Thereby, it was demonstrated that Si and Ge are collectively active along the cycling process, upon discharge with the formation of a ternary Li₁₅(Si₀.₅Ge₀.₅)₄ phase (with a step of overlithiation) and upon charge with the rebuilding of the a-Si-Ge phase. This process is undoubtedly behind the enhanced performance of Si₀.₅Ge₀.₅ compared to a physical mixture of Si and Ge.

Keywords: lithium ion battery, silicon germanium anode, in situ characterization, X-Ray diffraction

Procedia PDF Downloads 290

Authors: Annisa Dewanti Putri, Wang Juan, Y. Qing Shan

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The structural assessment is one of a crucial part for ancient timber structure, in which this phase will be the reference for the maintenance and preservation phase. The mechanical properties of a structure are one of an important component of the structural assessment of building. Feiyun as one of the particular preserved building in China will become one of the Pioneer of Timber Structure Building Assessment. The 3-storey building which is located in Shanxi Province consists of complex ancient timber structure. Due to condition and preservation purpose, assessments (visual inspections, Non-Destructive Test and a Semi Non-Destructive test) were conducted. The stress wave measurement, moisture content analyzer, and the micro-drilling resistance meter data will overview the prediction of Mechanical Properties. As a result, the mechanical properties can be used for the next phase as reference for structural damage solutions.

Keywords: ancient structure, mechanical properties, non destructive test, stress wave, structural assessment, timber structure

Procedia PDF Downloads 478

Authors: F. Massri, M. Shkarneh, B. Almassri

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Water scarcity and increasing water demand especially for residential use are major challenges facing Palestine. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. The main objective of this paper is to (i) study the major factors influencing the water consumption in Palestine, (ii) understand the general pattern of Household water consumption, (iii) assess the possible changes in household water consumption and suggest appropriate remedies and (iv) develop prediction model based on the Artificial Neural Network to the water consumption in Palestinian cities. The paper is organized in four parts. The first part includes literature review of household water consumption studies. The second part concerns data collection methodology, conceptual frame work for the household water consumption surveys, survey descriptions and data processing methods. The third part presents descriptive statistics, multiple regression and analysis of the water consumption in the two Palestinian cities. The final part develops the use of Artificial Neural Network for modeling the water consumption in Palestinian cities.

Keywords: water management, demand forecasting, consumption, ANN, Ramallah

Procedia PDF Downloads 223

Authors: Natnan Adelahu Dagne

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This paper focuses on an experimental investigation into the development of wall tiles made of a composite material consisting of egg shell powder (ESP), waste recycled low-density polyethylene (LDPE), and sisal fiber. Although waste plastic has been a popular material for packaging in recent years, its nonbiodegradability is generating contamination in the environment. Waste LDPE is a common material that is used extensively and discarded egg shell powder contributes to environmental contamination. By recycling them into usable items and reinforcing them with natural textile fibers to create composite materials, these waste plastics and egg shell powder can be eliminated from the environment. Natural fiber-based composites are ecofriendly, with better properties and low cost. The sisal fibers were treated with 6% NaOH in 24 hr., to improve the fiber-matrix interaction. The composites were manufactured by the melt-mixing method followed by compression molding. The effects of mixing time, egg shell powder content and fiber length and on the composite properties were investigated using tensile, flexural, impact, compressive, flame retardant and water absorption tests. The investigation showed that the optimum mixing time, ESP and fiber length for the optimal properties of the composite were achieved at 15.766 min, 1.668% and 10.096 mm respectively. The maximum optimized tensile strength of 57.572 Mpa, flexural strength of 59.262 Mpa, impact strength of 24.200 Mpa, compressive strength 120.307 Mpa, flame retardant of LOI values of 28.692 % of were obtained. Water absorption of the tiles increased with increase in the fiber length. Overall, the experimental findings demonstrate the possibility of using sisal reinforced LDPE filled with ESP composite as a sustainable substitute material to create wall tiles that are better for the environment, within low cost and have enhanced mechanical, physical, and chemical properties of composite.

