Search results for: forecasting methodologies review

Authors: Micheale Yifter Weldemichael, Stefaan Werbrouck, Hailay Mehari Gebremedhn

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Sesame (Sesamum indicum L.) is among the most important oilseed crops for its high edible oil quality and quantity. Sesame is grown for food, medicinal, pharmaceutical, and industrial uses. Sesame is also cultivated as a main cash crop in Asia and Africa by smallholder farmers. Despite the global exponential increase in sesame cultivation area, its production and productivity remain low, mainly due to biotic and abiotic constraints. Notwithstanding the efforts to solve these problems, a low level of genetic variation and inadequate genomic resources hinder the progress of sesame improvement. The objective of this paper is, therefore, to review recent advances in the area of molecular breeding and transformation to overcome major production constraints and could result in enhanced and sustained sesame production. This paper reviews various researches conducted to date on molecular breeding and genetic transformation in sesame focusing on molecular markers used in assessing the available online database resources, genes responsible for key agronomic traits as well as transgenic technology and genome editing. The review concentrates on quantitative and semi-quantitative studies on molecular breeding for key agronomic traits such as improvement of yield components, oil and oil-related traits, disease and insect/pest resistance, and drought, waterlogging and salt tolerance, as well as sesame genetic transformation and genome editing techniques. Pitfalls and limitations of existing studies and methodologies used so far are identified and some priorities for future research directions in sesame genetic improvement are identified in this review.

Keywords: molecular breeding, oil, sesame, shattering

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Authors: Saimir Heta, Ilma Robo, Dhimiter Papakozma, Eduart Kapaj, Vera Ostreni

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Introduction: The biocompatible materials applied to the implant surfaces are the target of recent literature studies. Methodologies: Modification of implant surfaces in different ways such as application of additional ions, surface microstructure change, surface or laser ultrasound alteration, or application of various substances such as recombinant proteins are among the most affected by articles published in the literature. The study is of review type with the main aim of finding the different ways that the mesenchymal cell reaction to these materials is, according to the literature, in the same percentage positive to the osteointegration process. Results: It is emphasized in the literature that implant success as a key evaluation key has more to implement implant treatment protocol ranging from dental health amenity and subsequent of the choice of implant type depending on the alveolar shape of the ridge level. Conclusions: Osteointegration is a procedure that should initially be physiologically independent of the type of implant pile material. With this physiological process, it can not "boast" for implant success or implantation depending on the brand of the selected implant, as the breadth of synthetic or natural materials that promote osteointegration is relatively large.

Keywords: mesenchymal cells, implants, review, biocompatible materials

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Authors: Natalie Rosales

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Based on an ethical vision which recognizes the vital role of human rights, shared values, social responsibility and justice, and environmental ethics, planning may be interpreted as a process aimed at reducing inequalities and overcoming marginality. Seen from this sustainability perspective, planning evaluation must utilize critical-evaluative and narrative receptive models which assist different stakeholders in their understanding of urban fabric while trigger reflexive processes that catalyze wider transformations. In this paper, this approach servers as a guide for the evaluation of Mexico´s urban planning systems, and postulates a framework to better integrate sustainability notions into planning evaluation. The paper is introduced by an overview of the current debate on evaluation in urban planning. The state of art presented includes: the different perspectives and paradigms of planning evaluation and their fundamentals and scope, which have focused on three main aspects; goal attainment (did planning instruments do what they were supposed to?); performance and effectiveness of planning (retrospective analysis of planning process and policy analysis assessment); and the effects of process-considering decision problems and contexts rather than the techniques and methods. As well as, methodological innovations and improvements in planning evaluation. This comprehensive literature review provides the background to support the authors’ proposal for a set of general principles to evaluate urban planning, grounded on a sustainability perspective. In the second part the description of the shortcomings of the approaches to evaluate urban planning in Mexico set the basis for highlighting the need of regulatory and instrumental– but also explorative- and collaborative approaches. As a response to the inability of these isolated methods to capture planning complexity and strengthen the usefulness of evaluation process to improve the coherence and internal consistency of the planning practice itself. In the third section the general proposal to evaluate planning is described in its main aspects. It presents an innovative methodology for establishing a more holistic and integrated assessment which considers the interdependence between values, levels, roles and methods, and incorporates different stakeholders in the evaluation process. By doing so, this piece of work sheds light on how to advance urban sustainability through the integration of evaluation methodologies into planning.

