Search results for: admission process model

Authors: C. Mekala, Indumathi M. Nambi

Abstract:

Nitrogen pollution in groundwater arising from wastewater and fertilizer application through vadose zone is a major problem and it causes a prime risk to groundwater based drinking water supplies. Nitrogenous compounds namely ammonium, nitrate and nitrite fate and transport in soil subsurface were studied experimentally. The major process like sorption, leaching, biotransformation involving microbial growth kinetics, and biological clogging due to biomass growth were assessed and modeled with advection-dispersion reaction equations for ammonium, nitrate and acetate in a saturated, heterogeneous soil medium. The transport process was coupled with freundlich sorption and monod inhibition kinetics for immobile bacteria and permeability reduction due to biomass growth will be verified and validated with the numerical model. This proposed mathematical model will be very helpful in the development of a management model for a sustainable and safe wastewater reuse strategies such as irrigation and groundwater recharge.

Keywords: nitrogen species transport, transformation, biological clogging, biokinetic parameters, contaminant transport model, saturated soil

Procedia PDF Downloads 372

Authors: Martha E. Ndeley

Abstract:

Operation management is the key element within the manufacturing process. However, during this process, there are a number of unforeseen disruptions that causes the process to a standstill which are, machine breakdown, employees absenteeism, improper scheduling. When this happens, it forces the shop flow to a rescheduling process and these strategy reschedules only a limited part of the initial schedule to match up with the pre-schedule at some point with the objective to create a new schedule that is reliable which in the long run gets disrupted. In this work, we have developed feedback control system that minimizes any form of disruption before the impact becomes severe, the model was tested in a paper manufacturing industries and the results revealed that, if the disruption is minimized at the initial state, the impact becomes unnoticeable.

Keywords: disruption, machine, absenteeism, scheduling

Procedia PDF Downloads 284

Authors: Yolina A. Petrova, Georgi I. Petkov

Abstract:

The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

Keywords: analogy-making, categorization, category learning, cognitive modeling, role-governed categories

Procedia PDF Downloads 120

Authors: Hsiu Hua Hu

Abstract:

Digital transformation has widely affected various industries. It provides technological innovation, process redesign, new business model construction, and talent value creation. This transformation not only allows organizations to obtain and deploy specific technologies and methods suitable for organizational reengineering but also is an important way to solve management problems in human resource (HR) reengineering, business efficiency, and process redesign. In this study, we present the results of a qualitative study that offers insight into a series of key feature of reengineering related to the digital transformation and how to create talent value when the companies successfully perform digital transformation and human resource reengineering, which is led by business digitalization strategies including talent planning, talent acquisition, talent adjustment, and talent development. Drawing from the qualitative investigation findings, we built an inductive model of HR reengineering, which aims to provide research and practical references on future digital transformation and management inquiry.

Keywords: talent value creation, digital transformation, HR reengineering, qualitative study

Procedia PDF Downloads 122

Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

Abstract:

Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

Procedia PDF Downloads 81

Authors: Camilo Souza, Lidia Melo, Fernanda Terra, Francisco Caio, Marcelo Reis

Abstract:

SIDIA is a research and development (R&D) institute that belongs to Samsung’s global software development process. The SIDIA’s Model Team (MT) is a part of Samsung’s Mobile Division Area, which is responsible for the development of Android releases embedded in Samsung mobile devices. Basically, in this software development process, the kickoff occurs in some strategic countries (e.g., South Korea) where some software requirements are applied and the initial software tests are performed. When the software achieves a more mature level, a new branch is derived, and the development continues in subsidiaries from other strategic countries (e.g., SIDIA-Brazil). However, even in the newly created branches, there are several interactions between developers from different nationalities in order to fix bugs reported during test activities, apply some specific requirements from partners and develop new features as well. Despite the GSD strategy contributes to improving software development, some challenges are also introduced as well. In this paper, we share the initial results about the application of the failure modes and effect analysis (FMEA) concept in the software development process followed by the SIDIA’s model team. The main goal was to identify and mitigate the process potential failures through the application of recommended actions. The initial results show that the application of the FMEA concept allows us to identify the potential failures in our GSD process as well as to propose corrective actions to mitigate them. Finally, FMEA encouraged members of different teams to take actions that contribute to improving our GSD process.

