Search results for: reduce order aeroelastic model (ROAM)

Authors: Mathias B. Michael, Esther T. Akinlabi, Tien-Chien Jen

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Thermal energy consumption in palm oil production plant comprises mainly of steam, hot water and hot air. In most efficient plants, hot water and air are generated from the steam supply system. Research has shown that thermal energy utilize in palm oil production plants is about 70 percent of the total energy consumption of the plant. In order to manage the plants’ energy efficiently, the energy systems are modelled and optimized. This paper aimed to present the model of steam supply systems of a typical palm oil production plant in Ghana. The models include exergy and energy models of steam boiler, steam turbine and the palm oil mill. The paper further simulates the virtual plant model to obtain the thermal energy performance of the plant under study. The simulation results show that, under normal operating condition, the boiler energy performance is considerably below the expected level as a result of several factors including intermittent biomass fuel supply, significant moisture content of the biomass fuel and significant heat losses. The total thermal energy performance of the virtual plant is set as a baseline. The study finally recommends number of energy efficiency measures to improve the plant’s energy performance.

Keywords: palm biomass, steam supply, exergy and energy models, energy performance benchmark

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Authors: Marulasiddappa H. B., Pushparajesh Viswanathan

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Day by day, the uses of internal combustion engines (ICE) are deteriorating because of pollution and less fuel availability. In the present scenario, the electric vehicle (EV) plays a major role in the place of an ICE vehicle. The performance of EVs can be improved by the proper selection of electric motors. Initially, EV preferred induction motors for traction purposes, but due to complexity in controlling induction motor, permanent magnet synchronous motor (PMSM) is replacing induction motor in EV due to its advantages. Direct torque control (DTC) is one of the known techniques for PMSM drive in EV to control the torque and speed. However, the presence of torque ripple is the main drawback of this technique. Many control strategies are followed to reduce the torque ripples in PMSM. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) controller technique is proposed to reduce torque ripples and settling time. Here the performance parameters like torque, speed and settling time are compared between conventional proportional-integral (PI) controller with ANFIS controller.

Keywords: direct torque control, electric vehicle, torque ripple, PMSM

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Authors: Ayna Yusubova, Bart Clarysse

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This paper explores role of research institutes in creation of support ecosystem for new technology-based ventures. Previous literature introduced research institutes as part of business and knowledge ecosystem, very few studies are available that consider a research institute as an ecosystem that support high-tech startups at every stage of development. Based on a resource-based view and a stage-based model of high-tech startups growth, this study aims to analyze how a research institute builds a startup support ecosystem by attracting different stakeholders in order to help startups to overcome resource. This paper is based on an in-depth case study of public research institute that focus on development of entrepreneurial ecosystem in a developed region. Analysis shows that the idea generation stage of high-tech startups that related to the invention and development of product or technology for commercialization is associated with a lack of critical knowledge resources. Second, at growth phase that related to market entrance, high-tech startups face challenges associated with the development of their business network. Accordingly, the study shows the support ecosystem that research institute creates helps high-tech startups overcome resource gaps in order to achieve a successful transition from one phase of growth to the next.

Keywords: new technology-based firms, ecosystems, resources, business incubators, research instutes

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Authors: Joseph Marae Djouda, Ashraf Kasmi, François Hild

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Fused filament fabrication is one of the most widespread additive manufacturing techniques because of its low-cost implementation. Its initial development was based on part fabrication with thermoplastic materials. The influence of the manufacturing parameters such as the filament orientation through the nozzle, the deposited layer thickness, or the speed deposition on the mechanical properties of the parts has been widely experimentally investigated. It has been recorded the remarkable variations of the anisotropy in the function of the filament path during the fabrication process. However, there is a lack in the development of constitutive models describing the mechanical properties. In this study, integrated digital image correlation (I-DIC) is used for the identification of mechanical constitutive parameters of two configurations of ABS samples: +/-45° and so-called “oriented deposition.” In this last, the filament was deposited in order to follow the principal strain of the sample. The identification scheme based on the gap reduction between simulation and the experiment directly from images recorded from a single sample (single edge notched tension specimen) is developed. The macroscopic and mesoscopic analysis are conducted from images recorded in both sample surfaces during the tensile test. The elastic and elastoplastic models in isotropic and orthotropic frameworks have been established. It appears that independently of the sample configurations (filament orientation during the fabrication), the elastoplastic isotropic model gives the correct description of the behavior of samples. It is worth noting that in this model, the number of constitutive parameters is limited to the one considered in the elastoplastic orthotropic model. This leads to the fact that the anisotropy of the architectured 3D printed ABS parts can be neglected in the establishment of the macroscopic behavior description.

