Search results for: measurement models

Authors: Sanja Podunavac-Kuzmanović, Denis Kučević, Strahinja Kovačević, Milica Karadžić, Lidija Jevrić

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The present study describes the application of chemometric methods in analysis of milk samples which were collected in a conventional dairy farm and an organic dairy farm in AP Vojvodina, Republic of Serbia. The chemometric analysis included the application of univariate regression modeling and Analysis of Variance (ANOVA) method. The ANOVA was used in order to determine the differences in fatty acids content in the milk samples from conventional and organic farm. The results of the ANOVA testing indicate that there is a highly statistically significant difference between the content of fatty acid (saturated fatty acid vs. unsaturated fatty acids) in different dairy farming. Besides, the linear univariate models have been obtained as a result of modeling the linear relationships between the milk fat content and saturated fatty acids content, and the linear relationships between the milk fat content and unsaturated fatty acids content. The models obtained on the basis of the milk samples which originate from the organic farming are statistically better than the models based on the milk samples from conventional farming.

Keywords: hemometrics, milk, organic farming, quality control

Procedia PDF Downloads 237

Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

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Authors: Arian Hosseini, Mahmudul Hasan

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To address the increasing need for efficient and accurate content moderation, we propose an efficient and lightweight deep classification ensemble structure. Our approach is based on a combination of simple visual features, designed for high-accuracy classification of violent content with low false positives. Our ensemble architecture utilizes a set of lightweight models with narrowed-down color features, and we apply it to both images and videos. We evaluated our approach using a large dataset of explosion and blast contents and compared its performance to popular deep learning models such as ResNet-50. Our evaluation results demonstrate significant improvements in prediction accuracy, while benefiting from 7.64x faster inference and lower computation cost. While our approach is tailored to explosion detection, it can be applied to other similar content moderation and violence detection use cases as well. Based on our experiments, we propose a "think small, think many" philosophy in classification scenarios. We argue that transforming a single, large, monolithic deep model into a verification-based step model ensemble of multiple small, simple, and lightweight models with narrowed-down visual features can possibly lead to predictions with higher accuracy.

Keywords: deep classification, content moderation, ensemble learning, explosion detection, video processing

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Authors: Nessreen Y. Ibrahim

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Designing the future according to realistic analytical study for the futuristic market needs can be a milestone strategy to make a huge improvement in developing countries economics. In developing countries, access to high technology and latest science approaches is very limited. The financial problems in low and medium income countries have negative effects on the kind and quality of imported new technologies and application for their markets. Thus, there is a strong need for shifting paradigm thinking in the design process to improve and evolve their development strategy. This paper discusses future possibilities in developing countries, and how they can design their own future according to specific future models FDM (Future Design Models), which established to solve certain economical problems, as well as political and cultural conflicts. FDM is strategic thinking framework provides an improvement in both content and process. The content includes; beliefs, values, mission, purpose, conceptual frameworks, research, and practice, while the process includes; design methodology, design systems, and design managements tools. In this paper the main objective was building an innovative economic model to design a chosen possible futuristic scenario; by understanding the market future needs, analyze real world setting, solve the model questions by future driven design, and finally interpret the results, to discuss to what extent the results can be transferred to the real world. The paper discusses Egypt as a potential case study. Since, Egypt has highly complex economical problems, extra-dynamic political factors, and very rich cultural aspects; we considered Egypt is a very challenging example for applying FDM. The paper results recommended using FDM numerical modeling as a starting point to design the future.

Keywords: developing countries, economic models, future design, possible futures

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

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Authors: Shahroz Khan, Erkan Gunpinar, Kemal Mart