Keywords: composite, sisal, ESP, LDPE

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

Procedia PDF Downloads 199

Authors: Yaniv Knop, Alva Peled

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Over the past few years there has been a growing interest in the development of Portland Composite Cement, by partial replacement of the clinker with mineral additives. The motivations to reduce the clinker content are threefold: (1) Ecological - due to lower emission of CO2 to the atmosphere; (2) Economical - due to cost reduction; and (3) Scientific\Technology – improvement of performances. Among the mineral additives being used and investigated, limestone is one of the most attractive, as it is considered natural, available, and with low cost. The goal of the research is to develop green cement, by partial replacement of the clinker with limestone powder while improving the performances of the cement paste. This work studied blended cements with three limestone powder particle diameters: smaller than, larger than, and similarly sized to the clinker particle. Blended cement with limestone consisting of one particle size distribution and limestone consisting of a combination of several particle sizes were studied and compared in terms of hydration rate, hydration degree, and water demand to achieve normal consistency. The performances of these systems were also compared with that of the original cement (without added limestone). It was found that the ability to replace an active material with an inert additive, while achieving improved performances, can be obtained by increasing the packing density of the cement-based particles. This may be achieved by replacing the clinker with limestone powders having a combination of several different particle size distributions. Mathematical and physical models were developed to simulate the setting history from initial to final setting time and to predict the packing density of blended cement with limestone having different sizes and various contents. Besides the effect of limestone, as inert additive, on the packing density of the blended cement, the influence of the limestone particle size on three different chemical reactions were studied; hydration of the cement, carbonation of the calcium hydroxide and the reactivity of the limestone with the hydration reaction products. The main results and developments will be presented.

Keywords: packing density, hydration degree, limestone, blended cement

Procedia PDF Downloads 290

Authors: Mauro Callejas-Cuervo, Andrea C. Alarcon-Aldana, Andres F. Ruiz-Olaya, Juan C. Alvarez

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Motion capture (MC) is the construction of a precise and accurate digital representation of a real motion. Systems have been used in the last years in a wide range of applications, from films special effects and animation, interactive entertainment, medicine, to high competitive sport where a maximum performance and low injury risk during training and competition is seeking. This paper presents an inertial and magnetic sensor based technological platform, intended for particular amplitude monitoring and telerehabilitation processes considering an efficient cost/technical considerations compromise. Our platform particularities offer high social impact possibilities by making telerehabilitation accessible to large population sectors in marginal socio-economic sector, especially in underdeveloped countries that in opposition to developed countries specialist are scarce, and high technology is not available or inexistent. This platform integrates high-resolution low-cost inertial and magnetic sensors with adequate user interfaces and communication protocols to perform a web or other communication networks available diagnosis service. The amplitude information is generated by sensors then transferred to a computing device with adequate interfaces to make it accessible to inexperienced personnel, providing a high social value. Amplitude measurements of the platform virtual system presented a good fit to its respective reference system. Analyzing the robotic arm results (estimation error RMSE 1=2.12° and estimation error RMSE 2=2.28°), it can be observed that during arm motion in any sense, the estimation error is negligible; in fact, error appears only during sense inversion what can easily be explained by the nature of inertial sensors and its relation to acceleration. Inertial sensors present a time constant delay which acts as a first order filter attenuating signals at large acceleration values as is the case for a change of sense in motion. It can be seen a damped response of platform virtual in other images where error analysis show that at maximum amplitude an underestimation of amplitude is present whereas at minimum amplitude estimations an overestimation of amplitude is observed. This work presents and describes the platform virtual as a motion capture system suitable for telerehabilitation with the cost - quality and precision - accessibility relations optimized. These particular characteristics achieved by efficiently using the state of the art of accessible generic technology in sensors and hardware, and adequate software for capture, transmission analysis and visualization, provides the capacity to offer good telerehabilitation services, reaching large more or less marginal populations where technologies and specialists are not available but accessible with basic communication networks.

Keywords: inertial sensors, joint amplitude measurement, MEMS, telerehabilitation

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Authors: R. Testoni, R. Levizzari, M. De Salve

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Radioactive waste management is fundamental to safeguard population and environment by radiological risks. Environmental assessment of a site, where nuclear activities are located, allows understanding the hydro geological system and the radionuclides transport in groundwater and subsoil. Use of dedicated software is the basis of transport phenomena investigation and for dynamic scenarios prediction; this permits to understand the evolution of accidental contamination events, but at the same time the potentiality of the software itself can be verified. The aim of this paper is to perform a numerical analysis by means of HYDRUS 1D code, so as to evaluate radionuclides transport in a nuclear site in Piedmont region (Italy). In particular, the behaviour in vadose zone was investigated. An iterative assessment process was performed for risk assessment of radioactive contamination. The analysis therein developed considers the following aspects: i) hydro geological site characterization; ii) individuation of the main intrinsic and external site factors influencing water flow and radionuclides transport phenomena; iii) software potential for radionuclides leakage simulation purposes.

