Search results for: restructuringbuilding information modeling

Authors: Mahalingam Palaniandi

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Higher education plays vital role in the nation building process for a country and the rest of world. The higher education sector develops the change-agents for the various fields which will help the human-kind wheel to run further. Conventional and traditional class-room based learning and teaching was followed in many decades which is one-to-one and one-to-many. In a way, these are simplest form of learners to be assembled in a class room wherein the teacher used the blackboard to demonstrate the theory and laboratories used for practical. As the technology evolved tremendously for the last 40 years, the teaching and learning environment changed slowly, wherein, the learning community will be anywhere in the world and teacher deliver the content through internet based tools such as video conferencing, web based conferencing tools or E-learning platforms such as Blackboard or noodle. Present day, the mobile technologies plays an important tool to deliver the teaching content on-the-go. Both PC based and mobile based learning technology brought the learning and teaching community together in various aspects. However, as the learning technology also brought various hurdles for learning processes such as plagiarism and not using the reference books entirely as most of the students wants the information instantaneously using internet without actually going to the library to take the notes from the millions of the books which are not available online as e-books which result lack of fundamental knowledge of the concepts complex theories. However, technology is inseparable in human life, now-a-days and every part of it contains piece of information technology right from computers to home appliances. To make use of the IT based learning and teaching at most efficiency, we should have a proper framework and recommendations laid to the learning community in order to derive the maximum efficiency from the IT based teaching and leaning. This paper discusses various IT based tools available for the learning community, efficiency from its usage and recommendations for the suitable framework that needs to be implemented at higher education institutions which makes the learners stronger in both theory as well as real-time knowledge of their studies that is going to be used in their future for the better world.

Keywords: higher education, e-learning, teaching learning, eLearning tools

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Authors: Silvia Lorenzo, Hongmian Gong

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With the influx of research assessing the economic impact of the global financial crisis of 2007-8, a spatial analysis based on empirical data is needed to better understand the spatial significance of the financial crisis in New York, a key international financial center also considered the origin of the crisis. Using spatial statistics, the existence of financial clusters specializing in credit and securities throughout the New York metropolitan area are identified for 2000 and 2010, the time period before and after the height of the global financial crisis. Geographically Weighted Regressions are then used to examine processes underlying the formation and movement of financial geographies across state, county and ZIP codes of the New York metropolitan area throughout the 2000s with specific attention to tax regimes, employment, household income, technology, and transportation hubs. This analysis provides useful inputs for financial risk management and public policy initiatives aimed at addressing regional economic sustainability across state boundaries, while also developing the groundwork for further research on a spatial analysis of the global financial crisis.

Keywords: financial clusters, New York, global financial crisis, geographically weighted regression

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Authors: Venkata Venugopal Rao Gudlur

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The Proposed Policies referred to as “SOP”, on the Internet of Things (IoT) based Forensic Investigation into Process Management is the latest revolution to save time and quick solution for investigators. The forensic investigation process has been developed over many years from time to time it has been given the required information with no policies in investigation processes. This research reveals that the current IoT based forensic investigation into Process Management based is more connected to devices which is the latest revolution and policies. All future development in real-time information on gathering monitoring is evolved with smart sensor-based technologies connected directly to IoT. This paper present conceptual framework on process management. The smart devices are leading the way in terms of automated forensic models and frameworks established by different scholars. These models and frameworks were mostly focused on offering a roadmap for performing forensic operations with no policies in place. These initiatives would bring a tremendous benefit to process management and IoT forensic investigators proposing policies. The forensic investigation process may enhance more security and reduced data losses and vulnerabilities.

Keywords: Internet of Things, Process Management, Forensic Investigation, M2M Framework