Keywords: global software development, potential failures, FMEA, recommended actions

Procedia PDF Downloads 203

Authors: Ayman M. Mansour, Bilal Hawashin, Hesham Alsalem

Abstract:

Faster delivery of laboratory test results is one of the most noticeable signs of good laboratory service and is often used as a key performance indicator of laboratory performance. Despite the availability of technology, the delivery time of clinical laboratory test results continues to be a cause of customer dissatisfaction which makes patients feel frustrated and they became careless to get their laboratory test results. The Medical Clinical Laboratory test results are highly sensitive and could harm patients especially with the severe case if they deliver in wrong time. Such results affect the treatment done by physicians if arrived at correct time efforts should, therefore, be made to ensure faster delivery of lab test results by utilizing new trusted, Robust and fast system. In this paper, we proposed a distributed Multi-Agent System to enhance and faster the process of laboratory test results delivery using SMS. The developed system relies on SMS messages because of the wide availability of GSM network comparing to the other network. The software provides the capability of knowledge sharing between different units and different laboratory medical centers. The system was built using java programming. To implement the proposed system we had many possible techniques. One of these is to use the peer-to-peer (P2P) model, where all the peers are treated equally and the service is distributed among all the peers of the network. However, for the pure P2P model, it is difficult to maintain the coherence of the network, discover new peers and ensure security. Also, security is a quite important issue since each node is allowed to join the network without any control mechanism. We thus take the hybrid P2P model, a model between the Client/Server model and the pure P2P model using GSM technology through SMS messages. This model satisfies our need. A GUI has been developed to provide the laboratory staff with the simple and easy way to interact with the system. This system provides quick response rate and the decision is faster than the manual methods. This will save patients life.

Keywords: multi-agent system, delivery process, GSM technology, clinical laboratory results

Procedia PDF Downloads 228

Authors: Yousuf N. Al Khamisi, Eduardo M. Hernandez, Khurshid M. Khan

Abstract:

Purpose: This paper presents a model of a Knowledge-Based (KB) using Lean Six Sigma (L6σ) principles to enhance the performance of healthcare leadership. Design/methodology/approach: Using L6σ principles to enhance healthcare leaders’ performance needs a pre-assessment of the healthcare organisation’s capabilities. The model will be developed using a rule-based approach of KB system. Thus, KB system embeds Gauging Absence of Pre-requisite (GAP) for benchmarking and Analytical Hierarchy Process (AHP) for prioritization. A comprehensive literature review will be covered for the main contents of the model with a typical output of GAP analysis and AHP. Findings: The proposed KB system benchmarks the current position of healthcare leadership with the ideal benchmark one (resulting from extensive evaluation by the KB/GAP/AHP system of international leadership concepts in healthcare environments). Research limitations/implications: Future work includes validating the implementation model in healthcare environments around the world. Originality/value: This paper presents a novel application of a hybrid KB combines of GAP and AHP methodology. It implements L6σ principles to enhance healthcare performance. This approach assists healthcare leaders’ decision making to reach performance improvement against a best practice benchmark.

Keywords: Lean Six Sigma (L6σ), Knowledge-Based System (KBS), healthcare leadership, Gauge Absence Prerequisites (GAP), Analytical Hierarchy Process (AHP)

Procedia PDF Downloads 143

Authors: D. Pérez, M. M. E. Alemany

Abstract:

In this work a Framework to model the Supply Chain (SC) Collaborative Planning (CP) Process is proposed, and particularly its Decisional view. The main Framework contributions with regards to previous related works are the following, 1) the consideration of not only the Decision view, the most important one due to the Process type, but other additional three views which are the Physical, Organisation and Information ones, closely related and complementing the Decision View, 2) the joint consideration of two interdependence types, the Temporal (among Decision Centres belonging to different Decision Levels) and Spatial (among Decision Centres belonging to the same Decision Level) to support the distributed Decision-Making process in SC where several decision Centres interact among them in a collaborative manner.

Keywords: collaborative planning, decision view, distributed decision-making, framework

Procedia PDF Downloads 440

Authors: Alonggot Limcharoen, Jintana Wannarat, Vorawat Panich

Abstract:

This study aims to investigate the balancing of the number of operators (Line Balancing technique) in the production line of hard disk drive components in order to increase efficiency. At present, the trend of using hard disk drives has continuously declined leading to limits in a company’s revenue potential. It is important to improve and develop the production process to create market share and to have the ability to compete with competitors with a higher value and quality. Therefore, an effective tool is needed to support such matters. In this research, the Arena program was applied to analyze the results both before and after the improvement. Finally, the precedent was used before proceeding with the real process. There were 14 work stations with 35 operators altogether in the RA production process where this study was conducted. In the actual process, the average production time was 84.03 seconds per product piece (by timing 30 times in each work station) along with a rating assessment by implementing the Westinghouse principles. This process showed that the rating was 123% underlying an assumption of 5% allowance time. Consequently, the standard time was 108.53 seconds per piece. The Takt time was calculated from customer needs divided by working duration in one day; 3.66 seconds per piece. Of these, the proper number of operators was 30 people. That meant five operators should be eliminated in order to increase the production process. After that, a production model was created from the actual process by using the Arena program to confirm model reliability; the outputs from imitation were compared with the original (actual process) and this comparison indicated that the same output meaning was reliable. Then, worker numbers and their job responsibilities were remodeled into the Arena program. Lastly, the efficiency of production process enhanced from 70.82% to 82.63% according to the target.

Keywords: hard disk drive, line balancing, ECRS, simulation, arena program

Procedia PDF Downloads 203

Authors: Anurag Savarnya

Abstract:

The reliability of the various parts of electric mining shovel has been assessed through the application of Weibull Model. The study was initiated to find reliability of components of electric mining shovel. The paper aims to optimize the reliability of components and increase the life cycle of component. A multilevel decomposition of the electric mining shovel was done and maintenance records were used to evaluate the failure data and appropriate system characterization was done to model the system in terms of reasonable number of components. The approach used develops a mathematical model to assess the reliability of the electric mining shovel components. The model can be used to predict reliability of components of the hydraulic mining shovel and system performance. Reliability is an inherent attribute to a system. When the life-cycle costs of a system are being analyzed, reliability plays an important role as a major driver of these costs and has considerable influence on system performance. It is an iterative process that begins with specification of reliability goals consistent with cost and performance objectives. The data were collected from an Indian open cast coal mine and the reliability of various components of the electric mining shovel has been assessed by following a Weibull Model.

Keywords: reliability, Weibull model, electric mining shovel

Procedia PDF Downloads 473

Authors: Aleš Kratochvíl, Svatomír Slavík

Abstract:

The paper presents an aircraft flutter suppression by active damping of supplementary control surface at trailing edge. The mathematical model of thin oscillation airfoil with control surface driven by pilot is developed. The supplementary control surface driven by control law is added. Active damping of flutter by several control law is present. The structural model of tailplane with an aerodynamic strip theory based on the airfoil model is developed by a finite element method. The optimization process of stiffens parameters is carried out to match the structural model with results from a ground vibration test of a small sport airplane. The implementation of supplementary control surface driven by control law is present. The active damping of tailplane model is shown.

Keywords: active damping, finite element method, flutter, tailplane model

Procedia PDF Downloads 271

Authors: Asmaa M. Marzouk, Abdallah M. Elshaer

Abstract:

Despite the well-recognized value of shopping as a revenue-generating resource, little effort was made to investigate what is the structure of tourists’ shopping behavior, which in turn, affect their travel experience. The purpose of this paper is to study the structure of tourists’ shopping process to better understand their shopping behavior by investigating factors that influence this activity other than hotels tyranny. This study specifically aims to propose a model incorporating those all variables. This empirical study investigates the shopping experience of international tourists using a questionnaire aimed to examine multinational samples selected from the tourist population visiting a specific destination in Egypt. This study highlights the various stakeholders that make tourists do shop independent of hotels. The results, therefore, demonstrate the relationship between the shopping process entities involved and configure the variables within the model in a way that provides a viable solution for visitors to avoid the tyranny of hotel facilities and amenities on the public markets.

Keywords: hotels’ amenities, shopping process, tourist behavior, tourist satisfaction

Procedia PDF Downloads 107

Authors: Hao Yan, Xiaobing Zhang

Abstract:

The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

Procedia PDF Downloads 62

Authors: Aid Asma, Dahdouh Nadjib, Amokrane Samira, Ladjali Samir, Nibou Djamel

Abstract:

The present work has for main objective the elimination of the textile dye Maxilon Red (MR) by two processes, adsorption on activated clay followed by photocatalysis in presence of ZnO as a photocatalyst. The influence of the physical parameters like the initial pH, adsorbent dose of the activated clay, the MR concentration and temperature has been studied. The best adsorption yield occurs at neutral pH ~ 7 within 60 min with an uptake percentage of 97% for a concentration of 25 mg L⁻¹ and a dose of 0.5 g L⁻¹. The adsorption data were suitably fitted by the Langmuir model with a maximum capacity of 176 mg g⁻¹. The MR adsorption is well described by the pseudo second order kinetic. The second part of this work was dedicated to the photocatalytic degradation onto ZnO under solar irradiation of the residual MR concentration, remained after adsorption. The effect of ZnO dose and MR concentration has also been investigated. The parametric study showed that the elimination is very effective by this process, based essentially on the in situ generation of free radicals *OH which are non-selective and very reactive. The photodegradation process follows a first order kinetic model according to the Langmuir-Hinshelwood model.

