Search results for: mixed effects models

Authors: Mohammad Alfaifi, Mohammed Alshehri

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Organophosphates are among the most widely used synthetic insect pesticides. The widespread use of organophosphates has stimulated research into the possible existence of effects related with their reproductive toxic activity. The present study aimed to assess the effects of diazinon (DIZ) on male reproductive system. DIZ at the dose levels of 1.5, 3.0 and 9.0 mg/kg b. wt./day was administered orally to male rats of Wistar strain for 30 days to evaluate the toxic alterations in testicular histology, biochemistry, sperm dynamics and testosterone levels. The body weight of animals did not show any significant changes; however, a significant reduction was observed in testes weight. DIZ also brought about a marked reduction in epididymal and testicular sperm counts in exposed males and a decrease in serum testosterone concentration. Histopathological examination of testes showed mild to severe degenerative changes in seminiferous tubules at various dose levels. Fertility test showed 79% negative results. All these toxic effects are moderate at low doses and become severe at higher dose levels. From the results of the present study, it is concluded that DIZ induces severe testicular damage and results in a reduction in sperm count and thus affect fertility. Small changes in sperm counts are known to have adverse effects on human fertility. Therefore, application of such insecticide should be limited to a designed programme.

Keywords: organophosphates, reproductive toxicity, diazinon, fertility

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Authors: Lee P. Leon, Raymond Charles

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This paper presents a method of evaluating the effect of aggregate angularity on hot mix asphalt (HMA) properties and its relationship to the Permanent Deformation resistance. The research concluded that aggregate particle angularity had a significant effect on the Permanent Deformation performance, and also that with an increase in coarse aggregate angularity there was an increase in the resistance of mixes to Permanent Deformation. A comparison between the measured data and predictive data of permanent deformation predictive models showed the limits of existing prediction models. The numerical analysis described the permanent deformation zones and concluded that angularity has an effect of the onset of these zones. Prediction of permanent deformation help road agencies and by extension economists and engineers determine the best approach for maintenance, rehabilitation, and new construction works of the road infrastructure.

Keywords: aggregate angularity, asphalt concrete, permanent deformation, rutting prediction

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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Authors: Naseem Uddin

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Impinging jets are used in variety of engineering and industrial applications. This paper is based on numerical simulations of heat transfer by turbulent impinging jet with velocity field excitations using different Reynolds Averaged Navier-Stokes Equations models. Also Detached Eddy Simulations are conducted to investigate the differences in the prediction capabilities of these two simulation approaches. In this paper the excited jet is simulated in non-commercial CFD code OpenFOAM with the goal to understand the influence of dynamics of impinging jet on heat transfer. The jet’s frequencies are altered keeping in view the preferred mode of the jet. The Reynolds number based on mean velocity and diameter is 23,000 and jet’s outlet-to-target wall distance is 2. It is found that heat transfer at the target wall can be influenced by judicious selection of amplitude and frequencies.

Keywords: excitation, impinging jet, natural frequency, turbulence models

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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Authors: Silvija ŠIljeg, Ante ŠIljeg, Ivan Marić

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Measuring housing quality is being done on objective and subjective level using different indicators. During the research 5 urban and housing indicators formed according to 58 variables from different housing, domains were used. The aims of the research were to measure housing quality based on GIS approach and to detect critical points of housing in the example of Croatian coastal Town Zadar. The purposes of GIS in the research are to generate models of housing quality indexes by standardisation and aggregation of variables and to examine accuracy model of housing quality index. Analysis of accuracy has been done on the example of variable referring to educational objects availability. By defining weighted coefficients and using different GIS methods high, middle and low housing quality zones were determined. Obtained results can be of use to town planners, spatial planners and town authorities in the process of generating decisions, guidelines, and spatial interventions.

Keywords: housing quality, GIS, housing quality index, indicators, models of housing quality

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Authors: Yue-Tzu Yang, Peng-Jen Chen

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The present study deals with the numerical optimization of wavy channel with the help of genetic algorithm (GA). Three design variables related to the wave amplitude (A), the wavelength (λ) and the channel aspect ratio (α) are chosen and their ranges are decided through preliminary calculations of three-dimensional Navier-stokes and energy equations. A parametric study is also performed to show the effects of different design variables on the overall performance of the wavy channel. Objective functions related to the heat transfer and pressure drop, performance factor (PF) is formulated to analyze the performance of the wavy channel. The numerical results show that the wave amplitude and the channel aspect ratio have significant effects on the thermal performance. It can improve the performance of the wavy channels by increasing wave amplitude or decreasing the channel aspect ratio. Increasing wavelengths have no significant effects on the heat transfer performance.

