Search results for: fingertip skin models

Authors: Beverley Gallimore

Abstract:

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 60

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

Abstract:

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 257

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

Abstract:

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

Procedia PDF Downloads 325

Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

Abstract:

The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

Procedia PDF Downloads 144

Authors: Ali Safdari-Vaighani

Abstract:

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 331

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

Abstract:

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 367

Authors: Elisa Pautasso, Matteo Castagno, Michele Osella

Abstract:

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 321

Authors: Souad Romdhane, Lotfi Belkacem

Abstract:

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 339

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

Abstract:

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

Procedia PDF Downloads 580

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

Abstract:

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

Procedia PDF Downloads 385

Authors: Ilma Robo, Saimir Heta, Rialda Xhizdari, Kers Kapaj

Abstract:

Background: The ideal disinfectant for surfaces, instruments, air, skin, both in dentistry and in the fields of medicine, does not exist.This is for the sole reason that all the characteristics of the ideal disinfectant cannot be contained in one; these are the characteristics that if one of them is emphasized, it will conflict with the other. A disinfectant must be stable, not be affected by changes in the environmental conditions where it stands, which means that it should not be affected by an increase in temperature or an increase in the humidity of the environment. Both of these elements contradict the other element of the idea of an ideal disinfectant, as they disrupt the solubility ratios of the base substance of the disinfectant versus the diluent. Material and methods: The study aims to extract the constant of each disinfectant/antiseptic used during dental disinfection protocols, accompanied by the side effects of the surface of the skin or mucosa where it is applied in the role of antiseptic. In the end, attempts were made to draw conclusions about the best possible combination for disinfectants after a dental procedure, based on the data extracted from the basic literature required during the development of the pharmacology module, as a module in the formation of a dentist, against data published in the literature. Results: The sensitivity of the disinfectant to changes in the atmospheric conditions of the environment where it is kept is a known fact. The care against this element is always accompanied by the advice on the application of the specific disinfectant, in order to have the desired clinical result. The constants of disinfectants according to the classification based on the data collected and presented are for alcohols 70-120, glycols 0.2, aldehydes 30-200, phenols 15-60, acids 100, povidone iodine halogens 5-75, hypochlorous acid halogens 150, sodium hypochlorite halogens 30-35, oxidants 18-60, metals 0.2-10. The part of halogens should be singled out, where specific results were obtained according to the representatives of this class, since it is these representatives that find scope for clinical application in dentistry. Conclusions: The search for the "ideal", in the conditions where its defining criteria are also established, not only for disinfectants but also for any medication or pharmaceutical product, is an ongoing search, without any definitive results. In this mine of data in the published literature if there is something fixed, calculable, such as the specific constant for disinfectants, the search for the ideal is more concrete. During the disinfection protocols, different disinfectants are applied since the field of action is different, including water, air, aspiration devices, tools, disinfectants used in full accordance with the production indications.

Keywords: disinfectant, constant, ideal, side effects

Procedia PDF Downloads 52

Authors: Cheng-yu Hsieh, Bo Zhang, Ahmed Hambaba

Abstract:

Stock market prediction has long been an area of interest for both expert analysts and investors, driven by its complexity and the noisy, volatile conditions it operates under. This research examines the efficacy of combining the Patch Time Series Transformer (PatchTST) with wavelet transforms, specifically focusing on Haar and Daubechies wavelets, in forecasting the adjusted closing price of the S&P 500 index for the following day. By comparing the performance of the augmented PatchTST models with traditional predictive models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, this study highlights significant enhancements in prediction accuracy. The integration of the Daubechies wavelet with PatchTST notably excels, surpassing other configurations and conventional models in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE). The success of the PatchTST model paired with Daubechies wavelet is attributed to its superior capability in extracting detailed signal information and eliminating irrelevant noise, thus proving to be an effective approach for financial time series forecasting.

