Abstract: Refugee Job Board Website is a web-based application that provides a platform for organizations to post jobs specifically for refugees. Organizations upload job opportunities and refugees can view them on the website. The website also allows refugees to input their skills and qualifications. The methodology used to develop this system is a waterfall (traditional) methodology. Software development tools include Brackets which will be used to code the website and PhpMyAdmin to store all the data in a database.
Abstract: Dynamic Voltage and Frequency Scaling (DVFS)
multicore platforms are promising execution platforms that enable
high computational performance, less energy consumption and
flexibility in scheduling the system processes. However, the
resulting interleaving and memory interference together with per-core
frequency tuning make real-time guarantees hard to be delivered.
Besides, energy consumption represents a strong constraint for the
deployment of such systems on energy-limited settings. Identifying
the system configurations that would achieve a high performance and
consume less energy while guaranteeing the system schedulability is
a complex task in the design of modern embedded systems. This work
studies the trade-off between energy consumption, cores utilization
and memory bottleneck and their impact on the schedulability of
DVFS multicore time-critical systems with a hierarchy of shared
memories. We build a model-based framework using Parametrized
Timed Automata of UPPAAL to analyze the mutual impact of
performance, energy consumption and schedulability of DVFS
multicore systems, and demonstrate the trade-off on an actual case
study.
Abstract: The generalization of relativistic theory of gravity based essentially on the principle of equivalence stipulates that for all bodies, the grave mass is equal to the inert mass which leads us to believe that gravitation is not a property of the bodies themselves, but of space, and the conclusion that the gravitational field must curved space-time what allows the abandonment of Minkowski space (because Minkowski space-time being nonetheless null curvature) to adopt Riemannian geometry as a mathematical framework in order to determine the curvature. Therefore the work presented in this paper begins with the evolution of the concept of gravity then tensor field which manifests by Riemannian geometry to formulate the general equation of the gravitational field.
Abstract: The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.
Abstract: In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.
Abstract: The goal of option pricing theory is to help the investors
to manage their money, enhance returns and control their financial
future by theoretically valuing their options. However, most of the
option pricing models have no analytical solution. Furthermore,
not all the numerical methods are efficient to solve these models
because they have nonsmoothing payoffs or discontinuous derivatives
at the exercise price. In this paper, we solve the American option
under jump diffusion models by using efficient time-dependent
numerical methods. several techniques are integrated to reduced
the overcome the computational complexity. Fast Fourier Transform
(FFT) algorithm is used as a matrix-vector multiplication solver,
which reduces the complexity from O(M2) into O(M logM).
Partial fraction decomposition technique is applied to rational
approximation schemes to overcome the complexity of inverting
polynomial of matrices. The proposed method is easy to implement
on serial or parallel versions. Numerical results are presented to prove
the accuracy and efficiency of the proposed method.
Abstract: The goal of option pricing theory is to help the
investors to manage their money, enhance returns and control their
financial future by theoretically valuing their options. Modeling
option pricing by Black-School models with jumps guarantees to
consider the market movement. However, only numerical methods
can solve this model. Furthermore, not all the numerical methods
are efficient to solve these models because they have nonsmoothing
payoffs or discontinuous derivatives at the exercise price. In this
paper, the exponential time differencing (ETD) method is applied
for solving partial integrodifferential equations arising in pricing
European options under Merton’s and Kou’s jump-diffusion models.
Fast Fourier Transform (FFT) algorithm is used as a matrix-vector
multiplication solver, which reduces the complexity from O(M2)
into O(M logM). A partial fraction form of Pad`e schemes is used
to overcome the complexity of inverting polynomial of matrices.
These two tools guarantee to get efficient and accurate numerical
solutions. We construct a parallel and easy to implement a version
of the numerical scheme. Numerical experiments are given to show
how fast and accurate is our scheme.
Abstract: Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.