Keywords: elastic anisotropy, fused filament fabrication, Acrylonitrile butadiene styrene, I-DIC identification

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Authors: Naeem Ullah, Menglan Duan, Mac Darlington Uche Onuoha

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The lowering of subsea equipment into the deep waters is a challenging job due to the harsh offshore environment. Many researchers have introduced various installation systems to deploy the payload safely into the deep oceans. In general practice, dual floating vessels are not employed owing to the prevalent safety risks and hazards caused by ever-increasing dynamical effects sourced by mutual interaction between the bodies. However, while keeping in the view of the optimal grounds, such as economical one, the Y-method, the two conventional tugboats supporting the equipment by the two independent strands connected to a tri-plate above the equipment, has been employed to study multibody dynamics of the dual barge lifting operations. In this study, the two tugboats and the suspended payload (Y-method) are deployed for the lowering of subsea equipment into the deep waters as a multibody dynamic system. The two-wire ropes are used for the lifting and installation operation by this Y-method installation system. 6-dof (degree of freedom) for each body are considered to establish coupled 18-dof multibody model by embedding technique or velocity transformation technique. The fundamental and prompt advantage of this technique is that the constraint forces can be eliminated directly, and no extra computational effort is required for the elimination of the constraint forces. The inertial frame of reference is taken at the surface of the water as the time-independent frame of reference, and the floating frames of reference are introduced in each body as the time-dependent frames of reference in order to formulate the velocity transformation matrix. The local transformation of the generalized coordinates to the inertial frame of reference is executed by applying the Euler Angle approach. The spherical joints are articulated amongst the multibody as the kinematic joints. The hydrodynamic force, the two-strand forces, the hydrostatic force, and the mooring forces are taken into consideration as the external forces. The radiation force of the hydrodynamic force is obtained by employing the Cummins equation. The wave exciting part of the hydrodynamic force is obtained by using force response amplitude operators (RAOs) that are obtained by the commercial solver ‘OpenFOAM’. The strand force is obtained by considering the wire rope as an elastic spring. The nonlinear hydrostatic force is obtained by the pressure integration technique at each time step of the wave movement. The mooring forces are evaluated by using Faltinsen analytical approach. ‘The Runge Kutta Method’ of Fourth-Order is employed to evaluate the coupled equations of motion obtained for 18-dof multibody model. The results are correlated with the simulated Orcaflex Model. Moreover, the results from Orcaflex Model are compared with the MOSES Model from previous studies. The MBDS of single barge lifting operation from the former studies are compared with the MBDS of the established dual barge lifting operation. The dynamics of the dual barge lifting operation are found larger in magnitude as compared to the single barge lifting operation. It is noticed that the traction at the top connection point of the cable decreases with the increase in the length, and it becomes almost constant after passing through the splash zone.

Keywords: dual barge lifting operation, Y-method, multibody dynamics, shipbuilding, installation of subsea equipment, shipbuilding

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Authors: Zhou Mo, Dennis Chow

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In the dynamic positioning system (DPS) for vessels, the reliable information transmission between each note basically relies on the wireless protocols. From the perspective of cluster-based routing pro-tocols for wireless sensor networks, the data fusion technology based on the sleep scheduling mechanism and remaining energy in network layer is proposed, which applies the sleep scheduling mechanism to the routing protocols, considering the remaining energy of node and location information when selecting cluster-head. The problem of uneven distribution of nodes in each cluster is solved by the Equilibrium. At the same time, Classified Forwarding Mechanism as well as Redelivery Policy strategy is adopted to avoid congestion in the transmission of huge amount of data, reduce the delay in data delivery and enhance the real-time response. In this paper, a simulation test is conducted to improve the routing protocols, which turns out to reduce the energy consumption of nodes and increase the efficiency of data delivery.