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Recently parametric design techniques became a vital concept in the field of Computer Aided Design (CAD), which helps to provide sophisticated platform to the designer in order to automate the design process in efficient time. In these techniques, design process starts by parameterizing the important features of design models (typically the key dimensions), with the implementation of design constraints. The design constraints help to retain the overall shape of the model while modifying its parameters. However, the process of initializing an appropriate number of design parameters and constraints is the crucial part of parametric design techniques, especially for complex surface models such as yacht hull. This paper introduces a method to create complex surface models in favor of parametric design techniques, a method to define the right number of parameters and respective design constraints, and a system to implement design parameters in contract to design constraints schema. For this, in our proposed approach the design process starts by dividing the yacht hull into three sections. Each section consists of different shape lines, which form the overall shape of yacht hull. The shape lines are created using Cubic Bezier Curves, which allow larger design flexibility. Design parameters and constraints are defined on the shape lines in 3D design space to facilitate the designers for better and individual handling of parameters. Afterwards, shape modifiers are developed, which allow the modification of each parameter while satisfying the respective set of criteria and design constraints. Such as, geometric continuities should be maintained between the shape lines of the three sections, fairness of the hull surfaces should be preserved after modification and while design modification, effect of a single parameter should be negligible on other parameters. The constraints are defined individually on shape lines of each section and mutually between the shape lines of two connecting sections. In order to validate and visualize design results of our shape modifiers, a real time graphic interface is created.

Keywords: design parameter, design constraints, shape modifies, yacht hull

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Authors: Gelacio JuáRez-Luna, Daniel Enrique GonzáLez-RamíRez, Enrique Tenorio-Montero

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Frame elements coupled with springs elements are used for modelling the development of hinges in segmented tunnels, the spring elements modelled the rotational, transversal and axial failure. These spring elements are equipped with constitutive models to include independently the moment, shear force and axial force, respectively. These constitutive models are formulated based on damage mechanics and experimental test reported in the literature review. The mesh of the segmented tunnels was discretized in the software GID, and the nonlinear analyses were carried out in the finite element software ANSYS. These analyses provide the capacity curve of the primary and secondary lining of a segmented tunnel. Two numerical examples of segmented tunnels show the capability of the spring elements to release energy by the development of hinges. The first example is a segmental concrete lining discretized with frame elements loaded until hinges occurred in the lining. The second example is a tunnel with primary and secondary lining, discretized with a double ring frame model. The outer ring simulates the segmental concrete lining and the inner ring simulates the secondary cast-in-place concrete lining. Spring elements also modelled the joints between the segments in the circumferential direction and the ring joints, which connect parallel adjacent rings. The computed load vs displacement curves are congruent with numerical and experimental results reported in the literature review. It is shown that the modelling of a tunnel with primary and secondary lining with frame elements and springs provides reasonable results and save computational cost, comparing with 2D or 3D models equipped with smeared crack models.

Keywords: damage, hinges, lining, tunnel

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Authors: Ramachandra C. G., Amarnath. M., Prashanth Pai M., Nagesh S. N.

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The machine's performance level drops down over a period of time due to the wear and tear of its components. The early detection of an emergent fault becomes very vital in order to obtain uninterrupted production in a plant. Maintenance is an activity that helps to keep the machine's performance at an anticipated level, thereby ensuring the availability of the machine to perform its intended function. At present, a number of modern maintenance techniques are available, such as preventive maintenance, predictive maintenance, condition-based maintenance, total productive maintenance, etc. Condition-based maintenance or condition monitoring is one such modern maintenance technique in which the machine's condition or health is checked by the measurement of certain parameters such as sound level, temperature, velocity, displacement, vibration, etc. It can recognize most of the factors restraining the usefulness and efficacy of the total manufacturing unit. This research work is conducted on a Batch Mill in a tire production unit located in the Southern Karnataka region. The health of the mill is assessed using amplitude of vibration as a parameter of measurement. Most commonly, the vibration level is assessed using various points on the machine bearing. The normal or standard level is fixed using reference materials such as manuals or catalogs supplied by the manufacturers and also by referring vibration standards. The Rio-Vibro meter is placed in different locations on the batch-off mill to record the vibration data. The data collected are analyzed to identify the malfunctioning components in the batch off the mill, and corrective measures are suggested.

Keywords: availability, displacement, vibration, rio-vibro, condition monitoring

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Authors: Pratiksha Dubey, Praveen Singh, Shantanu Sen Gupta, Anand K. Bachhawat

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5-oxoproline (pyroglutamic acid) is a cyclic lactam of glutamic acid. In the cell, it can be produced by several different pathways and is metabolized into glutamate with the help of the 5-oxoprolinase enzyme (OPLAH or OXP1). The inhibition of 5-oxoprolinase enzyme in mammals was found to result in heart failure and is thought to be a consequence of oxidative stress [1]. To analyze the consequences of 5-oxoproline accumulation more clearly, we are generating models for 5-oxoproline accumulation in yeast. The 5-oxoproline accumulation model in yeast is being developed by two different strategies. The first one is by overexpression of the mouse  -glutamylcyclotransferase enzyme. It degrades -glu-met dipeptide into 5-oxoproline and methionine taken by the cell from the medium. The second strategy is by providing high concentration of 5-oxoproline externally to the yeast cells. The intracellular 5-oxoproline levels in both models are being evaluated. In addition, the metabolic and cellular consequences are being investigated.

