Search results for: comprehensive model

Authors: Naser Kazemi Eilaki, Carolyn Ahmer, Ilona Heldal, Bjarne Christian Hagen

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Older people and people with disabilities are recognized as at-risk groups when it comes to egress and travel from hazard zone to safe places. One's disability can negatively influence her or his escape time, and this becomes even more important when people from this target group live alone. This research deals with the fire safety of mentioned people's buildings by means of probabilistic methods. For this purpose, fire safety is addressed by modeling the egress of our target group from a hazardous zone to a safe zone. A common type of detached house with a prevalent plan has been chosen for safety analysis, and a limit state function has been developed according to the time-line evacuation model, which is based on a two-zone and smoke development model. An analytical computer model (B-Risk) is used to consider smoke development. Since most of the involved parameters in the fire development model pose uncertainty, an appropriate probability distribution function has been considered for each one of the variables with indeterministic nature. To achieve safety and reliability for the at-risk groups, the fire safety index method has been chosen to define the probability of failure (causalities) and safety index (beta index). An improved harmony search meta-heuristic optimization algorithm has been used to define the beta index. Sensitivity analysis has been done to define the most important and effective parameters for the fire safety of the at-risk group. Results showed an area of openings and intervals to egress exits are more important in buildings, and the safety of people would improve with increasing dimensions of occupant space (building). Fire growth is more critical compared to other parameters in the home without a detector and fire distinguishing system, but in a home equipped with these facilities, it is less important. Type of disabilities has a great effect on the safety level of people who live in the same home layout, and people with visual impairment encounter more risk of capturing compared to visual and movement disabilities.

Keywords: fire safety, at-risk groups, zone model, egress time, uncertainty

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Authors: Khaled Ibrahim, Mathew Parackal, Damien Mather, Paul Hansen

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The integration between marketing and technology has given brands unprecedented opportunities to reach accurate customer data and competence to change customers' behaviour. Technology has generated a transformation within brands from traditional branding to algorithmic branding. However, brands have utilised customer data in non-cognitive programmatic targeting. This algorithmic persuasion may be effective in reaching the targeted audience. But it may encounter a moral conflict simultaneously, as it might not consider our social principles. Moral branding is a critical topic; particularly, with the increasing interest in commercial settings to teaching machines human morals, e.g., autonomous vehicles and chatbots; however, it is understudied in the marketing literature. Therefore, this paper aims to investigate the recent moral branding literature. Furthermore, applying human-like mind theory as initial framing to this paper explores a more comprehensive concept involving human morals, machine behaviour, and branding.

Keywords: brand machines, conceptual framework, moral branding, moral machines

Procedia PDF Downloads 166

Authors: G. M. Dominguez Almaraz, J. A. Ruiz Vilchez, M. A. Sanchez Miranda

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Ultrasonic fatigue tests have been carried out in the maraging 300 steel. Experimental results show that fatigue endurance under this modality of testing is closely related to the nature and geometrical properties of inclusions present in this alloy. A model was proposed to correlate the ultrasonic fatigue endurance with the nature and geometrical properties of the crack initiation inclusion. Scanning Electron Microscopy analyses were obtained on the fracture surfaces, in order to assess the crack initiation inclusion and to introduce these parameters in the proposed model, with good agreement for the fatigue life prediction.

Keywords: inclusions, ultrasonic fatigue, maraging 300 steel, crack initiation

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Authors: Michelle Siao-Cing Guo

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Technology is widely adopted to assist instruction and learning across disciplines. Traditional teaching method fails to capture the attention of the generation of digital native and does not accommodate diverse needs of today’s learners. The innovation in technology allows new pedagogical approaches. One approach that converts the traditional learning classroom to a more flexible learning time and space is known as the flipped classroom. This new pedagogy extends and enhances learning and accommodates different learning styles. The flipped classroom employs technology to offer course materials online 24 hours/day and to promote active class learning. However, will Taiwanese students who are used to more traditional instructional methods embrace the flipped classroom using E-learning? Will the flipped approach have an effect on Taiwanese students’ English mastery and learning autonomy? The researcher compares a flipped classroom model using E-learning and the traditional-lecture model. A pre- and post-test and a questionnaire were utilized to examine the effect of the flipped classroom on Taiwanese college students. The test results showed that the flipped approach had a positive effect on learners’ English proficiency level, topical knowledge, and willingness to participate in class. The questionnaire also demonstrates the acceptance of the new teaching model.

