Search results for: project progress prediction

Authors: S. Di Benedetto, M. P. Di Donato, A. Rispoli, S. Cardone, J. Riehmer, J. Steelant, L. Vecchione

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Thanks to a coordinated funding by the European Space Agency (ESA) and the European Commission (EC) within the 7th framework program, the High-Speed Experimental Fly Vehicles – International (HEXAFLY-INT) project is aimed at the flight validation of hypersonics technologies enabling future trans-atmospheric flights. The project, which is currently involving partners from Europe, Russian Federation and Australia operating under ESA/ESTEC coordination, will achieve the goal of designing, manufacturing, assembling and flight testing an unpowered high speed vehicle in a glider configuration by 2018. The main technical challenges of the project are specifically related to the design of the vehicle gliding configuration and to the complexity of integrating breakthrough technologies with standard aeronautical technologies, e.g. high temperature protection system and airframe cold structures. Also, the sonic boom impact, which is one of the environmental challenges of the high speed flight, will be assessed. This paper provides a comprehensive and detailed update on all the current projects activities carried out to date on both the vehicle and mission design.

Keywords: design, flight testing, HEXAFLY-INT, hypersonics

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Authors: Triano Nurhikmat

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Along with the progress of science and technology, the development of the industrialized world in Indonesia took place very rapidly. This leads to a process of industrialization of society Indonesia faster with the establishment of the company and the workplace are diverse. Development of the industry relates to the activity of the worker. Where in these work activities do not cover the possibility of an impending crash on either the workers or on a construction project. The cause of the occurrence of industrial accidents was the fault of electrical damage, work procedures, and error technique. The method of an association rule is one of the main techniques in data mining and is the most common form used in finding the patterns of data collection. In this research would like to know how relations of the association between the incidence of any industrial accidents. Therefore, by using methods of analysis association rule patterns associated with combination obtained two iterations item set (2 large item set) when every factor of industrial accidents with a West Jakarta so industrial accidents caused by the occurrence of an electrical value damage = 0.2 support and confidence value = 1, and the reverse pattern with value = 0.2 support and confidence = 0.75.

Keywords: association rule, data mining, industrial accidents, rules

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Authors: Samart Homthong, Wutthipong Moungnoi

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Critical success factors (CSFs) and the criteria to measure project success have received much attention over the decades and are among the most widely researched topics in the context of project management. However, although there have been extensive studies on the subject by different researchers, to date, there has been little agreement on the CSFs. The aim of this study is to identify the CSFs that influence the performance of construction projects, and determine their relative importance for different objectives across five stages in the project life cycle. A considerable literature review was conducted that resulted in the identification of 179 individual factors. These factors were then grouped into nine major categories. A questionnaire survey was used to collect data from three groups of respondents: client representatives, consultants, and contractors. Out of 164 questionnaires distributed, 93 were returned, yielding a response rate of 56.7%. Using the mean score, relative importance index, and weighted average method, the top 10 critical factors for each category were identified. The agreement of survey respondents on those categorised factors were analysed using Spearman’s rank correlation. A one-way analysis of variance was then performed to determine whether the mean scores among the various groups of respondents were statistically significant. The findings indicate the most CSFs in each category in procurement phase are: proper procurement programming of materials (time), stability in the price of materials (cost), and determining quality in the construction (quality). They are then followed by safety equipment acquisition and maintenance (health and safety), budgeting allowed in a contractual arrangement for implementing environmental management activities (environment), completeness of drawing documents (productivity), accurate measurement and pricing of bill of quantities (risk management), adequate communication among the project team (human resource), and adequate cost control measures (client satisfaction). An understanding of CSFs would help all interested parties in the construction industry to improve project performance. Furthermore, the results of this study would help construction professionals and practitioners take proactive measures for effective project management.

Keywords: critical success factors, procurement phase, project life cycle, project performance

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Amiel Satvedi, Richard Pyne

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Injuries in professional sports occur on a regular basis. Some may be minor, while others can cause huge impact on a player's career and earning potential. In soccer, there is a high risk of players picking up injuries during game time. This research work seeks to help soccer players reduce the risk of getting injured by predicting the likelihood of injury while playing in the near future and then providing recommendations for intervention. The injury prediction tool will use a soccer player's number of minutes played on the field, number of appearances, distance covered and performance data for the current and previous seasons as variables to conduct statistical analysis and provide injury predictive results using a machine learning linear regression model.

