Search results for: classification of factors

Authors: Aniket Alam, Sravya Gurram

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This paper proposes a categorization of sport that is based on the system of rules that a sport must adhere to. We focus on these systems of rules to examine how a winner is produced in different sports. The rules of a sport dictate the game play and the direction it takes. We propose to break down the game play into events. At this junction, we observe two kinds of events that constitute the game play of a sport –ones that follow sequential logic and ones that do not. Our focus is pertained to sports that are comprised of sequential events. To examine these events further, to understand how a winner emerges, we take the help of finite-state automaton from the theory of computation (Automata theory). We showcase how sequential sports are eligible to be represented as finite state machines. We depict these finite state machines as state diagrams. We examine these state diagrams to observe how a team/player reaches the final states of the sport, with a special focus on one final state –the final state which determines the winner. This exercise has been carried out for the following sports: Hurdles, Track, Shot Put, Long Jump, Bowling, Badminton, Pacman and Weightlifting (Snatch). Based on our observations of how this final state of winning is achieved, we propose a categorization of sports.

Keywords: sport classification, sport modelling, ontology, automata theory

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Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Marta Maria Aguilar Carceles

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This theoretical study argues how terrorism is rising around the world and which are the factors and situations that contribute to this process. Linked to aspects of human development, minors are one of the most vulnerable collectives to be engaged for this purpose. Its special weakness and lower possibility of self-defense makes them more likely to become victims as a result of a brainwashing process. Terrorism is an illicit way to achieve political and social changes and new technologies and available resources make it easier to spread. In this sense, throughout a theoretical revision of different recent and scientific articles, it is evaluated which risk factors can provoke its affiliation and later develop of antisocial and illicit behavior. An example of this group of factors could be the inter-generational continuity between parents and their children, as well as the sociodemographic aspects joined to cultural experiences (i.e. sense of dishonor, frustration, etc.). The assess of this kind of variables must be accompanied by the evaluation of protective factors, because the reasons through one person decides to join to terrorism are inherently idiosyncratic and we can only install mechanisms of prevention knowing those personal characteristics. To sum, both aspects underline the relevance of the internalizing and externalizing personal factors, each of them in one specific direction: a) to increase the possibility of being recruited or follow this type of criminal group by himself, and b) to be able of avoiding the effects and consequences of terrorism thanks to personal and resilient characteristics (resilience).

Keywords: criminality, minors, recruitment, resilience, terrorism

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Authors: Uyen Tran Tu

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The decision on capital structure is one of the most important and sophisticated decisions in financial management in order to improve firm performance. This article would study the two-way impact between capital structure and firm performance. The study use EVIEWS 6.0 software to determine a two-way relationship between the capital structure and firm performance based on two-stage regression (2SLS - Two-Stage Least Squares). The findings are: capital structure has the opposite effect on the business efficiency and vice versa, factors that effect on business efficiency include Size and Opportunities. Factors effects on the capital structure are size; liquidity. These factors also affect the ratio of capital structure (total debt/ total asset) of companies. In particular, liquidity has the opposite effect; and the size of the business has the same impact. The results of the study are in line with the theory and empirical studies presented, and the results of the study are unchanged for all three years 2015-2017.

Keywords: capital structure, firm performance, factors, two-way relationship

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Authors: Mona K. Fehling, Jakob Unger, Dietmar J. Hecker, Bernhard Schick, Joerg Lohscheller