Keywords: 5-oxoproline, pyroglutamic acid, yeast, genetics

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

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In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: S. Dalvi, N. Sane, V. Patil, D. Bansode, A. Tharakan, V. Mathur

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Measurement of routine clinical electrolyte tests is common in labs worldwide for screening of illness or diseases. All the analyzers for the measurement of electrolyte parameters have sensors, reagents, sampler, pump tubing, valve, other tubing’s separate that are either expensive, require heavy maintenance and have a short shelf-life. Moreover, the costs required to maintain such Lab instrumentation is high and this limits the use of the device to only highly specialized personnel and sophisticated labs. In order to provide Healthcare Diagnostics to ALL at affordable costs, there is a need for an All-in-one Universal Modular Cartridge that contains sensors, reagents, sampler, valve, pump tubing, and other tubing’s in one single integrated module-in-module cartridge that is affordable, reliable, easy-to-use, requires very low sample volume and is truly modular and maintenance-free. DiaSys India has developed a World’s first, Patent Pending, Versatile All-in-one Universal Module-in-Module Cartridge based Electrolyte Analyzer (QDx InstaLyte) that can perform sodium, potassium, chloride, calcium, pH, lithium tests. QDx InstaLyte incorporates High Performance, Inexpensive All-in-one Universal Cartridge for rapid quantitative measurement of electrolytes in body fluids. Our proposed methodology utilizes Advanced & Improved long life ISE sensors to provide a sensitive and accurate result in 120 sec with just 100 µl of sample volume. The All-in-One Universal Cartridge has a very low reagent consumption capable of maximum of 1000 tests with a Use-life of 3-4 months and a long Shelf life of 12-18 months at 4-25°C making it very cost-effective. Methods: QDx InstaLyte analyzers with All-in-one Universal Modular Cartridges were independently evaluated with three R&D lots for Method Performance (Linearity, Precision, Method Comparison, Cartridge Stability) to measure Sodium, Potassium, Chloride. Method Comparison was done against Medica EasyLyte Plus Na/K/Cl Electrolyte Analyzer, a mid-size lab based clinical chemistry analyzer with N = 100 samples run over 10 days. Within-run precision study was done using modified CLSI guidelines with N = 20 samples and day-to-day precision study was done for 7 consecutive days using Trulab N & P Quality Control Samples. Accelerated stability testing was done at 45oC for 4 weeks with Production Lots. Results: Data analysis indicates that the CV for within-run precision for Na is ≤ 1%, for K is ≤2%, and for Cl is ≤2% and with R2 ≥ 0.95 for Method Comparison. Further, the All-in-One Universal Cartridge is stable up to 12-18 months at 4-25oC storage temperature based on preliminary extrapolated data. Conclusion: The Developed Technology Platform of All-in-One Universal Module-in-Module Cartridge based QDx InstaLyte is Reliable and meets all the performance specifications of the lab and is Truly Modular and Maintenance-Free. Hence, it can be easily adapted for low cost, sensitive and rapid measurement of electrolyte tests in low resource settings such as in urban, semi-urban and rural areas in the developing countries and can be used as a Point-of-care testing system for worldwide applications.

Keywords: all-in-one modular catridge, electrolytes, maintenance free, QDx instalyte

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Authors: Jerome D. Donovan

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The pandemic has resulted in a dramatic re-consideration of the reliance on international student fees to support university models in Australia. A key resulting initiative for the Australian Federal Government has been shifting the way universities consider their research model, emphasising the importance of commercialising research. This study specifically examines this shift from the perspective of a university spin-off, examining how university support structures and incubation models have assisted in the translation of fundamental research into a high-growth university spin-off. A focused case study approach is adopted in this study, using an auto-ethnographic research method to document the experiences and insights drawn from being a co-founder in a university spin-off in a time where research commercialisation has emerged as a central focus in Australian universities.