Keywords: injury predictor, soccer injury prevention, machine learning in soccer, big data in soccer

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Authors: Kenneth O. Otasowie, Matthew Ikuabe, Clinton Aigbavboa, Ayodeji Oke

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The practice of feasibility studies plays a huge role in the success of construction projects. Feasibility studies according to several research should be the reason for embarking on any project. However, it has been discovered that in South Africa (SA), feasibility studies are mainly done in the private sector construction but skipped in the construction of most public projects. Hence, this study aims to evaluate the barriers to feasibility studies practice in public projects and the implications. A survey design was adopted. A total number of One hundred and fifty (150) questionnaires were administered to Quantity Surveyors, Construction managers, Construction project managers, Project managers, Architects and Civil and Structural engineers in Guateng Province, SA and ninety (90) were returned and found suitable for analysis. Collected data was analysed using percentage, mean item score, standard deviation, one-sample t-test. The findings show that political interference and corruption are the most significant barriers to feasibility studies practice in the public construction projects in SA, while late project completion, poor quality infrastructure are among the implication of not conducting feasibility studies in SA public projects. Therefore, the study recommends the development of a framework for public projects execution that will reduce the interference of the political class in the country, that way the risk of late project completion and poor quality infrastructure will be mitigated.

Keywords: arriers, feasibility studies, public construction, South Africa.

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Authors: Radoslaw Nagay

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The Paulina Project aims to address the challenges faced by immobilized individuals, such as those with multiple sclerosis, muscle dystrophy, or spinal cord injuries, by developing a flexible hardware and software solution. This paper presents the research and development efforts of our team, which commenced in 2019 and is now in its final stage. Recognizing the diverse needs and limitations of individuals with limited mobility, we conducted in-depth testing with a group of 30 participants. The insights gained from these tests led to the complete redesign of the system. Our presentation covers the initial project ideas, observations from in-situ tests, and the newly developed system that is currently under construction. Moreover, in response to the financial constraints faced by many disabled individuals, we propose an affordable business model for the future commercialization of our invention. Through the Paulina Project, we strive to empower immobilized individuals, providing them with greater independence and improved quality of life.

Keywords: UI, human-machine interface, social inclusion, multiple sclerosis, muscular dystrophy, spinal cord injury, quadriplegic

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Authors: May George

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Native speakers can play a significant role in improving second language learners speaking skills through weekly interaction. Speaking is one of the important skills that second language learners need to practice in order to be able to communicate the language. This study will examine Talkaboard as an important tool to achieve better outcomes in speaking a language. The subject of the study will be 16 advanced Arabic language learners at the college level. There will be a pre-test and post-test to examine the conversation outcomes using the Talkaborad tool. The students will be asked to write a summary and talk about their weekly conversation experience with the native speaker in class. The teacher will use a check list to determine the progress made in speaking the Arabic language. The results of this study will provide language teachers with information related to the native speakers’ role in language and the progress the second language learners made after interacting with native speakers.

Keywords: speaking, language, interaction, culture

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Authors: Ilana Lavy, Rami Rashkovits

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In this study, we describe Information Systems students' learning model which was applied by students in order to cope with professional knowledge gaps in the context of their final project. The students needed to implement a software system according to specifications and design they have made beforehand. They had to select certain technologies and use them. Most of them decided to use programming environments that were learned during their academic studies. The students had to cope with various levels of knowledge gaps. For that matter they used learning strategies that were organized by us as a learning model which includes two phases each suitable for different learning tasks. We analyze the learning model, describing advantages and shortcomings as perceived by the students, and provide excerpts to support our findings.

Keywords: knowledge gaps, independent learner skills, self-regulated learning, final project

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Jonas P. Reges, Jessyca A. Bessa, Auzuir R. Alexandria

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The research came up with the need of monitoring them of temperature and relative moisture in past work that enveloped the study of a greenhouse located in the Research and Extension Unit(UEPE). This research brought several unknowns that were resolved from bibliographical research. Based on the studies performed were found some monitoring methods, including the serial communication between the arduino and matlab which showed a great option due to the low cost. The project was conducted in two stages, the first, an algorithm was developed to the Arduino and Matlab, and second, the circuits were assembled and performed the monitoring tests the following variables: moisture, temperature, and distance. During testing it was possible to momentarily observe the change in the levels of monitored variables. The project showed satisfactory results, such as: real-time verification of the change of state variables, the low cost of acquisition of the prototype, possibility of easy change of programming for the execution of monitoring of other variables. Therefore, the project showed the possibility of monitoring through software and hardware that have easy programming and can be used in several areas. However, it is observed also the possibility of improving the project from a remote monitoring via Bluetooth or web server and through the control of monitored variables.