Keywords: DPS for vessel, wireless sensor network, data fusion, routing protocols

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Authors: Onyeke Paul Chuks

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Nigeria is ranked among the developing nations and a country with a low income per capita. The consequences of this economic situation have led to the low achievement records below UN benchmark especially in the area of basic education for her citizens. The country is, however, making relentless efforts at arresting the situation by making budgetary allocations to ensure the realization of Millennium Development Goal No. 2 which is achieving universal basic education, her distressed economy notwithstanding. Basic education which comprises primary and lower secondary education as well as pre-primary and/or adult literacy programs have suffered serious setbacks orchestrated by the dwindling of the nation’s economy. This category of education being the bedrock of all other levels of education is regarded as a priority by developing countries and also the focus of the Education for All Movement led by UNESCO. The introduction of child-friendly school model is one of the strategies designed by UNESCO to achieving this all important MDGs goal No. 2. Child-friendly education model is aimed at replacing the out-dated, mundane, regimented and officious school administrative model where the basic rights of school children are trampled upon with impunity and community participation in school activities is viewed as unnecessary interference by school managers. This paper ex-rayed the potential obstacles likely to impinge on the implementation of child-friendly school model in Nigeria especially from the angle of her distressed economy and the colossal effects of the corrupt practices bedeviling the nation. The paper as well outlines prospects for the successful implementation of the child-friendly school model in Nigeria.

Keywords: child-friendly school, distressed economy, model, Nigeria

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Authors: Nousheen Tariq Bhutta, Syed Zulfiqar Ali Shah

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Financial crises become a key impediment towards the development of countries especially in emerging economies. Based on standard finance, many researchers investigated the financial crises in different countries in order to find the underlying reason regarding occurrence these event; however they were unable to provide it. In this essence behavioral finance may be helpful in providing answers to some queries regarding occurrence and prevention of financial crises. In this paper, we explore the some psychological factors comprises of our inspiration, emotion, cognition and culture along with their reflection companies, financial markets and governments that present some supportive arguments. Moreover, we compared the views of Keynes and Minsky in order to validate the underling justification towards occurrence of financial crises and their prevention in future. This study helps the practitioners and policy makers through providing valuable recommendation in order to protect the economies.

Keywords: financial crises, behavioral finance, financial markets, emerging economies

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Authors: Lynn Sheridan, Dennis Alonzo, Hoa Nguyen, Andy Gao, Tracy Durksen

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Disruptions to teaching (e.g., COVID-related) have increased work demands for teachers. There is an opportunity for research to explore evidence-informed steps to support teachers. Collective evidence informs data on teachers’ personal attributes (e.g., self-efficacy beliefs) in the workplace are seen to promote success in teaching and support teacher engagement. Teacher engagement plays a role in students’ learning and teachers’ effectiveness. Engaged teachers are better at overcoming work-related stress, burnout and are more likely to take on active roles. Teachers’ commitment is influenced by a host of personal (e.g., teacher well-being) and environmental factors (e.g., job stresses). The job demands-resources model provided a conceptual basis for examining how teachers’ well-being, and is influenced by job demands and job resources. Job demands potentially evoke strain and exceed the employee’s capability to adapt. Job resources entail what the job offers to individual teachers (e.g., organisational support), helping to reduce job demands. The application of the job demands-resources model involves gathering an evidence-base of and connection to personal attributes (job resources). The study explored the association between constructs (resilience, adaptability, commitment, self/collective efficacy) and a teacher’s engagement with the job. The paper sought to elaborate on the model and determine the associations between key constructs of well-being (resilience, adaptability), commitment, and motivation (self and collective-efficacy beliefs) to teachers’ engagement in teaching. Data collection involved online a multi-dimensional instrument using validated items distributed from 2020-2022. The instrument was designed to identify construct relationships. The participant number was 170. Data Analysis: The reliability coefficients, means, standard deviations, skewness, and kurtosis statistics for the six variables were completed. All scales have good reliability coefficients (.72-.96). A confirmatory factor analysis (CFA) and structural equation model (SEM) were performed to provide measurement support and to obtain latent correlations among factors. The final analysis was performed using structural equation modelling. Several fit indices were used to evaluate the model fit, including chi-square statistics and root mean square error of approximation. The CFA and SEM analysis was performed. The correlations of constructs indicated positive correlations exist, with the highest found between teacher engagement and resilience (r=.80) and the lowest between teacher adaptability and collective teacher efficacy (r=.22). Given the associations; we proceeded with CFA. The CFA yielded adequate fit: CFA fit: X (270, 1019) = 1836.79, p < .001, RMSEA = .04, and CFI = .94, TLI = .93 and SRMR = .04. All values were within the threshold values, indicating a good model fit. Results indicate that increasing teacher self-efficacy beliefs will increase a teacher’s level of engagement; that teacher ‘adaptability and resilience are positively associated with self-efficacy beliefs, as are collective teacher efficacy beliefs. Implications for school leaders and school systems: 1. investing in increasing teachers’ sense of efficacy beliefs to manage work demands; 2. leadership approaches can enhance teachers' adaptability and resilience; and 3. a culture of collective efficacy support. Preparing teachers for now and in the future offers an important reminder to policymakers and school leaders on the importance of supporting teachers’ personal attributes when faced with the challenging demands of the job.