Abstract: Today, a consistent segment of the world’s population lives in urban areas, and this proportion will vastly increase in the next decades. Therefore, understanding the key trends in urbanization, likely to unfold over the coming years, is crucial to the implementation of sustainable urban strategies. In parallel, the daily amount of digital data produced will be expanding at an exponential rate during the following years. The analysis of various types of data sets and its derived applications have incredible potential across different crucial sectors such as healthcare, housing, transportation, energy, and education. Nevertheless, in city development, architects and urban planners appear to rely mostly on traditional and analogical techniques of data collection. This paper investigates the prospective of the data science field, appearing to be a formidable resource to assist city managers in identifying strategies to enhance the social, economic, and environmental sustainability of our urban areas. The collection of different new layers of information would definitely enhance planners' capabilities to comprehend more in-depth urban phenomena such as gentrification, land use definition, mobility, or critical infrastructural issues. Specifically, the research results correlate economic, commercial, demographic, and housing data with the purpose of defining the youth economic discomfort index. The statistical composite index provides insights regarding the economic disadvantage of citizens aged between 18 years and 29 years, and results clearly display that central urban zones and more disadvantaged than peripheral ones. The experimental set up selected the city of Rome as the testing ground of the whole investigation. The methodology aims at applying statistical and spatial analysis to construct a composite index supporting informed data-driven decisions for urban planning.
Abstract: Trends in education affect schooling, needing incorporation into design concepts to support desired learning processes with appropriate and stimulating environments. A design process for school architecture demands research, debates, reflections, and efficient decision-making methods. This paper presents research on evidence-based design, related to middle schools, based on a systematic literature review and the elaboration of a set of architectural design patterns, through a graphic translation of new concepts for classroom configurations, to support programming debates and the synthesis phase of design. The investigation resulted in nine patterns that configure the concepts of boundaries, flexibility, levels of openness, mindsets, neighborhoods, movement and interaction, territories, opportunities for learning, and sightlines for classrooms. The research is part of a continuous investigation of design methods, on contemporary school architecture to produce an architectural pattern matrix based on scientific information translated into an insightful graphic design language.
Abstract: In general, Covid-19 created many financial and non-financial damages to the economy and community. Level and severity of covid-19 as pandemic case varies over the region and due to different types of the projects. Covid-19 virus emerged as one of the most imperative risk management factors word-wide recently. Therefore, as part of portfolio management assessment, it is essential to evaluate severity of such risk on the project and program in portfolio management level to avoid any risky portfolio. Covid-19 appeared very effectively in South America, part of Europe and Middle East. Such pandemic infection affected the whole universe, due to lock down, interruption in supply chain management, health and safety requirements, transportations and commercial impacts. Therefore, this research proposes Analytical Hierarchy Process (AHP) to analyze and assess such pandemic case like Covid-19 and its impacts on the construction projects. The AHP technique uses four sub-criteria: Health and safety, commercial risk, completion risk and contractual risk to evaluate the project and program. The result will provide the decision makers with information which project has higher or lower risk in case of Covid-19 and pandemic scenario. Therefore, the decision makers can have most feasible solution based on effective weighted criteria for project selection within their portfolio to match with the organization’s strategies.
Abstract: Steel Metal Shear Wall is one of the most common and widely used energy dissipation systems in structures, which is used today as a damping system due to the increase in the construction of metal structures. In the present study, the shear wall of the steel plate with dimensions of 5×3 m and thickness of 0.024 m was modeled with 2 floors of total height from the base level with finite element method in Abaqus software. The loading is done as a concentrated load at the upper point of the shear wall on the second floor based on step type buckle. The mesh in the model is applied in two directions of length and width of the shear wall, equal to 0.02 and 0.033, respectively, and the mesh in the models is of sweep type. Finally, it was found that the steel plate shear wall with cavity (CSPSW) compared to the SPSW model, S (Mises), Smax (In-Plane Principal), Smax (In-Plane Principal-ABS), Smax (Min Principal) increased by 53%, 70%, 68% and 43%, respectively. The presence of cavities has led to an increase in the estimated stresses, but their presence has caused critical stresses and critical deformations created to be removed from the inner surface of the shear wall and transferred to the desired sections (regular cavities) which can be suggested as a solution in seismic design and improvement of the structure to transfer possible damage during the earthquake and storm to the desired and pre-designed location in the structure.
Abstract: This work shows the methodology of application and the effectiveness of the Flipped Learning technique for Civil Engineering laboratory classes. It was experimented by some of the professors of the Department of Civil Engineering at Tecnológico de Monterrey while teaching their laboratory classes. A total of 28 videos were created. The videos primarily demonstrate instructions of the experimental practices other than the usage of tools and materials. The technique allowed the students to prepare for their classes in advance. A survey was conducted on the participating professors and students (semester of August-December 2019) to quantify the effectiveness of the Flipped Learning technique. The students reported it as an excellent way of improving their learning aptitude, including self-learning whereas, the professors felt it as an efficient technique for optimizing their class session, which also provided an extra slot for class-interaction. A comparison of grades was analyzed between the students of the traditional classes and with Flipped Learning. It did not distinguish the benefits of Flipped Learning. However, the positive responses from the students and the professors provide an impetus for continuing and promoting the Flipped Learning technique in future classes.
