Search results for: strength prediction models

Authors: T. Alghamdi, G. Alaghband

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In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

Procedia PDF Downloads 154

Authors: Md. Nakib Ul Hasan, Md. Ariful Islam, Md. Sumon Miah, Misbah Ul Hoque, Bulbul Ahmed

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The variability of cotton fiber characteristics has always been influenced by origin, weather conditions, method of culturing, and harvesting. Spinners work tirelessly to ensure consistent yarn quality by using the different origins of fibers to maximizes the profit margin. Spinners often fail to select desired raw materials of various origins to achieve an appropriate mixing plan due to the lack of knowledge on the interrelationship among fiber properties. The purpose of this research is to investigate the correlations among dominating fiber properties such as micronaire, strength, breaking elongation, upper half mean length, length uniformity index, short fiber index, maturity, reflectance, and yellowness. For this purpose, fiber samples from 500 Indian cotton bales and 350 Greek cotton bales were collected and tested using the high volume instrument (HVI). The fiber properties dataset was then compiled and analyzed using python 3.7 to determine the correlations matrix. Results show that Indian cotton fiber have highest correlation between strength-mat = 0.84, followed by SFI-Unf =-0.83, and Neps-Unf = -0.72. Greek cotton fiber, in contrast, have highest correlation between SFI-Unf =-0.98, followed by SFI-Mat = 0.89, +b-Len = 0.84, and Str-Mat = 0.74. Overall, the Greek cotton fiber showed a higher correlational matrix than compared to that of Indian cotton fiber.

Keywords: cotton fiber, fiber properties correlation, Greek cotton, HVI, Indian cotton, spinning

Procedia PDF Downloads 163

Authors: Beverley Gallimore

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The Individuals with Disabilities Education Act of 2004 (IDEA) has helped to improve outcomes for students with special education needs. Through IDEA, students with Special Education Needs (SEN) have opportunities for more equitable education within the General Education classroom. However, students with disabilities lack access to instructions that can help them to maximize their fullest learning potential. Recently, educational stakeholders have emphasized Integrated Co-teaching as a tool to increase engagement and learning outcomes for students with disabilities in general education classrooms. As a result of this new approach, general and special education teachers are working collaboratively to teach students with disabilities. However, co-teaching models are not properly designed and structured to effectively benefit students with disabilities. Teachers must be oriented correctly in the co-teaching models if it is to be beneficial for students.

Keywords: CO-teaching, differentiation, equitable, collaborative

Procedia PDF Downloads 81

Authors: Moudar Zgoul, Hashem Alkhaldi

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A large number of transportable LPG cylinders are manufactured annually for domestic use. These LPG cylinders are manufactured from mild steel and filled maximally with 12.5 kg liquefied gas under internal pressure of 0.6 N/mm² at a temperature of 50°C. Many millions of such LPG cylinders are in daily use mainly, for purposes of space heating, water heating, and cooking. Thereby, they are imposed to severe conditions leading to their failure. Each year not less than 5000 of these LPG cylinders fail, some of those failures cause damage and loss in lives and properties. In this work, LPG cylinders were investigated; Stress calculations and deformations under dynamic (impact) loadings were carried out to simulate the effects of such loads on the cylinders while in service. Analysis of the LPG cylinders was carried out using the finite element method; shell and cylindrical elements were used at the top, bottom, and in middle (weld region), permitting elastic-plastic analysis for a thin-walled LPG cylinder. Variables such as maximum stresses and maximum deflections under the effect of impact loading were investigated in this work. Results showed that the maximum stresses reach 680 MPa when dropped from 3m-height. The maximum radial deformation occurs at the cylinder’s top in case of the top-position impact. This information should be useful for enhancing the strength of such cylinders and to for prolonging their service life.