Keywords: wavy channel, genetic algorithm, optimization, numerical simulation

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Authors: Nkwenti Mbelli Njah

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This research was motivated by a project requested by AXA on the topic of pensions payable under the workers compensation (WC) line of business. There are two types of pensions: the compulsorily recoverable and the not compulsorily recoverable. A pension is compulsorily recoverable for a victim when there is less than 30% of disability and the pension amount per year is less than six times the minimal national salary. The law defines that the mathematical provisions for compulsory recoverable pensions must be calculated by applying the following bases: mortality table TD88/90 and rate of interest 5.25% (maybe with rate of management). To manage pensions which are not compulsorily recoverable is a more complex task because technical bases are not defined by law and much more complex computations are required. In particular, companies have to predict the amount of payments discounted reflecting the mortality effect for all pensioners (this task is monitored monthly in AXA). The purpose of this research was thus to develop a stochastic model for the future mortality of the worker’s compensation pensioners of both the Portuguese market workers and AXA portfolio. Not only is past mortality modeled, also projections about future mortality are made for the general population of Portugal as well as for the two portfolios mentioned earlier. The global model was split in two parts: a stochastic model for population mortality which allows for forecasts, combined with a point estimate from a portfolio mortality model obtained through three different relational models (Cox Proportional, Brass Linear and Workgroup PLT). The one-year death probabilities for ages 0-110 for the period 2013-2113 are obtained for the general population and the portfolios. These probabilities are used to compute different life table functions as well as the not compulsorily recoverable reserves for each of the models required for the pensioners, their spouses and children under 21. The results obtained are compared with the not compulsory recoverable reserves computed using the static mortality table (TD 73/77) that is currently being used by AXA, to see the impact on this reserve if AXA adopted the dynamic tables.

Keywords: compulsorily recoverable, life table functions, relational models, worker’s compensation pensioners

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Eunice H. Li

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This paper discusses and analyses a small portion of the literature that has been reviewed for research work in Distance Education (DE) pedagogies that I am currently undertaking. It begins by presenting a brief overview of Taylor's (2001) five-generation models of Distance Education. The focus of the discussion will be on the 5th generation, Intelligent Flexible Learning Model. For this generation, educational and other institutions make portal access and interactive multi-media (IMM) an integral part of their operations. The paper then takes a brief look at current trends in technologies – for example smart-watch wearable technology such as Apple Watch. The emergent trends in technologies carry many new features. These are compared to former DE generational features. Also compared is the time span that has elapsed between the generations that are referred to in Taylor's model. This paper is a work in progress. The paper therefore welcome new insights, comparisons and critique of the issues discussed.

Keywords: distance education, e-learning technologies, pedagogy, generational models

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Yasaman Tohidi, Shidvash Vakilipour, Saeed Ebadi Tavallaee, Shahin Vakilipoor Takaloo, Hossein Amiri

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The numerical modeling is performed to study the effects of N₂ addition to the fuel stream on the flame structure and temperature distribution of methane-air swirl diffusion flames with different swirl intensities. The Open source Field Operation and Manipulation (OpenFOAM) has been utilized as the computational tool. Flamelet approach along with modified k-ε model is employed to model the flame characteristics.  The results indicate that the presence of N₂ in the fuel stream leads to the flame temperature reduction. By increasing of swirl intensity, the flame structure changes significantly. The flame has a conical shape in low swirl intensity; however, it has an hour glass-shape with a shorter length in high swirl intensity. The effects of N₂ dilution decrease the flame length in all swirl intensities; however, the rate of reduction is more noticeable in low swirl intensity.