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Voice disorders origin from disturbances of the vibration patterns of the two vocal folds located within the human larynx. Consequently, the visual examination of vocal fold vibrations is an integral part within the clinical diagnostic process. For an objective analysis of the vocal fold vibration patterns, the two-dimensional vocal fold dynamics are captured during sustained phonation using an endoscopic high-speed camera. In this work, we present an approach allowing a fully automatic analysis of the high-speed video data including a computerized classification of healthy and pathological voices. The approach bases on a wavelet-based analysis of so-called phonovibrograms (PVG), which are extracted from the high-speed videos and comprise the entire two-dimensional vibration pattern of each vocal fold individually. Using a principal component analysis (PCA) strategy a low-dimensional feature set is computed from each phonovibrogram. From the PCA-space clinically relevant measures can be derived that quantify objectively vibration abnormalities. In the first part of the work it will be shown that, using a machine learning approach, the derived measures are suitable to distinguish automatically between healthy and pathological voices. Within the approach the formation of the PCA-space and consequently the extracted quantitative measures depend on the clinical data, which were used to compute the principle components. Therefore, in the second part of the work we proposed a strategy to achieve a normalization of the PCA-space by registering the PCA-space to a coordinate system using a set of synthetically generated vibration patterns. The results show that owing to the normalization step potential ambiguousness of the parameter space can be eliminated. The normalization further allows a direct comparison of research results, which bases on PCA-spaces obtained from different clinical subjects.

Keywords: Wavelet-based analysis, Multiscale product, normalization, computer assisted classification, high-speed laryngoscopy, vocal fold analysis, phonovibrogram

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Authors: Dalia Suša Vugec

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Business process management (BPM) is widely used approach focused on designing, mapping, changing, managing and analyzing business processes of an organization, which eventually leads to better performance and derives many other benefits. Since every organization strives to improve its performance in order to be sustainable and to remain competitive on the market in long-term period, numerous organizations are nowadays adopting and implementing BPM. However, not all organizations are equally successful in that. One of the ways of measuring BPM success is by measuring its maturity by calculating Process Performance Index (PPI) using ten BPM success factors. Still, although BPM is a holistic concept, organizational culture is not taken into consideration in calculating PPI. Hence, aim of this paper is twofold; first, it aims to explore and analyze the current state of BPM success factors within the big organizations from Slovenia, Croatia, and Austria and second, it aims to analyze the structure of organizational culture within the observed companies, focusing on the link with BPM success factors as well. The presented study is based on the results of the questionnaire conducted as the part of the PROSPER project (IP-2014-09-3729) and financed by Croatian Science Foundation. The results of the questionnaire reveal differences in the achieved levels of BPM success factors and therefore BPM maturity in total between the three observed countries. Moreover, the structure of organizational culture across three countries also differs. This paper discusses the revealed differences between countries as well as the link between organizational culture and BPM success factors.

Keywords: business process management, BPM maturity, BPM success factors, organizational culture, process performance index

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: Andrew Couch, Nicholas Loyd, Nathan Tenhundfeld

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The rapid emergence of technology observably induces disruptive effects that carry implications for internal organizational dynamics as well as external market opportunities, strategic pressures, and threats. An examination of the historical tendencies of technology innovation shows that the body of managerial knowledge for addressing such disruption is underdeveloped. Fundamentally speaking, the impacts of innovation are unique and situationally oriented. Hence, the appropriate managerial response becomes a complex function that depends on the nature of the emerging technology, the posturing of internal organizational dynamics, the rate of technological growth, and much more. This research considers a particular case of mismanagement, the BP Texas City Refinery explosion of 2005, that carries notable discrepancies on the basis of human factors principles. Moreover, this research considers the modern technological climate (shaped by Industry 4.0 technologies) and seeks to arrive at an appropriate conceptual lens by which human factors principles and Industry 4.0 may be favorably integrated. In this manner, the careful examination of these phenomena helps to better support the sustainment of human factors principles despite the disruptive impacts that are imparted by technological innovation. In essence, human factors considerations are assessed through the application of principles that stem from usability engineering, the Swiss Cheese Model of accident causation, human-automation interaction, signal detection theory, alarm design, and other factors. Notably, this stream of research supports a broader framework in seeking to guide organizations amid the uncertainties of Industry 4.0 to capture higher levels of adoption, implementation, and transparency.