Keywords: dynamic analysis, finite element method, impact load, LPG cylinders

Procedia PDF Downloads 324

Authors: Habib Khodaverdiloo, Fatemeh Afrasiabi, Farrokh Asadzadeh, Martinus Th. Van Genuchten

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The soil particle-size distribution (PSD) is known to affect a broad range of soil physical, mechanical and hydraulic properties. Complete descriptions of a PSD curve should provide more information about these properties as opposed to having only information about soil textural class or the soil sand, silt and clay (SSC) fractions. We compared the accuracy of 19 different models of the cumulative PSD in terms of fitting observed data from a large number of Iranian soils. Parameters of the six most promising models were correlated with measured values of the field saturated hydraulic conductivity (Kfs), the mean weight diameter of soil aggregates (MWD), bulk density (ρb), and porosity (∅). These same soil properties were correlated also with conventional PSD parameters (SSC fractions), selected geometric PSD parameters (notably the mean diameter dg and its standard deviation σg), and several other PSD parameters (D50 and D60). The objective was to find the best predictions of several soil physical quality indices and the soil hydraulic properties. Neither SSC nor dg, σg, D50 and D60 were found to have a significant correlation with both Kfs or logKfs, However, the parameters of several cumulative PSD models showed statistically significant correlation with Kfs and/or logKfs (|r| = 0.42 to 0.65; p ≤ 0.05). The correlation between MWD and the model parameters was generally also higher than either with SSC fraction and dg, or with D50 and D60. Porosity (∅) and the bulk density (ρb) also showed significant correlation with several PSD model parameters, with ρb additionally correlating significantly with various geometric (dg), mechanical (D50 and D60), and agronomic (clay and sand) representations of the PSD. The fitted parameters of selected PSD models furthermore showed statistically significant correlations with Kfs,, MWD and soil porosity, which may be viewed as soil quality indices. Results of this study are promising for developing more accurate pedotransfer functions.

Keywords: particle size distribution, soil texture, hydraulic conductivity, pedotransfer functions

Procedia PDF Downloads 279

Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 234

Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

Procedia PDF Downloads 519

Authors: Sílvia tuyà Viñas, Bruno Fernández-Valdés, Carla Pérez-Chirinos, Monica Morral-Yepes, Lucas del Campo Montoliu, Gerard Moras Feliu

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Some researchers stated that whole body vibration (WBV) generates postural destabilization, although there is no extensive research. Therefore, the aim of this study was to analyze movement variability when performing a half-squat with a different type of ballasts and rhythms with (V) and without (NV) WBV in male athletes using entropy. Twelve experienced in strength training males (age: 21.24  2.35 years, height: 176.83  5.80 cm, body mass: 70.63  8.58 kg) performed a half-squat with weighted vest (WV), dumbbells (D), and a bar with the weights suspended with elastic bands (B), in V and NV at 40 bpm and 60 bpm. Subjects performed one set of twelve repetitions of each situation, composed by the combination of the three factors. The movement variability was analyzed by calculating the Sample Entropy (SampEn) of the total acceleration signal recorded at the waist. In V, significant differences were found between D and WV (p<0.001; ES: 2.87 at 40 bpm; p<0.001; ES: 3.17 at 60 bpm) and between the B and WV at both rhythms (p<0.001; ES: 3.12 at 40 bpm; p<0.001; ES: 2.93 at 60 bpm) and a higher SampEn was obtained at 40 bpm with all ballasts (p<0.001; ES of WV: 1.22; ES of D: 4.49; ES of B: 4.03). No significant differences were found in NV. WBV is a disturbing and destabilizing stimulus. Strength and conditioning coaches should choose the combination of ballast and rhythm of execution according to the level and objectives of each athlete.

Keywords: accelerometry, destabilization, entropy, movement variability, resistance training