Keywords: deep learning, financial forecasting, stock market prediction, patch time series transformer, wavelet transform

Procedia PDF Downloads 20

Authors: Stanislav Babaev, Vladimir Cuk, Sjef Cobben, Jan Desmet

Abstract:

The procedures for performing harmonic studies for medium-voltage distribution feeders have become relatively mature topics since the early 1980s. The efforts of various electric power engineers and researchers were mainly focused on handling large harmonic non-linear loads connected scarcely at several buses of medium-voltage feeders. In order to assess the impact of these loads on the voltage quality of the distribution system, specific modeling and simulation strategies were proposed. These methodologies could deliver a reasonable estimation accuracy given the requirements of least computational efforts and reduced complexity. To uphold these requirements, certain analysis assumptions have been made, which became de facto standards for establishing guidelines for harmonic analysis. Among others, typical assumptions include balanced conditions of the study and the negligible impact of impedance frequency characteristics of various power system components. In latter, skin and proximity effects are usually omitted, and resistance and reactance values are modeled based on the theoretical equations. Further, the simplifications of the modelling routine have led to the commonly accepted practice of neglecting phase angle diversity effects. This is mainly associated with developed load models, which only in a handful of cases are representing the complete harmonic behavior of a certain device as well as accounting on the harmonic interaction between grid harmonic voltages and harmonic currents. While these modelling practices were proven to be reasonably effective for medium-voltage levels, similar approaches have been adopted for low-voltage distribution systems. Given modern conditions and massive increase in usage of residential electronic devices, recent and ongoing boom of electric vehicles, and large-scale installing of distributed solar power, the harmonics in current low-voltage grids are characterized by high degree of variability and demonstrate sufficient diversity leading to a certain level of cancellation effects. It is obvious, that new modelling algorithms overcoming previously made assumptions have to be accepted. In this work, a simulation approach aimed to deal with some of the typical assumptions is proposed. A practical low-voltage feeder is modeled in PowerFactory. In order to demonstrate the importance of diversity effect and harmonic interaction, previously developed measurement-based models of photovoltaic inverter and battery charger are used as loads. The Python-based script aiming to supply varying voltage background distortion profile and the associated current harmonic response of loads is used as the core of unbalanced simulation. Furthermore, the impact of uncertainty of feeder frequency-impedance characteristics on total harmonic distortion levels is shown along with scenarios involving linear resistive loads, which further alter the impedance of the system. The comparative analysis demonstrates sufficient differences with cases when all the assumptions are in place, and results indicate that new modelling and simulation procedures need to be adopted for low-voltage distribution systems with high penetration of non-linear loads and renewable generation.

Keywords: electric power system, harmonic distortion, power quality, public low-voltage network, harmonic modelling

Procedia PDF Downloads 138

Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

Abstract:

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

Procedia PDF Downloads 170

Authors: M. S. Mahmoud

Abstract:

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

Procedia PDF Downloads 434

Authors: Zhengyi Kong, Seung-Eock Kim

Abstract:

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

Procedia PDF Downloads 409

Authors: Oluwaponmile D. Alao

Abstract:

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

Procedia PDF Downloads 323

Authors: Azita Ramezani, Atousa Ramezani

Abstract:

In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense.

Keywords: fusion models, cyber threat defense, windows malware, clustering, random forests, support vector machines (SVM), accuracy, f1-score, cybersecurity, malicious code detection

Procedia PDF Downloads 47

Authors: Mette Dalgaard Nielsen

Abstract:

The paper serves the purpose of providing an eco-systemic typology of fashion resale business models in Denmark while pointing to possibilities to learn from its wisdom during a time when a fundamental break with the dominant linear fashion paradigm has become inevitable. As we transgress planetary boundaries and can no longer continue the unsustainable path of over-exploiting the Earth’s resources, the global fashion industry faces a tremendous need for change. One of the preferred answers to the fashion industry’s sustainability crises lies in the circular economy, which aims to maximize the utilization of resources by keeping garments in use for longer. Thus, in the context of fashion, resale business models that allow pre-owned garments to change hands with the purpose of being reused in continuous cycles are considered to be among the most efficient forms of circularity. Methodologies: The paper is based on empirical data from an ongoing project and a series of qualitative pilot studies that have been conducted on the Danish resale market over a 2-year time period from Fall 2021 to Fall 2023. The methodological framework is comprised of (n) ethnography and fieldwork in selected resale environments, as well as semi-structured interviews and a workshop with eight business partners from the Danish fashion and textiles industry. By focusing on the real-world circulation of pre-owned garments, which is enabled by the identified resale business models, the research lets go of simplistic hypotheses to the benefit of dynamic, vibrant and non-linear processes. As such, the paper contributes to the emerging research field of circular economy and fashion, which finds itself in a critical need to move from non-verified concepts and theories to empirical evidence. Findings: Based on the empirical data and anchored in the business partners, the paper analyses and presents five distinct resale business models with different product, service and design characteristics. These are 1) branded resale, 2) trade-in resale, 3) peer-2-peer resale, 4) resale boutiques and consignment shops and 5) resale shelf/square meter stores and flea markets. Together, the five business models represent a plurality of resale-promoting business model design elements that have been found to contribute to the circulation of pre-owned garments in various ways for different garments, users and businesses in Denmark. Hence, the provided typology points to the necessity of prioritizing several rather than single resale business model designs, services and initiatives for the resale market to help reconfigure the linear fashion model and create a circular-ish future. Conclusions: The article represents a twofold research ambition by 1) presenting an original, up-to-date eco-systemic typology of resale business models in Denmark and 2) using the typology and its eco-systemic traits as a tool to understand different business model design elements and possibilities to help fashion grow out of its linear growth model. By basing the typology on eco-systemic mechanisms and actual exemplars of resale business models, it becomes possible to envision the contours of a genuine alternative to business as usual that ultimately helps bend the linear fashion model towards circularity.