Keywords: flipped classroom , E-learning, innovative teaching, technology

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Authors: Gülay Dalgıç, Gildis Tachir

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Architectural design studios are ambiencein where architecture design is realized as a palpable product in architectural education. In the design studios that the architect candidate will use in the design processthe information, the methods of approaching the design problem, the solution proposals, etc., are set uptogetherwith the studio coordinators. The architectural design process, on the other hand, is complex and uncertain.Candidate architects work in a process that starts with abstre and ill-defined problems. This process starts with the generation of alternative solutions with the help of representation tools, continues with the selection of the appropriate/satisfactory solution from these alternatives, and then ends with the creation of an acceptable design/result product. In the studio ambience, many designs and thought relationships are evaluated, the most important step is the early design phase. In the early design phase, the first steps of converting the information are taken, and converted information is used in the constitution of the first design decisions. This phase, which positively affects the progress of the design process and constitution of the final product, is complex and fuzzy than the other phases of the design process. In this context, the aim of the study is to investigate the effects of face-to-face learning model and e-learning model on the early design phase. In the study, the early design phase was defined by literature research. The data of the defined early design phase criteria were obtained with the feedback graphics created for the architect candidates who performed e-learning in the first year of architectural education and continued their education with the face-to-face learning model. The findings of the data were analyzed with the common graphics program. It is thought that this research will contribute to the establishment of a contemporary architectural design education model by reflecting the evaluation of the data and results on architectural education.

Keywords: education modeling, architecture education, design education, design process

Procedia PDF Downloads 143

Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

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Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

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Authors: Randa Alharbi, Vladislav Vyshemirsky

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Systems biology is an important field in science which focuses on studying behaviour of biological systems. Modelling is required to produce detailed description of the elements of a biological system, their function, and their interactions. A well-designed model requires selecting a suitable mechanism which can capture the main features of the system, define the essential components of the system and represent an appropriate law that can define the interactions between its components. Complex biological systems exhibit stochastic behaviour. Thus, using probabilistic models are suitable to describe and analyse biological systems. Continuous-Time Markov Chain (CTMC) is one of the probabilistic models that describe the system as a set of discrete states with continuous time transitions between them. The system is then characterised by a set of probability distributions that describe the transition from one state to another at a given time. The evolution of these probabilities through time can be obtained by chemical master equation which is analytically intractable but it can be simulated. Uncertain parameters of such a model can be inferred using methods of Bayesian inference. Yet, inference in such a complex system is challenging as it requires the evaluation of the likelihood which is intractable in most cases. There are different statistical methods that allow simulating from the model despite intractability of the likelihood. Approximate Bayesian computation is a common approach for tackling inference which relies on simulation of the model to approximate the intractable likelihood. Particle Markov chain Monte Carlo (PMCMC) is another approach which is based on using sequential Monte Carlo to estimate intractable likelihood. However, both methods are computationally expensive. In this paper we discuss the efficiency and possible practical issues for each method, taking into account the computational time for these methods. We demonstrate likelihood-free inference by performing analysing a model of the Repressilator using both methods. Detailed investigation is performed to quantify the difference between these methods in terms of efficiency and computational cost.

Keywords: Approximate Bayesian computation(ABC), Continuous-Time Markov Chains, Sequential Monte Carlo, Particle Markov chain Monte Carlo (PMCMC)

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Authors: M. Mamor

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GaN-based compounds have attracted much interest in the fabrication of high-power, high speed and high-frequency electronic devices. Other examples of GaN-based applications are blue and ultraviolet (UV) light-emitting diodes (LEDs). All these devices require high-quality ohmic and Schottky contacts. Gaining an understanding of the electrical characteristics of metal/GaN contacts is of fundamental and technological importance for developing GaN-based devices. In this work, the barrier characteristics of Pt and Pd Schottky contacts on n-type GaN were studied using temperature-dependent forward current-voltage (I-V) measurements over a wide temperature range 80–400 K. Our results show that the barrier height and ideality factor, extracted from the forward I-V characteristics based on thermionic emission (TE) model, exhibit an abnormal dependence with temperature; i.e., by increasing temperature, the barrier height increases whereas the ideality factor decreases. This abnormal behavior has been explained based on the TE model by considering the presence of double Gaussian distribution (GD) of nonhomogeneous barrier height at the metal/GaN interface. However, in the high-temperature range (160-400 K), the extracted value for the effective Richardson constant A* based on the barrier inhomogeneity (BHi) model is found in fair agreement with the theoretically predicted value of about 26.9 A.cm-2 K-2 for n-type GaN. This result indicates that in this temperature range, the conduction current transport is dominated by the thermionic emission mode. On the other hand, in the lower temperature range (80-160 K), the corresponding effective Richardson constant value according to the BHi model is lower than the theoretical value, suggesting the presence of other current transport, such as tunneling-assisted mode at lower temperatures.