Procedia PDF Downloads 179

Authors: Jie Zheng, Ruifeng Luo

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The United States is the world’s most developed economy with the strongest military power. China is the fastest growing country with growing comprehensive strength and its economic strength is second only to the US. However, the conflict between them is getting serious in recent years. President’s address is the representative of a nation’s ideology. The paper has built up a small sized corpus of President Xi Jinping and Trump’s speech in Davos to investigate their respective use and types of metaphors and calculate the respective percentage of each type of metaphor. The result shows President Xi Jinping employs more metaphors than Trump. The metaphors of Xi includes “building” metaphor, “plant” metaphor, “journey” metaphor, “ship” metaphor, “traffic” metaphor, “nation is a person” metaphor, “show” metaphor, etc while Trump’s comprises “war” metaphor, “building” metaphor, “journey” metaphor, “traffic” metaphor, “tax” metaphor, “book” metaphor, etc. After investigating metaphor use differences, the paper makes an analysis of the underlying ideology between the two nations. China is willing to strengthen ties with all the countries all over the world and has built a platform of development for them and itself to go to the destination of social well being while the US pays much concern to itself, emphasizing its first leading position and is also willing to help its alliances to development. The paper’s comparison of the ideology difference between the two countries will help them get a better understanding and reduce the conflict to some extent.

Keywords: metaphor; corpus; ideology; conflict

Procedia PDF Downloads 147

Authors: Reuben Newell, Edward Archer, Alistair McIlhagger, Calvin Ralph

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Modern industry has developed a need for innovative 3D composite materials due to their attractive material properties. Composite materials are composed of a fibre reinforcement encased in a polymer matrix. The fibre reinforcement consists of warp, weft and binder yarns or tows woven together into a preform. The mechanical performance of composite material is largely controlled by the properties of the preform. As a result, the bulk of recent textile research has been focused on the design of high-strength preform architectures. Studies looking at optimisation of the weaving process have largely been neglected. It has been reported that yarns experience varying levels of damage during weaving, resulting in filament breakage and ultimately compromised composite mechanical performance. The weaving parameters involved in causing this yarn damage are not fully understood. Recent studies indicate that poor yarn tension control may be an influencing factor. As tension is increased, the yarn-to-yarn and yarn-to-weaving-equipment interactions are heightened, maximising damage. The correlation between yarn tension variation and weaving damage severity has never been adequately researched or quantified. A novel study is needed which accesses the influence of tension variation on the mechanical properties of woven yarns. This study has looked to quantify the variation of yarn tension throughout weaving and sought to link the impact of tension to weaving damage. Multiple yarns were randomly selected, and their tension was measured across the creel and shedding stages of weaving, using a hand-held tension meter. Sections of the same yarn were subsequently cut from the loom machine and tensile tested. A comparison study was made between the tensile strength of pristine and tensioned yarns to determine the induced weaving damage. Yarns from bobbins at the rear of the creel were under the least amount of tension (0.5-2.0N) compared to yarns positioned at the front of the creel (1.5-3.5N). This increase in tension has been linked to the sharp turn in the yarn path between bobbins at the front of the creel and creel I-board. Creel yarns under the lower tension suffered a 3% loss of tensile strength, compared to 7% for the greater tensioned yarns. During shedding, the tension on the yarns was higher than in the creel. The upper shed yarns were exposed to a decreased tension (3.0-4.5N) compared to the lower shed yarns (4.0-5.5N). Shed yarns under the lower tension suffered a 10% loss of tensile strength, compared to 14% for the greater tensioned yarns. Interestingly, the most severely damaged yarn was exposed to both the largest creel and shedding tensions. This study confirms for the first time that yarns under a greater level of tension suffer an increased amount of weaving damage. Significant variation of yarn tension has been identified across the creel and shedding stages of weaving. This leads to a variance of mechanical properties across the woven preform and ultimately the final composite part. The outcome from this study highlights the need for optimised yarn tension control during preform manufacture to minimize yarn-induced weaving damage.

Keywords: optimisation of preform manufacture, tensile testing of damaged tows, variation of yarn weaving tension, weaving damage

Procedia PDF Downloads 236

Authors: Ali Safdari-Vaighani

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Pricing financial contracts on several underlying assets received more and more interest as a demand for complex derivatives. The option pricing under asset price involving jump diffusion processes leads to the partial integral differential equation (PIDEs), which is an extension of the Black-Scholes PDE with a new integral term. The aim of this paper is to show how basket option prices in the jump diffusion models, mainly on the Merton model, can be computed using RBF based approximation methods. For a test problem, the RBF-PU method is applied for numerical solution of partial integral differential equation arising from the two-asset European vanilla put options. The numerical result shows the accuracy and efficiency of the presented method.