Keywords: Industry 4.0, human factors engineering, management, case study

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Authors: Aya Salama

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Digital Twin is an emerging research topic that attracted researchers in the last decade. It is used in many fields, such as smart manufacturing and smart healthcare because it saves time and money. It is usually related to other technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, Human digital twin (HDT), in specific, is still a novel idea that still needs to prove its feasibility. HDT expands the idea of Digital Twin to human beings, which are living beings and different from the inanimate physical entities. The goal of this research was to create a Human digital twin that is responsible for real-time human replies automation by simulating human behavior. For this reason, clustering, supervised classification, topic extraction, and sentiment analysis were studied in this paper. The feasibility of the HDT for personal replies generation on social messaging applications was proved in this work. The overall accuracy of the proposed approach in this paper was 63% which is a very promising result that can open the way for researchers to expand the idea of HDT. This was achieved by using Random Forest for clustering the question data base and matching new questions. K-nearest neighbor was also applied for sentiment analysis.

Keywords: human digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification, clustering

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Josephine O. Okocha, Ifeanyi Adigwe

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This research aims to examine the factors impacting women's retention in STEM in Nigeria and Georgia. In a bid to come up with strategies to enhance women’s participation in STEM, this study identifies and juxtaposes the factors impacting the retention of women in STEM and how they vary from one country to another are discussed. This study adopted the literature review method to perform the critical analysis. A total of 76 papers were retrieved from the Scopus database and were published between 2018 and 2023. Only 12 papers met the criteria for inclusion in the analysis. The findings reveal that the factors impacting women’s retention in STEM include funding (NGOs and government agencies), scholarship, specialized recruitment, mentoring, the establishment of women-only higher institutions, creating a balanced work and family environment, combating stereotypes, and enabling policies and laws. The paper highlights some key recommendations to help improve the retention of women in STEM in Africa and Nigeria in particular.

Keywords: STEM, women, retention, career, Nigeria, Georgia, women’s retention, women representation

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Authors: Asiri Wijesinghe, N. D. Kodikara, Damitha Sandaruwan

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The Diabetic Retinopathy (DR) is a rapidly growing interrogation around the world which can be annotated by abortive metabolism of glucose that causes long-term infection in human retina. This is one of the preliminary reason of visual impairment and blindness of adults. Information on retinal pathological mutation can be recognized using ocular fundus images. In this research, we are mainly focused on resurrecting an automated diagnosis system to detect DR anomalies such as severity level classification of DR patient (Non-proliferative Diabetic Retinopathy approach) and vessel tortuosity measurement of untwisted vessels to assessment of vessel anomalies (Proliferative Diabetic Retinopathy approach). Severity classification method is obtained better results according to the precision, recall, F-measure and accuracy (exceeds 94%) in all formats of cross validation. In ROC (Receiver Operating Characteristic) curves also visualized the higher AUC (Area Under Curve) percentage (exceeds 95%). User level evaluation of severity capturing is obtained higher accuracy (85%) result and fairly better values for each evaluation measurements. Untwisted vessel detection for tortuosity measurement also carried out the good results with respect to the sensitivity (85%), specificity (89%) and accuracy (87%).

Keywords: fundus image, exudates, microaneurisms, hemorrhages, tortuosity, diabetic retinopathy, optic disc, fovea

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Mutshinye Manguvhewa, Maria Florence, Mansoo Yu, Elize Koch, Kamal Kamaloodien

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Substance use among pregnant women is a perennial problem in the Western Cape Province of South Africa. There are many influential factors are associated with substance use among women of childbearing age. The study explored factors associated with substance use among pregnant women using a qualitative research design and the bio-ecological theoretical framework to explore and guide the researcher throughout the study. Participants were selected using purposive sampling. Only participants accessed from the Department of Social Development meeting the inclusion criteria of the study were interviewed using semi structured interviews. Immediate referral for psychological intervention during the interview was available for participants who needed it. Braun and Clarke's six phases of thematic analysis were utilised to analyse the data. The study adheres to ethical guidelines for the participants' protection. Participants were informed about the study before the initiation of the interviews and the details of their voluntary participation were explained. The key findings from this study illustrate that socio-cultural factors, personal factors, emotional response and intimate relationships are the major contributing factors to substance use among pregnant women in this sample. The results outline the preventative measures that pregnant women implement. Lastly, the study reveals the positive and negative perceptions of substance use programmes that participants share. Some of the study findings are similar to the existing literature and some of the findings differed. Recommendations emanating from the study include that the stakeholders, rehabilitation centres, Department of Health and future researchers should act proactively against substance use during pregnancy.