Abstract: Nowadays, Corporate Social responsibility (CSR) is becoming a buzz word, and more and more academics are putting efforts on CSR studies. It is believed that CSR could influence Corporate Reputation (CR), and they hold a favourable view that CSR leads to a positive CR. To be specific, the CSR related activities in the reputational context have been regarded as ways that associate to excellent financial performance, value creation, etc. Also, it is argued that CSR and CR are two sides of one coin; hence, to some extent, doing CSR is equal to establishing a good reputation. Still, there is no consensus of the CSR-CR relationship in the literature; thus, a systematic literature review is highly in need. This research conducts a systematic literature review with both bibliometric and content analysis. Data are selected from English language sources, and academic journal articles only, then, keyword combinations are applied to identify relevant sources. Data from Scopus and WoS are gathered for bibliometric analysis. Scopus search results were saved in RIS and CSV formats, and Web of Science (WoS) data were saved in TXT format and CSV formats in order to process data in the Bibexcel software for further analysis which later will be visualised by the software VOSviewer. Also, content analysis was applied to analyse the data clusters and the key articles. In terms of the topic of CSR-CR, this literature review with bibliometric analysis has made four achievements. First, this paper has developed a systematic study which quantitatively depicts the knowledge structure of CSR and CR by identifying terms closely related to CSR-CR (such as ‘corporate governance’) and clustering subtopics emerged in co-citation analysis. Second, content analysis is performed to acquire insight on the findings of bibliometric analysis in the discussion section. And it highlights some insightful implications for the future research agenda, for example, a psychological link between CSR-CR is identified from the result; also, emerging economies and qualitative research methods are new elements emerged in the CSR-CR big picture. Third, a multidisciplinary perspective presents through the whole bibliometric analysis mapping and co-word and co-citation analysis; hence, this work builds a structure of interdisciplinary perspective which potentially leads to an integrated conceptual framework in the future. Finally, Scopus and WoS are compared and contrasted in this paper; as a result, Scopus which has more depth and comprehensive data is suggested as a tool for future bibliometric analysis studies. Overall, this paper has fulfilled its initial purposes and contributed to the literature. To the author’s best knowledge, this paper conducted the first literature review of CSR-CR researches that applied both bibliometric analysis and content analysis; therefore, this paper achieves its methodological originality. And this dual approach brings advantages of carrying out a comprehensive and semantic exploration in the area of CSR-CR in a scientific and realistic method. Admittedly, its work might exist subjective bias in terms of search terms selection and paper selection; hence triangulation could reduce the subjective bias to some degree.
Abstract: The study and application of composite materials are a truly interdisciplinary endeavor that has been enriched by contributions from chemistry, physics, materials science, mechanics and manufacturing engineering. The understanding of the interface (or interphase) in composites is the central point of this interdisciplinary effort. From the early development of composite materials of various nature, the optimization of the interface has been of major importance. Even more important, the ideas linking the properties of composites to the interface structure are still emerging. In our study, we need a direct characterization of the interface; the micromechanical tests we are addressing seem to meet this objective and we chose to use two complementary tests simultaneously. The microindentation test that can be applied to real composites and the drop test, preferred to the pull-out because of the theoretical possibility of studying systems with high adhesion (which is a priori the case with our systems). These two tests are complementary because of the principle of the model specimen used for both the first "compression indentation" and the second whose fiber is subjected to tensile stress called the drop test. Comparing the results obtained by the two methods can therefore be rewarding.
Abstract: Noise estimation is essential in today wireless systems
for power control, adaptive modulation, interference suppression and
quality of service. Deep learning (DL) has already been applied in the
physical layer for modulation and signal classifications. Unacceptably
low accuracy of less than 50% is found to undermine traditional
application of DL classification for SNR prediction. In this paper,
we use divide-and-conquer algorithm and classifier fusion method
to simplify SNR classification and therefore enhances DL learning
and prediction. Specifically, multiple CNNs are used for classification
rather than a single CNN. Each CNN performs a binary classification
of a single SNR with two labels: less than, greater than or equal.
Together, multiple CNNs are combined to effectively classify over a
range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained
CNNs to predict SNR over a wide range of joint channel parameters
including multiple Doppler shifts (0, 60, 120 Hz), power-delay
profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The
approach achieves individual SNR prediction accuracy of 92%,
composite accuracy of 70% and prediction convergence one order
of magnitude faster than that of traditional estimation.