Keywords: collective teacher efficacy, teacher self-efficacy, job demands, teacher engagement

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Authors: Alireza Bahramian

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High-purity TiO₂ nanoparticles (NPs) with size ranging between 50 nm and 100 nm are synthesized from titanium slag through sulphate route under sonochemical procedure. The effect of dissolution parameters such as the sulfuric acid/slag weight ratio, caustic soda concentration, digestion temperature and time, and initial particle size of the dried slag on the extraction efficiency of TiO₂ and removal of iron are examined. By optimizing the digestion conditions, a rutile TiO₂ powder with surface area of 42 m²/g and mean pore diameter of 22.4 nm were prepared. A thermo-kinetic analysis showed that the digestion temperature has an important effect, while the acid/slag weight ratio and initial size of the slag has a moderate effect on the dissolution rate. The shrinking-core model including both chemical surface reaction and surface diffusion is used to describe the leaching process. A low value of activation energy, 38.12 kJ/mol, indicates the surface chemical reaction model is a rate-controlling step. The kinetic analysis suggested a first order reaction mechanism with respect to the acid concentrations.

Keywords: TiO₂ nanoparticles, titanium slag, dissolution rate, sonochemical method, thermo-kinetic study

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Masrur Alam Khan

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The purpose of this study was to examine the dyadic interaction techniques that social media users utilize to reduce uncertainty in their day to day business engagements in the absence of their physical interaction. The study empirically tested assumptions of uncertainty reduction theory while addressing self-disclosure, seeking questions to develop consensus, and subsequently to achieve intimacy in very conducive environment. Moreover, this study examined the effect of dyadic interaction through social media among business community while identifying the strength of their reciprocity in relationships and compares it with those having no dyadic relations due to absence of social media. Using socio-metric survey, the study revealed a better understanding of their partners for upholding their professional relations more credible. A sample of unacquainted, both male and female, was randomly asked questions regarding their nature of dyadic interaction within their office while using social media (face-to-face, visual CMC (webcam) or text-only). Primary results explored that the social media users develop their better know-how about their professional obligations to reduce ambiguity and align with one to one interact.

Keywords: dyadic-interaction, social media, uncertainty reduction, socio-metric survey, self-disclosure, intimacy, reciprocity in relationship

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Authors: K. Zehouani, I. Oldal

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Purpose: In the human knee joint, degenerative joint disease may happen with time. The standard treatment of this disease is the total knee replacement through prosthesis implanting. The reason lies in the fact that this phenomenon causes different material abrasion as compare to pure sliding or rolling alone. This study focuses on developing a knee prosthesis geometry, which fulfills the mechanical and kinematical requirements. Method: The MSC ADAMS program is used to describe the rotation of the human knee joint as a function of flexion, and to investigate how the flexion and rotation movement changes between the condyles of a multi-body model of the knee prosthesis as a function of flexion angle (in the functional arc of the knee (20-120º)). Moreover, the multi-body model with identical boundary conditions is constituted, and the numerical simulations are carried out using the MSC ADAMS program system. Results: It is concluded that the use of the multi-body model reduces time and cost since it does not need to manufacture the tibia and the femur as it requires for the knee prosthesis of the test machine. Moreover, without measuring or by dispensing with a test machine for the knee prosthesis geometry, approximation of the results of our model to a human knee is carried out directly. Conclusion: The pattern obtained by the multi-body model provides an insight for future experimental tests related to the rotation and flexion of the knee joint concerning the actual average and friction load.

Keywords: biomechanics, knee joint, rotation, flexion, kinematics, MSC ADAMS

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Authors: Yeliz Mert Kantar, İsmail Yeni̇lmez, Ibrahim Arik

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In this study, the efficiency measurement of the top fifty Turkish Universities has been conducted. The top fifty Turkish Universities are listed by The Scientific and Technological Research Council of Turkey (TÜBITAK) according to the Entrepreneur and Innovative University Index every year. The index is calculated based on four components since 2018. Four components are scientific and technological research competency, intellectual property pool, cooperation and interaction, and economic and social contribution. The four components consist of twenty-three sub-components. The 2021 list announced in January 2022 is discussed in this study. Efficiency analysis have been carried out using the Stochastic Frontier Model. Statistical significance of the sub-components that make up the index with certain weights has been examined in terms of the efficiency measurement calculated through the Stochastic Frontier Model. The relationship between the efficiency ranking estimated based on the Stochastic Frontier Model and the Entrepreneur and Innovative University Index ranking is discussed in detail.