Keywords: Schottky diodes, inhomogeneous barrier height, GaN semiconductors, Schottky barrier heights

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Authors: Muazzam A. Khan, Muhammad Wasim

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Vehicular adhoc network (VANET) Cars for network (VANET) allowing vehicles to talk to each other, which is committed to building a strong network of mobile vehicles is technical. In VANETs vehicles are equipped with special devices that can get and share info with the atmosphere and other vehicles in the network. Depending on this data security and safety of the vehicles can be enhanced. Broadcast routing is dispersion of any audio or visual medium of mass communication scattered audience distribute audio and video content, but usually using electromagnetic radiation (waves). The lack of server or fixed infrastructure media messages in VANETs plays an important role for every individual application. Broadcast Message VANETs still open research challenge and requires some effort to come to good solutions. This paper starts with a brief introduction of VANET, its applications, and the law of the message-trends in this network starts. This work provides an important and comprehensive study of reliable broadcast routing in VANET scenario.

Keywords: vehicular ad-hoc network , broadcasting, networking protocols, traffic pattern, low intensity conflict

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Authors: Sameer Jung Karki, Gokhan Saygili

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The allowable bearing capacity of foundation systems is determined by applying a factor of safety to the ultimate bearing capacity. Conventional ultimate bearing capacity calculations routines are based on deterministic input parameters where the nonuniformity and inhomogeneity of soil and site properties are not accounted for. Hence, the laws of mathematics like probability calculus and statistical analysis cannot be directly applied to foundation engineering. It’s assumed that the Factor of Safety, typically as high as 3.0, incorporates the uncertainty of the input parameters. This factor of safety is estimated based on subjective judgement rather than objective facts. It is an ambiguous term. Hence, a probabilistic analysis of the bearing capacity of an isolated footing on a clayey soil is carried out by using the Monte Carlo Simulation method. This simulated model was compared with the traditional discrete model. It was found out that the bearing capacity of soil was found higher for the simulated model compared with the discrete model. This was verified by doing the sensitivity analysis. As the number of simulations was increased, there was a significant % increase of the bearing capacity compared with discrete bearing capacity. The bearing capacity values obtained by simulation was found to follow a normal distribution. While using the traditional value of Factor of safety 3, the allowable bearing capacity had lower probability (0.03717) of occurring in the field compared to a higher probability (0.15866), while using the simulation derived factor of safety of 1.5. This means the traditional factor of safety is giving us bearing capacity that is less likely occurring/available in the field. This shows the subjective nature of factor of safety, and hence probability method is suggested to address the variability of the input parameters in bearing capacity equations.

Keywords: bearing capacity, factor of safety, isolated footing, montecarlo simulation

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Authors: Arun Prasad Jaganathan

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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper introduces an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization

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Authors: Asadullah I. Qazi, Mansoor Ahsan, Zahir Ashraf, Uzair Ahmad

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System identification of an Unmanned Aerial Vehicle (UAV), to acquire its mathematical model, is a significant step in the process of aircraft flight automation. The need for reliable mathematical model is an established requirement for autopilot design, flight simulator development, aircraft performance appraisal, analysis of aircraft modifications, preflight testing of prototype aircraft and investigation of fatigue life and stress distribution etc.  This research is aimed at system identification of a fixed wing UAV by means of specifically designed flight experiment. The purposely designed flight maneuvers were performed on the UAV and aircraft states were recorded during these flights. Acquired data were preprocessed for noise filtering and bias removal followed by parameter estimation of longitudinal dynamics transfer functions using MATLAB system identification toolbox. Black box identification based transfer function models, in response to elevator and throttle inputs, were estimated using least square error   technique. The identification results show a high confidence level and goodness of fit between the estimated model and actual aircraft response.

