Search results for: stock price prediction

Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Mukesh Khadka, Alexander Paz, Hanns de la Fuente-Mella

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Typically, pavement performance models are developed in two steps: (i) pavement segments with similar characteristics are grouped together to form a cluster, and (ii) the corresponding performance models are developed using statistical techniques. A challenge is to select the characteristics that define clusters and the segments associated with them. If inappropriate characteristics are used, clusters may include homogeneous segments with different performance behavior or heterogeneous segments with similar performance behavior. Prediction accuracy of performance models can be improved by grouping the pavement segments into more uniform clusters by including both characteristics and a performance measure. This grouping is not always possible due to limited information. It is impractical to include all the potential significant factors because some of them are potentially unobserved or difficult to measure. Historical performance of pavement segments could be used as a proxy to incorporate the effect of the missing potential significant factors in clustering process. The current state-of-the-art proposes Clusterwise Linear Regression (CLR) to determine the pavement clusters and the associated performance models simultaneously. CLR incorporates the effect of significant factors as well as a performance measure. In this study, a mathematical program was formulated for CLR models including multiple explanatory variables. Pavement data collected recently over the entire state of Nevada were used. International Roughness Index (IRI) was used as a pavement performance measure because it serves as a unified standard that is widely accepted for evaluating pavement performance, especially in terms of riding quality. Results illustrate the advantage of the using CLR. Previous studies have used CLR along with experimental data. This study uses actual field data collected across a variety of environmental, traffic, design, and construction and maintenance conditions.

Keywords: clusterwise regression, pavement management system, performance model, optimization

Procedia PDF Downloads 225

Authors: Albert Suryaprawira

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The Nasion-Sella Line (NSL) has been used for several years as a reference line in longitudinal growth study. Therefore this line is considered to be stable not only to evaluate treatment outcome and to predict relapse possibility but also to manage prognosis. This is a radiographic superimposition of an adult male aged 19 years who complained of difficulty in aesthetic, talking and chewing. Patient has a midface hypoplasia profile (concave). He was diagnosed to have a severe Skeletal Class III with Class III malocclusion, increased lower vertical height, and an anterior open bite. A pre-treatment cephalometric radiograph was taken to analyse the skeletal problem and to measure the amount of bone movement and the prediction soft tissue response. A panoramic radiograph was also taken to analyse bone quality, bone abnormality, third molar impaction, etc. Before the surgery, a pre-surgical cephalometric radiograph was taken to re-evaluate the plan and to settle the final amount of bone cut. After the surgery, a post-surgical cephalometric radiograph was taken to confirm the result with the plan. The superimposition using NSL as a reference line between those radiographs was performed to analyse the outcome. It is important to describe the amount of hard and soft tissue movement and to predict the possibility of relapse after the surgery. The patient also needs to understand all the surgical plan, outcome and relapse prevention. The surgical management included maxillary impaction and advancement of Le Fort I osteotomy. The evaluation using NSL as a reference was a very useful method in determining the outcome and prognosis.

Keywords: Nasion-Sella Line, midface hypoplasia, Le Fort 1, maxillary advancement

Procedia PDF Downloads 115

Authors: Ayanda Ndokwana, Stanley Fore

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Industrial prosperity and rapid expansion of human population in South Africa over the past two decades, have increased the use of conventional fossil fuels such as crude oil, coal and natural gas to meet the country’s energy demands. However, the inevitable depletion of fossil fuel reserves, global volatile oil price and large carbon footprint are some of the crucial reasons the South African Government needs to make a considerable investment in the development of the biofuel industry. In South Africa, this industry is still at the introductory stage with no large scale manufacturing plant that has been commissioned yet. Bioethanol is a potential replacement of gasoline which is a fossil fuel that is used in motor vehicles. Using bioethanol for the transport sector as a source of fuel will help Government to save heavy foreign exchange incurred during importation of oil and create many job opportunities in rural farming. In 2007, the South African Government developed the National Biofuels Industrial Strategy in an effort to make provision for support and attract investment in bioethanol production. However, capital investment in the production of bioethanol on a large scale, depends on the sound economic assessment of the available manufacturing technologies. The aim of this study is to evaluate the profitability of an advanced bioethanol manufacturing technology which uses maize as a feedstock in South Africa. The impact of fiber or bran fractionation in this technology causes it to possess a number of merits such as energy efficiency, low capital expenditure, and profitability compared to a conventional dry-mill bioethanol technology. Quantitative techniques will be used to collect and analyze numerical data from suitable organisations in South Africa. The dependence of three profitability indicators such as the Discounted Payback Period (DPP), Net Present Value (NPV) and Return On Investment (ROI) on plant capacity will be evaluated. Profitability analysis will be done on the following plant capacities: 100 000 ton/year, 150 000 ton/year and 200 000 ton/year. The plant capacity with the shortest Discounted Payback Period, positive Net Present Value and highest Return On Investment implies that a further consideration in terms of capital investment is warranted.