Keywords: efficiency, entrepreneur and innovative universities, turkish universities, stochastic frontier model, tübi̇tak

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Authors: Shahana Sharmin

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In the present study, the effect of cellulose polymers Ethyl Cellulose and Hydroxy Propyl Methyl Cellulose was evaluated on the release profile of drug from sustained release pellet. Diltiazem Hydrochloride, an antihypertensive, cardio-protective agent and slow channel blocker were used as a model drug to evaluate its release characteristics from different pellets formulations. Diltiazem Hydrochloride sustained release pellets were prepared by drug loading (drug binder suspension) on neutral pellets followed by different percentages of spraying, i.e. 2%,4%, 6%, 8% and 10% coating suspension using ethyl cellulose and hydroxy-propyl methyl cellulose polymer in a fixed 85:15 ratios respectively. The in vitro dissolution studies of Diltiazem Hydrochloride from these sustained release pellets were carried out in pH 7.2 phosphate buffer for 1, 2, 3, 4, 5, 6, 7, and 8 hrs using USP-I method. Statistically, significant differences were found among the drug release profile from different formulations. Polymer content with the highest concentration of Ethyl cellulose on the pellets shows the highest release retarding rate of the drug. The retarding capacity decreases with the decreased concentration of ethyl cellulose. The release mechanism was explored and explained with zero order, first order, Higuchi and Korsmeyer’s equations. Finally, the study showed that the profile and kinetics of drug release were functions of polymer type, polymer concentration & the physico-chemical properties of the drug.

Keywords: diltiazem hydrochloride, ethyl cellulose, hydroxy propyl methyl cellulose, release kinetics, sustained release pellets

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Authors: Azzam Alosaimi

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Different countries attempt to reduce energy demands and Greenhouse Gas (GHG) emissions to mitigate global warming potential. They set different building codes to regulate excessive building’s energy losses. Energy losses occur due to pressure difference between the indoor and outdoor environments, and thus, heat transfers from one region to another. One major sources of energy loss is known as building airtightness. Building airtightness is the fundamental feature of the building envelope that directly impacts infiltration. Most of international building codes require minimum performance for new construction to ensure acceptable airtightness. The execution of airtightness required standards has become more challenging in recent years due to a lack of expertise and equipment, making it costly and time-consuming. Hence, researchers have developed predictive models to predict buildings infiltration rates to meet building codes and to reduce energy and cost. This research applies a theoretical modeling approach using Matlab software to predict mean infiltration rate distributions and total heat loss of Saudi Arabia’s housing stock.

Keywords: infiltration rate, energy demands, heating loss, cooling loss, carbon emissions

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Authors: Olusola Bamisile, Ferdinard Dika, Mustafa Dagbasi, Serkan Abbasoglu

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The aim of this study was to model an air conditioning system that brings about effective cooling and reduce fossil fuel consumption with solar energy as an alternative source of energy. The objective of the study is to design a system with high COP, low usage of electricity and to integrate solar energy into AC systems. A hybrid solar assisted air conditioning system is designed to produce 30kW cooling capacity and R744 (CO₂) is used as a refrigerant. The effect of discharge pressure on the performance of the system is studied. The subcool temperature, evaporating temperature (5°C) and suction gas return temperature (12°C) are kept constant for the four different discharge pressures considered. The cooling gas temperature is set at 25°C, and the discharge pressure includes 80, 85, 90 and 95 bars. Copeland Scroll software is used for the simulation. A pressure-enthalpy graph is also used to deduce each enthalpy point while numerical methods were used in making other calculations. From the result of the study, it is observed that a higher COP is achieved with the use of solar assisted systems. As much as 46% of electricity requirements will be save using solar input at compressor stage.