Keywords: urban planning, evaluation methodologies, urban sustainability, innovative approaches

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Authors: Khalid Ali, Manar Jammal

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In order to meet the stringent latency and reliability requirements of the upcoming 5G networks, Open Radio Access Networks (O-RAN) have been proposed. The virtualization of O-RAN has allowed it to be treated as a Network Function Virtualization (NFV) architecture, while its components are considered Virtualized Network Functions (VNFs). Hence, intelligent Machine Learning (ML) based solutions can be utilized to apply different resource management and allocation techniques on O-RAN. However, intelligently allocating resources for O-RAN VNFs can prove challenging due to the dynamicity of traffic in mobile networks. Network providers need to dynamically scale the allocated resources in response to the incoming traffic. Elastically allocating resources can provide a higher level of flexibility in the network in addition to reducing the OPerational EXpenditure (OPEX) and increasing the resources utilization. Most of the existing elastic solutions are reactive in nature, despite the fact that proactive approaches are more agile since they scale instances ahead of time by predicting the incoming traffic. In this work, we propose and evaluate traffic forecasting models based on the ML algorithm. The algorithms aim at predicting future O-RAN traffic by using previous traffic data. Detailed analysis of the traffic data was carried out to validate the quality and applicability of the traffic dataset. Hence, two ML models were proposed and evaluated based on their prediction capabilities.

Keywords: O-RAN, traffic forecasting, NFV, ARIMA, LSTM, elasticity

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Authors: Yamini Raju, Parminder S. Kang, Adam Moroz, Ross Clement, Alistair Duffy, Ashley Hopwell

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Accurate prediction of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. Current research in this area focused on limited number of factors specific to a single product or a business type. This paper gives an overview of the current literature on the variability factors used to predict demand and the existing forecasting techniques of short shelf life products. It then extends it by adding new factors and investigating if there is a time lag and possibility of noise in the orders. It also identifies the most important factors using correlation and Principal Component Analysis (PCA).

Keywords: demand forecasting, deteriorating products, food wholesalers, principal component analysis, variability factors

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Emmanuel A. Oriaifo, Noel Perera, Alan Guy, Pak. S. Leung, Kian T. Tan

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Concerns on corrosion and effective coating protection of double hull tankers and bulk carriers in service have been raised especially in water ballast tanks (WBTs). Test protocols/methodologies specifically that which is incorporated in the International Maritime Organisation (IMO), Performance Standard for Protective Coatings for Dedicated Sea Water ballast tanks (PSPC) are being used to assess and evaluate the performance of the coatings for type approval prior to their application in WBTs. However, some of the type approved coatings may be applied as very thick films to less than ideally prepared steel substrates in the WBT. As such films experience hygrothermal cycling from operating and environmental conditions, they become embrittled which may ultimately result in cracking. This embrittlement of the coatings is identified as an undesirable feature in the PSPC but is not mentioned in the test protocols within it. There is therefore renewed industrial research aimed at understanding this issue in order to eliminate cracking and achieve the intended coating lifespan of 15 years in good condition. This paper will critically review test protocols currently used for assessing and evaluating coating performance, particularly the IMO PSPC.