Keywords: bioethanol, economic evaluation, maize, profitability indicators

Procedia PDF Downloads 200

Authors: Kei Kamada, Rikito Murakami, Masahiko Ito, Mototaka Arakawa, Yasuhiro Shoji, Toshiyuki Ueno, Masao Yoshino, Akihiro Yamaji, Shunsuke Kurosawa, Yuui Yokota, Yuji Ohashi, Akira Yoshikawa

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Techniques of energy harvesting y have been widely developed in recent years, due to high demand on the power supply for ‘Internet of things’ devices such as wireless sensor nodes. In these applications, conversion technique of mechanical vibration energy into electrical energy using magnetostrictive materials n have been brought to attention. Among the magnetostrictive materials, Fe-Ga and Fe-Al alloys are attractive materials due to the figure of merits such price, mechanical strength, high magnetostrictive constant. Up to now, bulk crystals of these alloys are produced by the Bridgman–Stockbarger method or the Czochralski method. Using these method big bulk crystal up to 2~3 inch diameter can be grown. However, non-uniformity of chemical composition along to the crystal growth direction cannot be avoid, which results in non-uniformity of magnetostriction constant and reduction of the production yield. The micro-pulling down (μ-PD) method has been developed as a shaped crystal growth technique. Our group have reported shaped crystal growth of oxide, fluoride single crystals with different shape such rod, plate tube, thin fiber, etc. Advantages of this method is low segregation due to high growth rate and small diffusion of melt at the solid-liquid interface, and small kerf loss due to near net shape crystal. In this presentation, we report the shaped long plate crystal growth of Fe-Ga and Fe-Al alloys using the μ-PD method. Alloy crystals were grown by the μ-PD method using calcium oxide crucible and induction heating system under the nitrogen atmosphere. The bottom hole of crucibles was 5 x 1mm² size. A <100> oriented iron-based alloy was used as a seed crystal. 5 x 1 x 320 mm³ alloy crystal plates were successfully grown. The results of crystal growth, chemical composition analysis, magnetostrictive properties and a prototype vibration energy harvester are reported. Furthermore, continuous crystal growth using powder supply system will be reported to minimize the chemical composition non-uniformity along the growth direction.

Keywords: crystal growth, micro-pulling-down method, Fe-Ga, Fe-Al

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Authors: Shidvash Vakilipour, Masoud Mohammadi, Rouzbeh Riazi, Scott Ormiston, Kimia Amiri, Sahar Barati

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An essential component of a finite volume method (FVM) is the advection scheme that estimates values on the cell faces based on the calculated values on the nodes or cell centers. The most widely used advection schemes are upwind schemes. These schemes have been developed in FVM on different kinds of structured and unstructured grids. In this research, the physical influence scheme (PIS) is developed for a cell-centered FVM that uses an implicit coupled solver. Results are compared with the exponential differencing scheme (EDS) and the skew upwind differencing scheme (SUDS). Accuracy of these schemes is evaluated for a lid-driven cavity flow at Re = 1000, 3200, and 5000 and a backward-facing step flow at Re = 800. Simulations show considerable differences between the results of EDS scheme with benchmarks, especially for the lid-driven cavity flow at high Reynolds numbers. These differences occur due to false diffusion. Comparing SUDS and PIS schemes shows relatively close results for the backward-facing step flow and different results in lid-driven cavity flow. The poor results of SUDS in the lid-driven cavity flow can be related to its lack of sensitivity to the pressure difference between cell face and upwind points, which is critical for the prediction of such vortex dominant flows.

Keywords: cell-centered finite volume method, coupled solver, exponential differencing scheme (EDS), physical influence scheme (PIS), pressure weighted interpolation method (PWIM), skew upwind differencing scheme (SUDS)

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: F. Q. Shao, W. Q. Wang, Y. Yin, T. P. Yu, D. B. Zou, J. M. Ouyang

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The Radiation Pressure Acceleration (RPA) mechanism is very promising in laser-driven ion acceleration because of high laser-ion energy conversion efficiency. Although some experiments have shown the characteristics of RPA, the energy of ions is quite limited. The ion energy obtained in experiments is only several MeV/u, which is much lower than theoretical prediction. One possible limiting factor is the transverse instability incited in the RPA process. The transverse instability is basically considered as the Rayleigh-Taylor (RT) instability, which is a kind of interfacial instability and occurs when a light fluid pushes against a heavy fluid. Multi-dimensional particle-in-cell (PIC) simulations show that the onset of transverse instability will destroy the acceleration process and broaden the energy spectrum of fast ions during the RPA dominant ion acceleration processes. The evidence of the RT instability driven by radiation pressure has been observed in a laser-foil interaction experiment in a typical RPA regime, and the dominant scale of RT instability is close to the laser wavelength. The development of transverse instability in the radiation-pressure-acceleration dominant laser-foil interaction is numerically examined by two-dimensional particle-in-cell simulations. When a laser interacts with a foil with modulated surface, the internal instability is quickly incited and it develops. The linear growth and saturation of the transverse instability are observed, and the growth rate is numerically diagnosed. In order to optimize interaction parameters, a method of information entropy is put forward to describe the chaotic degree of the transverse instability. With moderate modulation, the transverse instability shows a low chaotic degree and a quasi-monoenergetic proton beam is produced.