Keywords: air conditioning, solar energy, performance, energy saving

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Authors: Pravin Gaikwad, Prakash Mahanwar

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The main objective of incorporating natural fibers such as Henequen microfibers (NF) into the High-Density Polyethylene (HDPE) polymer matrix is to reduce the cost and to enhance the mechanical as well as other properties. The Henequen microfibers were chopped manually to 5-7mm in length and added into the polymer matrix at the optimized concentration of 8 wt %. In order to facilitate the link between Henequen microfibers (NF) and HDPE matrix, coupling agent such as Glycidoxy (Epoxy) Functional Methoxy Silane (GPTS) at various concentrations from 0.1%, 0.3%, 0.5%, 0.7%, 0.9%, and 1% by weight to the total fibers were added. The tensile strength of the composite increased marginally while % elongation at break of the composites decreased with increase in silane loading by wt %. Tensile modulus and stiffness observed increased at 0.9 wt % GPTS loading. Flexural as well as impact strength of the composite decreased with increase in GPTS loading by weight %. Dielectric strength of the composite also found increased marginally upto 0.5wt % silane loading and thereafter remained constant.

Keywords: Henequen microfibers (NF), polymer composites, HDPE, coupling agent, GPTS

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Authors: Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, William Templier, Costin Vasile

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Industrial end-users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both machine learning and first principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed by breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data are rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems, and humans -including asset maintenance operations- in order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.

Keywords: degradation models, ageing, anomaly detection, soft sensor, incremental learning

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Authors: B. S. Fatoyinbo, D. Stretch, O. T. Amoo, D. Allopi

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This study extends the use of the Drainage Area Regionalization (DAR) method in generating synthetic data and calibrating PyTOPKAPI stream yield for an ungauged basin at a daily time scale. The generation of runoff in determining a river yield has been subjected to various topographic and spatial meteorological variables, which integers form the Catchment Characteristics Model (CCM). Many of the conventional CCM models adapted in Africa have been challenged with a paucity of adequate, relevance and accurate data to parameterize and validate the potential. The purpose of generating synthetic flow is to test a hydrological model, which will not suffer from the impact of very low flows or very high flows, thus allowing to check whether the model is structurally sound enough or not. The employed physically-based, watershed-scale hydrologic model (PyTOPKAPI) was parameterized with GIS-pre-processing parameters and remote sensing hydro-meteorological variables. The validation with mean annual runoff ratio proposes a decent graphical understanding between observed and the simulated discharge. The Nash-Sutcliffe efficiency and coefficient of determination (R²) values of 0.704 and 0.739 proves strong model efficiency. Given the current climate variability impact, water planner can now assert a tool for flow quantification and sustainable planning purposes.

Keywords: catchment characteristics model, GIS, synthetic data, ungauged basin

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Authors: Eva L. Suarez, Daniel E. Meeroff, Yan Yong

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Current flooding risk modeling focuses on resilience, defined as the probability of recovery from a severe flooding event. However, the long-term damage to property and well-being by nuisance flooding and its long-term effects on communities are not typically included in risk assessments. An approach was developed to address the probability of recovering from a severe flooding event combined with the probability of community performance during a nuisance event. A consolidated model, namely the conflation flooding recovery (&FR) model, evaluates risk-coping mitigation strategies for communities based on the recovery time from catastrophic events, such as hurricanes or extreme surges, and from everyday nuisance flooding events. The &FR model assesses the variation contribution of each independent input and generates a weighted output that favors the distribution with minimum variation. This approach is especially useful if the input distributions have dissimilar variances. The &FR is defined as a single distribution resulting from the product of the individual probability density functions. The resulting conflated distribution resides between the parent distributions, and it infers the recovery time required by a community to return to basic functions, such as power, utilities, transportation, and civil order, after a flooding event. The &FR model is more accurate than averaging individual observations before calculating the mean and variance or averaging the probabilities evaluated at the input values, which assigns the same weighted variation to each input distribution. The main disadvantage of these traditional methods is that the resulting measure of central tendency is exactly equal to the average of the input distribution’s means without the additional information provided by each individual distribution variance. When dealing with exponential distributions, such as resilience from severe flooding events and from nuisance flooding events, conflation results are equivalent to the weighted least squares method or best linear unbiased estimation. The combination of severe flooding risk with nuisance flooding improves flood risk management for highly populated coastal communities, such as in South Florida, USA, and provides a method to estimate community flood recovery time more accurately from two different sources, severe flooding events and nuisance flooding events.