Keywords: corrosion test, hygrothermal cycling, coating test protocols, water ballast tanks

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Authors: Montserrrat Dopico Gonzalez, Ramon Lopez Facal

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The development of a critical, open and plural citizenship constitutes one of the main challenges of the school institution in the present multicultural societies. Didactics in Social Sciences has conducted important contributions to the development of active methodologies to promote the development of the intercultural competencies of the student body. Research in intercultural education has demonstrated the efficiency of the cooperative learning techniques to improve the intercultural relations in the classroom. Our study proposes to check the effect that, concerning the development of intercultural competencies of the student body, the emotional education can have in the context of the use of active methodologies such as the learning by projects and the cooperative learning. To that purpose, a programme of intervention based on activities focussed on controversial issues related to cultural diversity has been implemented in several secondary schools. Through a methodology which combines intercultural competence scales with interviews and also with the analysis of the school body’s productions, the persistence of stereotypes against immigration and the efficacy of the introduction of emotional education elements in the development of intercultural competencies have both been observed.

Keywords: active methodologies, didactics in social sciences, intercultural competences, intercultural education

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Authors: Gurmail Singh

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Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS).

Keywords: bullwhip effect, hybrid techniques, net stock amplification, supply chain flexibility

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Authors: Mark Andrew

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Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Ulrike Dowie, Ralph Grothmann

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Rational planning decisions are based upon forecasts. Precise forecasting has, therefore, a central role in business. The prediction of customer demand is a prime example. This paper introduces recurrent neural networks to model customer demand and combines the forecast with uncertainty measures to derive decision support of the demand planning department. It identifies and describes the keys to the successful implementation of an AI-based solution: bringing together data with business knowledge, AI methods, and user experience, and applying agile software development practices.

Keywords: agile software development, AI project success factors, deep learning, demand forecasting, forecast uncertainty, neural networks, supply chain management

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: D. Prangchumpol

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This research presents a forecasting method of requisition document demands for Military units by using Exponential Smoothing methods to analyze data. The data used in the forecast is an actual data requisition document of The Adjutant General Department. The results of the forecasting model to forecast the requisition of the document found that Holt–Winters’ trend and seasonality method of α=0.1, β=0, γ=0 is appropriate and matches for requisition of documents. In addition, the researcher has developed a requisition online system to improve the performance of requisition documents of The Adjutant General Department, and also ensuring that the operation can be checked.

Keywords: requisition, holt–winters, time series, royal thai army

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Authors: Danielle Guerra, Marden Silva

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The use of active methodologies in the teaching of English has been widely encouraged recently, due to its potential to create propitious conditions for the learners to develop autonomy and studying skills that tend to keep them motivated throughout the learning process. The constant use of technology by the students makes it possible to implement strategies such as blended learning, which blends regular classes with online instruction and practice. (Horn and Staker, 2015) For that reason, the aim of this study was to implement the blended approach in a High School second-grade English class in Brazil, in order to analyze the impacts of this methodology on the students' autonomy. The teacher's role was that of a mediator, being responsible for selecting the best resources for students to study with, and also for helping them with questions when necessary. The results show that taking learner characteristics and learning experiences into account and allowing the students to follow their learning paths at their own pace was crucial to promoting engagement that led to the desired outcomes. In conclusion, the research shows that blended learning is a helpful strategy to foster autonomy and promote motivation in EFL students.

Keywords: active methodologies, autonomy, blended learning, motivation

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Authors: Umit Cali

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The integration of intermittent energy sources like wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating behaviour of wind power generation. Wind power predictions improve the economic and technical integration of large amounts of wind energy into the existing electricity grid. Trading, balancing, grid operation, controllability and safety issues increase the importance of predicting power output from wind power operators. Therefore, wind power forecasting systems have to be integrated into the monitoring and control systems of the transmission system operator (TSO) and wind farm operators/traders. The wind forecasts are relatively precise for the time period of only a few hours, and, therefore, relevant with regard to Spot and Intraday markets. In this work predictive data mining techniques are applied to identify a statistical and neural network model or set of models that can be used to predict wind power output of large onshore and offshore wind farms. These advanced data analytic methods helps us to amalgamate the information in very large meteorological, oceanographic and SCADA data sets into useful information and manageable systems. Accurate wind power forecasts are beneficial for wind plant operators, utility operators, and utility customers. An accurate forecast allows grid operators to schedule economically efficient generation to meet the demand of electrical customers. This study is also dedicated to an in-depth consideration of issues such as the comparison of day ahead and the short-term wind power forecasting results, determination of the accuracy of the wind power prediction and the evaluation of the energy economic and technical benefits of wind power forecasting.