Keywords: circular business models, circular economy, fashion, resale, strategic design, sustainability

Procedia PDF Downloads 40

Authors: M. K. Witek-Hajduk, T. M. Napiórkowski

Abstract:

A number of studies discussed the topic of benefits of retailers-manufacturers cooperation and coopetition. However, there are only few publications focused on the benefits of cooperation and coopetition between retailers and their suppliers of durable consumer goods; especially in the context of business model of cooperating partners. This paper aims to provide a clustering approach to segment retailers selling consumer durables according to the benefits they obtain from their cooperation with key manufacturers and differentiate the said retailers’ in term of the business models of cooperating partners. For the purpose of the study, a survey (with a CATI method) collected data on 603 consumer durables retailers present on the Polish market. Retailers are clustered both, with hierarchical and non-hierarchical methods. Five distinctive groups of consumer durables’ retailers are (based on the studied benefits) identified using the two-stage clustering approach. The clusters are then characterized with a set of exogenous variables, key of which are business models employed by the retailer and its partnering key manufacturer. The paper finds that the a combination of a medium sized retailer classified as an Integrator with a chiefly domestic capital and a manufacturer categorized as a Market Player will yield the highest benefits. On the other side of the spectrum is medium sized Distributor retailer with solely domestic capital – in this case, the business model of the cooperating manufactrer appears to be irreleveant. This paper is the one of the first empirical study using cluster analysis on primary data that defines the types of cooperation between consumer durables’ retailers and manufacturers – their key suppliers. The analysis integrates a perspective of both retailers’ and manufacturers’ business models and matches them with individual and joint benefits.

Keywords: benefits of cooperation, business model, cluster analysis, retailer-manufacturer cooperation

Procedia PDF Downloads 239

Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

Abstract:

Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

Procedia PDF Downloads 358

Authors: Xian Li, Qing-Guo Wang, Jiangshuai Huang, Jidong Liu, Ming Yu, Tan Kok Poh

Abstract:

In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly.

Keywords: power industry, electricity demand, modeling, forecasting

Procedia PDF Downloads 621

Authors: Prajakta Musale, Bhargav Parlikar, Sakshi Parkhi, Anshu Parihar, Aryan Parikh, Diksha Parasharam, Parth Jadhav

Abstract:

AR technology is basically a development of VR technology that harnesses the power of computers to be able to read the surroundings and create projections of digital models in the real world for the purpose of visualization, demonstration, and education. It has been applied to education, fields of prototyping in product design, development of medical models, battle strategy in the military and many other fields. Our Engineering Design and Innovation (EDAI) project focuses on the usage of augmented reality, visual mapping, and 3d-visualization along with animation and text boxes to help students in fields of education get a rough idea of the concepts such as flow and mechanical movements that may be hard to visualize at first glance.

Keywords: spatial mapping, ARKit, depth sensing, real-time rendering

Procedia PDF Downloads 47

Authors: Abbas Kamalibandpey, Alain Beland, Joseph Mukendi Kabuya

Abstract:

Since a rock mass is a discontinuum medium, its behaviour is governed by discontinuities such as faults, joint sets, lithologic contact, and bedding planes. Thus, rock slope stability analysis in jointed rock masses is largely dependent upon discontinuities constitutive equations. This paper studies the difference between the Mohr-Coulomb (MC) and the Barton-Bandis (BB) joint constitutive numerical models for lithological contacts and joint sets. For the rock in these models, generalized Hoek-Brown criteria have been considered. The joint roughness coefficient (JRC) and the joint wall compressive strength (JCS) are vital parameters in the BB model. The numerical models are applied to the rock slope stability analysis in the Mont-Wright (MW) mine. The Mont-Wright mine is owned and operated by ArcelorMittal Mining Canada (AMMC), one of the largest iron-ore open pit operations in Canada. In this regard, one of the high walls of the mine has been selected to undergo slope stability analysis with RS2D software, finite element method. Three piezometers have been installed in this zone to record pore water pressure and it is monitored by radar. In this zone, the AMP-IF and QRMS-IF contacts and very persistent and altered joint sets in IF control the rock slope behaviour. The height of the slope is more than 250 m and consists of different lithologies such as AMP, IF, GN, QRMS, and QR. To apply the B-B model, the joint sets and geological contacts have been scanned by Maptek, and their JRC has been calculated by different methods. The numerical studies reveal that the JRC of geological contacts, AMP-IF and QRMS-IF, and joint sets in IF had a significant influence on the safety factor. After evaluating the results of rock slope stability analysis and the radar data, the B-B constitutive equation for discontinuities has shown acceptable results to the real condition in the mine. It should be noted that the difference in safety factors in MC and BB joint constitutive models in some cases is more than 30%.

Keywords: barton-Bandis criterion, Hoek-brown and Mohr-Coulomb criteria, open pit, slope stability

Procedia PDF Downloads 80

Authors: Anastasiia Yu. Timofeeva

Abstract:

Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: grade point average, orthogonal regression, penalized regression spline, locally weighted regression

Procedia PDF Downloads 394

Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez

Abstract:

In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: information theory, prediction model, prosthetic alignment, transtibial prosthesis

Procedia PDF Downloads 235

Authors: Ahmed Elbeheri, Tarek Zayed

Abstract:

Steel bridges deterioration has been one of the problems in North America for the last years. Steel bridges deterioration mainly attributed to the difficult weather conditions. Steel bridges suffer fatigue cracks and corrosion, which necessitate immediate inspection. Visual inspection is the most common technique for steel bridges inspection, but it depends on the inspector experience, conditions, and work environment. So many Non-destructive Evaluation (NDE) models have been developed use Non-destructive technologies to be more accurate, reliable and non-human dependent. Non-destructive techniques such as The Eddy Current Method, The Radiographic Method (RT), Ultra-Sonic Method (UT), Infra-red thermography and Laser technology have been used. Digital Image processing will be used for Corrosion detection as an Alternative for visual inspection. Different models had used grey-level and colored digital image for processing. However, color image proved to be better as it uses the color of the rust to distinguish it from the different backgrounds. The detection of the rust is an important process as it’s the first warning for the corrosion and a sign of coating erosion. To decide which is the steel element to be repainted and how urgent it is the percentage of rust should be calculated. In this paper, an image processing approach will be developed to detect corrosion and its severity. Two models were developed 1st to detect rust and 2nd to detect rust percentage.

Keywords: steel bridge, bridge inspection, steel corrosion, image processing

Procedia PDF Downloads 282

Authors: Pranav Gunhal

Abstract:

This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.

Keywords: sentiment analysis, twitter, Karnataka elections, congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, lok sabha elections

Procedia PDF Downloads 66

Authors: Woo Young Jung, Min Ho Kwon, Bu Seog Ju

Abstract:

A failure of the non-structural component can cause significant damages in critical facilities such as nuclear power plants and hospitals. Historically, it was reported that the damage from the leakage of sprinkler systems, resulted in the shutdown of hospitals for several weeks by the 1971 San Fernando and 1994 North Ridge earthquakes. In most cases, water leakages were observed at the cross joints, sprinkler heads, and T-joint connections in piping systems during and after the seismic events. Hence, the primary objective of this study was to understand the seismic performance of T-joint connections and to develop an analytical Finite Element (FE) model for the T-joint systems of 2-inch fire protection piping system in hospitals subjected to seismic ground motions. In order to evaluate the FE models of the piping systems using OpenSees, two types of materials were used: 1) Steel 02 materials and 2) Pinching 4 materials. Results of the current study revealed that the nonlinear moment-rotation FE models for the threaded T-joint reconciled well with the experimental results in both FE material models. However, the system-level fragility determined from multiple nonlinear time history analyses at the threaded T-joint was slightly different. The system-level fragility at the T-joint, determined by Pinching 4 material was more conservative than that of using Steel 02 material in the piping system.

Keywords: fragility, t-joint, piping, leakage, sprinkler

Procedia PDF Downloads 284

Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

Abstract:

The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

Procedia PDF Downloads 176