Keywords: information entropy, radiation pressure acceleration, Rayleigh-Taylor instability, transverse instability

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Authors: Pavel A. Strizhak, Geniy V. Kuznetsov, Ivan S. Voytkov, Dmitri V. Antonov

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This paper presents the results of experiments carried out at a specialized test site for establishing macroscopic patterns of heat and mass transfer processes at localizing model combustion sources of ground forest fires with the use of barrier lines in the form of a wetted lay of material in front of the zone of flame burning and thermal decomposition. The experiments were performed using needles, leaves, twigs, and mixtures thereof. The dimensions of the model combustion source and the ranges of heat release correspond well to the real conditions of ground forest fires. The main attention is paid to the complex analysis of the effect of dispersion of water aerosol (concentration and size of droplets) used to form the barrier line. It is shown that effective conditions for localization and subsequent suppression of flame combustion and thermal decomposition of forest fuel can be achieved by creating a group of barrier lines with different wetting width and depth of the material. Relative indicators of the effectiveness of one and combined barrier lines were established, taking into account all the main characteristics of the processes of suppressing burning and thermal decomposition of forest combustible materials. We performed the prediction of the necessary and sufficient parameters of barrier lines (water volume, width, and depth of the wetted lay of the material, specific irrigation density) for combustion sources with different dimensions, corresponding to the real fire extinguishing practice.

Keywords: forest fire, barrier water lines, pyrolysis front, flame front

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Wenbo Wang, Yi-Fang Brook Wu

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The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Mona Solvoll, Ragnhild Kr. Olsen

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Background and significance of the study: Newspapers are going through radical changes with increased competition, eroding readerships and declining advertising resulting in plummeting overall revenues. This has lead to a quest for new business models, focusing on monetizing content. This research paper investigates both how local online newspapers have introduced user payment and how the audience has received these changes. Given the role of local media in keeping their communities informed and those in power accountable, their potential impact on civic engagement and cultural integration in local communities, the business model innovations of local media deserves far more research interest. Empirically, the findings are interesting for local journalists, local media managers as well as local advertisers. Basic methodologies: The study is based on interviews with commercial leaders in 20 Norwegian local newspapers in addition to a national survey data from 1600 respondents among local media users. The interviews were conducted in the second half of 2015, while the survey was conducted in September 2016. Theoretically, the study draws on the business model framework. Findings: The analysis indicates that paywalls aim more at reducing digital cannibalisation of print revenue than about creating new digital income. The newspapers are mostly concerned with retaining “old” print subscribers and transform them into digital subscribers. However, this strategy may come at a high price for newspapers if their defensive print strategy drives away younger digital readership and hamper their recruitment potential for new audiences as some previous studies have indicated. Analysis of young reader news habits indicates that attracting the younger audience to traditional local news providers is particularly challenging and that they are more prone to seek alternative news sources than the older audience is. Conclusion: The paywall strategy applied by the local newspapers may be well fitted to stabilise print subscription figures and facilitate more tailored and better services for already existing customers, but far less suited for attracting new ones. The paywall is a short-sighted strategy, which drives away younger readers and paves the road for substitute offerings, particularly Facebook.

Keywords: business model, newspapers, paywall, user payment

Procedia PDF Downloads 241

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 164

Authors: Bambang Antoro, Lasito Soebari, Geoffrey de Jong, Fernandy Meiriyanto, Michael Siahaan, Eko Wibowo, Pormando Silalahi, Ruswanto, Adi Budirumantyo

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The Ertsberg East Skarn System (EESS) is located in the Ertsberg Mining District, Papua, Indonesia. EESS is a sub-vertical zone of copper-gold mineralization hosted in both diorite (vein-style mineralization) and skarn (disseminated and vein style mineralization). Deep Mill Level Zone (DMLZ) is a mining zone in the lower part of East Ertsberg Skarn System (EESS) that product copper and gold. The Deep Mill Level Zone deposit is located below the Deep Ore Zone deposit between the 3125m to 2590m elevation, measures roughly 1,200m in length and is between 350 and 500m in width. DMLZ planned start mined on Q2-2015, being mined at an ore extraction rate about 60,000 tpd by the block cave mine method (the block cave contain 516 Mt). Mineralization and associated hydrothermal alteration in the DMLZ is hosted and enclosed by a large stock (The Main Ertsberg Intrusion) that is barren on all sides and above the DMLZ. Late porphyry dikes that cut through the Main Ertsberg Intrusion are spatially associated with the center of the DMLZ hydrothermal system. DMLZ orebody hosted in diorite and skarn, both dominantly by vein style mineralization. Percentage Material Mined at DMLZ compare with current Reserves are diorite 46% (with 0.46% Cu; 0.56 ppm Au; and 0.83% EqCu); Skarn is 39% (with 1.4% Cu; 0.95 ppm Au; and 2.05% EqCu); Hornfels is 8% (with 0.84% Cu; 0.82 ppm Au; and 1.39% EqCu); and Marble 7 % possible mined waste. Correlation between Ertsberg intrusion, major structure, and vein style mineralization is important to determine characteristic orebody in DMLZ Mine. Generally Deep Mill Level Zone has 2 type of vein filling mineralization from both hosted (diorite and skarn), in diorite hosted the vein system filled by chalcopyrite-bornite-quartz and pyrite, in skarn hosted the vein filled by chalcopyrite-bornite-pyrite and magnetite without quartz. Based on orientation the stockwork vein at diorite hosted and shallow vein in skarn hosted was generally NW-SE trending and NE-SW trending with shallow-moderate dipping. Deep Mill Level Zone control by two main major faults, geologist founded and verified local structure between major structure with NW-SE trending and NE-SW trending with characteristics slickenside, shearing, gauge, water-gas channel, and some has been re-healed.