Abstract: This study aims at determining the extent to which occupant control of microenvironment influences, improves thermal sensation and comfort, and saves energy in spaces equipped with ceiling personalized ventilation (CPV) system assisted by chair fans (CF) and desk fans (DF) in 2 experiments in a climatic chamber equipped with two-station CPV systems, one that allows control of fan flow rate and the other is set to the fan speed of the selected participant in control. Each experiment included two participants each entering the cooled space from transitional environment at a conventional mixed ventilation (MV) at 24 °C. For CPV diffuser, fresh air was delivered at a rate of 20 Cubic feet per minute (CFM) and a temperature of 16 °C while the recirculated air was delivered at the same temperature but at a flow rate 150 CFM. The macroclimate air of the space was at 26 °C. The full speed flow rates for both the CFs and DFs were at 5 CFM and 20 CFM, respectively. Occupant 1 was allowed to operate the CFs or the DFs at (1/3 of the full speed, 2/3 of the full speed, and the full speed) while occupant 2 had no control on the fan speed and their fan speed was selected by occupant 1. Furthermore, a parametric study was conducted to study the effect of increasing the fresh air flow rate on the occupants’ thermal comfort and whole body sensations. The results showed that most occupants in the CPV+CFs, who did not control the CF flow rate, felt comfortable 6 minutes. The participants, who controlled the CF speeds, felt comfortable in around 24 minutes because they were preoccupied with the CFs. For the DF speed control experiments, most participants who did not control the DFs felt comfortable within the first 8 minutes. Similarly to the CPV+CFs, the participants who controlled the DF flow rates felt comfortable at around 26 minutes. When the CPV system was either supported by CFs or DFs, 93% of participants in both cases reached thermal comfort. Participants in the parametric study felt more comfortable when the fresh air flow rate was low, and felt cold when as the flow rate increased.
Abstract: Cold-start is a notoriously difficult problem which
can occur in recommendation systems, and arises when there is
insufficient information to draw inferences for users or items. To
address this challenge, a contextual bandit algorithm – the Fast
Approximate Bayesian Contextual Cold Start Learning algorithm
(FAB-COST) – is proposed, which is designed to provide improved
accuracy compared to the traditionally used Laplace approximation
in the logistic contextual bandit, while controlling both algorithmic
complexity and computational cost. To this end, FAB-COST uses
a combination of two moment projection variational methods:
Expectation Propagation (EP), which performs well at the cold
start, but becomes slow as the amount of data increases; and
Assumed Density Filtering (ADF), which has slower growth of
computational cost with data size but requires more data to obtain an
acceptable level of accuracy. By switching from EP to ADF when
the dataset becomes large, it is able to exploit their complementary
strengths. The empirical justification for FAB-COST is presented, and
systematically compared to other approaches on simulated data. In a
benchmark against the Laplace approximation on real data consisting
of over 670, 000 impressions from autotrader.co.uk, FAB-COST
demonstrates at one point increase of over 16% in user clicks. On
the basis of these results, it is argued that FAB-COST is likely to
be an attractive approach to cold-start recommendation systems in a
variety of contexts.
Abstract: Here a methodology is considered aiming at evaluating the economic benefit of the provision of a primary frequency control unit using a Battery Energy Storage System (BESS). In this methodology, two control types (basic and hysteresis) are implemented and the corresponding minimum energy storage system power allowing to maintain the frequency drop inside a given threshold under a given contingency is identified and compared using DigSilent’s PowerFactory software. Following this step, the corresponding energy storage capacity (in MWh) is calculated. As PowerFactory is dedicated to dynamic simulation for transient analysis, a first order model related to the IEEE 9 bus grid used for the analysis under PowerFactory is characterized and implemented on MATLAB-Simulink. Primary frequency control is simulated using the two control types over one-month grid's frequency deviation data on this Simulink model. This simulation results in the energy throughput both basic and hysteresis BESSs. It emerges that the 15 minutes operation band of the battery capacity allocated to frequency control is sufficient under the considered disturbances. A sensitivity analysis on the width of the control deadband is then performed for the two control types. The deadband width variation leads to an identical sizing with the hysteresis control showing a better frequency control at the cost of a higher delivered throughput compared to the basic control. An economic analysis comparing the cost of the sized BESS to the potential revenues is then performed.
Abstract: Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.