Keywords: HYDRUS 1D, radionuclides transport phenomena, site characterization, radiation protection

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Authors: Ranveet Saggu, Saad Bin Ahmed

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Cardiovascular Disease (CVD) is the leading cause of death worldwide. One of its main manifestations, myocardial infarction (commonly known as a heart attack), occurs about 750,000 times a year, caused by insufficient blood flow to a portion of the heart muscle. A quick and accurate diagnosis of a heart attack or heart failure is crucial in the treatment of the patient. The aim of this research project is to improve the prediction of cardiovascular diseases by automating risk assessment using binary classifiers. The methodology includes Exploratory Data Analysis (EDA), which helps to obtain information about the dataset with the help of visualizations and metrics. Additionally, Feature Engineering techniques is employed to address missing values, outliers, feature extraction, and normalizing the dataset. Subsequently, various classification machine learning algorithms are trained, and their accuracy along with other metrics are evaluated to identify the most efficient model in terms of processing time and predictive performance.

Keywords: cardiovascular disease, machine learning, deci- sion trees, logistic regression, k-nearest neighbor, xgboost, random forest, gradient boosting

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Authors: Mequanint Birhan

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Modern urban environments present a dynamically growing field where, notwithstanding shared goals, several mutually conflicting interests frequently collide. However, it has a big impact on the city's socioeconomic standing, waiting lines and queues are common occurrences. This results in extremely long lines for both vehicles and people on incongruous routes, service coagulation, customer murmuring, unhappiness, complaints, and looking for other options sometimes illegally. The root cause of this is corruption, which leads to traffic jams, stopping, and packing vehicles beyond their safe carrying capacity, and violating the human rights and freedoms of passengers. This study focused on the optimizing time of passengers had to wait in public vehicle stations. This applied research employed both data gathering sources and mixed approaches, then 166 samples of key informants of transport station were taken by using the Slovin sampling formula. The length of time vehicles, including the drivers and auxiliary drivers ‘Weyala', had to wait was also studied. To maximize the service level at vehicle stations, a queuing model was subsequently devised ‘Menaharya’. Time, cost, and quality encompass performance, scope, and suitability for the intended purposes. The minimal response time for passengers and vehicles queuing to reach their final destination at the stations of the Tepi, Mizan, and Bonga towns was determined. A new bus station system was modeled and simulated by Arena simulation software in the chosen study area. 84% improvement on cost reduced by 56.25%, time 4hr to 1.5hr, quality, safety and designed load performance calculations employed. Stakeholders are asked to put the model into practice and monitor the results obtained.

Keywords: Arena 14 automatic rockwell, queue, transport services, vehicle stations

Procedia PDF Downloads 81

Authors: Sanjay M. Tailor, Urmila H. Patel

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Phthalyl Sulfacetamide belongs to well-known member of antimicrobial sulfonamide family. It is a potent antitumor drug. Structural characteristics of 4-amino-N-(2quinoxalinyl) benzene-sulfonamides (Phthalyl Sulfacetamide), C14H12N4O2S has been studied by method of X-ray crystallography. The compound crystallizes in monoclinic space group P21/n with unit cell parameters a= 7.9841 Ǻ, b= 12.8208 Ǻ, c= 16.6607 Ǻ, α= 90˚, β= 93.23˚, γ= 90˚and Z=4. The X-ray based three-dimensional structure analysis has been carried out by direct methods and refined to an R-value of 0.0419. The crystal structure is stabilized by intermolecular N-H…N, N-H…O and π-π interactions. The Hirshfeld surfaces and consequently the fingerprint analysis have been performed to study the nature of interactions and their quantitative contributions towards the crystal packing. An analysis of Hirshfeld surfaces and fingerprint plots facilitates a comparison of intermolecular interactions, which are the key elements in building different supramolecular architectures. Docking is used for virtual screening for the prediction of the strongest binders based on various scoring functions. Docking studies are carried out on Phthalyl Sulfacetamide for better activity, which is important for the development of a new class of inhibitors.