Keywords: copper-gold, DMLZ, skarn, structure

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

Procedia PDF Downloads 39

Authors: I. Kurashvili, G. Darsavelidze, T. Kimeridze, G. Chubinidze, I. Tabatadze

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At present, a number of modern semiconductor devices based on SiGe alloys have been created in which the latest achievements of high technologies are used. These devices might cause significant changes to networking, computing, and space technology. In the nearest future new materials based on SiGe will be able to restrict the A³B⁵ and Si technologies and firmly establish themselves in medium frequency electronics. Effective realization of these prospects requires the solution of prediction and controlling of structural state and dynamical physical –mechanical properties of new SiGe materials. Based on these circumstances, a complex investigation of structural defects and structural-sensitive dynamic mechanical characteristics of SiGe alloys under different external impacts (deformation, radiation, thermal cycling) acquires great importance. Internal friction (IF) and shear modulus temperature and amplitude dependences of the monocrystalline boron-doped Si_1-xGe_x(x≤0.05) alloys grown by Czochralski technique is studied in initial and ⁶⁰Co gamma-irradiated states. In the initial samples, a set of dislocation origin relaxation processes and accompanying modulus defects are revealed in a temperature interval of 400-800 ⁰C. It is shown that after gamma-irradiation intensity of relaxation internal friction in the vicinity of 280 ⁰C increases and simultaneously activation parameters of high temperature relaxation processes reveal clear rising. It is proposed that these changes of dynamical mechanical characteristics might be caused by a decrease of the dislocation mobility in the Cottrell atmosphere enriched by the radiation defects.

Keywords: internal friction, shear modulus, gamma-irradiation, SiGe alloys

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Authors: Idrissa Kayijuka

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The logistic regression and Cox regression models (proportional hazard model) at present are being employed in the analysis of prospective epidemiologic research looking into risk factors in their application on chronic diseases. However, a theoretical relationship between the two models has been studied. By definition, Cox regression model also called Cox proportional hazard model is a procedure that is used in modeling data regarding time leading up to an event where censored cases exist. Whereas the Logistic regression model is mostly applicable in cases where the independent variables consist of numerical as well as nominal values while the resultant variable is binary (dichotomous). Arguments and findings of many researchers focused on the overview of Cox and Logistic regression models and their different applications in different areas. In this work, the analysis is done on secondary data whose source is SPSS exercise data on BREAST CANCER with a sample size of 1121 women where the main objective is to show the application difference between Cox regression model and logistic regression model based on factors that cause women to die due to breast cancer. Thus we did some analysis manually i.e. on lymph nodes status, and SPSS software helped to analyze the mentioned data. This study found out that there is an application difference between Cox and Logistic regression models which is Cox regression model is used if one wishes to analyze data which also include the follow-up time whereas Logistic regression model analyzes data without follow-up-time. Also, they have measurements of association which is different: hazard ratio and odds ratio for Cox and logistic regression models respectively. A similarity between the two models is that they are both applicable in the prediction of the upshot of a categorical variable i.e. a variable that can accommodate only a restricted number of categories. In conclusion, Cox regression model differs from logistic regression by assessing a rate instead of proportion. The two models can be applied in many other researches since they are suitable methods for analyzing data but the more recommended is the Cox, regression model.

Keywords: logistic regression model, Cox regression model, survival analysis, hazard ratio

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Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

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The present study compares the semi-empirical wake-oscillator models that are used to predict vortex-induced vibration of structures. These models include those proposed by Facchinetti, Farshidian, and Dolatabadi, and Skop and Griffin. These models combine a wake oscillator model resembling the Van der Pol oscillator model and a single degree of freedom oscillation model. In order to use these models for estimating the top displacement of chimneys, the first mode vibration of the chimneys is only considered. The modal equation of the chimney constitutes the single degree of freedom model (SDOF). The equations of the wake oscillator model and the SDOF are simultaneously solved using an iterative procedure. The empirical parameters used in the wake-oscillator models are estimated using a newly developed approach, and response is compared with experimental data, which appeared comparable. For carrying out the iterative solution, the ode solver of MATLAB is used. To carry out the comparative study, a tall concrete chimney of height 210m has been chosen with the base diameter as 28m, top diameter as 20m, and thickness as 0.3m. The responses of the chimney are also determined using the linear model proposed by E. Simiu and the deterministic model given in Eurocode. It is observed from the comparative study that the responses predicted by the Facchinetti model and the model proposed by Skop and Griffin are nearly the same, while the model proposed by Fashidian and Dolatabadi predicts a higher response. The linear model without considering the aero-elastic phenomenon provides a less response as compared to the non-linear models. Further, for large damping, the prediction of the response by the Euro code is relatively well compared to those of non-linear models.