Procedia PDF Downloads 87

Authors: Siti Zabedah Saidin, Aidi Ahmi, Azharudin Ali, Wan Norhayati Wan Ahmad

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In today's increasingly complex and interconnected world, the concept of sustainability has transcended mere corporate social responsibility, evolving into a fundamental driver of organizational behaviour and disclosure. This content analysis study delves into the Malaysian federal-level statutory bodies’ annual report for the year 2021, aiming to elucidate the extent of sustainability disclosures within the non-financial sections of these reports. The escalating global emphasis on sustainability has prompted organizations to embrace transparency as a means to demonstrate their commitment to environmental, social, and governance (ESG) considerations. Voluntary sustainability disclosure has emerged as a crucial channel through which organizations communicate their efforts, initiatives, and impacts in these areas, thereby fostering trust and accountability with stakeholders. The study aims to identify and examine the types of sustainability information disclosed voluntarily by the federal-level statutory bodies, concentrating on the non-financial sections of the annual reports. To achieve this, the study adopts a simplified disclosure index, a pragmatic tool that quantifies the extent of sustainability reporting in a standardized manner. Using convenience sampling, the study selects a sample of annual reports from the federal-level statutory bodies in Malaysia, as provided on their respective websites. The content analysis is centred on the non-financial sections of these reports, allowing for an in-depth exploration of sustainability disclosures. The findings of the study present the extent to which Malaysian federal-level statutory bodies embrace sustainability reporting. Through thorough content analysis, the study uncovered diverse dimensions of sustainability information, encompassing environmental impact assessments, social engagement endeavours, and governance frameworks. This reveals a deliberate effort by these bodies to encapsulate their holistic organizational contributions and challenges, transcending traditional financial metrics. This research contributes to the existing literature by providing insights into the evolving landscape of sustainability disclosure practices among Malaysian federal-level statutory bodies. The findings underline the proactive nature of these bodies in voluntarily sharing sustainability-related information, reflecting their recognition of the interconnectedness between organizational success and societal well-being. Furthermore, the study underscores the potential influence of regulatory guidelines and societal expectations in shaping the extent and nature of voluntary sustainability disclosures. Organizations are not merely responding to regulatory mandates but are actively aligning with global sustainability goals and stakeholder expectations. As organizations continue to navigate the intricate web of stakeholder expectations and sustainability imperatives, this study enriches the discourse surrounding transparency and sustainability reporting. The analysis emphasizes the important role of non-financial disclosures in portraying a holistic organizational narrative. In an era where stakeholders demand accountability, and the interconnectedness of global challenges necessitates collaborative action, the voluntary disclosure of sustainability information stands as a testament to the commitment of Malaysian federal-level statutory bodies in shaping a more sustainable future.

Keywords: voluntary disclosure, sustainability information, annual report, federal-level statutory body

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Authors: Javad Khazaei, Rick Blum

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Distributed control is an efficient and flexible approach for coordination of multi-agent systems. One of the main challenges in designing a distributed controller is identifying the governing dynamics of the dynamical systems. Data-driven system identification is currently undergoing a revolution. With the availability of high-fidelity measurements and historical data, model-free identification of dynamical systems can facilitate the control design without tedious modeling of high-dimensional and/or nonlinear systems. This paper develops a distributed control design using consensus theory for linear and nonlinear dynamical systems using sparse identification of system dynamics. Compared with existing consensus designs that heavily rely on knowing the detailed system dynamics, the proposed model-free design can accurately capture the dynamics of the system with available measurements and input data and provide guaranteed performance in consensus and tracking problems. Heterogeneous damped oscillators are chosen as examples of dynamical system for validation purposes.

Keywords: consensus tracking, distributed control, model-free control, sparse identification of dynamical systems

Procedia PDF Downloads 247

Authors: Eddegdag Nasser, Naamane Azzeddine, Radouani Mohammed, Ensam Meknes

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In this study, we are interested in the asymptotic modeling of the two-dimensional stationary supersonic flow of a viscous compressible fluid around wing airfoil. The aim of this article is to solve the partial differential equations of the flow far from the leading edge and near the wall using the triple-deck technique is what brought again in precision according to the principle of least degeneration. In order to validate our theoretical model, these obtained results will be compared with the experimental results. The comparison of the results of our model with experimentation has shown that they are quantitatively acceptable compared to the obtained experimental results. The experimental study was conducted using the AF300 supersonic wind tunnel and a NACA Reduced airfoil model with two pressure Taps on extrados. In this experiment, we have considered the incident upstream supersonic Mach number over a dissymmetric NACA airfoil wing. The validation and the accuracy of the results support our model.

Keywords: supersonic, viscous, triple deck technique, asymptotic methods, AF300 supersonic wind tunnel, reduced airfoil model

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Authors: Zahid Ullah, Atlas Khan

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The presented research is dedicated to an extensive examination of the regularity properties associated with a specific class of equations, namely extremely degenerate elliptic equations. This study holds significance in unraveling the complexities inherent in these equations and understanding the smoothness of their solutions. The focus is on analyzing the regularity of results, aiming to contribute to the broader field of mathematical theory. By delving into the intricacies of extremely degenerate elliptic equations, the research seeks to advance our understanding beyond conventional analyses, addressing challenges posed by degeneracy and pushing the boundaries of classical analytical methods. The motivation for this exploration lies in the practical applicability of mathematical models, particularly in real-world scenarios where physical phenomena exhibit characteristics that challenge traditional mathematical modeling. The research aspires to fill gaps in the current understanding of regularity properties within solutions to extremely degenerate elliptic equations, ultimately contributing to both theoretical foundations and practical applications in diverse scientific fields.