Keywords: fixed wing UAV, system identification, black box modeling, longitudinal dynamics, least square error

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Martin Popescu D. Dana Maria

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The aims of this research are to broaden the study on the relationship between emotional intelligence and counterproductive work behavior (CWB). The study sample consisted in 441 Romanian employees from companies all over the country. Data has been collected through web surveys and processed with SPSS. The results indicated an average correlation between the two constructs and their sub variables, employees with a high level of emotional intelligence tend to be less aggressive. In addition, labeling was considered an individual difference which has the power to influence the level of employee aggression. A regression model was used to underline the importance of emotional intelligence together with labeling as predictors of CWB. Results have shown that this regression model enforces the assumption that labeling and emotional intelligence, taken together, predict CWB. Employees, who label themselves as victims and have a low degree of emotional intelligence, have a higher level of CWB.

Keywords: aggression, CWB, emotional intelligence, labeling

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Authors: Birhanu Abera Kibret

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The abstract highlights findings from a seismicity study conducted in the Ethiopian Rift Valley and adjacent cities, including Semera, Adama, and Hawasa, located in Afar and the Main Ethiopian Rift system. The region experiences high seismicity, characterized by small to moderate earthquakes situated in the mid-to-upper crust. Additionally, the capital city of Ethiopia, Addis Ababa, situated in the rift margin, experiences seismic activity, with small to relatively moderate earthquakes observed to the east and southeast of the city, alongside the rift valley. These findings underscore the seismic vulnerability of the region, emphasizing the need for comprehensive seismic risk assessment and mitigation strategies to enhance resilience and preparedness.

Keywords: seismic hazard, seismicity, crustal structure, magmatic intrusion, partial meltung

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Authors: Hicham Amar, Mustapha El Ghorfi, Yassine Taha, Abdellatif Elghali, Rachid Hakkou, Mostafa Benzaazoua

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Phosphate mine waste rock (PMWR) generated during ore extraction is continuously increasing, resulting in a significant environmental footprint. The main objectives of this study consist of i) elaboration of the sampling strategy of PMWR piles, ii) a mineralogical and chemical characterization of PMWR piles, and iii) 3D block model creation to evaluate the potential valorization of the existing PMWR. Destructive drilling using reverse circulation from 13 drills was used to collect samples for chemical (X-ray fluorescence analysis) and mineralogical assays. The 3D block model was created based on the data set, including chemical data of the realized drills using Datamine RM software. The optical microscopy observations showed that the sandy phosphate from drills in the PMWR piles is characterized by the abundance of carbonate fluorapatite with the presence of calcite, dolomite, and quartz. The mean grade of composite samples was around 19.5±2.7% for P₂O₅. The mean grade of P₂O₅ exhibited an increasing tendency by depth profile from bottom to top of PMWR piles. 3D block model generated with chemical data confirmed the tendency of the mean grades’ variation and may allow a potential selective extraction according to %P₂O₅. The 3D block model of P₂O₅ grade is an efficient sampling approach that confirmed the variation of P₂O₅ grade. This integrated approach for PMWR management will be a helpful tool for decision-making to recover the residual phosphate, adopting the circular economy and sustainability in the phosphate mining industry.

Keywords: 3D modelling, reverse circulation drilling, circular economy, phosphate mine waste rock, sampling