Keywords: automation, monitoring, programming, arduino, matlab

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Koo Ja-Choon, Seok Hyun-Deok, Park So-Hee

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Even though developed countries have supported aid projects for restoring degraded and deforested mountain, recent North Korean authorities announced that North Korean forest is still very serious. Last 12 years, more than 16 thousand ha of forest were destroyed. Most of previous researches concluded that food and fuel problems should be solved for preventing people from deforesting and degrading forest in North Korea. It means that mountain restoration projects such as A/R(afforestation/reforestation) and REDD(Reducing Emissions from Deforestation and Forest Degradation) project should be implemented with the agroforestry and the forest tending project. Because agroforestry and the forest tending can provide people in the project area with foods and fuels, respectively. Especially, Agroforestry has been operated well with the support of Swiss agency of Development and cooperation since 2003. This paper aims to find the priority sites for mountain restoration project where all types of projects including agroforesty can be implemented simultaneously. We tried to find the primary counties where the areas of these activities were distributed widely and evenly. Recent spatial data of 186 counties representing altitude, gradient and crown density were collected from World Forest Watch. These 3 attributes were used to determine the type of activities; A/R, REDD, Agroforestry and forest tending project. Finally, we calculated the size of 4 activities in 186 counties by using GIS technique. Result shows that Chongjin in Hamgyeongbuk-do, Hoeryong in Hamgyeongbuk-do and Tongchang in Pyeonganbuk-do are on the highest priority of counties. Most of feasible counties whose value of richness and uniformity were greater than the average were located near the eastern coast of North Korea. South Korean government has not supported any aid projects in North Korea since 2010. Recently, South Korea is trying to continue the aid projects for North Korea. Forest project which is not affected by the political situation between North- and South- Korea can be considered as a priority activities. This result can be used when South Korean government determine the priority sites for North Korean mountain restoration project in near future.

Keywords: agroforestry, forest restoration project, GIS, North Korea, priority

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Authors: Nopadol Burananuth

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This research was aimed to investigate (1) the relationship among local social movements, non-governmental Organization activities and state measures deployment; and (2) the effects of local social movements, non-governmental Organization activities, and state measures deployment on conflict of local people towards the Thai-Malaysian gas pipeline project. These people included 1,000 residents of the four districts in Songkhla province. The methods of data analysis consist of multiple regression analysis. The results of the analysis showed that: (1) local social movements depended on information, and mass communication; deployment of state measures depended on compromise, coordination, and mass communication; and (2) the conflict of local people depended on mobilization, negotiation, and campaigning for participation of people in the project. Thus, it is recommended that to successfully implement any government policy, consideration must be paid to the conflict of local people, mobilization, negotiation, and campaigning for people’s participation in the project.

Keywords: conflict, NGO activities, social movements, state measures

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Akunna Agunwah, Kevin Gallimore, Kathryn Kinnmond

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Entrepreneurship and project management are widely associated and seen as a vehicle for economic growth, but are studied separately. A few authors have considered the interconnectivity existing between these two fields, but relatively little empirical data currently exist in the literature. The purpose of the present empirical study is to explore whether successful entrepreneurs utilise project management approaches in enhancing enterprise growth by understanding the working practices and experiences of the entrepreneurs’ using the Theory of Planned Behaviour (TPB) as a lens. In order to understand those experiences, ten successful entrepreneurs in various business sectors in the North West of England were interviewed through a face-to-face semi-structured interview method. The collected audio tape-recorded data was transcribed and analysed using the deductive thematic technique (qualitative approach). The themes were viewed through the lens of Theory of Planned Behaviour to identify the three intentional antecedents (attitude, subjective norms, and perceived behavioural control) and to understand how they relate to the project management approaches (Planning, execution, and monitoring). The findings are twofold, the first evidence of the three intentional antecedents, which make up Theory of Planned Behaviour was present. Secondly, the analysis of project management approaches themes (planning, execution, and monitoring) using the lens of the theory of planned behaviour shows evidence of the three intentional antecedents. There were more than one intentional antecedents found in a particular project management theme, which indicates that the entrepreneur does utilise these approaches without categorising them into definite themes. However, the entrepreneur utilised these intentional antecedents as processes to enhanced business growth. In conclusion, the work presented here showed a way of understanding the interconnectivity between entrepreneurship and project management towards enhancing enterprise growth by examining the working practices and experiences of the successful entrepreneurs in the North-West England.