Keywords: phthalyl sulfacetamide, crystal structure, hirshfeld surface analysis, docking

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Riktesh Srivastava

Abstract:

Online Shopping have established an astonishing evolution in the last few years. And it is now apparent that B2C architecture is becoming progressively imperative channel for even traditional brick and mortar type traders as well. In this completion knowing customers and predicting behavior are extremely important. More important, when any customer logs onto the B2C architecture, the traces of their buying patterns can be stored and used for future predictions. Such a prediction is called Customer Lifetime Value (CLV). Earlier, we used Net Present Value to do so, however, it ignores two important aspects of B2C architecture, “market risks” and “big amount of customer data”. Now, we use RFM- Recency, Frequency and Monetary Value to estimate the CLV, and as the term exemplifies, market risks, is well sheltered. Big Data Analysis is also roofed in RFM, which gives real exploration of the Big Data and lead to a better estimation for future cash flow from customers. In the present paper, 6 factors (collected from varied sources) are used to determine as to what attracts the customers to the B2C architecture. For these 6 factors, RFM is computed for 3 years (2013, 2014 and 2015) respectively. CLV and Revenue are the two parameters defined using RFM analysis, which gives the clear picture of the future predictions.

Keywords: CLV, RFM, revenue, recency, frequency, monetary value

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Authors: Jinyoung Choi, Sunmook Lee

Abstract:

In general, single parameter, i.e. temperature, as an accelerating parameter is used to assess the accelerated stability of Point-of-Care Testing (POCT) diagnostic devices. However, humidity also plays an important role in deteriorating the strip performance since major components of test strips are proteins such as enzymes. 4 different Temp./Humi. Conditions were used to assess the lifetime of strips. Degradation of test strips were studied through the accelerated stability test and the lifetime was assessed using commercial POCT products. The life distribution of strips, which were obtained by monitoring the failure time of test strip under each stress condition, revealed that the weibull distribution was the most proper distribution describing the life distribution of strips used in the present study. Equal shape parameters were calculated to be 0.9395 and 0.9132 for low and high concentrations, respectively. The lifetime prediction was made by adopting Peck Eq. Model for Stress-Life relationship, and the B10 life was calculated to be 70.09 and 46.65 hrs for low and high concentrations, respectively.

Keywords: accelerated degradation, diagnostic device, lifetime assessment, POCT

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Authors: Abdelsalam Elawwad, Mostafa Ragab, Hisham Abdel-Halim

Abstract:

Leakage in sewerage system can cause groundwater and soil contamination in urban areas, especially in area with a high groundwater table. This is a serious problem in small villages in developing countries that rely on ground water as a source for irrigation and drinking purposes. In the developed countries, the recent trend in areas with low population densities is vacuum sewerage system, which is environmentally safer than conventional gravity system, protecting public health, preventing exfiltration to the ground water, very easily applied in a relatively short time and can cope with a faster expansion of the urbanized areas. The aim of this work is to assess the feasibility of using vacuum sewerage in developing country, such as Egypt. Knowledge of local conditions can determine the most suitable sewer system for a specific region. Technical, environmental and financial comparisons between conventional sewerage system and vacuum sewerage system were held using statistical analysis. Different conditions, such as population densities, geometry of area, and ground water depths were evaluated. Sample comprising of 30 Egyptian villages was selected, where a complete design for conventional sewerage system and vacuum sewerage system was done. Based on this study, it is recommended from the environmental point of view to construct the vacuum sewerage system in such villages with low population densities; however, it is not economic for all cases. From financial point of view, vacuum sewerage system was a good competitor to conventional systems in flat areas and areas with high groundwater table. The local market supplying of the construction equipment especially collection chambers will greatly affect the investment cost. Capacity building and social mobilization will also play a great role in sustainability of this system. At the end, it is noteworthy that environmental sustainability and public health are more important than the financial aspects.

Keywords: ground water, conventional system, vacuum system, statistics, cost, density, terrain

Procedia PDF Downloads 280

Authors: A. Szekrényes

Abstract:

This paper revisits the free vibration problem of delaminated composite beams. It is shown that during the vibration of composite beams the delaminated parts are subjected to the parametric excitation. This can lead to the dynamic buckling during the motion of the structure. The equation of motion includes time-dependent stiffness and so it leads to a system of Mathieu-Hill differential equations. The free vibration analysis of beams is carried out in the usual way by using beam finite elements. The dynamic buckling problem is investigated locally, and the critical buckling forces are determined by the modified harmonic balance method by using an imposed time function of the motion. The stability diagrams are created, and the numerical predictions are compared to experimental results. The most important findings are the critical amplitudes at which delamination buckling takes place, the stability diagrams representing the instability of the system, and the realistic mode shape prediction in contrast with the unrealistic results of models available in the literature.