Keywords: community resilience, conflation, flood risk, nuisance flooding

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Authors: Toru Shionoya, Yosuke Kurihara, Takashi Kaburagi, Kajiro Watanabe

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Mass-produced bidirectional microphones have attractive characteristics. They work as a microphone as well as a sensor with high gain over a wide frequency range; they are also highly reliable and economical. We present novel multiple functional uses of the microphones. A mathematical model for explaining the high-pass-filtering characteristics of bidirectional microphones was presented. Based on the model, the characteristics of the microphone were investigated, and a novel use for the microphone as a sensor with a wide frequency range was presented. In this study, applications for using the microphone as a security sensor and a human biosensor were introduced. The mathematical model was validated through experiments, and the feasibility of the abovementioned applications for security monitoring and the biosignal monitoring were examined through experiments.

Keywords: bidirectional microphone, low-frequency, mathematical model, frequency response

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Authors: Ashraf Muharam, Nasser Al-Hemiddi, El Sayed Amer

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Green roof is one of sustainable practice for reducing the environmental impact of a building. Green roofs are vegetation roofs that are partially or completely covered building's roof. It can provide multiple environmental benefits such as mitigation of urban heat island effect and protecting buildings against solar radiation. In Riyadh city buildings consume about 70 % of the total energy used in the building for cooling and heating because of the Riyadh's harsh and tropical climate. So, the study aim was identifying the thermal performance of extensive green roof and comparing its performance with concrete roof performance during summer season. The experimental validations results indicated that the extensive green roofs system was better than concrete roof system for lowering the indoor air temperature. It could reduce the indoor air temperature from 2°C to 5.5°C compared to the concrete roof system. Also, the finding of this study demonstrated that extensive green roof system could reduce 12% to 33% of energy consumption of air conditioning in Riyadh city during summer seasons by using environmentally friendly insulation.

Keywords: thermal performance, green roof system, concrete roof system, tropical climatic, internal temperatures

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Authors: J.Zambrano Nájera, M.Gómez Valentín

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Increases in urbanization during XX century, have brought as one major problem the increased of sediment production. Hydraulic erosion is one of the major causes of increasing of sediments in small urban catchments. Such increments in sediment yielding in header urban catchments can caused obstruction of drainage systems, making impossible to capture urban runoff, increasing runoff volumes and thus exacerbating problems of urban flooding. For these reasons, it is more and more important to study of sediment production in urban watershed for properly analyze and solve problems associated to sediments. The study of sediments production has improved with the use of mathematical modeling. For that reason, it is proposed a new physically based model applicable to small header urban watersheds that includes the advantages of distributed physically base models, but with more realistic data requirements. Additionally, in this paper the model proposed is compared with a lumped model, reviewing the results, the advantages and disadvantages between the both of them.

Keywords: erosion, hydrologic modeling, urban runoff, sediment modeling, sediment yielding, urban planning

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Authors: Uzma Saqib, Mirza S. Baig

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Myeloid differentiation primary response protein 88 (MYD88) has long been considered a central player in the inflammatory pathway. Recent studies clearly suggest that it is an important therapeutic target in inflammation. On the other hand, a recent study on the interaction between the orphan nuclear receptor (Nur77) and p38α, leading to increased lipopolysaccharide-induced hyperinflammatory response, suggests this binary complex as a therapeutic target. In this study, we have designed inhibitors that can inhibit both MYD88 and Nur77 at the same time. Since both MYD88 and Nur77 are an integral part of the pathways involving lipopolysaccharide-induced activation of NF-κB-mediated inflammation, we tried to target both proteins with the same library in order to retrieve compounds having dual inhibitory properties. To perform this, we developed a homodimeric model of MYD88 and, along with the crystal structure of Nur77, screened a virtual library of compounds from the traditional Chinese medicine database containing ~61,000 compounds. We analyzed the resulting hits for their efficacy for dual binding and probed them for developing a common pharmacophore model that could be used as a prototype to screen compound libraries as well as to guide combinatorial library design to search for ideal dual-target inhibitors. Thus, our study explores the identification of novel leads having dual inhibiting effects due to binding to both MYD88 and Nur77 targets.

Keywords: drug design, Nur77, MYD88, inflammation

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Authors: Kadir Mokrane, Souag Doudja

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In Algeria, one of the Mediterranean countries, water resources are limited and unevenly distributed in space and in time. Boumerdes, coastal town of Algeria, known for its farming and fishing activities. The region is also known for its semi-arid climate and a large water deficit. In order to preserve the quality of water bodies and to reduce withdrawals in the natural environment, it is necessary to seek alternative supplies. The reuse of treated wastewater seems to be a good alternative, especially for irrigation. In the framework of sustainable development, it is imperative to rationalize the use of water resources conventional and unconventional. That is why the re-use agricultural of by-products of the treatment is an alternative expected to preserve the environment and promotion of the agricultural sector. The present work aims, to search for the possibility of reuse of treated wastewater, and sludge resulting from treatment plant of the city of Boumerdes in agriculture, through the analysis of physical, chemical and bacteriological on the samples, and the continuous monitoring of the evolution of several elements during the period of study extended over 12 months, and then, the comparison of these test results to standards and guidelines established in the framework of irrigation and land application.