Keywords: maxilon red, adsorption, photodegradation, ZnO, coupling

Procedia PDF Downloads 158

Authors: Fu Jinyu, Lin Jinguan

Abstract:

This paper introduces a novel approach that unifies two types of models: one is the continuous-time jump-diffusion used to model high-frequency data, and the other is discrete-time GQARCH employed to model low-frequency financial data by embedding the discrete GQARCH structure with jumps in the instantaneous volatility process. This model is named “GQARCH-It\^{o} -Jumps mode.” We adopt the realized range-based threshold estimation for high-frequency financial data rather than the realized return-based volatility estimators, which entail the loss of intra-day information of the price movement. Meanwhile, a quasi-likelihood function for the low-frequency GQARCH structure with jumps is developed for the parametric estimate. The asymptotic theories are mainly established for the proposed estimators in the case of finite activity jumps. Moreover, simulation studies are implemented to check the finite sample performance of the proposed methodology. Specifically, it is demonstrated that how our proposed approaches can be practically used on some financial data.

Keywords: It\^{o} process, GQARCH, leverage effects, threshold, realized range-based volatility estimator, quasi-maximum likelihood estimate

Procedia PDF Downloads 130

Authors: Adnan A. Y. Mustafa

Abstract:

Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

Procedia PDF Downloads 121

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

Procedia PDF Downloads 269

Authors: Bruno Trstenjak, Dzenana Donko

Abstract:

Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

Procedia PDF Downloads 346

Authors: G. R. Constantine, M.C.K. Thilakasiri, V.S. Mohottala, T.V. Soundaram, D.S. Rathnayake, E.G.H.E. De Silva, A.L.S. Mohamed, V.R. Weerasekara

Abstract:

Orthostatic hypotension is prevalent in the elderly population, and it is an important risk factor contributing to falls in the elderly. This study aims to evaluate the prevalence of orthostatic hypotension in hospitalized elderly patients, changes in blood pressure during the hospital stay, morbidities associated with it and its association with falls in the elderly. A cross-sectional descriptive study was conducted in the National Hospital of Sri Lanka (NHSL) in a sample of 120 patients of age 65 years or above who were admitted to the medical wards. The demographic, clinical data was obtained by an interviewer-administered questionnaire. Two validated questionnaires were used to assess symptoms and effects of orthostatic hypotension and risk factors associated with falls. Orthostatic hypotension on admission and after 3 days of hospital stay was measured by bed-side mercury sphygmomanometer. Prevalence of orthostatic hypotension among the study population was 63.3%(76 patients). But no significant change in the orthostatic hypotension noted after 3 days of hospital admission (SND 0.61, SE= 5.59, p=0.27). There was no significant association found between orthostatic hypotension and its symptoms (dizziness and vertigo, vision problems, malaise, fatigue, poor concentration, neck stiffness), impact on standing or walking and non-communicable diseases. Falls were experienced by 27.5 % (33 patients) of the study population and prevalence of patients with orthostatic hypotension who had experienced falls was 25.9% (28 patients). In conclusions, orthostatic hypotension is more prevalent among elderly patients, but It wasn’t associated with symptoms, and non-communicable diseases, or as a risk factor for falls in elderly.

Keywords: orthostatic hypotension, elderly falls, emergency geriatric, Sri Lanka

Procedia PDF Downloads 86

Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

Abstract:

Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

Procedia PDF Downloads 186

Authors: Abdelmalek Arrouf

Abstract:

This article sets up a cognitive schema of the architectural designing activity. It begins by outlining, theoretically, an a priori model of its general cognitive mechanisms. The obtained theoretical framework represents the designing activity as a complex system composed of three interrelated subsystems of cognitive actions: a subsystem of meaning production, one of morphology production and finally a subsystem of navigation between the two formers. A protocol analysis that uses statistical and informational tools is then used to measure the validity of the built schema. The model thus achieved shows that the designer begins by conceiving abstract meanings, which he then translates into shapes. That’s why we call it a semio-morphic model of the designing activity.