Keywords: delamination, free vibration, parametric excitation, sweep excitation

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Authors: Maziar Ramezani, Thomas Neitzert, Timotius Pasang

Abstract:

This paper aims at experimental and numerical investigation of springback behavior of sheet metals during L-bending process with emphasis on Stribeck-type friction modeling. The coefficient of friction in Stribeck curve depends on sliding velocity and contact pressure. The springback behavior of mild steel and aluminum alloy 6022-T4 sheets was studied experimentally and using numerical simulations with ABAQUS software with two types of friction model: Coulomb friction and Stribeck friction. The influence of forming speed on springback behavior was studied experimentally and numerically. The results showed that Stribeck-type friction model has better results in predicting springback in sheet metal forming. The FE prediction error for mild steel and 6022-T4 AA is 23.8%, 25.5% respectively, using Coulomb friction model and 11%, 13% respectively, using Stribeck friction model. These results show that Stribeck model is suitable for simulation of sheet metal forming especially at higher forming speed.

Keywords: friction, L-bending, springback, Stribeck curves

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Authors: Ibrahim Shaker, Amr El Hossany, Moustafa Osman, Mohamed El Raey

Abstract:

This paper will explore integration model between GIS–SCADA system and enclosure quantification model to approach the impact of failure-safe event. There are real demands to identify spatial objects and improve control system performance. Nevertheless, the employed methodology is predicting electro-mechanic operations and corresponding time to environmental incident variations. Open processing, as object systems technology, is presented for integration enclosure database with minimal memory size and computation time via connectivity drivers such as ODBC:JDBC during main stages of GIS–SCADA connection. The function of Geographic Information System is manipulating power distribution in contrast to developing issues. In other ward, GIS-SCADA systems integration will require numerical objects of process to enable system model calibration and estimation demands, determine of past events for analysis and prediction of emergency situations for response training.

Keywords: air dispersion model, environmental management, SCADA systems, GIS system, integration power system

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Authors: Benjamin Bobbia, Matthias Picard

Abstract:

In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Mahdi Fakoor, Hannaneh Manafi Farid

Abstract:

In order to predict the fracture behavior of cracked orthotropic materials under mixed-mode loading, well-known minimum strain energy density (SED) criterion is extended. The crack is subjected along the fibers at plane strain conditions. Despite the complicities to solve the nonlinear equations which are requirements of SED criterion, SED criterion for anisotropic materials is derived. In the present research, fracture limit curve of SED criterion is depicted by a numerical solution, hence the direction of crack growth is figured out by derived criterion, MSED. The validated MSED demonstrates the improvement in prediction of fracture behavior of the materials. Also, damaged factor that plays a crucial role in the fracture behavior of quasi-brittle materials is derived from this criterion and proved its dependency on mechanical properties and direction of crack growth.

Keywords: mixed-mode fracture, minimum strain energy density criterion, orthotropic materials, fracture limit curve, mode II critical stress intensity factor

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Authors: Yoftahe Nigussie Worku

Abstract:

This report unveils a meticulous project focused on the design intricacies of a Fire Tube Boiler tailored for the efficient generation of saturated steam. The overarching objective is to produce 2000kg/h of saturated steam at 12-bar design pressure, achieved through the development of an advanced fire tube boiler. This design is meticulously crafted to harmonize cost-effectiveness and parameter refinement, with a keen emphasis on material selection for component parts, construction materials, and production methods throughout the analytical phases. The analytical process involves iterative calculations, utilizing pertinent formulas to optimize design parameters, including the selection of tube diameters and overall heat transfer coefficients. The boiler configuration incorporates two passes, a strategic choice influenced by tube and shell size considerations. The utilization of heavy oil fuel no. 6, with a higher heating value of 44000kJ/kg and a lower heating value of 41300kJ/kg, results in a fuel consumption of 140.37kg/hr. The boiler achieves an impressive heat output of 1610kW with an efficiency rating of 85.25%. The fluid flow pattern within the boiler adopts a cross-flow arrangement strategically chosen for inherent advantages. Internally, the welding of the tube sheet to the shell, secured by gaskets and welds, ensures structural integrity. The shell design adheres to European Standard code sections for pressure vessels, encompassing considerations for weight, supplementary accessories (lifting lugs, openings, ends, manhole), and detailed assembly drawings. This research represents a significant stride in optimizing fire tube boiler technology, balancing efficiency and safety considerations in the pursuit of enhanced saturated steam production.

Keywords: fire tube, saturated steam, material selection, efficiency

Procedia PDF Downloads 87