Procedia PDF Downloads 81

Authors: Tanmoy Basu

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The dynamic theory of criminal psychology and its physiological effects on human organs presents a novel perspective that bridges the gap between behavioral sciences and medical research, with significant implications for judicial interpretation and global societal impact. This study seeks to explore the intricate interplay between psychological factors driving criminal behavior and their measurable effects on the human body, hypothesizing that psychological stressors inherent in criminal tendencies produce detectable physiological changes. These insights have the potential to reshape approaches to crime prevention, judicial fairness, and rehabilitation strategies worldwide. Criminal psychology, often confined to behavioral and cognitive dimensions, rarely considers its direct impact on human biology. This research proposes that criminal tendencies and behavior's, characterized by heightened psychological stress and deviant mental patterns, trigger physiological responses in the cardiovascular, endocrine, and neurological systems. The scientific questions addressed here are pivotal: Can criminal psychology leave biological imprints? If so, can these markers provide early warning systems or contribute to judicial evaluations of criminal accountability? Addressing these questions can transform the intersection of science, law, and society. Criminological theories traditionally focus on socio-economic, cultural, or psychological triggers for criminal acts. However, emerging research underscores the psychosomatic connections between mental states and bodily health. Psychological stressors such as anxiety, guilt, or fear—common in individuals predisposed to criminal behavior—may lead to systemic changes in hormone levels, cardiovascular strain, and neural activity. Despite these connections, their implications for understanding criminal behavior remain underexplored, leaving a critical gap in the literature. This study adopts a multidisciplinary, mixed-methods approach that combines empirical data collection with theoretical analysis. Neurological imaging, biomarkers, and physiological testing are employed to identify and quantify changes in the human body associated with individuals exhibiting criminal tendencies. These data are correlated with detailed case histories, enabling an integrative perspective on how psychological and physiological factors converge in criminal behavior. Complementary qualitative analyses provide insights into contextual factors, such as socio-environmental stressors, that influence these physiological responses. Preliminary results reveal a strong correlation between criminal psychology and physiological dysfunction. Specifically, individuals displaying persistent criminal tendencies exhibit elevated cortisol levels, irregular heart rate patterns, and abnormal neural activity in regions associated with impulse control and decision-making. These findings suggest that criminal psychology is not merely a cognitive or emotional phenomenon but one with tangible biological markers. The results are interpreted through the lens of judicial applications, suggesting that physiological markers could supplement psychological evaluations in assessing criminal intent and responsibility. This perspective raises ethical considerations about the use of biological data in legal systems, highlighting the need for careful policy-making. The study advocates for integrating scientific activism into judicial frameworks, enabling more evidence-based decisions that consider both psychological and physiological dimensions of criminal behavior. This research holds transformative potential for global society. By recognizing the biological underpinnings of criminal psychology, policymakers can devise more holistic crime prevention strategies and rehabilitation programmed. Furthermore, this understanding promotes equitable judicial interpretations, ensuring that decisions are informed by comprehensive, evidence-based analyses. This comprehensive investigation not only deepens the understanding of criminal psychology but also paves the way for innovative intersections between science, law, and societal reform.

Keywords: behavioral science, criminal psychology, cognitive dimensions, dysfunction, dynamic theory, emotional phenomenon, global societal impact, human organs, judicial interpretation, psychological changes, rehabilitation strategies

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Authors: Armansyah, I. P. Almanar, M. Saiful Bahari Shaari, M. Shamil Jaffarullah, Nur’amirah Busu, M. Arif Fadzleen Zainal Abidin, M. Amlie A. Kasim

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Temperature distribution in Friction Stir Welding (FSW) of 6061-T6 Aluminum Alloy is modeled using the Finite Element Method (FEM). In order to obtain temperature distribution in the welded aluminum plates during welding operation, transient thermal finite element analyses are performed. Heat input from tool shoulder and tool pin are considered in the model. A moving heat source with a heat distribution simulating the heat generated by frictions between tool shoulder and workpiece is used in the analysis. Three-dimensional model for simulated process is carried out by using Altair HyperWork, a commercially available software. Transient thermal finite element analyses are performed in order to obtain the temperature distribution in the welded Aluminum plates during welding operation. The developed model was then used to show the effect of various input parameters such as total rate of welding speed and rotational speed on temperature distribution in the workpiece.

Keywords: frictions stir welding, temperature distribution, finite element method, altair hyperwork

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Authors: Salim Belouettar, Kodjo Attipou

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The thermal wrinkling behavior of thin membranes is investigated. The Fourier double scale series are used to deduce the macroscopic membrane wrinkling equations. The obtained equations account for the global and local wrinkling modes. Numerical examples are conducted to assess the validity of the approach developed. Compared to the finite element full model, the present model needs only few degrees of freedom to recover accurately the bifurcation curves and wrinkling paths. Different parameters such as membrane’s aspect ratio, wave number, pre-stressed membranes are discussed from a numerical point of view and the properties of the wrinkles (critical load, wavelength, size and location) are presented.

Keywords: wrinkling, thermal stresses, Fourier series, thin membranes

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Authors: Amar Sabih, James Nemes

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The occurrence of internal ductile failure within the Adiabatic Shear Band (ASB) in cold-headed products presents a significant barrier in the fast-expanding cold-heading (CH) industry. The presence of internal ductile failure in cold-headed products may lead to catastrophic fracture under tensile loads despite the ductile nature of the material causing expensive industrial recalls. Therefore, this paper presents a workability criterion that uses stress instability as an indicator to accurately reveal the locus of initiation of internal ductile failures. The concept of the instability criterion is to use the stress ratio at failure as a weighting function to indicate the initiation of ductile failure inside the ASBs. This paper presents a comprehensive experimental, metallurgical, and finite element simulation study to calculate the material constants used in this criterion.