Keywords: designing actions, model of the design process, morphosis, protocol analysis, semiosis

Procedia PDF Downloads 147

Authors: Ayoub. El Bourtali, Abdessamed.Najine, Amrou Moussa. Benmoussa

Abstract:

One of the main goals of river engineering is river training, which is defined as controlling and predicting the behavior of a river. It is taking effective measurements to eliminate all related risks and thus improve the river system. In some rivers, the riverbed continues to erode and degrade; therefore, equilibrium will never be reached. Generally, river geometric characteristics and riverbed erosion analysis are some of the most complex but critical topics in river engineering and sediment hydraulics; riverbank erosion is the second answering process in hydrodynamics, which has a major impact on the ecological chain and socio-economic process. This study aims to integrate the new computer technology that can analyze erosion and hydraulic problems through computer simulation and modeling. Choosing the right model remains a difficult and sensitive job for field engineers. This paper makes use of the 5.0.4 version of the HEC-RAS model. The river section is adopted according to the gauged station and the proximity of the adjustment. In this work, we will demonstrate how 2D hydraulic modeling helped clarify the design and cover visuals to set up depth and velocities at riverbanks and throughout advanced structures. The hydrologic engineering center's-river analysis system (HEC-RAS) 2D model was used to create a hydraulic study of the erosion model. The geometric data were generated from the 12.5-meter x 12.5-meter resolution digital elevation model. In addition to showing eroded or overturned river sections, the model output also shows patterns of riverbank changes, which can help us reduce problems caused by erosion.

Keywords: 2D hydraulics model, erosion, floodplain, hydrodynamic, HEC-RAS, riverbed erosion, river morphology, resolution digital data, sediment

Procedia PDF Downloads 165

Authors: Sarra Roubi, Mohammed Erramdani, Samir Mbarki

Abstract:

This paper presents an approach for the model-driven generating of Rich Internet Application (RIA) focusing on the graphical aspect. We used well known Model-Driven Engineering (MDE) frameworks and technologies, such as Eclipse Modeling Framework (EMF), Graphical Modeling Framework (GMF), Query View Transformation (QVTo) and Acceleo to enable the design and the code automatic generation of the RIA. During the development of the approach, we focused on the graphical aspect of the application in terms of interfaces while opting for the Model View Presenter pattern that is designed for graphics interfaces. The paper describes the process followed to define the approach, the supporting tool and presents the results from a case study.

Keywords: metamodel, model-driven engineering, MVP, rich internet application, transformation, user interface

Procedia PDF Downloads 325

Authors: Mohammed A. Almomani, Dina W. Al-Qudah

Abstract:

Product performance depends on the type and quality of its building material. Successful product must be made using high quality material, and using the right methods. Many foot problems took place as a result of using poor insole material. Therefore, selecting a proper insole material is crucial to eliminate these problems. In this study, the analytical hierarchy process (AHP) is used to provide a systematic procedure for choosing the best material adequate for this application among three material alternatives (polyurethane, poron, and plastzote). Several comparison criteria are used to build the AHP model including: density, stiffness, durability, energy absorption, and ease of fabrication. Poron was selected as the best choice. Inconsistency testing indicates that the model is reasonable, and the materials alternative ranking is effective.

Keywords: AHP, footwear insole, insole material, materials selection

Procedia PDF Downloads 323

Authors: Ezil Sam Leni, Shalen S.

Abstract:

Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

Procedia PDF Downloads 58

Authors: Yi-Hsiang Pan, Ching-Hsiang Chen, Wei-Ting Hsu

Abstract:

The purpose of this study was to compare the learning effects of the sports education model (SEM) to those of the traditional teaching model (TTM) in physical education classes in terms of students learning motivation, action control, learning strategies, and learning performance. A quasi-experimental design was utilized in this study, and participants included two physical educators and four classes with a total of 94 students in grades 5 and 6 of elementary schools. Two classes implemented the SEM (n=47, male=24, female=23; age=11.89, SD=0.78) and two classes implemented the TTM (n=47, male=25, female=22, age=11.77; SD=0.66). Data were collected from these participants using a self-report questionnaire (including a learning motivation scale, action control scale, and learning strategy scale) and a game performance assessment instrument, and multivariate analysis of covariance was used to conduct statistical analysis. The findings of the study revealed that the SEM was significantly better than the TTM in promoting students learning motivation, action control, learning strategies, and game performance. It was concluded that the SEM could promote the mechanics of students self-regulated learning process, and thereby improve students movement performance.