Keywords: investigating smoothness, extremely degenerate elliptic equations, regularity properties, mathematical analysis, complexity solutions

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Authors: Şebnem Aslan, Demet Akarçay

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The objective of this study is to investigate the feelings of health employees occurred by bullying and the relationships between these feelings at work place. In this context, the relationships between bullying and the feelings of self-esteem, employee silence, anger, self- blame and shame. This study was conducted among 512 health employees in three hospitals in Konya by using survey method and simple random sampling. The scales of bullying, self-esteem, employee silence, anger, self-blame, and shame were performed within the study. The obtained data were analyzed with descriptive analysis, correlation, confirmative factor analysis, structural equation modeling and path analysis. The results of the study showed that while bullying had a positive effect on self-esteem (.61), employee silence (.41), anger (.18), a negative effect on self-blame and shame (-.26) was observed. Employee silence affected self-blame and shame (.83) as positively. Besides, self-esteem impacted on self- blame and shame (.18), employee silence (.62) positively and self-blame and shame was observed as negatively affecting on anger (-.20). Similarly, self-esteem was found as negatively affected on anger (-.13).

Keywords: bullying, self-esteem, employee silence, anger, shame and guilt, healthcare employee

Procedia PDF Downloads 286

Authors: Youngji Yoo, Seung Hwan Park, Daewoong An, Sung-Shick Kim, Jun-Geol Baek

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The yield management system is very important to produce high-quality semiconductor chips in the semiconductor manufacturing process. In order to improve quality of semiconductors, various tests are conducted in the post fabrication (FAB) process. During the test process, large amount of data are collected and the data includes a lot of information about defect. In general, the defect on the wafer is the main causes of yield loss. Therefore, analyzing the defect data is necessary to improve performance of yield prediction. The wafer bin map (WBM) is one of the data collected in the test process and includes defect information such as the fail bit patterns. The fail bit has characteristics of spatial point patterns. Therefore, this paper proposes the feature extraction method using the spatial point pattern analysis. Actual data obtained from the semiconductor process is used for experiments and the experimental result shows that the proposed method is more accurately recognize the fail bit patterns.

Keywords: semiconductor, wafer bin map, feature extraction, spatial point patterns, contour map

Procedia PDF Downloads 371

Authors: Hasan Keshavarzian, Tayebeh Nesari

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Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

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In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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Authors: I. F. Ejim, F. L. Kamen

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Aims: In this experiment, algae oil extraction with a combination of n-hexane and ethanol was investigated. The effects of extraction solvent concentration, extraction time and temperature on the yield and quality of oil were studied using Response Surface Methodology (RSM). Experimental Design: Optimization of algae oil extraction using Box-Behnken design was used to generate 17 experimental runs in a three-factor-three-level design where oil yield, specific gravity, acid value and saponification value were evaluated as the response. Result: In this result, a minimum oil yield of 17% and maximum of 44% was realized. The optimum values for yield, specific gravity, acid value and saponification value from the overlay plot were 40.79%, 0.8788, 0.5056 mg KOH/g and 180.78 mg KOH/g respectively with desirability of 0.801. The maximum point prediction was yield 40.79% at solvent concentration 66.68 n-hexane, temperature of 40.0°C and extraction time of 4 hrs. Analysis of Variance (ANOVA) results showed that the linear and quadratic coefficient were all significant at p<0.05. The experiment was validated and results obtained were with the predicted values. Conclusion: Algae oil extraction was successfully optimized using RSM and its quality indicated it is suitable for many industrial uses.