Keywords: adiabatic shear band, workability criterion, ductile failure, stress instability

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Authors: Nithipattara Balsiri

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The purposes of this research was to develop instructional model by using research-based learning enhancing critical thinking, cognitive skills, and culture and local wisdom knowledge of undergraduate students. The sample consisted of 307 undergraduate students. Critical thinking and cognitive skills test were employed for data collection. Second-order confirmatory factor analysis, t-test, and one-way analysis of variance were employed for data analysis using SPSS and LISREL programs. The major research results were as follows; 1) the instructional model by using research-based learning enhancing critical thinking, cognitive skills, and culture and local wisdom knowledge should be consists of 6 sequential steps, namely (1) the setting research problem (2) the setting research hypothesis (3) the data collection (4) the data analysis (5) the research result conclusion (6) the application for problem solving, and 2) after the treatment undergraduate students possessed a higher scores in critical thinking and cognitive skills than before treatment at the 0.05 level of significance.

Keywords: critical thinking, cognitive skills, culture and local wisdom knowledge

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Authors: Abdelmalk Brahma

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Drying is a phenomenon that accompanies the hardening of hydraulic materials. It can, if it is not prevented, lead to significant spontaneous dimensional variations, which the cracking is one of events. In this context, cracking promotes the transport of aggressive agents in the material, which can affect the durability of concrete structures. Drying shrinkage develops over a long period almost 30 years although most occurred during the first three years. Drying shrinkage stabilizes when the material is water balance with the external environment. The drying shrinkage of cementitious materials is due to the formation of capillary tensions in the pores of the material, which has the consequences of bringing the solid walls of each other. Knowledge of the shrinkage characteristics of concrete is a necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable shrinkage movement in reinforced or prestressed concrete and the appropriate steps can be taken in design to accommodate this movement. This study is concerned the modelling of drying shrinkage of the hydraulic materials and the prediction of the rate of spontaneous deformations of hydraulic materials during hardening. The model developed takes in consideration the main factors affecting drying shrinkage. There was agreement between drying shrinkage predicted by the developed model and experimental results. In last we show that developed model describe the evolution of the drying shrinkage of high performances concretes correctly.

Keywords: drying, hydraulic concretes, shrinkage, modeling, prediction

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Authors: Zhaopeng Xuan, Yuan Bian, C. Cailleaux, Jing Qin, S. Traore

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Traditional software engineering allows engineers to propose to their clients multiple specialized software distributions assembled from a shared set of software assets. The management of these assets however requires a trade-off between client satisfaction and software engineering process. Clients have more and more difficult to find a distribution or components based on their needs from all of distributed repositories. This paper proposes a software engineering for a user-driven software product line in which engineers define a feature model but users drive the actual software distribution on demand. This approach makes the user become final actor as a release manager in software engineering process, increasing user product satisfaction and simplifying user operations to find required components. In addition, it provides a way for engineers to manage and assembly large software families. As a proof of concept, a user-driven software product line is implemented for eclipse, an integrated development environment. An eclipse feature model is defined, which is exposed to users on a cloud-based built platform from which clients can download individualized Eclipse distributions.

Keywords: software product line, model-driven development, reverse engineering and refactoring, agile method

Procedia PDF Downloads 438

Authors: Benedictus Asriparusa, Lathifah Al Hakimi, Aulia Husada

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A large amount of energy is being wasted by the release of natural gas associated with the oil industry. This release interrupts the environment particularly atmosphere layer condition globally which contributes to global warming impact. This research presents an overview of the methods employed by researchers in PT. Chevron Pacific Indonesia in the Minas area to determine a new prediction method of measuring and reducing gas flaring and its emission. The method emphasizes advanced research which involved analytical studies, numerical studies, modeling, and computer simulations, amongst other techniques. A flaring system is the controlled burning of natural gas in the course of routine oil and gas production operations. This burning occurs at the end of a flare stack or boom. The combustion process releases emissions of greenhouse gases such as NO2, CO2, SO2, etc. This condition will affect the chemical composition of air and environment around the boundary layer mainly during transition season. Transition season in Indonesia is absolutely very difficult condition to predict its pattern caused by the difference of two air mass conditions. This paper research focused on transition season in 2013. A simulation to create the new pattern of the pollutants distribution is needed. This paper has outlines trends in gas flaring modeling and current developments to predict the dominant variables in the pollutants distribution. A Fluent model is used to simulate the distribution of pollutants gas coming out of the stack, whereas WRF model output is used to overcome the limitations of the analysis of meteorological data and atmospheric conditions in the study area. Based on the running model, the most influence factor was wind speed. The goal of the simulation is to predict the new pattern based on the time of fastest wind and slowest wind occurs for pollutants distribution. According to the simulation results, it can be seen that the fastest wind (last of March) moves pollutants in a horizontal direction and the slowest wind (middle of May) moves pollutants vertically. Besides, the design of flare stack in compliance according to EPA Oil and Gas Facility Stack Parameters likely shows pollutants concentration remains on the under threshold NAAQS (National Ambient Air Quality Standards).