Keywords: basket option, jump diffusion, ‎radial basis function, RBF-PUM

Procedia PDF Downloads 354

Authors: Asim Majeed, Rehan Bhana, Mak Sharma, Rebecca Goode, Nizam Bolia, Mike Lloyd-Williams

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This study aims to investigate and explore the underlying causes of security concerns of customers emerged when WHSmith transformed its physical system to virtualized business model through NetSuite. NetSuite is essentially fully integrated software which helps transforming the physical system to virtualized business model. Modern organisations are moving away from traditional business models to cloud based models and consequently it is expected to have a better, secure and innovative environment for customers. The vital issue of the modern age race is the security when transforming virtualized through cloud based models and designers of interactive systems often misunderstand privacy and even often ignore it, thus causing concerns for users. The content analysis approach is being used to collect the qualitative data from 120 online bloggers including TRUSTPILOT. The results and finding provide useful new insights into the nature and form of security concerns of online users after they have used the WHSmith services offered online through their website. Findings have theoretical as well as practical implications for the successful adoption of cloud computing Business-to-Business model and similar systems.

Keywords: innovation, virtualization, cloud computing, organizational flexibility

Procedia PDF Downloads 384

Authors: Elisa Pautasso, Matteo Castagno, Michele Osella

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In the last decade, the emergence of new social needs as an effect of the economic crisis has stimulated the flourishing of business endeavours characterised by explicit social goals. Social start-ups, social enterprises or Corporate Social Responsibility operations carried out by traditional companies are quintessential examples in this regard. This paper analyses these kinds of initiatives in order to discover the main characteristics of social business models and to provide insights to social entrepreneurs for developing or improving their strategies. The research is conducted through the integration of literature review and case study analysis and, thanks to the recognition of the importance of both profits and social impacts as the key success factors for a social business model, proposes a framework for identifying indicators suitable for measuring the social impacts generated.

Keywords: business model, case study, impacts, social business

Procedia PDF Downloads 349

Authors: Al. I. Diwedar, Moheb Iskander, Mohamed Yossef, Ahmed ElKut, Noha Fouad, Radwa Fathy, Mustafa M. Almaghraby, Amira Samir, Ahmed Romya, Nourhan Hassan, Asmaa Abo Zed, Bas Reijmerink, Julien Groenenboom

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Coastal problems are stressing the coastal environment due to its complexity. The dynamic interaction between the sea and the land results in serious problems that threaten coastal areas worldwide, in addition to human interventions and activities. This makes the coastal environment highly vulnerable to natural processes like flooding, erosion, and the impact of human activities as pollution. Protecting and preserving this vulnerable coastal zone with its valuable ecosystems calls for addressing the coastal problems. This, in the end, will support the sustainability of the coastal communities and maintain the current and future generations. Consequently applying suitable management strategies and sustainable development that consider the unique characteristics of the coastal system is a must. The coastal management philosophy aims to solve the conflicts of interest between human development activities and this dynamic nature. Modeling emerges as a successful tool that provides support to decision-makers, engineers, and researchers for better management practices. Modeling tools proved that it is accurate and reliable in prediction. With its capability to integrate data from various sources such as bathymetric surveys, satellite images, and meteorological data, it offers the possibility for engineers and scientists to understand this complex dynamic system and get in-depth into the interaction between both the natural and human-induced factors. This enables decision-makers to make informed choices and develop effective strategies for sustainable development and risk mitigation of the coastal zone. The application of modeling tools supports the evaluation of various scenarios by affording the possibility to simulate and forecast different coastal processes from the hydrodynamic and wave actions and the resulting flooding and erosion. The state-of-the-art application of modeling tools in coastal management allows for better understanding and predicting coastal processes, optimizing infrastructure planning and design, supporting ecosystem-based approaches, assessing climate change impacts, managing hazards, and finally facilitating stakeholder engagement. This paper emphasizes the role of hydraulic models in enhancing the management of coastal problems by discussing the diverse applications of modeling in coastal management. It highlights the modelling role in understanding complex coastal processes, and predicting outcomes. The importance of informing decision-makers with modeling results which gives technical and scientific support to achieve sustainable coastal development and protection.