Keywords: renewable energy sources, wind power, forecasting, data mining, big data, artificial intelligence, energy economics, power trading, power grids

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Authors: Etienne Gaborit, Dorothy Durnford, Daniel Deacu, Marco Carrera, Nathalie Gauthier, Camille Garnaud, Vincent Fortin

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A new experimental streamflow forecasting system was recently implemented at the Environment and Climate Change Canada’s (ECCC) Canadian Centre for Meteorological and Environmental Prediction (CCMEP). It relies on CaLDAS (Canadian Land Data Assimilation System) for the assimilation of surface variables, and on a surface prediction system that feeds a routing component. The surface energy and water budgets are simulated with the SVS (Soil, Vegetation, and Snow) Land-Surface Scheme (LSS) at 2.5-km grid spacing over Canada. The routing component is based on the Watroute routing scheme at 1-km grid spacing for the Great Lakes and Nelson River watersheds. The system is run in two distinct phases: an analysis part and a forecast part. During the analysis part, CaLDAS outputs are used to force the routing system, which performs streamflow assimilation. In forecast mode, the surface component is forced with the Canadian GEM atmospheric forecasts and is initialized with a CaLDAS analysis. Streamflow performances of this new system are presented over 2019. Performances are compared to the current ECCC’s operational streamflow forecasting system, which is different from the new experimental system in many aspects. These new streamflow forecasts are also compared to persistence. Overall, the new streamflow forecasting system presents promising results, highlighting the need for an elaborated assimilation phase before performing the forecasts. However, the system is still experimental and is continuously being improved. Some major recent improvements are presented here and include, for example, the assimilation of snow cover data from remote sensing, a backward propagation of assimilated flow observations, a new numerical scheme for the routing component, and a new reservoir model.

Keywords: assimilation system, distributed physical model, offline hydro-meteorological chain, short-term streamflow forecasts

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Authors: Aurelio Palacardo, Giulio Mangano, Alberto De Marco

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Nowadays, the global economic framework is extremely competitive, and it consequently requires an efficient deployment of the resources provided by EU. In this context, Project management practices are intended to be one of the levers for increasing such an efficiency. The objective of this work is to explore the usage of Project Management methodologies and good practices in the European-wide research program “Horizon2020” and establish whether their maturity might impact the project's success. This allows to identify strengths in terms of application of PM methodologies and good practices and, in turn, to provide feedback and opportunities for improvements to be implemented in future programs. In order to achieve this objective, the present research makes use of a survey-based data retrieval and correlation analysis to investigate the level of perceived PM maturity in H2020 projects and the correlation of maturity with project success. The results show the Project Managers involved in H2020 to hold a high level of PM maturity, confirming PM standards, which are imposed by the EU commission as a binding process, are effectively enforced.

Keywords: project management, project management maturity, maturity models, project success

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Karthikeyan Srinivasan, Ranjana Singh, Yatin Talwar, Karthikeyan Srinivasan

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Healthcare services play a vital role in the life of human being. The Setup of Hospital varies in wide spectrum of cost, technology, and access. Hospital’s of Public sector satisfies need of a common man to poorer, which can differ at private owned hospitals on cost and treatment. Patient assessing hospital frequently assumes spending time at the hospital is miserable and not aware of what is happening around them. Mostly they are queued up round the clock waiting to be admitted on hospital beds. The idea here is to highlight the role in admitting patient population of Outdoor as well as Emergency entering the Post Graduate Institute of Medical Education and Research, Chandigarh with available hospital beds. This study emphasizes the trend forecasting and acquiring beds needed. The conception “if patient population increases’ likewise increasing hospital beds advertently perceived. If tend to increase the hospital beds, thereby exploring budget, Manpower, space, and infrastructure make compulsion. This survey ideally draws out planning and forecasting beds to cater patient population in and around neighboring state of Chandigarh for admission at territory healthcare and research institute on available hospital beds. Executing healthcare services for growing population needs to know Roemer’s law indicating "in an insured population, a hospital bed built is a filled bed".