Keywords: flare motion, new prediction, pollutants distribution, transition season, WRF model

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Authors: Yegnanew A. Shiferaw

Abstract:

Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems.

Keywords: commodity prices, MS-GARCH model, MS regression model, South Africa, volatility

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Authors: P. Kavuma, J. Mukasa, M. Lusunku

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Background and Problem: The widespread poverty in Africa- together with the negative impacts of climate change-are two great global challenges that call for everyone’s involvement including Architects. This in particular places serious challenges on architects to have additional skills in both Entrepreneurship and Environmental Design (SEED). Regrettably, while Architectural Training in most African Universities including those from Uganda lack comprehensive implementation of SEED in their curricula, regulatory bodies have not contributed towards the effective integration of SEED in their professional practice. In response to these challenges, Nkumba University (NU) under Architect Kavuma Paul supported by the Uganda Chambers of Architects– initiated the SEED integration in the undergraduate Architectural curricula to cultivate SEED know-how and examples of best practices. Main activities: Initiated in 2007, going beyond the traditional Architectural degree curriculum, the NU Architect department offers SEED courses including provoking passions for creating desirable positive changes in communities. Learning outcomes are assessed theoretically and practically through field projects. The first set of SEED graduates came out in 2012. As part of the NU post-graduation and alumni survey, in October 2014, the pioneer SEED graduates were contacted through automated reminder emails followed by individual, repeated personal follow-ups via email and phone. Out of the 36 graduates who responded to the survey, 24 have formed four (4) private consortium agencies of 5-7 graduates all of whom have pioneered Ugandan-own-cultivated Architectural social projects that include: fishing farming in shipping containers; solar powered mobile homes in shipping containers, solar powered retail kiosks in rural and fishing communities, and floating homes in the flood-prone areas. Primary outcomes: include being business self –reliant in creating the social change the architects desired in the communities. Examples of the SEED project returns to communities reported by the graduates include; employment creation via fabrication, retail business, marketing, improved diets, safety of life and property, decent shelter in the remote mining and oil exploration areas. Negative outcomes-though not yet evaluated include the disposal of used-up materials. Conclusion: The integration of SEED in Architectural Training has established a baseline benchmark and a replicable model based on best practice projects.

Keywords: architectural training, entrepreneurship, environment, integration

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Authors: Nir Schreiber, Reuven Cohen, Simi Haber

Abstract:

The Potts model has been widely explored in the literature for the last few decades. While many analytical and numerical results concern with the traditional two site interaction model in various geometries and dimensions, little is yet known about models where more than two spins simultaneously interact. We consider a ferromagnetic four site interaction Potts model on the square lattice (FFPS), where the four spins reside in the corners of an elementary square. Each spin can take an integer value 1,2,...,q. We write the partition function as a sum over clusters consisting of monochromatic faces. When the number of faces becomes large, tracing out spin configurations is equivalent to enumerating large lattice animals. It is known that the asymptotic number of animals with k faces is governed by λᵏ, with λ ≈ 4.0626. Based on this observation, systems with q < 4 and q > 4 exhibit a second and first order phase transitions, respectively. The transition nature of the q = 4 case is borderline. For any q, a critical giant component (GC) is formed. In the finite order case, GC is simple, while it is fractal when the transition is continuous. Using simple equilibrium arguments, we obtain a (zero order) bound on the transition point. It is claimed that this bound should apply for other lattices as well. Next, taking into account higher order sites contributions, the critical bound becomes tighter. Moreover, for q > 4, if corrections due to contributions from small clusters are negligible in the thermodynamic limit, the improved bound should be exact. The improved bound is used to relate the critical point to the finite correlation length. Our analytical predictions are confirmed by an extensive numerical study of FFPS, using the Wang-Landau method. In particular, the q=4 marginal case is supported by a very ambiguous pseudo-critical finite size behavior.