Keywords: coastal problems, coastal management, hydraulic model, numerical model, physical model

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Authors: Souad Romdhane, Lotfi Belkacem

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When referring to actuarial analysis of lifetime, only models accounting for observable risk factors have been developed. Within this context, Cox proportional hazards model (CPH model) is commonly used to assess the effects of observable covariates as gender, age, smoking habits, on the hazard rates. These covariates may fail to fully account for the true lifetime interval. This may be due to the existence of another random variable (frailty) that is still being ignored. The aim of this paper is to examine the shared frailty issue in the Cox proportional hazard model by including two different parametric forms of frailty into the hazard function. Four estimated methods are used to fit them. The performance of the parameter estimates is assessed and compared between the classical Cox model and these frailty models through a real-life data set from the Quebec Pension Plan and then using a more general simulation study. This performance is investigated in terms of the bias of point estimates and their empirical standard errors in both fixed and random effect parts. Both the simulation and the real dataset studies showed differences between classical Cox model and shared frailty model.

Keywords: life insurance-pension plan, survival analysis, risk factors, cox proportional hazards model, multivariate failure-time data, shared frailty, simulations study

Procedia PDF Downloads 359

Authors: Achim Kampker, Peter Burggräf, Moritz Krunke, Hanno Voet

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As a reaction to current challenges in factory planning, many companies think about introducing factory standards to lower planning times and decrease planning costs. If these factory standards are set-up with a high level of modularity, they are defined as modular factory systems. This paper deals with the main current problems in the application of modular factory systems in practice and presents a solution approach with its basic models. The methodology is based on methods from factory planning but also uses the tools of other disciplines like product development or technology management to deal with the high complexity, which the development of modular factory systems implies. The four basic models that such a methodology has to contain are introduced and pointed out.

Keywords: factory planning, modular factory systems, factory standards, cost-benefit analysis

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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: L. Joseph, B. Farid, F. Ravelet

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Fundamental studies were performed on bifurcation, instabilities and turbulence in Taylor-Couette flow and applied to many engineering applications like astrophysics models in the accretion disks, shrouded fans, and electric motors. Such rotating machinery performances need to have a better understanding of the fluid flow distribution to quantify the power losses and the heat transfer distribution. The present investigation is focused on high gap ratio of Taylor-Couette flow with high rotational speeds, for smooth and grooved surfaces. So far, few works has been done in a very narrow gap and with very high rotation rates and, to the best of our knowledge, not with this combination with grooved surface. We study numerically the turbulent flow between two coaxial cylinders where R1 and R2 are the inner and outer radii respectively, where only the inner is rotating. The gap between the rotor and the stator varies between 0.5 and 2 mm, which corresponds to a radius ratio η = R1/R2 between 0.96 and 0.99 and an aspect ratio Γ= L/d between 50 and 200, where L is the length of the rotor and d being the gap between the two cylinders. The scaling of the torque with the Reynolds number is determined at different gaps for different smooth and grooved surfaces (and also with different number of grooves). The fluid in the gap is air. Re varies between 8000 and 30000. Another dimensionless parameter that plays an important role in the distinction of the regime of the flow is the Taylor number that corresponds to the ratio between the centrifugal forces and the viscous forces (from 6.7 X 105 to 4.2 X 107). The torque will be first evaluated with RANS and U-RANS models, and compared to empirical models and experimental results. A mesh convergence study has been done for each rotor-stator combination. The results of the torque are compared to different meshes in 2D dimensions. For the smooth surfaces, the models used overestimate the torque compared to the empirical equations that exist in the bibliography. The closest models to the empirical models are those solving the equations near to the wall. The greatest torque achieved with grooved surface. The tangential velocity in the gap was always higher in between the rotor and the stator and not on the wall of rotor. Also the greater one was in the groove in the recirculation zones. In order to avoid endwall effects, long cylinders are used in our setup (100 mm), torque is measured by a co-rotating torquemeter. The rotor is driven by an air turbine of an automotive turbo-compressor for high angular velocities. The results of the experimental measurements are at rotational speed of up to 50 000 rpm. The first experimental results are in agreement with numerical ones. Currently, quantitative study is performed on grooved surface, to determine the effect of number of grooves on the torque, experimentally and numerically.