Keywords: admissions, average length of stay, bed days, hospital beds, occupancy rates

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Authors: Cosette Maiky

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Background: In the context of the Syrian crisis, the past few years have witnessed an exponential growth in the number of refugee mental health studies, which have essentially focused either on the affected Syrian population and/or host communities. However, the Palestinian communities in the region did not receive sufficient that much of attention. Aim: The study aimed at identifying trends and patterns of mental health and and psychosocial conditions among Palestinian refugees in the context of the Syrian crisis, including the recognition of gaps in appropriate services. Methods: The research model comprised a systematic documentary review, a mapping of available contextual analyses, a quantitative survey, focus group discussions as well as key informant interviews (with relevant stakeholders and beneficiaries). Findings: Content analysis revealed multiple effects of transgenerational transmission of trauma among Palestinian refugees in the context of the Syrian crisis, which showed to be neither linear nor one-dimensional occurrence. In addition to highlights on exposure to traumatic events and psychological sequelae, the review outlines the most prevailing coping mechanisms and essential protective factors. Conclusion: Away from a trauma-centered or symptom-focused exercise, practitioners may take account of the present study to better focus research and intervention methodologies.

Keywords: Palestine refugees, Syria crisis, psychosocial, mental health

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Authors: Ilaria Lucrezia Amerise

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Electricity markets throughout the world have undergone substantial changes. Accurate, reliable, clear and comprehensible modeling and forecasting of different variables (loads and prices in the first instance) have achieved increasing importance. In this paper, we describe the actual state of the art focusing on reg-SARMA methods, which have proven to be flexible enough to accommodate the electricity price/load behavior satisfactory. More specifically, we will discuss: 1) The dichotomy between point and interval forecasts; 2) The difficult choice between stochastic (e.g. climatic variation) and non-deterministic predictors (e.g. calendar variables); 3) The confrontation between modelling a single aggregate time series or creating separated and potentially different models of sub-series. The noteworthy point that we would like to make it emerge is that prices and loads require different approaches that appear irreconcilable even though must be made reconcilable for the interests and activities of energy companies.

Keywords: interval forecasts, time series, electricity prices, reg-SARIMA methods

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Authors: Sarah Al-Lumai, Fatima Al-Attar, Nour Jamal, Badria Al-Dabbous, Manal Abdulla

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Today, many commonly used Industrial Engineering tools and techniques are being used in hospitals around the world for the goal of producing a more efficient and effective healthcare system. A common quality improvement methodology known as Lean Six-Sigma has been successful in manufacturing industries and recently in healthcare. The objective of our project is to use the Lean Six-Sigma methodology to reduce waiting time in the Emergency Department (ED), in a local private hospital. Furthermore, a comprehensive literature review was conducted to evaluate the success of Lean Six-Sigma in the ED. According to the study conducted by Ibn Sina Hospital, in Morocco, the most common problem that patients complain about is waiting time. To ensure patient satisfaction many hospitals such as North Shore University Hospital were able to reduce waiting time up to 37% by using Lean Six-Sigma. Other hospitals, such as John Hopkins’s medical center used Lean Six-Sigma successfully to enhance the overall patient flow that ultimately decreased waiting time. Furthermore, it was found that capacity constraints, such as staff shortages and lack of beds were one of the main reasons behind long waiting time. With the use of Lean Six-Sigma and bed management, hospitals like Memorial Hermann Southwest Hospital were able to reduce patient delays. Moreover, in order to successfully implement Lean Six-Sigma in our project, two common methodologies were considered, DMAIC and DMADV. After the assessment of both methodologies, it was found that DMAIC was a more suitable approach to our project because it is more concerned with improving an already existing process. With many of its successes, Lean Six-Sigma has its limitation especially in healthcare; but limitations can be minimized if properly approached.