Keywords: entropic sampling, lattice animals, phase transitions, Potts model

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Authors: Idris M. Olatunde, Kareem B.

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In manufacturing systems, especially job shops, service performance is a key factor that determines customer satisfaction. Service performance depends not only on the quality of the output but on the delivery lead times as well. Besides product quality enhancement, delivery lead time must be minimized for optimal patronage. Quoting accurate due dates is sine quo non for job shop operational survival in a global competitive environment. Quoting accurate due dates in job shops has been a herculean task that nearly defiled solutions from many methods employed due to complex jobs routing nature of the system. This class of NP-hard problems possessed no rigid algorithms that can give an optimal solution. Jobshop operational problem is more complex in developing nations due to some peculiar factors. Operational complexity in job shops emanated from political instability, poor economy, technological know-how, and the non-promising socio-political environment. The mentioned exogenous factors were hardly considered in the previous studies on scheduling problem related to due date determination in job shops. This study has filled the gap created in the past studies by developing a dynamic model that incorporated the exogenous factors for accurate determination of due dates for varying jobs complexity. Real data from six job shops selected from the different part of Nigeria, were used to test the efficacy of the model, and the outcomes were analyzed statistically. The results of the analyzes showed that the model is more promising in determining accurate due dates than the traditional models deployed by many job shops in terms of patronage and lead times minimization.

Keywords: due dates prediction, improved performance, customer satisfaction, dynamic model, exogenous factors, job shops

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Authors: Chakrit Chotamonsak

Abstract:

In this study, the Weather Research and Forecasting (WRF) model was used as regional climate model to dynamically downscale the ECHAM5 Global Climate Model projection for the regional climate change impact on air quality–related meteorological conditions in the upper northern Thailand. The analyses were focused on meteorological variables that potentially impact on the regional air quality such as sea level pressure, planetary boundary layer height (PBLH), surface temperature, wind speed and ventilation. Comparisons were made between the present (1990–2009) and future (2045–2064) climate downscaling results during majority air pollution season (dry season, January-April). Analyses showed that the sea level pressure will be stronger in the future, suggesting more stable atmosphere. Increases in temperature were obvious observed throughout the region. Decreases in surface wind and PBLH were predicted during air pollution season, indicating weaker ventilation rate in this region. Consequently, air quality-related meteorological variables were predicted to change in almost part of the upper northern Thailand, yielding a favorable meteorological condition for pollutant accumulation in the future.

Keywords: climate change, climate impact, air quality, air pollution, Thailand

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Authors: Frida Carrillo Morales, Maria Maricela Carrasco Yépez, Saúl Rojas Hernández

Abstract:

Naegleria fowleri is the causative agent of primary amebic meningoencephalitis, this disease is acute and fulminant that affects humans. It has been reported that despite the existence of therapeutic options against this disease, its mortality rate is 97%. Therefore, the need arises to have vaccines that confer protection against this disease and, in addition to developing adjuvants to enhance the immune response. In this regard, in our work group, we obtained a peptide designed from the membrane protein MP2CL5 of Naegleria fowleri called Smp145 that was shown to be immunogenic; however, it would be of great importance to enhance its immunological response, being able to co-administer it with a non-toxic adjuvant. Therefore, the objective of this work was to carry out the bioinformatic design of a peptide of the Naegleria fowleri membrane protein MP2CL5 conjugated with a non-toxic modified adjuvant from the native A1 structure of Cholera Toxin. For which different bioinformatics tools were used to obtain a model with a modification in amino acid 61 of the A1 subunit of the CT (CTA1), to which the Smp145 peptide was added and both molecules were joined with a 13-glycine linker. As for the results obtained, the modification in CTA1 bound to the peptide produces a reduction in the toxicity of the molecule in in silico experiments, likewise, the prediction in the binding of Smp145 to the receptor of B cells suggests that the molecule is directed in specifically to the BCR receptor, decreasing its native enzymatic activity. The stereochemical evaluation showed that the generated model has a high number of adequately predicted residues. In the ERRAT test, the confidence with which it is possible to reject regions that exceed the error values was evaluated, in the generated model, a high score was obtained, which determines that the model has a good structural resolution. Therefore, the design of the conjugated peptide in this work will allow us to proceed with its chemical synthesis and subsequently be able to use it in the mouse meningitis protection model caused by N. fowleri.

Keywords: immunology, vaccines, pathogens, infectious disease

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