Keywords: swirl diffusion flame, N2 dilution, OpenFOAM, swirl intensity

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Authors: Zhongzhong Zeng, Zichen Liang

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In recent years, Beijing courtyards have been facing the problem of renewal and renovation, and the residents are faced with the problems of small house areas, large household sizes, old and dangerous houses, etc. Among the many renovation methods, the authors note two more common practices of using the front porch to expand the floor area and adding a loft. Residents and architects, however, did not give the ventilation performance of the significant interior consideration before beginning the remodeling. The aim of this article is to explore the good or negative impacts of both front porch and loft structures on the manner of interior ventilation in the courtyard. Ventilation, in turn, is crucial to the indoor environmental quality of a home. The major method utilized in this study is the comparative analysis method, in which the authors create four alternative house models with or without a front porch and an attic as two variables and examine internal ventilation using the CFD(Computational Fluid Dynamics) technique. The authors compare the indoor ventilation of four different architectural models with or without front porches and lofts as two variables. The results obtained from the analysis of the sectional airflow and the plane 1.5m height cloud are the existence of the loft, to a certain extent, disrupts the airflow organization of the building and makes the rear wall high windows of the building less effective. Occupying the front porch to become the area of the house has no significant effect on ventilation, but try not to occupy the front porch and add the loft at the same time in the building renovation. The findings of this study led to the following recommendations: strive to preserve the courtyard building's original architectural design and make adjustments to only the inappropriate elements or constructions. The ventilation in the loft portion is inadequate, and the inhabitants typically use the loft as a living area. This may lead to the building relying more on air conditioning in the summer, which would raise energy demand. The front porch serves as a transition place as well as a source of shade, weather protection, and inside ventilation. In conclusion, the examination of interior environments in upcoming studies should concentrate on cross-disciplinary, multi-angle, and multi-level research topics.

Keywords: Beijing courtyard renewal, CFD, indoor environment, ventilation analysis

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Authors: Carolina Sanchez, Juan R. Gallego, Manuel Gamez, Tomas Cabello

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Currently, biological control programs in greenhouse crops involve the use, at the same time, several natural enemies during the crop cycle. Also, large number of plant species grown in greenhouses, among them, the used cultivars are also wide. However, the cultivar effects on entomophagous species efficacy (predators and parasitoids) have been scarcely studied. A new method had been developed, using the factitious prey or host Ephestia kuehniella. It allows us to evaluate, under greenhouse or controlled conditions (semi-field), the cultivar effects on the entomophagous species effectiveness. The work was carried out in greenhouse tomato crop. It has been found the biological and ecological activities of predatory species (Nesidiocoris tenuis) and egg-parasitoid (Trichogramma achaeae) can be well represented with the use of the factitious prey or host; being better in the former than the latter. The data found in the trial are shown and discussed. The developed method could be applied to evaluate new plant materials before making available to farmers as commercial varieties, at low costs and easy use.

Keywords: cultivar effects, efficiency, predators, parasitoids

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Authors: S. N. T. I. Sampath, J. M. S. Jayasinghe, A. P. Attanayake, V. Karunaratne