Keywords: treated water, sewage sludge, recycling, agriculture

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Authors: Lidon Lashley

Abstract:

This paper presents how the social model of disability can be used to reshape inclusive education practices in Guyana. Inclusive education in Guyana is metamorphosizing but still firmly held in the tenets of the Medical Model of Disability which influences the experiences of children with Special Education Needs and/or Disabilities (SEN/D). An ethnographic approach to data gathering was employed in this study. Qualitative data was gathered from the voices of children with and without SEN/D as well as their mainstream teachers to present the interplay of discourses and subjectivities in the situation. The data was analyzed using Adele Clarke's postmodern approach to grounded theory analysis called situational analysis. The data suggest that it is possible but will be challenging to fully contextualize and adopt Loreman's synthesis and Booths and Ainscow's Index in the two mainstream schools studied. In addition, the data paved the way for the presentation of the social model framework specific to Guyana called 'Southern Inclusive Education Framework for Guyana' and its support tool called 'The Inclusive Checker created for Southern mainstream primary classrooms.

Keywords: social model of disability, medical model of disability, subjectivities, metamorphosis, special education needs, postcolonial Guyana, inclusion, culture, mainstream primary schools, Loreman's synthesis, Booths and Ainscow's index

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Authors: Giulio Mangano, Alberto De Marco, Giovanni Zenezini

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The concept of City Logistics (CL) has emerged to improve the impacts of last mile freight distribution in urban areas. In this paper, a System Dynamics (SD) model exploring the dynamics of the diffusion of a ICT platform for CL management across different populations is proposed. For the development of the model two sources have been used. On the one hand, the major diffusion variables and feedback loops are derived from a literature review of existing diffusion models. On the other hand, the parameters are represented by the value propositions delivered by the platform as a response to some of the users’ needs. To extract the most important value propositions the Business Model Canvas approach has been used. Such approach in fact focuses on understanding how a company can create value for her target customers. These variables and parameters are thus translated into a SD diffusion model with three different populations namely municipalities, logistics service providers, and own account carriers. Results show that, the three populations under analysis fully adopt the platform within the simulation time frame, highlighting a strong demand by different stakeholders for CL projects aiming at carrying out more efficient urban logistics operations.

Keywords: city logistics, simulation, system dynamics, business model

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Authors: Moses Mwangi, Geert Verbeke, Geert Molenberghs

Abstract:

Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment with respect to a reference treatment (placebo or standard). The main advantage of using cross-over design over conventional parallel design is its flexibility, where every subject become its own control, thereby reducing confounding effect. Jones & Kenward, discuss in detail more recent developments in the analysis of cross-over trials. We revisit the simple piecewise linear mixed-effects model, proposed by Mwangi et. al, (in press) for its first application in the analysis of cross-over trials. We compared performance of the proposed piecewise linear mixed-effects model with two commonly cited statistical models namely, (1) Grizzle model; and (2) Jones & Kenward model, used in estimation of the treatment effect, in the analysis of randomized cross-over trial. We estimate two performance measurements (mean square error (MSE) and coverage probability) for the three methods, using data simulated from the proposed piecewise linear mixed-effects model. Piecewise linear mixed-effects model yielded lowest MSE estimates compared to Grizzle and Jones & Kenward models for both small (Nobs=20) and large (Nobs=600) sample sizes. It’s coverage probability were highest compared to Grizzle and Jones & Kenward models for both small and large sample sizes. A piecewise linear mixed-effects model is a better estimator of treatment effect than its two competing estimators (Grizzle and Jones & Kenward models) in the analysis of cross-over trials. The data generating mechanism used in this paper captures two time periods for a simple 2-Treatments x 2-Periods cross-over design. Its application is extendible to more complex cross-over designs with multiple treatments and periods. In addition, it is important to note that, even for single response models, adding more random effects increases the complexity of the model and thus may be difficult or impossible to fit in some cases.

Keywords: Evaluation, Grizzle model, Jones & Kenward model, Performance measures, Simulation

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

Abstract:

Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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