Keywords: substance addiction, antenatal care, pregnancy, substance use

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Authors: Hossein Boromandfar, Ahmad Ghalavandi

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This paper examines the importance of human resources as a strategic resource and the factors that lead to increased Labor productivity in Applied Science Center Andimeshk pay. First, the concepts and definitions of productivity and factors affecting it, and then determine the center Recommendations for improving the productivity of the university at a high level its improvement. What leads to increased productivity of labor is worth. The most competent human resources infrastructure is set, because by moving towards the development and promotion. The use of qualified employees in the university with a focus on specific objectives can be effective on its promotion.

Keywords: productivity, manage, human resources, center for applied science

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Authors: Osa D Egonwa

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In the 21^st century, the disciplinary boundaries of past centuries that we often create through mainstream art historical classification, techniques and sources may have been eroded by visual culture, which seems to provide a more inclusive umbrella for the new ways artists go about the creative process and its resultant commodities. Over the past four decades, artists in Africa have resorted to new materials, techniques and themes which have affected our ways of research on these artists and their art. Frontline artists such as El Anatsui, Yinka Shonibare, Erasmus Onyishi are demonstrating that any material is just suitable for artistic expression. Most of times, these materials come with their own techniques/effects and visual syntax: a combination of materials compounds techniques, formal aesthetic indexes, halo effects, and iconography. This tends to challenge the categories and we lean on to view, think and talk about them. This renders our main stream art historical research methods inadequate, thus suggesting new discursive concepts, terms and theories. This paper proposed the Africanist eclectic methods derived from the dual framework of Masquerade Theory and What Comes Behind Six is More Than Seven. This paper shares thoughts/research on art historical methods, terminological re-alignments on classification/source data, presentational format and interpretation arising from the emergent trends in our subject. The outcome provides useful tools to mediate new thoughts and experiences in recent African art and visual culture.

Keywords: art historical methods, classifications, concepts, re-alignment

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Aleksander V. Zakharov, Marat R. Bogdanov, Ramil F. Malikov, Irina N. Dumchikova

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Classification of persecution movement laws is proposed. Modes of persecution in number of specific cases were researched. Modes of movement control using GLONASS/GPS are discussed.

Keywords: UAV Management, mathematical algorithms of targeting and persecution, GLONASS, GPS

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Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

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Authors: Violetta Parutis

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This qualitative study aims to answer the following research questions: i) What are the factors that students perceive as relevant to a) promoting and b) preventing good grades? ii) How does socio-economic status (SES) feature in those beliefs? We conducted in-depth interviews with 19 first- and second-year undergraduates of varying SES at a research-intensive university in the UK. The interviews yielded eight factors that students perceived as promoting and six perceived as preventing good grades. The findings suggested one significant difference between the beliefs of low and high SES students in that low SES students perceive themselves to be at a greater disadvantage to their peers while high SES students do not have such beliefs. This could have knock-on effects on their performance.

Keywords: social class, education, academic performance, students’ beliefs

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Authors: Danish Farooq, Janos Juhasz

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Car-motorcycle accidents have been observed higher in recent years, which caused mainly riders’ fatalities and serious injuries. In-depth crash investigation methods aim to investigate the main factors which are likely involved in fatal road accidents and injury outcomes. The main objective of this study is to investigate the involved factors in car-motorcycle accidents in Budapest city. The procedure included statistical analysis and data sampling to identify car-motorcycle accidents by dominant accident types based on collision configurations. The police report was used as a data source for specified accidents, and simulation models were plotted according to scale (M 1:200). Car-motorcycle accidents were simulated in Virtual Crash software for 5 seconds before the collision. The simulation results showed that the main involved factors to car-motorcycle accidents were human behavior and view obstructions. The comprehensive, in-depth analysis also found that most of the car drivers and riders were unable to perform collision avoidance manoeuvres before the collision. This study can help the traffic safety authorities to focus on simulated involved factors to solve road safety issues in car-motorcycle accidents. The study also proposes safety measures to improve safe movements among road users.