Keywords: chimney, deterministic model, van der pol, vortex-induced vibration

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Authors: Khalda Amr, Noha Eltaweel, Sherif Ismail, Hala Raslan

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Introduction: Osteoarthritis is the most common form of arthritis that involves the wearing away of the cartilage that caps the bones in the joints. While rheumatoid arthritis is an autoimmune disease in which the immune system attacks the joints, beginning with the lining of joints. In this study, we aimed to study the top deregulated miRNAs that might be the cause of pathogenesis in both diseases. Methods: Eight cases were recruited in this study: 4 rheumatoid arthritis (RA), 2 osteoarthritis (OA) patients, as well as 2 healthy controls. Total RNA was isolated from plasma to be subjected to miRNA profiling by NGS. Sequencing libraries were constructed and generated using the NEBNextR UltraTM small RNA Sample Prep Kit for Illumina R (NEB, USA), according to the manufacturer’s instructions. The quality of samples were checked using fastqc and multiQC. Results were compared RA vs Controls and OA vs. Controls. Target gene prediction and functional annotation of the deregulated miRNAs were done using Mienturnet. The top deregulated miRNAs in each disease were selected for further validation using qRT-PCR. Results: The average number of sequencing reads per sample exceeded 2.2 million, of which approximately 57% were mapped to the human reference genome. The top DEMs in RA vs controls were miR-6724-5p, miR-1469, miR-194-3p (up), miR-1468-5p, miR-486-3p (down). In comparison, the top DEMs in OA vs controls were miR-1908-3p, miR-122b-3p, miR-3960 (up), miR-1468-5p, miR-15b-3p (down). The functional enrichment of the selected top deregulated miRNAs revealed the highly enriched KEGG pathways and GO terms. Six of the deregulated miRNAs (miR-15b, -128, -194, -328, -542 and -3180) had multiple target genes in the RA pathway, so they are more likely to affect the RA pathogenesis. Conclusion: Six of our studied deregulated miRNAs (miR-15b, -128, -194, -328, -542 and -3180) might be highly involved in the disease pathogenesis. Further functional studies are crucial to assess their functions and actual target genes.

Keywords: next generation sequencing, mirnas, rheumatoid arthritis, osteoarthritis

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Authors: Anjelika Kremer, Irina Nachimov, Dan Justo

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Background: C-reactive protein (CRP) serum levels are commonly measured in hospitalized patients. Elevated admission CRP serum levels and in-hospital mortality has been seldom studied in the general population of elderly patients admitted to the acute Geriatrics department. Methods: A retrospective cross-sectional study was conducted at a tertiary medical center. Included were all elderly patients (age 65 years or more) admitted to a single acute Geriatrics department from the emergency room between April 2014 and January 2015. CRP serum levels were measured routinely in all patients upon the first 24 hours of admission. A logistic regression analysis was used to study if admission CRP serum levels were associated with in-hospital mortality independent of age, gender, functional status, and co-morbidities. Results: Overall, 498 elderly patients were included in the analysis: 306 (61.4%) female patients and 192 (38.6%) male patients. The mean age was 84.8±7.0 years (median: 85 years; IQR: 80-90 years). The mean admission CRP serum levels was 43.2±67.1 mg/l (median: 13.1 mg/l; IQR: 2.8-51.7 mg/l). Overall, 33 (6.6%) elderly patients died during the hospitalization. A logistic regression analysis showed that in-hospital mortality was independently associated with history of stroke (p < 0.0001), heart failure (p < 0.0001), and admission CRP serum levels (p < 0.0001) – and to a lesser extent with age (p = 0.042), collagen vascular disease (p=0.011), and recent venous thromboembolism (p=0.037). Receiver operating characteristic (ROC) curve showed that admission CRP serum levels predict in-hospital mortality fairly with an area under the curve (AUC) of 0.694 (p < 0.0001). Cut-off value with maximal sensitivity and specificity was 19.7 mg/L. Conclusions: Admission CRP serum levels may be used to predict in-hospital mortality in the general population of elderly patients admitted to the acute Geriatrics department.

Keywords: c-reactive protein, elderly, mortality, prediction

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Authors: Fatih Eren

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This research aims to find an answer to the question of which knowledge and skills do the Turkish urban planners need in their business practice. Understanding change in cities, making a prediction, making an urban decision and putting it into practice, working together with actors from different organizations from various academic disciplines, persuading people to accept something and developing good personal and professional relationships have become very complex and difficult in today’s world. The truth is that urban planners work in many institutions under various positions which are not similar to each other by field of activity and all planners are forced to develop some knowledge and skills for success in their business in Turkey. This study targets to explore what urban planners do in the global information age. The study is the product of a comprehensive nation-wide research. In-depth interviews were conducted with 174 experienced urban planners, who work in different public institutions and private companies under varied positions in the Turkish Planning System, to find out knowledge and skills needed by next-generation urban planners. The main characteristics of next-generation urban planners are defined; skills that planners needed today are explored in this paper. Findings show that the positivist (traditional) planning approach has given place to anti-positivist planning approaches in the Turkish Planning System so next-generation urban planners who seek success and want to carve out a niche for themselves in business life have to equip themselves with innovative skills. The result section also includes useful and instructive findings for planners about what is the meaning of being an urban planner and what is the ideal content and context of planning education at universities in the global age.