Keywords: self-regulated learning theory, learning process, curriculum model, physical education

Procedia PDF Downloads 318

Authors: Ravi Raj, Louis Chiu, Deepak Marla, Aijun Huang

Abstract:

Hybridization of the directed laser deposition (DLD) process using an in-situ micro-roller to impart a vertical compressive load on the deposited bead at elevated temperatures can relieve tensile residual stresses incurred in the process. To investigate this stress relief mechanism and its relationship with the in-situ rolling parameters, a fully coupled dynamic thermo-mechanical model is presented in this study. A single bead deposition of Ti-6Al-4V alloy with an in-situ roller made of mild steel moving at a constant speed with a fixed nominal bead reduction is simulated using the explicit solver of the finite element software, Abaqus. The thermal model includes laser heating during the deposition process and the heat transfer between the roller and the deposited bead. The laser heating is modeled using a moving heat source with a Gaussian distribution, applied along the pre-formed bead’s surface using the VDFLUX Fortran subroutine. The bead’s cross-section is assumed to be semi-elliptical. The interfacial heat transfer between the roller and the bead is considered in the model. Besides, the roller is cooled internally using axial water flow, considered in the model using convective heat transfer. The mechanical model for the bead and substrate includes the effects of rolling along with the deposition process, and their elastoplastic material behavior is captured using the J2 plasticity theory. The model accounts for strain, strain rate, and temperature effects on the yield stress based on Johnson-Cook’s theory. Various aspects of this material behavior are captured in the FE software using the subroutines -VUMAT for elastoplastic behavior, VUHARD for yield stress, and VUEXPAN for thermal strain. The roller is assumed to be elastic and does not undergo any plastic deformation. Also, contact friction at the roller-bead interface is considered in the model. Based on the thermal results of the bead, the distance between the roller and the deposition nozzle (roller o set) can be determined to ensure rolling occurs around the beta-transus temperature for the Ti-6Al-4V alloy. It is identified that roller offset and the nominal bead height reduction are crucial parameters that influence the residual stresses in the hybrid process. The results obtained from a simulation at roller offset of 20 mm and nominal bead height reduction of 7% reveal that the tensile residual stresses decrease to about 52% due to in-situ rolling throughout the deposited bead. This model can be used to optimize the rolling parameters to minimize the residual stresses in the hybrid DLD process with in-situ micro-rolling.

Keywords: directed laser deposition, finite element analysis, hybrid in-situ rolling, thermo-mechanical model

Procedia PDF Downloads 82

Authors: F. L. Motta, M. H. A. Santana

Abstract:

Humic Acids (HA) were produced by a Trichoderma viride strain under submerged fermentation in a medium based on the oil palm Empty Fruit Bunch (EFB) and the main variables of the process were optimized by using response surface methodology. A temperature of 40°C and concentrations of 50g/L EFB, 5.7g/L potato peptone and 0.11g/L (NH4)2SO4 were the optimum levels of the variables that maximize the HA production, within the physicochemical and biological limits of the process. The optimized conditions led to an experimental HA concentration of 428.4±17.5 mg/L, which validated the prediction from the statistical model of 412.0mg/L. This optimization increased about 7–fold the HA production previously reported in the literature. Additionally, the time profiles of HA production and fungal growth confirmed our previous findings that HA production preferably occurs during fungal sporulation. The present study demonstrated that T. viride successfully produced HA via the submerged fermentation of EFB and the process parameters were successfully optimized using a statistics-based response surface model. To the best of our knowledge, the present work is the first report on the optimization of HA production from EFB by a biotechnological process, whose feasibility was only pointed out in previous works.

Keywords: empty fruit bunch, humic acids, submerged fermentation, Trichoderma viride

Procedia PDF Downloads 272

Authors: Hao Hsi Tseng

Abstract:

Taiwan’s infrastructure is gradually deteriorating, while resources for maintenance and replacement are increasingly limited, raising the urgent need for methods for maintaining existing infrastructure within constrained budgets. Infrastructure value evaluation is used to enhance the efficiency of infrastructure maintenance work, allowing administrators to quickly assess the maintenance needs and performance by observing variation in infrastructure value. This research establishes a value evaluation model for Taiwan’s highway box girder bridges. The operating mechanism and process of the model are illustrated in a practical case.

Keywords: box girder bridge, deterioration, infrastructure, maintenance, value evaluation

Procedia PDF Downloads 164