Keywords: algae oil, response surface methodology, optimization, Box-Bohnken, extraction

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Chukwukelue Uzodinma Umenyilorah

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The advent of the New Media as enabled by information and communication technology from the 19th through the 21st century has no doubt taken its toll on all fronts of human existence; especially in Africa. Apart from shortening the distance between all parts of the world, technology and the new media has also succeeded in making the world a global village. Hence, it is now easy to relay live audio and visual signals across the length and breadth of the world in real time. People now contract and execute businesses across countries, conferences are held and ideas are shared with a simple push of a button. Likewise, political leaders and diplomats are now just a click away from reaching those important decisions that take their country’s fortunes to the next level. On the flip side, ICT and the New Media have also contributed in no small measure in aiding global terrorism and general insecurity around the world. More interesting is the fact that as developing economies, African countries have massively embraced the information technology and this has helped them in keeping up with the trends in the polity of other model democracies around the world. This paper is therefore designed to determine the how much effect ICT and the New Media has exerted on the economic, social and political lives of African. Nigeria shall be used as a case in point for the purpose of this paper.

Keywords: Africa, ICT, new media, Nigeria

Procedia PDF Downloads 241

Authors: Nan Ding, Motoki Shino, Nobuyasu Tomokuni, Genki Murata

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The wheelchair is the major means of transport for physically disabled people. However, it cannot overcome architectural barriers such as curbs and stairs. In this paper, the authors proposed a method to avoid falling down of a wheeled inverted pendulum type robotic wheelchair for climbing stairs. The problem of this system is that the feedback gain of the wheels cannot be set high due to modeling errors and gear backlash, which results in the movement of wheels. Therefore, the wheels slide down the stairs or collide with the side of the stairs, and finally the wheelchair falls down. To avoid falling down, the authors proposed a slider control strategy based on skyhook model in order to decrease the movement of wheels, and a rotary link control strategy based on the staircase dimensions in order to avoid collision or slide down. The effectiveness of the proposed fall avoidance control strategy was validated by ODE simulations and the prototype wheelchair.

Keywords: EPW, fall avoidance control, skyhook, wheeled inverted pendulum

Procedia PDF Downloads 319

Authors: Yoonsuh Jung

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As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an "optimal" value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: cross validation, parameter averaging, parameter selection, regularization parameter search

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Authors: A. M. Taleb, A. G. Tail, A. F. Kara, B. B. Djedouani, C. T. Moussa

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Forensic entomology is the use of insects to aid legal investigations. The main purpose of forensic entomology is to establish the postmortem interval (PMI). In order to estimate the PMI, a forensic entomologist compares the case data with certain reference information relevant to the particular location and time of year. This reference information, including the local distribution of species, are not available in Algeria. Therefore, experiments need to be conducted to provide references for entomological evidence. The objective of this study was to identify the necrophagous flies species which arrive first to carrion using liver baits in Ghardaia, South Algeria. The study was carried out during the spring season in the palmeral of Beni Isguen, Ghardaia which is well known by its hot arid climate. The experiment site (32°28’0’’ N, 3°42’0’’ E), is situated at an altitude of about 526 metres above mean sea level. On April the 4th, 2014, a number of three replicates of liver baited traps were placed in the shade and other three baits were exposed to the sun. Flying insects and larvae were captured and identified. After few minutes, flies invaded the traps which were exposed to the sun. In contrast, no flies were observed in the other traps. A total number of fourty five (45) adult specimens belonging to three taxa were identified: Calliphora vicina (Robineau-Desvoidy, 1830) (Diptera, Calliphoridae) (51.11 %), Lucilia sericata (Meigen, 1826) (Diptera, Calliphoridae) (33.33 %) and Sarcophaga africa (Wiedemann, 1824) (Diptera: Sarcophagidae) (15.55 %). Six hundred and three (603) maggots belonging to two taxa were identified: Calliphora vicina (76.28 %) and Lucilia sericata (23.71 %). The data obtained from this study provides baseline information regarding the carrion fauna of this area. It will also form a basis for similar studies in different geographical and climatological regions of Algeria.

Keywords: forensic entomology, liver baits, necrophagous fly, Ghardaia, South Algeria

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Authors: Zhongyang Sun, Shu Zhang

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In osteoblasts, L-type voltage sensitive calcium channels (LTCCs), especially the Cav1.2 LTCCs, play fundamental roles in cellular responses to external stimuli including both mechanical forces and hormonal signals. Several lines of evidence have revealed that the density of bone is increased and the resorption of bone is decreased when these calcium channels in osteoblasts are activated. And numerous studies have shown that mechanical loading promotes bone formation in the modeling skeleton, whereas removal of this stimulus in microgravity results in a reduction in bone mass. However, the effect of microgravity on LTCCs in osteoblasts is still unknown. The aim of this study was to determine whether microgravity exerts influence on LTCCs in osteoblasts and the possible mechanisms underlying. In this study, we demonstrate that simulated microgravity substantially inhibits LTCCs in osteoblast by suppressing the expression of Cav1.2. Then we show that the up-regulation of miR-103 is involved in the down-regulation of Cav1.2 expression and inhibition of LTCCs by simulated microgravity in osteoblasts. Our study provides a novel mechanism of simulated microgravity-induced adverse effects on osteoblasts, offering a new avenue to further investigate the bone loss caused by microgravity.