Keywords: Taylor-Couette flow, high gap ratio, grooved surface, high speed

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Authors: Abdulsamee Halahla, Emad Allout

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The purpose of this research is to study the behavior of exterior beam-column joints (BCJs) strengthened with ultra-high performance concrete (UHPC), in terms of the shear strength and maximum displacement using pushover analysis at the tip of the beam. A finite element (F.E) analysis was performed to study three main parameters – the level of the axial load in the column (N), the beam shear reinforcement (Av/s)B, and the effect of using UHPC. The normal concrete at the studied joint region was replaced by UHPC. The model was verified by using experimental results taken from the literature. The results showed that the UHPC contributed to the transference of the plastic hinge from the joint to the beam-column interface. In addition, the strength of the UHPC-strengthened joints was enhanced dramatically from 8% to 38% for the joints subjected to 12.8MPa and zero axial loads, respectively. Moreover, the UHPC contributed in improving the maximum deflection. This improvement amounted to 1% and 176% for the joints subjected to zero and 12.8MPa axial load, respectively.

Keywords: ultra high performance concrete, ductility, reinforced concrete joints, finite element modeling, nonlinear behavior; pushover analysis

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Authors: Laurent Debailleux

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Although past research has focused on parameters influencing the vulnerability of brick and its decay, in practice ancient fired clay bricks are usually replaced without any particular assessment of their characteristics. This paper presents results of non-destructive Schmidt hammer tests performed on ancient fired clay bricks sampled from historic masonry. Samples under study were manufactured between the 18th and 20th century and came from facades and interior walls. Tests were performed on three distinct brick surfaces, depending on their position within the masonry unit. Schmidt hammer tests were carried out in order to measure the mean rebound value (Rn), which refers to the resistance of the surface to successive impacts of the hammer plunger tip. Results indicate that rebound values increased with successive impacts at the same point. Therefore, mean Schmidt hammer rebound values (Rn), limited to the first impact on a surface minimises the estimation of compressive strength. In addition, the results illustrate that this technique is sensitive enough to measure weathering differences, even for different surfaces of a particular sample. Finally, the paper also highlights the relevance of considering the position of the brick within the masonry when conducting particular assessments of the material’s strength.

Keywords: brick, non-destructive tests, rebound number, Schmidt hammer, weathering grade

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Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

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Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: M. S. Mahmoud

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In this paper, the adsorption performance of a novel environmental friendly material, calcium alginate gel beads as a non-conventional technique for the successful removal of copper ions from aqueous solution are reported on. Batch equilibrium studies were carried out to evaluate the adsorption capacity and process parameters such as pH, adsorbent dosages, initial metal ion concentrations, stirring rates and contact times. It was observed that the optimum pH for maximum copper ions adsorption was at pH 5.0. For all contact times, an increase in copper ions concentration resulted in decrease in the percent of copper ions removal. Langmuir and Freundlich's isothermal models were used to describe the experimental adsorption. Adsorbent was characterization using Fourier transform-infrared (FT-IR) spectroscopy and Transmission electron microscopy (TEM).

Keywords: adsorption, alginate polymer, isothermal models, equilibrium

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Authors: Zhengyi Kong, Seung-Eock Kim

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Single angle connections, which are bolted to the beam web and the column flange, are studied to investigate moment-rotation behavior. Elastic–perfectly plastic material behavior is assumed. ABAQUS software is used to analyze the nonlinear behavior of a single angle connection. The same geometric and material conditions with Yanglin Gong’s test are used for verifying finite element models. Since Kishi and Chen’s Power model and Lee and Moon’s Log model are accurate only for a limited range, simpler and more accurate hyperbolic function models are proposed. The equation for calculating rotation at ultimate moment is first proposed.