Keywords: research commercialisation, spin-offs, university incubation, entrepreneurship

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Authors: Wi Sung Yoo, Seung Woo Lee, Youn Kyoung Hur, Sung Hwan Kim

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Large-scale construction projects are continuously increasing, and the need for tools to monitor and evaluate the project success is emphasized. At the construction industry level, there are limitations in deriving performance evaluation factors that reflect the diversity of construction sites and systems that can objectively evaluate and manage performance. Additionally, there are difficulties in integrating structured and unstructured data generated at construction sites and deriving improvements. In this study, we propose the Key Performance Indicators (KPIs) to enable performance evaluation that reflects the increased diversity of construction sites and the unstructured data generated, and present a model for measuring performance by the derived indicators. The comprehensive performance of a unit construction site is assessed based on 6 areas (Time, Cost, Quality, Safety, Environment, Productivity) and 26 indicators. We collect performance indicator information from 30 construction sites that meet legal standards and have been successfully performed. And We apply data augmentation and optimization techniques into establishing measurement standards for each indicator. In other words, the KPI for construction site performance evaluation presented in this study provides standards for evaluating performance in six areas using institutional requirement data and document data. This can be expanded to establish a performance evaluation system considering the scale and type of construction project. Also, they are expected to be used as a comprehensive indicator of the construction industry and used as basic data for tracking competitiveness at the national level and establishing policies.

Keywords: key performance indicator, performance measurement, structured and unstructured data, data augmentation

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Authors: Wang Mimi, Jiang Changjun, Liu Guanjun, Fang Xianwen

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Loop structure often appears in the business process modeling, analyzing the consistency of corresponding workflow net models containing loop structure is a problem, the existing behavior consistency methods cannot analyze effectively the process models with the loop structure. In the paper, by analyzing five kinds of behavior relations of transitions, a three-dimensional figure and two-dimensional behavior relation matrix are proposed. Based on this, analysis method of behavior consistency of business process based on Petri net branching processes is proposed. Finally, an example is given out, which shows the method is effective.

Keywords: workflow net, behavior consistency measures, loop, branching process

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Authors: Tadashi Watanabe, Jinya Katsuyama, Akihiro Mano

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The estimation of leak flow rates through narrow cracks in structures is of importance for nuclear reactor safety, since the leak flow could be detected before occurrence of loss-of-coolant accidents. The two-phase critical leak flow rates are calculated using the system analysis code, and two representative non-homogeneous critical flow models, Henry-Fauske model and Ransom-Trapp model, are compared. The pressure decrease and vapor generation in the crack, and the leak flow rates are found to be larger for the Henry-Fauske model. It is shown that the leak flow rates are not affected by the structural temperature, but affected largely by the roughness of crack surface.

Keywords: crack, critical flow, leak, roughness

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Authors: Jiri Cecrdle

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This paper describes the concept of the W-WING whirl flutter aeroelastic demonstrator. Whirl flutter is the specific case of flutter that accounts for the additional dynamic and aerodynamic influences of the engine rotating parts. The instability is driven by motion-induced unsteady aerodynamic propeller forces and moments acting in the propeller plane. Whirl flutter instability is a serious problem that may cause the unstable vibration of a propeller mounting, leading to the failure of an engine installation or an entire wing. The complicated physical principle of whirl flutter required the experimental validation of the analytically gained results. W-WING aeroelastic demonstrator has been designed and developed at Czech Aerospace Research Centre (VZLU) Prague, Czechia. The demonstrator represents the wing and engine of the twin turboprop commuter aircraft. Contrary to the most of past demonstrators, it includes a powered motor and thrusting propeller. It allows the changes of the main structural parameters influencing the whirl flutter stability characteristics. Propeller blades are adjustable at standstill. The demonstrator is instrumented by strain gauges, accelerometers, revolution-counting impulse sensor, sensor of airflow velocity, and the thrust measurement unit. Measurement is supported by the in house program providing the data storage and real-time depiction in the time domain as well as pre-processing into the form of the power spectral densities. The engine is linked with a servo-drive unit, which enables maintaining of the propeller revolutions (constant or controlled rate ramp) and monitoring of immediate revolutions and power. Furthermore, the program manages the aerodynamic excitation of the demonstrator by the aileron flapping (constant, sweep, impulse). Finally, it provides the safety guard to prevent any structural failure of the demonstrator hardware. In addition, LMS TestLab system is used for the measurement of the structure response and for the data assessment by means of the FFT- and OMA-based methods. The demonstrator is intended for the experimental investigations in the VZLU 3m-diameter low-speed wind tunnel. The measurement variant of the model is defined by the structural parameters: pitch and yaw attachment stiffness, pitch and yaw hinge stations, balance weight station, propeller type (duralumin or steel blades), and finally, angle of attack of the propeller blade 75% section (). The excitation is provided either by the airflow turbulence or by means of the aerodynamic excitation by the aileron flapping using a frequency harmonic sweep. The experimental results are planned to be utilized for validation of analytical methods and software tools in the frame of development of the new complex multi-blade twin-rotor propulsion system for the new generation regional aircraft. Experimental campaigns will include measurements of aerodynamic derivatives and measurements of stability boundaries for various configurations of the demonstrator.