Keywords: car motorcycle accidents, in-depth analysis, microscopic simulation, safety measures

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Authors: Olena Kavats, Dmitry Khramov, Kateryna Sergieieva, Vladimir Vasyliev, Iurii Kavats

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Geoinformation technologies of space agromonitoring are a means of operative decision making support in the tasks of managing the agricultural sector of the economy. Existing technologies use satellite images in the optical range of electromagnetic spectrum. Time series of optical images often contain gaps due to the presence of clouds and haze. A geoinformation technology is created. It allows to fill gaps in time series of optical images (Sentinel-2, Landsat-8, PROBA-V, MODIS) with radar survey data (Sentinel-1) and use information about agrometeorological conditions of the growing season for individual monitoring years. The technology allows to perform crop classification and mapping for spring-summer (winter and spring crops) and autumn-winter (winter crops) periods of vegetation, monitoring the dynamics of crop state seasonal changes, crop yield forecasting. Crop classification is based on supervised classification algorithms, takes into account the peculiarities of crop growth at different vegetation stages (dates of sowing, emergence, active vegetation, and harvesting) and agriculture land state characteristics (row spacing, seedling density, etc.). A catalog of samples of the main agricultural crops (Ukraine) is created and crop spectral signatures are calculated with the preliminary removal of row spacing, cloud cover, and cloud shadows in order to construct time series of crop growth characteristics. The obtained data is used in grain crop growth tracking and in timely detection of growth trends deviations from reference samples of a given crop for a selected date. Statistical models of crop yield forecast are created in the forms of linear and nonlinear interconnections between crop yield indicators and crop state characteristics (temperature, precipitation, vegetation indices, etc.). Predicted values of grain crop yield are evaluated with an accuracy up to 95%. The developed technology was used for agricultural areas monitoring in a number of Great Britain and Ukraine regions using EOS Crop Monitoring Platform (https://crop-monitoring.eos.com). The obtained results allow to conclude that joint use of Sentinel-1 and Sentinel-2 images improve separation of winter crops (rapeseed, wheat, barley) in the early stages of vegetation (October-December). It allows to separate successfully the soybean, corn, and sunflower sowing areas that are quite similar in their spectral characteristics.

Keywords: geoinformation technology, crop classification, crop yield prediction, agricultural monitoring, EOS Crop Monitoring Platform

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Authors: Arundhati Devi Maibam, K. Ingocha Singh

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Cervical cancer is preventable if detected early. Determination of risk factors is essential to plan screening programmes to prevent the disease. To study the demographic risk factors of cervical cancer among Manipuri women, information on age, marital status, educational level, monthly family income and socioeconomic status were collected through a pre-tested interview schedule. In this study, 64 incident cases registered at the RT Dept, RIMS (Regional Institute of Medical Sciences), Imphal, Manipur, India during 2008-09 participated. Data were entered in Microsoft Excel and the results were expressed in percentages. Among the 64 patients with cervical cancer, 56 (88.9%) were in the age group of 40+ years. The majority of the patients were from rural areas (68.75%) and 31.25% were from urban areas. The majority of the patients were Hindus (73%), 55(85.9%) were of low educational level, 43(67.2%) were married, and 36 (56.25%) belonged to Class IV socioeconomic status. In conclusion, if detected early, cervical cancer is preventable and curable. The potential risk factors need to be identified and women in the risk group need to be motivated for screening. Affordable screening programmes and health care resources will help in lessening the burden of the disease.