Keywords: business growth, entrepreneurship, project management approaches, theory of planned behaviour

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Introducing Principles of Land Surveying by Assigning a Practical Project

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A practical project is used in an engineering surveying course to expose sophomore and junior civil engineering students to several important issues related to the use of basic principles of land surveying. The project, which is the design of a two-lane rural highway to connect between two arbitrary points, requires students to draw the profile of the proposed highway along with the existing ground level. Areas of all cross-sections are then computed to enable quantity computations between them. Lastly, Mass-Haul Diagram is drawn with all important parts and features shown on it for clarity. At the beginning, students faced challenges getting started on the project. They had to spend time and effort thinking of the best way to proceed and how the work would flow. It was even more challenging when they had to visualize images of cut, fill and mixed cross sections in three dimensions before they can draw them to complete the necessary computations. These difficulties were then somewhat overcome with the help of the instructor and thorough discussions among team members and/or between different teams. The method of assessment used in this study was a well-prepared-end-of-semester questionnaire distributed to students after the completion of the project and the final exam. The survey contained a wide spectrum of questions from students' learning experience when this course development was implemented to students' satisfaction of the class instructions provided to them and the instructor's competency in presenting the material and helping with the project. It also covered the adequacy of the project to show a sample of a real-life civil engineering application and if there is any excitement added by implementing this idea. At the end of the questionnaire, students had the chance to provide their constructive comments and suggestions for future improvements of the land surveying course. Outcomes will be presented graphically and in a tabular format. Graphs provide visual explanation of the results and tables, on the other hand, summarize numerical values for each student along with some descriptive statistics, such as the mean, standard deviation, and coefficient of variation for each student and each question as well. In addition to gaining experience in teamwork, communications, and customer relations, students felt the benefit of assigning such a project. They noticed the beauty of the practical side of civil engineering work and how theories are utilized in real-life engineering applications. It was even recommended by students that such a project be exercised every time this course is offered so future students can have the same learning opportunity they had.

Keywords: land surveying, highway project, assessment, evaluation, descriptive statistics

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Anila Xhahysa, Migena Ceyhan, Neki Kuka, Klajdi Qoshi, Damiano Koxhaj

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The seismic event catalog has been updated in the framework of a bilateral project supported by the Central European Investment Fund and with the extensive support of Global Earthquake Model Foundation to update Albania's national seismic hazard model. The earthquake catalogue prepared within this project covers the Western Balkan area limited by 38.0° - 48°N, 12.5° - 24.5°E and includes 41,806 earthquakes that occurred in the region between 510 BC and 2022. Since the moment magnitude characterizes the earthquake size accurately and the selected ground motion prediction equations for the seismic hazard assessment employ this scale, it was chosen as the uniform magnitude scale for the catalogue. Therefore, proxy values of moment magnitude had to be obtained by using new magnitude conversion equations between the local and other magnitude types to this unified scale. The Global Centroid Moment Tensor Catalogue was considered the most authoritative for moderate to large earthquakes for moment magnitude reports; hence it was used as a reference for calibrating other sources. The best fit was observed when compared to some regional agencies, whereas, with reports of moment magnitudes from Italy, Greece and Turkey, differences were observed in all magnitude ranges. For teleseismic magnitudes, to account for the non-linearity of the relationships, we used the exponential model for the derivation of the regression equations. The obtained regressions for the surface wave magnitude and short-period body-wave magnitude show considerable differences with Global Earthquake Model regression curves, especially for low magnitude ranges. Moreover, a conversion relation was obtained between the local magnitude of Albania and the corresponding moment magnitude as reported by the global and regional agencies. As errors were present in both variables, the Deming regression was used.