Keywords: L-type voltage sensitive calcium channels, Cav1.2, osteoblasts, microgravity

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Authors: Myoung Je Song

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Ureteral stent insertion is being used as one of the clinical interventional treatments due to stenosis and/or obstruction in the ureter. For the development of the ureteral stents, we have to know the flow patterns with and without peristalsis in the ureter. The purpose of this study is to understand the flow characteristics and movement of the ureter for the ureter model according to the presence or absence of peristalsis and to use it as fundamental information to design the optimal ureteral stent. In this study, CFD (Computational Fluid Dynamics) and FSI (Fluid-Structure Interaction) approaches were applied and compared the flow characteristics in the ureter. The distribution of streamlines was different in the near ureteropelvic junction. As a result of analyzing the area change of the ureter, the area change was large at the frontal and posterior ends, and the frontal and posterior aspects of the area change were reversed. There was no significant difference in the flow rate at the ureter outlet, and the movement of the ureter was larger when peristalsis was considered. Finally, as an introductory stage for the development of ureteral stents, basic information about the ureters according to the presence or absence of peristalsis is acquired.

Keywords: computational fluid dynamics, fluid-structure interaction, peristalsis, urine flow

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: M. Lu

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The paper presents part of the results of EU-funded projects: SoCool@EU (Sustainable Organisation between Clusters Of Optimized Logistics @ Europe), DG-RTD (Research and Innovation), Regions of Knowledge Programme (FP7-REGIONS-2011-1). It will provide an in-depth review of emerging technologies for further improving urban mobility and freight transport systems, such as (information and physical) infrastructure, ICT-based Intelligent Transport Systems (ITS), vehicles, advanced logistics, and services. Furthermore, the paper will provide an analysis of the barriers and will review business models for the market uptake of innovations. From a perspective of science and technology, the challenges of urbanization could be mainly handled through adequate (human-oriented) solutions for urban planning, sustainable energy, the water system, building design and construction, the urban transport system (both physical and information aspects), and advanced logistics and services. Implementation of solutions for these domains should be follow a highly integrated and balanced approach, a silo approach should be avoided. To develop a sustainable urban transport system (for people and goods), including inter-hubs and intra-hubs, a holistic view is needed. To achieve a sustainable transport system for people and goods (in terms of cost-effectiveness, efficiency, environment-friendliness and fulfillment of the mobility, transport and logistics needs of the society), a proper network and information infrastructure, advanced transport systems and operations, as well as ad hoc and seamless services are required. In addition, a road map for an enhanced urban transport system until 2050 will be presented. This road map aims to address the challenges of urban transport, and to provide best practices in inter-city and intra-city environments from various perspectives, including policy, traveler behaviour, economy, liability, business models, and technology.

Keywords: synchromodality, multimodal transport, logistics, Intelligent Transport Systems (ITS)

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

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Authors: Ki Hyun Park, Hye Min Park, Jeong Ho Kim, Chan Jong Park, Koan Sik Joo

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In the modern days, the use of radioactive substances is on the rise in the areas like chemical weaponry, industrial usage, and power plants. Although there are various technologies available to detect and monitor radioactive substances in the air, the technologies to detect underwater radioactive substances are scarce. In this study, computer simulation of the underwater detection system measuring beta-ray, a radioactive substance, has been done through MCNP. CaF₂, YAP(Ce) and YAG(Ce) have been used in the computer simulation to detect beta-ray as scintillator. Also, the source used in the computer simulation is Sr-90 and Y-90, both of them emitting only pure beta-ray. The distance between the source and the detector was shifted from 1mm to 10mm by 1 mm in the computer simulation. The result indicated that Sr-90 was impossible to measure below 1 mm since its emission energy is low while Y-90 was able to be measured up to 10mm underwater. In addition, the detector designed with CaF₂ had the highest efficiency among 3 scintillators used in the computer simulation. Since it was possible to verify the detectable range and the detection efficiency according to modeling through MCNP simulation, it is expected that such result will reduce the time and cost in building the actual beta-ray detector and evaluating its performances, thereby contributing the research and development.