Keywords: cervical cancer, Manipuri women, RIIMS, socio-demographic risk factors

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Authors: Bassey Odiong Akan

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In recent decades potentially significant changes have taken place in the world of work and these have led to the emergence of new challenges in occupational safety and health. The working environment is now not only wroth with concerns about physical, biological and chemical risks but also emerging risks which are completely new risks that have never been seen before or previously known risks that are evolving in unexpected ways with unanticipated consequences. Psychosocial risk factors and its attendant hazards happen to be one of them and can impact health directly or indirectly, mediated by work-related stress. These risks are related to the way work is designed, organised and managed, as well as the economic and social contexts of work. It has become necessary to identify, explore and anticipate the dynamics of these risks factors and hazards with regards to how it affects women. This presentation is a review of information gathered from books of distinguished authors, research work and scientific/professional journals on the psychosocial work environment intended as a guide to stimulate discussion, raise awareness and encourage research and action at different levels.

Keywords: emerging risks, psychosocial hazards, psychosocial risk factors, work related stress

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Authors: W. C. Lucas, S. Titus, M. E. M. Young

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Gymnastics is the umbrella term that represents seven different and unique disciplines of gymnastics. Men and women of all ages and abilities practice this sport, and participation in gymnastics can develop both gross and fine motor skills, strength, flexibility, coordination and balance. There are various social factors, such as a family’s socioeconomic status or accessibility to sports facilities that may play a role in affecting levels of participation. The aim of this study is to investigate the social factors that have an influence on gymnastics participation in the Western Cape. To this end, a qualitative approach is adopted to collect data. This study also adopts the ecological systems theory as the theoretical framework, and is used to analyze and interpret current social factors that directly or indirectly influence participation in gymnastics. The study’s objectives were to ascertain which social factors hinder participation, and which social factors promote participation, thus, coaches, parents and gymnasts participated in focus group discussions. Key informant interviews took place with experts in the field of gymnastics in the Western Cape. A thematic analysis was conducted on transcriptions from the focus group discussions and key informant interviews. Social factors investigated in this study occurred in the chronosystem, macrosystem, exosystem, mesosystem, and microsystem, and had both a direct and indirect influence on the gymnast’s continued participation. These systems are defined as the environment of the individual, in which they grow and develop. The research findings of this paper are used to draw conclusions and make specific recommendations for practice and further research. The information gathered in this study can assist all stakeholders within the field of gymnastics, such as parents, judges, coaches, gymnasts, and the supporting community which surround the participating gymnast.

Keywords: developing child, ecological systems theory, facilities, federation, gymnastics, influence, participation, social factors, socioeconomic status, sport

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Authors: Haider Karar, Saghir Ahmad, Amjad Ali, Ibrar Ul Haq

Abstract:

The experiment was conducted at Cotton Research Station, Multan to study the impact of weather factors and red cotton bug (RCB) on cotton boll disease yielded yellowish lint during 2012. The population on RCB along with abiotic factors was recorded during three consecutive years i.e. 2012, 2013, and 2014. Along with population of RCB and abiotic factors, the number of unopened/opened cotton bolls (UOB), percent yellowish lint (YL) and whitish lint (WL) were also recorded. The data revealed that the population per plant of RCB remain 0.50 and 0.34 during years 2012, 2013 but increased during 2014 i.e. 3.21 per plant. The number of UOB were more i.e. 13.43% in 2012 with YL 76.30 and WL 23.70% when average maximum temperature 34.73◦C, minimum temperature 22.83◦C, RH 77.43% and 11.08 mm rainfall. Similarly in 2013 the number of UOB were less i.e. 0.34 per plant with YL 1.48 and WL 99.53 per plant when average maximum temperature 34.60◦C, minimum temperature 23.37◦C, RH 73.01% and 9.95 mm rainfall. During 2014 RCB population per plant was 3.22 with no UOB and YL was 0.00% and WL was 100% when average maximum temperature 23.70◦C, minimum temperature 23.18◦C, RH 71.67% and 4.55 mm rainfall. So it is concluded that the cotton bolls disease was more during 2012 due to more rainfall and more percent RH. The RCB may be the carrier of boll rot disease pathogen during more rainfall.