Keywords: global information age, Turkish Planning System, the institutional approach, urban planners, roles, skills, values

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Authors: Tigist Mekonnen Melesse

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Since rural households are basically entitled to food through own production, improving productivity might lead to enhance the welfare of rural population through higher food availability at the household level and lowering the price of agricultural products. Increasing agricultural productivity through the use of improved technology is one of the desired outcomes from sensible food security and agricultural policy. The ultimate objective of this study was to evaluate the potential impact of improved agricultural technology adoption on smallholders’ crop productivity and welfare. The study is conducted in Ethiopia covering 1500 rural households drawn from four regions and 15 rural villages based on data collected by Ethiopian Rural Household Survey. Endogenous treatment effect model is employed in order to account for the selection bias on adoption decision that is expected from the self-selection of households in technology adoption. The treatment indicator, technology adoption is a binary variable indicating whether the household used improved seeds and chemical fertilizer or not. The outcome variables were cereal crop productivity, measured in real value of production and welfare of households, measured in real per capita consumption expenditure. Results of the analysis indicate that there is positive and significant effect of improved technology use on rural households’ crop productivity and welfare in Ethiopia. Adoption of improved seeds and chemical fertilizer alone will increase the crop productivity by 7.38 and 6.32 percent per year of each. Adoption of such technologies is also found to improve households’ welfare by 1.17 and 0.25 percent per month of each. The combined effect of both technologies when adopted jointly is increasing crop productivity by 5.82 percent and improving welfare by 0.42 percent. Besides, educational level of household head, farm size, labor use, participation in extension program, expenditure for input and number of oxen positively affect crop productivity and household welfare, while large household size negatively affect welfare of households. In our estimation, the average treatment effect of technology adoption (average treatment effect on the treated, ATET) is the same as the average treatment effect (ATE). This implies that the average predicted outcome for the treatment group is similar to the average predicted outcome for the whole population.

Keywords: Endogenous treatment effect, technologies, productivity, welfare, Ethiopia

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Authors: Paula Pascoal-Faria, Rúben Pereira, Elodie Pinto, Miguel Belbut, Ana Rosa, Inês Sousa, Nuno Alves

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Apple bruise damage from harvesting, handling, transporting and sorting is considered to be the major source of reduced fruit quality, resulting in loss of profits for the entire fruit industry. The three factors which can physically cause fruit bruising are vibration, compression load and impact, the latter being the most common source of bruise damage. Therefore, prediction of the level of damage, stress distribution and deformation of the fruits under external force has become a very important challenge. In this study, experimental and numerical methods were used to better understand the impact caused when an apple is dropped from different heights onto a plastic surface and a conveyor belt. Results showed that the extent of fruit damage is significantly higher for plastic surface, being dependent on the height. In order to support the development of a biomimetic electronic device for the determination of fruit damage, the mechanical properties of the apple fruit were determined using mechanical tests. Preliminary results showed different values for the Young’s modulus according to the zone of the apple tested. Along with the mechanical characterization of the apple fruit, the development of the first two prototypes is discussed and the integration of the results obtained to construct the final element model of the apple is presented. This work will help to reduce significantly the bruise damage of fruits or vegetables during the entire processing which will allow the introduction of exportation destines and consequently an increase in the economic profits in this sector.

Keywords: apple, fruit damage, impact during crop and post-crop, mechanical characterization of the apple, numerical evaluation of fruit damage, electronic device

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Authors: Nothando Gwazani, K. R. Marembo

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An increase in the atmospheric concentration of carbon dioxide (CO₂) from fossil fuel and land use change necessitates identification of strategies for mitigating threats associated with global warming. Oceans are insufficient to offset the accelerating rate of carbon emission. However, the challenges of oceans as a source of reducing carbon footprint can be effectively overcome by the storage of carbon in terrestrial carbon sinks. The gases with special optical properties that are responsible for climate warming include carbon dioxide (CO₂), water vapors, methane (CH₄), nitrous oxide (N₂O), nitrogen oxides (NOₓ), stratospheric ozone (O₃), carbon monoxide (CO) and chlorofluorocarbons (CFC’s). Amongst these, CO₂ plays a crucial role as it contributes to 50% of the total greenhouse effect and has been linked to climate change. Because plants act as carbon sinks, interest in terrestrial carbon sequestration has increased in an effort to explore opportunities for climate change mitigation. Removal of carbon from the atmosphere is a topical issue that addresses one important aspect of an overall strategy for carbon management namely to help mitigate the increasing emissions of CO₂. Thus, terrestrial ecosystems have gained importance for their potential to sequester carbon and reduce carbon sink in oceans, which have a substantial impact on the ocean species. Field data and electromagnetic spectrum bands were analyzed using ArcGIS 10.2, QGIS 2.8 and ERDAS IMAGINE 2015 to examine the vegetation distribution. Satellite remote sensing data coupled with Normalized Difference Vegetation Index (NDVI) was employed to assess future potential changes in vegetation distributions in Eastern Cape Province of South Africa. The observed 5-year interval analysis examines the amount of carbon absorbed using vegetation distribution. In 2015, the numerical results showed low vegetation distribution, therefore increased the acidity of the oceans and gravely affected fish species and corals. The outcomes suggest that the study area could be effectively utilized for carbon sequestration so as to mitigate ocean acidification. The vegetation changes measured through this investigation suggest an environmental shift and reduced vegetation carbon sink, and that threatens biodiversity and ecosystem. In order to sustain the amount of carbon in the terrestrial ecosystems, the identified ecological factors should be enhanced through the application of good land and forest management practices. This will increase the carbon stock of terrestrial ecosystems thereby reducing direct loss to the atmosphere.