Keywords: finite element method, moment and rotation, rotation at ultimate moment, single-web angle connections

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Authors: Muhammad Naveed Tahir, Wang Yingkuan, Huang Wenjiang, Raheel Osman

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Numerous models used in prediction and decision-making process but most of them are linear in natural environment, and linear models reach their limitations with non-linearity in data. Therefore accurate estimation is difficult. Artificial Neural Networks (ANN) found extensive acceptance to address the modeling of the complex real world for the non-linear environment. ANN’s have more general and flexible functional forms than traditional statistical methods can effectively deal with. The link between information technology and agriculture will become more firm in the near future. Monitoring crop biophysical properties non-destructively can provide a rapid and accurate understanding of its response to various environmental influences. Crop chlorophyll content is an important indicator of crop health and therefore the estimation of crop yield. In recent years, remote sensing has been accepted as a robust tool for site-specific management by detecting crop parameters at both local and large scales. The present research combined the ANN model with satellite-derived chlorophyll indices from LANDSAT 8 imagery for predicting real-time wheat chlorophyll estimation. The cloud-free scenes of LANDSAT 8 were acquired (Feb-March 2016-17) at the same time when ground-truthing campaign was performed for chlorophyll estimation by using SPAD-502. Different vegetation indices were derived from LANDSAT 8 imagery using ERADAS Imagine (v.2014) software for chlorophyll determination. The vegetation indices were including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Chlorophyll Absorbed Ratio Index (CARI), Modified Chlorophyll Absorbed Ratio Index (MCARI) and Transformed Chlorophyll Absorbed Ratio index (TCARI). For ANN modeling, MATLAB and SPSS (ANN) tools were used. Multilayer Perceptron (MLP) in MATLAB provided very satisfactory results. For training purpose of MLP 61.7% of the data, for validation purpose 28.3% of data and rest 10% of data were used to evaluate and validate the ANN model results. For error evaluation, sum of squares error and relative error were used. ANN model summery showed that sum of squares error of 10.786, the average overall relative error was .099. The MCARI and NDVI were revealed to be more sensitive indices for assessing wheat chlorophyll content with the highest coefficient of determination R²=0.93 and 0.90 respectively. The results suggested that use of high spatial resolution satellite imagery for the retrieval of crop chlorophyll content by using ANN model provides accurate, reliable assessment of crop health status at a larger scale which can help in managing crop nutrition requirement in real time.

Keywords: ANN, chlorophyll content, chlorophyll indices, satellite images, wheat

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: B. S. Al-Tulaian, M. J. Al-Shannag, A. M. Al-Hozaimy

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The development of new construction materials using recycled plastic is important to both the construction and the plastic recycling industries. Manufacturing of fibers from industrial or post-consumer plastic waste is an attractive approach with such benefits as concrete performance enhancement, and reduced needs for land filling. The main objective of this study is to investigate the effect of plastic fibers obtained locally from recycled waste on plastic shrinkage cracking of ordinary cement based mortar. Parameters investigated include: Fiber length ranging from 20 to 50 mm, and fiber volume fraction ranging from 0% to 1.5% by volume. The test results showed significant improvement in crack arresting mechanism and substantial reduction in the surface area of cracks for the mortar reinforced with recycled plastic fibers compared to plain mortar. Furthermore, test results indicated that there was a slight decrease in compressive strength of mortar reinforced with different lengths and contents of recycled fibers compared to plain mortar. This study suggests that adding more than 1% of RP fibers to mortar, can be used effectively for controlling plastic shrinkage cracking of cement based mortar, and thus results in waste reduction and resources conservation.