Keywords: red cotton bug, cotton, weather factors, years

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Authors: Arpan Paul, Joy Sen

Abstract:

Livability is a holistic concept whose factors include many complex characteristics and levels of interrelationships among them. It has been considered as people’s need for public amenities and is recognized as a major element to create social welfare. The concept and principles of livability are essential for recognizing the significance of community well-being. The attributes and dimensions of livability are also important aspects to measure the overall quality of environment. Livability potential is mainly considered as the capacity to develop into the overall well-being of an urban area in future. The intent of the present study is to identify the prime factors to evaluate livability potential within a metropolis. For ground level case study, the paper has selected Kolkata Metropolitan Area (KMA) as it has wide physical, social, and economic variations within it. The initial part of the study deals with detailed literature review on livability and its significance of evaluating its potential within a metropolis. The next segment is dedicated for identifying the primary factors which would evaluate livability potential within a metropolis. In pursuit of identifying primary factors, which have a direct impact on urban livability, this study delineates the metropolitan area into various clusters, having their distinct livability potential. As a final outcome of the study, variations of livability potential of those selected clusters are highlighted to explain the complexity of the metropolitan development.

Keywords: Kolkata Metropolitan Area (KMA), livability potential, metropolis, wellbeing

Procedia PDF Downloads 266

Authors: Ahmed M. Rashed

Abstract:

Culicoides midges are known to be vectors of disease to both man and animals. For providing information necessary for control methods to be applied to the best advantage, a New jersey light-trap was used. Twenty species were identified during this study and eight species were recorded from Chantilly for the first time, these include C.grisescens, C.nubeculosus, C.cubitalis, C.achrayi, C.circumscriptus, C.stigma, C.reconditus, and C.parroti. The environmental factors, wind speed and temperature were found to have a marked effect on the activity of Culicoides midges. The temperature was found to be positively correlated and the wind speed negatively correlated with the light-trap catch. However, humidioty could not be shown to have any effect on the catch.

Keywords: culicoides, meteorological factors, wind speed, disease

Procedia PDF Downloads 451

Authors: Maha Mourad, Fady Todros

Abstract:

The objective of this research is to find an explanatory model to define the most important variables and factors that affect the acceptance of Mobile Internet in the Egyptian market. A qualitative exploratory research was conducted to support the conceptual framework followed with a quantitative research in the form of a survey distributed among 411 respondents. It was clear that relative advantage, complexity, compatibility, perceived price level and perceived playfulness have a dominant role in influencing consumers to adopt mobile internet, while observability is correlated to the adoption but when measured with the other factors it lost its value. The perceived price level has a negative relationship with the adoption as well the compatibility.

Keywords: innovation, Egypt, communication technologies, diffusion, innovation adoption, emerging market

Procedia PDF Downloads 447

Authors: Morteza Hamidpour, Kambeez Shahroudi

Abstract:

The aim of this research was to identify the factors affecting the measurement and management of marketing performance in Iran's airports and air navigation companies (Economics in Air and Airport Transport). This study was exploratory and used a qualitative content analysis technique. The study population consisted of university professors in the field of air transportation and senior airport managers, with 15 individuals selected as samples using snowball technique. Based on the results, 15 main indicators were identified for measuring the marketing performance of Iran's airports and air navigation companies. These indicators include airport staff, general and operational expenses, annual passenger reception capacity, number of counter receptions and passenger dispatches, airport runway length, airline companies' loyalty to using airport space and facilities, regional market share of transit and departure flights, claims and net profit (aviation and non-aviation). By keeping the input indicators constant, the output indicators can be improved, enhancing performance efficiency and consequently increasing the economic situation in air transportation.

Keywords: air transport economics, marketing performance management, marketing performance input factors, marketing performance intermediary factors, marketing performance output factors, content analysis

Procedia PDF Downloads 63