Keywords: regression, seismic catalogue, local magnitude, tele-seismic magnitude, moment magnitude

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Authors: Seyed Ali Mohammadiborna, Mehdi Ravanshadnia

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Project delays usually detrimentally affect perceptions of project success and can in some instances, result in increased costs and other time-related damages to project stakeholders. One of the realities in the national infrastructure projects is that since the primary stakeholders are state-affiliated, the delay factors of the projects have not been seriously taken into account despite the importance of on-time completion of projects. Project postponement has different economic and social consequences and leads to the technical and economic infeasibility of the infrastructure projects in the form of reduced productivity and exploitation capacity. The present study aimed at investigating delay factors of Iranian national infrastructure projects according to regulatory reports of the Plan and Budget Organization (BPO) of Iran. The present study scrutinized the influence of each of the factors that caused delays in national Iranian infrastructure projects according to the supervision reports of the planning and budget organization in 8 years. For this purpose, the study analyzed the information regarding the impact of 12 key delay factors causing delays in average 4867 projects per year in all provinces. The said factors were classified into the three groups of executive, credit, and financial and environmental-procurement factors.

Keywords: delays, infrastructure, projects, regulatory

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Authors: Kuo-Wei Liao, Jessica Gitomarsono

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Bridge reliability against natural hazards such as floods or earthquakes is an interdisciplinary problem that involves a wide range of knowledge. Moreover, due to the global climate change, engineers have to design a structure against the multi-hazard threats. Currently, few of the practical design guideline has included such concept. The bridge foundation in Taiwan often does not have a uniform width. However, few of the researches have focused on safety evaluation of a bridge with a complex pier. Investigation of the scouring depth under such situation is very important. Thus, this study first focuses on investigating and improving the scour prediction formula for a bridge with complicated foundation via experiments and artificial intelligence. Secondly, a probabilistic design procedure is proposed using the established prediction formula for practical engineers under the multi-hazard attacks.

Keywords: bridge, reliability, multi-hazards, scour

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: David Oyewumi Oyekunle, David Preston, Florence Ibeh

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The paper provides an overview of the impacts of the human dimension in project management and team management on projects, which is increasingly affecting the performance of organizations. Recognizing its crucial significance, the research focuses on analyzing the psychological and interpersonal dynamics within project teams. This research is highly significant in the dynamic field of project management, as it addresses important gaps and offers vital insights that align with the constantly changing demands of the profession. A case study was conducted at the University of Salford to examine how human activity affects project management and performance. The study employed a mixed methodology to gain a deeper understanding of the real-world experiences of the subjects and project teams. Data analysis procedures to address the research objectives included the deductive approach, which involves testing a clear hypothesis or theory, as well as descriptive analysis and visualization. The survey comprised a sample size of 40 participants out of 110 project management professionals, including staff and final students in the Salford Business School, using a purposeful sampling method. To mitigate bias, the study ensured diversity in the sample by including both staff and final students. A smaller sample size allowed for more in-depth analysis and a focused exploration of the research objective. Conflicts, for example, are intricate occurrences shaped by a multitude of psychological stimuli and social interactions and may have either a deterrent perspective or a positive perspective on project performance and project management productivity. The study identified conflict elements, including culture, environment, personality, attitude, individual project knowledge, team relationships, leadership, and team dynamics among team members, as crucial human activities to minimize conflict. The findings are highly significant in the dynamic field of project management, as they address important gaps and offer vital insights that align with the constantly changing demands of the profession. It provided project professionals with valuable insights that can help them create a collaborative and high-performing project environment. Uncovering human influence on project performance, effective communication, optimal team synergy, and a keen understanding of project scope are necessary for the management of projects to attain exceptional performance and efficiency. For the research to achieve the aims of this study, it was acknowledged that the productive dynamics of teams and strong group cohesiveness are crucial for effectively managing conflicts in a beneficial and forward-thinking manner. Addressing the identified human influence will contribute to a more sustainable project management approach and offer opportunities for exploration and potential contributions to both academia and practical project management.

Keywords: human dimension, project management, team dynamics, conflict resolution

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

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Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

Abstract:

In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: alpha waves, antidepressant, treatment outcome, wavelet

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Authors: Yassir Abdelrazig, Ren Moses

Abstract:

Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: gemoetric design, optimization, planning, roadway planning, roadway design

Procedia PDF Downloads 338