Keywords: Beta-ray, CaF₂, detector, MCNP simulation, scintillator

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Authors: I. O. Nascimento, J. T. Manzi

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The drying process is an important operation in the chemical industry and it is widely used in the food, grain industry and fertilizer industry. However, for demanding a considerable consumption of energy, such a process requires a deep energetic analysis in order to reduce operating costs. This paper deals with thermodynamic optimization applied to rotary dryers based on the entropy production minimization, aiming at to reduce the energy consumption. To do this, the mass, energy and entropy balance was used for developing a relationship that represents the rate of entropy production. The use of the Second Law of Thermodynamics is essential because it takes into account constraints of nature. Since the entropy production rate is minimized, optimals conditions of operations can be established and the process can obtain a substantial gain in energy saving. The minimization strategy had been led using classical methods such as Lagrange multipliers and implemented in the MATLAB platform. As expected, the preliminary results reveal a significant energy saving by the application of the optimal parameters found by the procedure of the entropy minimization It is important to say that this method has shown easy implementation and low cost.

Keywords: thermodynamic optimization, drying, entropy minimization, modeling dryers

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Authors: Jean Marie Tshimula, Atsushi Togashi

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We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

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Authors: Hussam Alrabaiah

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Some new projects (new proposed harbor, recreational projects) are considered in the eastern coasts of Dammam city, Saudi Arabia. Dredging operations would significantly alter coast hydrological and sediment transport processes. It is important that the project areas must keep flushing the fresh sea water in and out with good water quality parameters, which are currently facing increased pressure from urbanization and navigation requirements in conjunction with industrial developments. A suspended solids or sediments are expected to affect the flora and fauna in that area. Governing advection-diffusion equations are considered to understand the consequences of such projects. A numerical modeling study is developed to study the effect of dredging and, in particular, the suspended sediments concentrations (mg/L) changed in the region. The results were obtained using finite element method using an in-house or commercial software. Results show some consistency with data observed in that region. Recommendations based on results could be formulated for decision makers to protect the environment in the long term.

Keywords: finite element, method, suspended solids transport, advection-diffusion

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Authors: Li Yan Hei, Au Wing Tung

Abstract:

Knowledge sharing is a process in which individuals mutually exchange existing knowledge and co-create new knowledge. Previous research has confirmed that trust is positively associated with knowledge sharing. However, only few studies systematically examined the antecedents of trust and these antecedents’ impacts on knowledge sharing. In order to explore and understand the relationships between trust and knowledge sharing in depth, this study proposed a relationship maintenance-based model to examine the antecedents of trust in knowledge sharing in project teams. Three critical elements within a project team were measured, including the environment, project team partner and interaction. It was hypothesized that the trust would lead to knowledge sharing and in turn result in perceived good team performance. With a sample of 200 Hong Kong employees, the proposed model was evaluated with structural equation modeling. Expected findings are trust will contribute to knowledge sharing, resulting in better team performance. The results will also offer insights into antecedents of trust that play a heavy role in the focal relationship. The present study contributes to a more holistic understanding of relationship between trust and knowledge sharing by linking the antecedents and outcomes. The findings will raise the awareness of project managers on ways to promote knowledge sharing.

Keywords: knowledge sharing, project management, team, trust

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

Abstract:

Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Faezeh Shalchy

Abstract:

Concrete is the most used materials in the world. It is also one of the most versatile while complex materials which human have used for construction. However, concrete is weak in tension, over the past thirty years many studies were accomplished to improve the tensile properties of concrete (cement-based materials) using a variety of methods. One of the most successful attempts is to use polymeric fibers in the structure of concrete to obtain a composite with high tensile strength and ductility. Understanding the mechanical behavior of fiber reinforced concrete requires the knowledge of the fiber/matrix interfaces at the small scale. In this study, a combination of numerical simulations and experimental techniques have been used to study the nano structure of fiber/matrix interfaces. A new model for calcium-silicate-hydrate (C-S-H)/fiber interfaces is proposed based on Scanning Electron Microscopy (SEM) and Energy-dispersive X-ray spectroscopy (EDX) analysis. The adhesion energy between the C-S-H gel and 2 different polymeric fibers (polyvinyl alcohol and polypropylene) was numerically studied at the atomistic level since adhesion is one of the key factors in the design of fiber reinforced composites. The mechanisms of adhesion as a function of the nano structure of fiber/matrix interfaces are also studied and discussed.

Keywords: fiber-reinforced concrete, adhesion, molecular modeling

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