Keywords: remote sensing, vegetation dynamics, carbon sequestration, terrestrial carbon sink

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Authors: V. Kuzhandai Velu, R. Ramesh

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Background and objectives: Acute myocardial infarction (AMI) is the most common cause of mortality and morbidity. Over the last decade, microRNAs (miRs) have emerged as a potential marker for detecting AMI. The current study evaluates the kinetics and importance of miRs in the differential diagnosis of ST-segment elevated MI (STEMI) and non-STEMI (NSTEMI) and its correlation to conventional biomarkers and to predict the immediate outcome of AMI for arrhythmias and left ventricular (LV) dysfunction. Materials and Method: A total of 100 AMI patients were recruited for the study. Routine cardiac biomarker and miRNA levels were measured during diagnosis and serially at admission, 6, 12, 24, and 72hrs. The baseline biochemical parameters were analyzed. The expression of miRs was compared between STEMI and NSTEMI at different time intervals. Diagnostic utility of miR-1, miR-133, miR-208, and miR-499 levels were analyzed by using RT-PCR and with various diagnostics statistical tools like ROC, odds ratio, and likelihood ratio. Results: The miR-1, miR-133, and miR-499 showed peak concentration at 6 hours, whereas miR-208 showed high significant differences at all time intervals. miR-133 demonstrated the maximum area under the curve at different time intervals in the differential diagnosis of STEMI and NSTEMI which was followed by miR-499 and miR-208. Evaluation of miRs for predicting arrhythmia and LV dysfunction using admission sample demonstrated that miR-1 (OR = 8.64; LR = 1.76) and miR-208 (OR = 26.25; LR = 5.96) showed maximum odds ratio and likelihood respectively. Conclusion: Circulating miRNA showed a highly significant difference between STEMI and NSTEMI in AMI patients. The peak was much earlier than the conventional biomarkers. miR-133, miR-208, and miR-499 can be used in the differential diagnosis of STEMI and NSTEMI, whereas miR-1 and miR-208 could be used in the prediction of arrhythmia and LV dysfunction, respectively.

Keywords: myocardial infarction, cardiac biomarkers, microRNA, arrhythmia, left ventricular dysfunction

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Authors: Ese Urhie, Olabisi Popoola, Obindah Gershon

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Several policies and programs initiated to address the challenge of unemployment in Nigeria seem to be inadequate. The desired structural transformation which is expected to absorb the excess labour in the economy is yet to be achieved. The agricultural sector accounts for almost half of the labour force with very low productivity. This could partly explain why the much anticipated structural transformation has not been achieved. A major reason for the low productivity is the fact that the production process is predominantly based on the use of traditional tools. In view of the underdeveloped nature of the agricultural sector, Nigeria still has huge potentials for productivity enhancement through modern technology. Aside from productivity enhancement, modern agriculture also stimulates both backward and forward linkages that promote investment and thus generate employment. Contrary to the apprehension usually expressed by many stake-holders about the adoption of modern technology by labour-abundant less-developed countries, this study showed that though there will be job loss initially, the reverse will be the case in the long-run. The outcome of this study will enhance the understanding of all stakeholders in the sector and also encourage them to adopt modern techniques of farming. It will also aid policy formulation at both sectoral and national levels. The recursive model and analysis adopted in the study is useful because it exhibits a unilateral cause-and-effect relationship which most simultaneous equation models do not. It enables the structural equations to be ordered in such a way that the first equation includes only predetermined variables on the right-hand side, while the solution for the final endogenous variable is completely determined by all equations of the system. The study examines the transmission channels and effect of modern agriculture on agricultural productivity and employment growth in Nigeria, via its forward and backward linkages. Using time series data spanning 1980 to 2014, the result of the analyses shows that: (i) a significant and positive relationship between agricultural productivity growth and modern agriculture; (ii) a significant and negative relationship between export price index and agricultural productivity growth; (iii) a significant and positive relationship between export and investment; and (iv) a significant and positive relationship between investment and employment growth. The unbalanced growth theory will be a good strategy to adopt by developing countries such as Nigeria.