Keywords: lean six sigma, DMAIC, hospital, methodology

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Authors: Sasha Goodwin

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Sex offenders are often viewed as a homogenous group, but they encompass a diverse range of individuals with varying characteristics and offenses. The principal aim of this study was to ascertain how members of the Australian public perceive and define a sex offender while also investigating the emotional underpinnings associated with these attitudes and definitions. To assess public attitude, this study used the innovative utilization of visual methodologies to assess the public's perception of sex offenders. The study employed the iSquare approach, a visual methodology framework that offers unique viewpoints and insights into public attitudes toward sex offenders. Through the utilization of this approach, this study established an academic foundation for a deeper understanding of the public's perception of sex offenders. The data analysis revealed that most participants associated sex offenders with strong negative emotions, primarily disgust and anger. The findings of this research point towards the potential for fostering a social environment characterized by evidence-based discussions instead of reactionary punitive responses. Promoting a comprehensive understanding of the diverse nature of sexual offenders aims to broaden perceptions, fostering constructive attitudes.

Keywords: visual methodologies, public perception, sex offenders, offender characteristics, emotional attitudes, isquare approach, attitudes

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Authors: Pauline E. Ngo-Henha

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Existing turnover intention theories are reviewed in this paper. This review was conducted with the help of the search keyword “turnover intention theories” in Google Scholar during the month of July 2017. These theories include: The Theory of Organizational Equilibrium (TOE), Social Exchange Theory, Job Embeddedness Theory, Herzberg’s Two-Factor Theory, the Resource-Based View, Equity Theory, Human Capital Theory, and the Expectancy Theory. One of the limitations of this review paper is that data were only collected from Google Scholar where many papers were sometimes not freely accessible. However, this paper attempts to contribute to the research in clarifying the distinction between theories and models in the context of turnover intention.

Keywords: Literature Review, Theory, Turnover, Turnover intention

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Authors: Eti Mizrahi, Gizem İntepe

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The geographical location of Turkey that stretches from Asia to Europe and Russia to Africa makes it an important logistics hub in the region. Although logistics is a developing sector in Turkey, the stock market representation is still low with only two companies listed in Turkey’s stock exchange since 2010. In this paper, we use the daily values of these two listed stocks as a benchmark for the logistics sector. After modeling logistics stock prices, an empirical examination is conducted between the existing logistics indices and these stock prices. The paper investigates whether the measures of logistics stocks are correlated with newly available logistics indices. It also shows the reflection of the economic activity in the logistics sector on the stock exchange market. The results presented in this paper are the first analysis of the behavior of logistics indices and logistics stock prices for Turkey.

Keywords: forecasting, logistic stock exchange, modeling, Africa

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Authors: Ioannis Andrianakis, Vasileios Gkatas, Nikos Eleftheriadis, Alexios Ellinidis, Ermioni Avramidou

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The paper discusses the main aspects involved in the development of a supply chain management system using the newly developed PredictionSCMS software as a basis for the discussion. The discussion is focused on three topics: the first is demand forecasting, where we present the predictive algorithms implemented and discuss related concepts such as the calculation of the safety stock, the effect of out-of-stock days etc. The second topic concerns the design of a supply chain, where the core parameters involved in the process are given, together with a methodology of incorporating these parameters in a meaningful order creation strategy. Finally, the paper discusses some critical events that can happen during the operation of a supply chain management system and how the developed software notifies the end user about their occurrence.