Keywords: mortar, plastic, shrinkage cracking, compressive strength, RF recycled fibers

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Yelbek B. Utepov, Assel S. Tulebekova, Alizhan B. Kazkeyev

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Maturity sensors are used to determine concrete strength by the non-destructive method. The method of placement of the maturity sensors determines their number required for a certain frame of a monolithic building. Previous studies weakly describe this aspect, giving only logical assumptions. This paper proposes a cheap prototype of an embedded wireless sensor for monitoring concrete structures, as well as an alternative strategy for placing sensors based on the transitional boundaries of the temperature distribution of concrete curing, which were determined by building a heat map of the temperature distribution, where unknown values are calculated by the method of inverse distance weighing. The developed prototype can simultaneously measure temperature and relative humidity over a smartphone-controlled time interval. It implements a maturity method to assess the in-situ strength of concrete, which is considered an alternative to the traditional shock impulse and compression testing method used in Kazakhstan. The prototype was tested in laboratory and field conditions. The tests were aimed at studying the effect of internal and external temperature and relative humidity on concrete's strength gain. Based on an experimentally poured concrete slab with randomly integrated maturity sensors, it was determined that the transition boundaries form elliptical forms. Temperature distribution over the largest diameter of the ellipses was plotted, resulting in correct and inverted parabolas. As a result, the distance between the closest opposite crossing points of the parabolas is accepted as the maximum permissible step for setting the maturity sensors. The proposed placement strategy can be applied to sensors that measure various continuous phenomena such as relative humidity. Prototype testing has also revealed Bluetooth inconvenience due to weak signal and inability to access multiple prototypes simultaneously. For this reason, further prototype upgrades are planned in future work.

Keywords: heat map, placement strategy, temperature and relative humidity, wireless embedded sensor

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Authors: Amir Asadi, Karim Habib, Robert J. Moon, Kyriaki Kalaitzidou

Abstract:

There has been considerable interest in cellulose nanomaterials (CN) as polymer and polymer composites reinforcement due to their high specific modulus and strength, low density and toxicity, and accessible hydroxyl side groups that can be readily chemically modified. The focus of this study is making lightweight composites for better fuel efficiency and lower CO2 emission in auto industries with no compromise on mechanical performance using a scalable technique that can be easily integrated in sheet molding compound (SMC) manufacturing lines. Light weighting will be achieved by replacing part of the heavier components, i.e. glass fibers (GF), with a small amount of cellulose nanocrystals (CNC) in short GF/epoxy composites made using SMC. CNC will be introduced as coating of the GF rovings prior to their use in the SMC line. The employed coating method is similar to the fiber sizing technique commonly used and thus it can be easily scaled and integrated to industrial SMC lines. This will be an alternative route to the most techniques that involve dispersing CN in polymer matrix, in which the nanomaterials agglomeration limits the capability for scaling up in an industrial production. We have demonstrated that incorporating CNC as a coating on GF surface by immersing the GF in CNC aqueous suspensions, a simple and scalable technique, increases the interfacial shear strength (IFSS) by ~69% compared to the composites produced by uncoated GF, suggesting an enhancement of stress transfer across the GF/matrix interface. As a result of IFSS enhancement, incorporation of 0.17 wt% CNC in the composite results in increases of ~10% in both elastic modulus and tensile strength, and 40 % and 43 % in flexural modulus and strength respectively. We have also determined that dispersing 1.4 and 2 wt% CNC in the epoxy matrix of short GF/epoxy SMC composites by sonication allows removing 10 wt% GF with no penalty on tensile and flexural properties leading to 7.5% lighter composites. Although sonication is a scalable technique, it is not quite as simple and inexpensive as coating the GF by passing through an aqueous suspension of CNC. In this study, the above findings are integrated to 1) investigate the effect of CNC content on mechanical properties by passing the GF rovings through CNC aqueous suspension with various concentrations (0-5%) and 2) determine the optimum ratio of the added CNC to the removed GF to achieve the maximum possible weight reduction with no cost on mechanical performance of the SMC composites. The results of this study are of industrial relevance, providing a path toward producing high volume lightweight and mechanically enhanced SMC composites using cellulose nanomaterials.

Keywords: cellulose nanocrystals, light weight polymer-matrix composites, mechanical properties, sheet molding compound (SMC)

Procedia PDF Downloads 225