Keywords: employment, modern agriculture, productivity, recursive model

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Authors: Sana Maidullah, Ankita Sharma

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The present study takes naturalistic decision-making approach to examine the role of personality in information processing in consumer decision making. In the technological era, most of the information comes in form of HTML or similar language via the internet; processing of this situation could be ambiguous, laborious and painful. The present study explores the role of impulsivity in creating an extreme effect on consumer decision making. Specifically, the study explores the role of impulsivity in extreme effect, i.e., extremeness avoidance (compromise effect) and extremeness seeking; the role of demographic variables, i.e. age and gender, in the relation between impulsivity and extreme effect. The study was conducted with the help of a questionnaire and two experiments. The experiment was designed in the form of two shopping websites with two product types: Hotel choice and Mobile choice. Both experimental interfaces were created with the Xampp software, the frontend of interfaces was HTML CSS JAVASCRIPT and backend was PHP MySQL. The mobile experiment was designed to measure the extreme effect and hotel experiment was designed to measure extreme effect with alignability of attributes. To observe the possibilities of the combined effect of individual difference and context effects, the manipulation of price, a number of alignable attributes and number of the non-alignable attributes is done. The study was conducted on 100 undergraduate and post-graduate engineering students within the age range of 18-35. The familiarity and level of use of internet and shopping website were assessed and controlled in the analysis. The analysis was done by using a t-test, ANOVA and regression analysis. The results indicated that the impulsivity leads to compromise effect and at the same time it also increases the relationship between alignability of attribute among choices and the compromise effect. The demographic variables were found to play a significant role in the relationship. The subcomponents of impulsivity were significantly influencing compromise effect, but the cognitive impulsivity was significant for women, and motor impulsivity was significant for males only. The impulsivity was significantly positively predicted by age, though there were no significant gender differences in impulsivity. The results clearly indicate the importance of individual factors in decision making. The present study, with precise and direct results, provides a significant suggestion for market analyst and business providers.

Keywords: impulsivity, extreme effect, personality, alignability, consumer decision making

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Authors: Sejin Jung, Dongho Kang, Byungsik Kim

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Assessing the impact of climate change requires the use of a multi-model ensemble (MME) to quantify uncertainties between scenarios and produce downscaled outlines for simulation of climate under the influence of different factors, including topography. This study decreases climate change scenarios from the 13 global climate models (GCMs) to assess the impacts of future climate change. Unlike South Korea, North Korea lacks in studies using climate change scenarios of the CoupledModelIntercomparisonProject (CMIP5), and only recently did the country start the projection of extreme precipitation episodes. One of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates high applicability of the Multi-Model Ensemble (MME). Furthermore, the study classifies climatic zones by applying the Köppen-Geiger climate classification system to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate (D) that covers the inland area for the reference climate is expected to shift into the temperate climate (C). The coefficient of variation (CVs) in the temperature ensemble is particularly low for the southern coast of the Korean peninsula, and accordingly, a high possibility of the shifting climatic zone of the coast is predicted. This research was supported by a grant (MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety (MOIS, Korea).

Keywords: MME, North Korea, Koppen–Geiger, climatic zones, coefficient of variation, CV

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Authors: Filipa Lancastre, Carmen Lages, Filipe Santos

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The concept of social entrepreneurship (SE) remains ambiguous and deprived of a widely accepted operational definition. We argue that an awareness about the consensual constituent elements of SE from all key players from its ecosystem as well as a deeper understanding of apparently divergent perspectives will allow the different stakeholders (social entrepreneurs, corporations, investors, policymakers, the beneficiaries themselves) to bridge and cooperate for societal value co-creation in trying to solve our most pressing societal issues. To address our research question –what are the dimensions of SE that are consensual and controversial across existing perspectives? – We designed a two-step qualitative study. In a first step, we conducted an extensive literature review, collecting and analyzing 155 different SE definitions. From this initial step, we extracted and characterized three consensual and six controversial dimensions of the SE concept. In a second step, we conducted 20 semi-structured interviews with practitioners that are actively involved in the SE field. The goal of this second step was to verify if the literature did not capture any key dimension, understand how the dimensions related to each other and to understand the rationale behind them. The dimensions of the SE concept were extracted based on the relevance of each theme and on the theoretical relationship among them. To identify the relevance, we used as a proxy the frequency of each theme was referred to in our sample of definitions. To understand relationships, as identified in the previous section, we included concepts from both the management and psychology literature, such as the Entrepreneurial Orientation concept from the entrepreneurship literature, the Subjective Well Being construct from psychology literature, and the Resource-Based Theory from the strategy literature. This study has two main contributions; First, the identification of (consensual and controversial) dimensions of SE that exist across scattered definitions from the academic and practitioner literature. Second, a framework that parsimoniously synthesizes four dominant perspectives of SE and relates them with the SE dimensions. Assuming the contested nature of the SE concept, it is not expected that these views will be reconciled at the academic or practitioner field level. In future research, academics can, however, be aware of the existence of different understandings of SE and avoid bias towards a single view, developing holistic studies on SE phenomena or comparing differences by studying their underlying assumptions. Additionally, it is important that researchers make explicit the perspective they are embracing to ensure consistency among the research question, sampling procedures and implications of results. At the practitioner level, individuals or groups following different logics are predictably mutually suspicious and might benefit from taking stock of other perspectives on SE, building bridges and fostering cross-fertilization to the benefit of the SE ecosystem for which all contribute.

Keywords: social entrepreneurship, conceptualization, dimensions, perspectives

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