Keywords: aeroelasticity, flutter, whirl flutter, W WING demonstrator

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Authors: Elisabeth Maidl

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The interdependence of climate change and natural hazard goes along with large uncertainties regarding future risks. Regional stakeholders, experts in natural hazards management and scientists have specific knowledge, resp. mental models on such risks. This diversity of views makes it difficult to find common and broadly accepted prevention measures. If the specific knowledge of these types of actors is shared in an interactive knowledge production process, this enables a broader and common understanding of complex risks and allows to agree on long-term solution strategies. Previous studies on mental models confirm that actors with specific vulnerabilities perceive different aspects of a topic and accordingly prefer different measures. In bringing these perspectives together, there is the potential to reduce uncertainty and to close blind spots in solution finding. However, studies that examine the mental models of regional actors on future concrete mass movement risks are lacking so far. The project tests and evaluates the feasibility of knowledge co-creation for the anticipatory prevention of climate change-induced mass movement risks in the Alps. As a key element, mental models of the three included groups of actors are compared. Being integrated into the research program Climate Change Impacts on Alpine Mass Movements (CCAMM2), this project is carried out in two Swiss mountain regions. The project is structured in four phases: 1) the preparatory phase, in which the participants are identified, 2) the baseline phase, in which qualitative interviews and a quantitative pre-survey are conducted with actors 3) the knowledge-co-creation phase, in which actors have a moderated exchange meeting, and a participatory modelling workshop on specific risks in the region, and 4) finally a public information event. Results show that participants' mental models are based on the place of origin, profession, believes, values, which results in narratives on climate change and hazard risks. Further, the more intensively participants interact with each other, the more likely is that they change their views. This provides empirical evidence on how changes in opinions and mindsets can be induced and fostered.

Keywords: climate change, knowledge-co-creation, participatory process, natural hazard risks

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Authors: Sibylle Tellenbach, Julie Haddock-Millar, Francis Bidault

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The aim of this research is to understand the extent to which hybrid working models have influenced employee engagement in the Swiss financial sector. The context for this research is the transition out of the pandemic and the changes that have occurred between 2020 and 2023. Since the pandemic, many financial services companies have had to rethink their working model for office-based employees, as this group of employees has been able to experience a new way of working and, thus, greater freedom and flexibility. For a large number of companies, it was a huge change to shift from the traditional office-based to a new hybrid working model. A heightened focus on employee engagement has become a necessity in order to understand and respond to the challenges presented by the shift in a working model. This new way of working, partly office-based and partly virtual, has led to ambiguities about the impact on the engagement of hybrid teams. Therefore, the research question is: How hybrid working models have influenced employee engagement to what extent? The methodological approach is a narrative inquiry with four similar functional teams within four Swiss financial companies. Semi-structured interviews will be conducted with managers from middle management and their individual team members. The findings will demonstrate whether this shift in the working model influenced individual team members’ engagement and to what extent. The contribution of this research is two-fold. First, the research makes a theoretical contribution, presenting evidence of the impact of hybrid working on individual team members’ engagement in a specific sector and context, enhancing current knowledge on the challenges in working model transition. Second, this research will make a practice-based contribution, recommending ways to enhance the engagement of hybrid teams in a specific context. These recommendations may be applied in wider sectors and teams.