Keywords: demand forecasting, machine learning, risk management, supply chain design

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Authors: Michal Krčál

Abstract:

Since their introduction in 1990’s, Enterprise Information Portals (EIPs) were investigated from different perspectives (e.g. project management, technology acceptance, IS success). However, no systematic literature review was produced to systematize both the research efforts and the technology itself. This paper reports first results of an extent systematic literature review study focused on research of EIPs and its categorization, specifically it reports a conceptual model of EIP features. The previous attempt to categorize EIP features was published in 2002. For the purpose of the literature review, content of 89 articles was analyzed in order to identify and categorize features of EIPs. The methodology of the literature review was as follows. Firstly, search queries in major indexing databases (Web of Science and SCOPUS) were used. The results of queries were analyzed according to their usability for the goal of the study. Then, full-texts were coded in Atlas.ti according to previously established coding scheme. The codes were categorized and the conceptual model of EIP features was created.

Keywords: enterprise information portal, content analysis, features, systematic literature review

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Authors: Patricio Alberto Cullen

Abstract:

Teaching Science in high school has been decreasing its quality for several years, and it is an obvious theme of discussion over more than 30 years. As a teacher of Secondary Education and a Professor of Technological University was necessary to work with some projects that attempt to articulate the different methodologies and concepts between both levels. Teaching Physics in Engineering Career is running between two waters. Disciplinary content and inconsistent training students got in high school. In the heady times facing humanity, teaching Science has become a race against time, and this is where it is worth stopping. Professor of Physics has outdated teaching tools against the relentless growth of knowledge in the Academic World. So we have raised from a pedagogical point of view the following question: Laboratory practices must continue to focus on traditional physics or should develop alternatives between old practices and new physics methodologies. Faced with this paradox, we stopped to try to answer from our experience, and our teaching and learning practice. These are one of the greatest difficulties presented in the Engineering work. The physics team will try to find new methodologies that are appealing to the population of students in the 21st century. Currently, the methodology used is question students about their personal interests. Once discovered mentioned interests, will be held some lines of action to facilitate achieving the goals.

Keywords: high school and university, level, students, physics, teaching physics

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Authors: Nur Azaliah Abu Bakar, Harihodin Selamat, Mohd Nazri Kama

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This article provides a systematic review of existing research related to the Service-oriented Enterprise Architecture (SoEA) adoption and maturity measurement model. The review’s main goals are to support research, to facilitate other researcher’s search for relevant studies and to propose areas for future studies within this area. In addition, this article provides useful information on SoEA adoption issues and its related maturity model, based on research-based knowledge. The review results suggest that motives, critical success factors (CSFs), implementation status and benefits are the most frequently studied areas and that each of these areas would benefit from further exposure.

Keywords: systematic literature review, service-oriented architecture, adoption, maturity model

Procedia PDF Downloads 290

Authors: Jhonas Geraldo Peixoto Flauzino, Pedro Pompeo Boechat Araujo, Alexia Allis Rocha Lima, Giovanna Biângulo Lacerda Chaves, Victor Ryan Ferrão Chaves

Abstract:

Schizophrenia is characterized as a set of psychiatric signs and symptoms (syndrome) that commonly erupt in the stages of adolescence or early adulthood, being recognized as one of the most serious diseases, as it causes important problems during the life of the patient. carrier - both in mental health and in physical health and in social life. Objectives: This is an integrative literature review that aimed to verify what has been produced of scientific knowledge in the field of child and adolescent psychiatry regarding schizophrenia in these stages of life, correlated to the most discussed themes and methodologies of choice for the preparation of studies. Methods: Articles were selected from the following databases: Virtual Health Library and CAPES Journal Portal, published in the last five years; and on Google Scholar, published in 2021, totaling 62 works, searched in September 2021. Results: The studies focus mainly on diagnosis through the DSM-V (25.8%), on drug treatment (25.8%) and in psychotherapy (24.2%), most of them in the literature review format: integrative (27.4%) and systematic (24.2%). Conclusion: The themes and study methods are redundant, and do not cover in depth the immense aspects that encompass Schizophrenia in Childhood and Adolescence, giving attention to the disease in a general way or focusing on the adult patient.

Keywords: schizophrenia, mental health, childhood, adolescence

Procedia PDF Downloads 136