Keywords: employee engagement, hybrid teams, hybrid working models, Swiss financial sector, team engagement

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Authors: Ismail Aslantas

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Value-Added Models (VAMs), the statistical methods for evaluating the effectiveness of teachers and schools based on student achievement growth, has attracted decision-makers’ and researchers’ attention over the last decades. As a result of this attention, many studies have conducted in recent years to discuss these statistical models from different aspects. This research focused on the importance of conceptual variables in VAM estimations; therefor, this research was undertaken to examine the extent to which value-added effectiveness estimates for teachers can be affected by using context predictions. Using longitudinal data over three years from the international school context, value-added teacher effectiveness was estimated by ordinary least-square value-added models, and the effectiveness of the teachers was examined. The longitudinal dataset in this study consisted of three major sources: students’ attainment scores up to three years and their characteristics, teacher background information, and school characteristics. A total of 1,027 teachers and their 35,355 students who were in eighth grade were examined for understanding the impact of model specifications on the value-added teacher effectiveness evaluation. Models were created using selection methods that adding a predictor on each step, then removing it and adding another one on a subsequent step and evaluating changes in model fit was checked by reviewing changes in R² values. Cohen’s effect size statistics were also employed in order to find out the degree of the relationship between teacher characteristics and their effectiveness. Overall, the results indicated that prior attainment score is the most powerful predictor of the current attainment score. 47.1 percent of the variation in grade 8 math score can be explained by the prior attainment score in grade 7. The research findings raise issues to be considered in VAM implementations for teacher evaluations and make suggestions to researchers and practitioners.

Keywords: model specification, teacher effectiveness, teacher performance evaluation, value-added model

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Authors: Mohsen Yaghmoor, Sima Radmanesh

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Rapid environmental changes have been threaten the life of many organizations .Enabling and productivity of human resource should be considered as the most important issue in order to increase performance and ensure survival of the organizations. In this research, the effectiveness of management factory in productivity & inability of human resource have been identified and reviewed at glance. Afterward there were two questions they are “what are the factors effecting productivity and enabling of human resource” . And ”what are the priority order based on effective management of human resource in Fars Poultry Complex". A specified questionnaire has been designed in order to priorities and effectiveness of the identified factors. Six factors specify to consist of: Individual characteristics, teaching, motivation, partnership management, authority or power submission and job development that have most effect on organization. Then specify a questionnaire for priority and effect measurement of specified factor that reach after collect information and using statistical tests of keronchbakh alpha coefficient r=0.792 that we can say the questionnaire has sufficient reliability. After information analysis of specified six factors by Friedman test categorize their effect. Measurement on organization respectively consists of individual characteristics, job development or enrichment, authority submission, partnership management, teaching and motivation. At last it has been indicated to approaches to increase making power full and productivity of manpower.

Keywords: productivity, empowerment, enrichment, authority submission, partnership management, teaching, motivation

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Authors: Duvert A. Puentes T., Javier A. Maldonado E., Ivan Quintero., Diego F. Villegas

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Mechanical Computation is a great tool to study the performance of complex models. An example of it is the study of the human body structure. This paper took advantage of different types of software to make a 3D model of the glenohumeral joint and apply a finite element analysis. The main objective was to study the change in the biomechanical properties of the joint when it presents an injury. Specifically, a BANKART lesion, which consists in the detachment of the anteroinferior labrum from the glenoid. Stress and strain distribution of the soft tissues were the focus of this study. First, a 3D model was made of a joint without any pathology, as a control sample, using segmentation software for the bones with the support of medical imagery and a cadaveric model to represent the soft tissue. The joint was built to simulate a compression and external rotation test using CAD to prepare the model in the adequate position. When the healthy model was finished, it was submitted to a finite element analysis and the results were validated with experimental model data. With the validated model, it was sensitized to obtain the best mesh measurement. Finally, the geometry of the 3D model was changed to imitate a BANKART lesion. Then, the contact zone of the glenoid with the labrum was slightly separated simulating a tissue detachment. With this new geometry, the finite element analysis was applied again, and the results were compared with the control sample created initially. With the data gathered, this study can be used to improve understanding of the labrum tears. Nevertheless, it is important to remember that the computational analysis are approximations and the initial data was taken from an in vitro assay.

Keywords: biomechanics, computational model, finite elements, glenohumeral joint, bankart lesion, labrum

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Authors: Alisha Khanal, Gokhan Saygili

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It is commonly observed that aftershocks follow the mainshock. Aftershocks continue over a period of time with a decreasing frequency and typically there is not sufficient time for repair and retrofit between a mainshock–aftershock sequence. Usually, aftershocks are smaller in magnitude; however, aftershock ground motion characteristics such as the intensity and duration can be greater than the mainshock due to the changes in the earthquake mechanism and location with respect to the site. The seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Various empirical models are available that predict sliding displacement as a function of seismic loading parameters, ground motion parameters, and site parameters but these models do not include the aftershocks. The seismic risks associated with the post-mainshock slopes ('damaged slopes') subjected to aftershocks is significant. This paper extends the empirical sliding displacement models for flexible slopes subjected to earthquake mainshock-aftershock sequences (a multi hazard approach). A dataset was developed using 144 pairs of as-recorded mainshock-aftershock sequences using the Pacific Earthquake Engineering Research Center (PEER) database. The results reveal that the combination of mainshock and aftershock increases the seismic demand on slopes relative to the mainshock alone; thus, seismic risks are underestimated if aftershocks are neglected.

Keywords: seismic slope stability, mainshock, aftershock, landslide, earthquake, flexible slopes

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Authors: Yuliya Radanova, Agnė Tvaronavičienė

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With the growing trend for mandating parties of family conflicts to out-of-court processes, the adopted statutory regulations often remain silent on the way the voice of the child is integrated into the procedure. Convention on the Rights of the Child (Art. 12) clearly states the obligation to assure to the child who can form his or her own views the right to express those views freely in all matters affecting him. This article seeks to explore the way children participate in the mandatory mediation schemes applicable to family disputes in the European Union. A review of scientific literature and empirical data has been conducted on those EU Member States that coerce parties to family mediation to establish that different models of practice are deployed, and there is a lack of synchronicity on how children’s role in mediation is viewed. Child-inclusive mediation processes are deemed to produce sustainable results over time but necessitate professional qualifications and skills for the purpose of mediators to accommodate that such discussions are aligned with the best interest of the child. However, there is no unanimous guidance, standards or protocols on the peculiar characteristics and manner through which children are involved in mediation. Herewith, it is suggested that the lack of such rigorous approaches and coherence in an ever-changing mediation setting transitioning towards mandatory mediation models jeopardizes the importance of children’s voices in the process. Thus, it is suggested that there is a need to consider the adoption of uniform guidelines on the specific role children have in mediation, particularly in its mandatory models.

Keywords: family mediation, child involvement, mandatory mediation, child-inclusive, child-focused

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: Rafal Bryk, Holger Schmidt, Thomas Mull, Ingo Ganzmann, Oliver Herbst

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Prediction of the two-phase water mixture level during fast depressurization of the Reactor Pressure Vessel (RPV) resulting from an accident scenario is an important issue from the view point of the reactor safety. Since the level swell may influence the behavior of some passive safety systems, it has been recognized that an assumption which at the beginning may be considered as a conservative one, not necessary leads to a conservative result. This paper discusses outcomes obtained during simulations of the water dynamics and heat transfer during sudden depressurization of a vessel filled up to a certain level with liquid water under saturation conditions and with the rest of the vessel occupied by saturated steam. In case of the pressure decrease e.g. due to the main steam line break, the liquid water evaporates abruptly, being a reason thereby, of strong transients in the vessel. These transients and the sudden emergence of void in the region occupied at the beginning by liquid, cause elevation of the two-phase mixture. In this work, several models calculating the water collapse and swell levels are presented and validated against experimental data. Each of the models uses different approach to calculate void fraction. The object-oriented models were developed with the Modelica modelling language and the OpenModelica environment. The models represent the RPV of the Integral Test Facility Karlstein (INKA) – a dedicated test rig for simulation of KERENA – a new Boiling Water Reactor design of Framatome. The models are based on dynamic mass and energy equations. They are divided into several dynamic volumes in each of which, the fluid may be single-phase liquid, steam or a two-phase mixture. The heat transfer between the wall of the vessel and the fluid is taken into account. Additional heat flow rate may be applied to the first volume of the vessel in order to simulate the decay heat of the reactor core in a similar manner as it is simulated at INKA. The comparison of the simulations results against the reference data shows a good agreement.

Keywords: boiling water reactor, level swell, Modelica, RPV depressurization, thermal-hydraulics

Procedia PDF Downloads 210