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Diabetes mellitus is one of the major public health posers throughout the world today that incidence and associated with increasing mortality. Insufficient regulation of the blood glucose level might be serious effects for health and its necessity to identify new therapeutics that have ability to reduce hyperglycaemic condition in the human body. Even though synthetic antidiabetic drugs are more effective to control diabetes mellitus, there are considerable side effects have been reported. Thus, there is an increasing demand for searching new natural products having high antidiabetic activity with lesser side effects. The purposes of the present study were to evaluate different physical parameters and in-vitro and in-vivo antidiabetic potential of the selected combined medicinal plant extracts mixture composed of leaves of Murraya koenigii, cloves of Allium sativum, fruits of Garcinia queasita and seeds of Piper nigrum. The selected plants parts were mixed and ground together and extracted sequentially into the hexane, ethyl acetate and methanol. Solvents were evaporated and they were further dried by freeze-drying to obtain a fine powder of each extract. Various physical parameters such as moisture, total ash, acid insoluble ash and water soluble ash were evaluated using standard test procedures. In-vitro antidiabetic activity of combined plant extracts mixture was screened using enzyme assays such as α-amylase inhibition assay and α-glucosidase inhibition assay. The acute anti-hyperglycaemic activity was performed using oral glucose tolerance test for the streptozotocin induced diabetic Wistar rats to find out in-vivo antidiabetic activity of combined plant extracts mixture and it was assessed through total oral glucose tolerance curve (TAUC) values. The percentage of moisture content, total ash content, acid insoluble ash content and water soluble ash content were ranged of 7.6-17.8, 8.1-11.78, 0.019-0.134 and 6.2-9.2 respectively for the plant extracts and those values were less than standard values except the methanol extract. The hexane and ethyl acetate extracts exhibited highest α-amylase (IC50 = 25.7 ±0.6; 27.1 ±1.2 ppm) and α-glucosidase (IC50 = 22.4 ±0.1; 33.7 ±0.2 ppm) inhibitory activities than methanol extract (IC50 = 360.2 ±0.6; 179.6 ±0.9 ppm) when compared with the acarbose positive control (IC50 = 5.7 ±0.4; 17.1 ±0.6 ppm). The TAUC values for hexane, ethyl acetate, and methanol extracts and glibenclamide (positive control) treated rats were 8.01 ±0.66; 8.05 ±1.07; 8.40±0.50; 5.87 ±0.93 mmol/L.h respectively, whereas in diabetic control rats the TAUC value was 13.22 ±1.07 mmol/L.h. Administration of plant extracts treated rats significantly suppressed (p<0.05) the rise in plasma blood glucose levels compared to control rats but less significant than glibenclamide. The obtained results from in-vivo and in-vitro antidiabetic study showed that the hexane and ethyl acetate extracts of selected combined plant mixture might be considered as a potential source to isolate natural antidiabetic agents and physical parameters of hexane and ethyl acetate extracts will helpful to develop antidiabetic drug with further standardize properties.

Keywords: diabetes mellitus, in-vitro antidiabetic assays, medicinal plants, standardization

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Authors: Fares Hedayatit, Arnauld Joly, Panagiotis Papadimitriou

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Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library.

Keywords: big data, sparsely representable data, tree-based models, scalable learning

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Authors: Quy Dang Nguyen, Reza Nakhaie Jazar

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The paper investigates vibration dynamics of tire-road separation for a quarter-car model; this separation model is developed to be close to the real situation considering the tire is able to separate from the ground plane. A set of piecewise linear mathematical models is developed and matches the in-contact and no-contact states to be considered as mother models for further investigations. The bound dynamics are numerically simulated in the time response and phase portraits. The separation analysis may determine which values of suspension parameters can delay and avoid the no-contact phenomenon, which results in improving ride comfort and eliminating the potentially dangerous oscillation. Finally, model verification is carried out in the MSC-ADAMS environment.

Keywords: quarter-car vibrations, tire-road separation, separation analysis, separation dynamics, ride comfort, ADAMS validation

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

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Authors: Daphne Sobolev, Niklas Voege

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During the last three decades, consumer ethics perceptions have attracted the attention of a large number of researchers. Nevertheless, little is known about the effect of the cognitive and situational contexts of the decision on ethics judgments. In this paper, the interrelationship between consumers’ ethics perceptions, legality evaluations and mind-sets are explored. Legality evaluations represent the cognitive context of the ethical judgments, whereas mind-sets represent their situational context. Drawing on moral development theories and priming theories, it is hypothesized that both factors are significantly related to consumer ethics perceptions. To test this hypothesis, 289 participants were allocated to three mind-set experimental conditions and a control group. Participants in the mind-set conditions were primed for aggressiveness, politeness or awareness to the negative legal consequences of breaking the law. Mind-sets were induced using a sentence-unscrambling task, in which target words were included. Ethics and legality judgments were assessed using consumer ethics and internet ethics questionnaires. All participants were asked to rate the ethicality and legality of consumer actions described in the questionnaires. The results showed that consumer ethics and legality perceptions were significantly correlated. Moreover, including legality evaluations as a variable in ethics judgment models increased the predictive power of the models. In addition, inducing aggressiveness in participants reduced their sensitivity to ethical issues; priming awareness to negative legal consequences increased their sensitivity to ethics when uncertainty about the legality of the judged scenario was high. Furthermore, the correlation between ethics and legality judgments was significant overall mind-set conditions. However, the results revealed conflicts between ethics and legality perceptions: consumers considered 10%-14% of the presented behaviors unethical and legal, or ethical and illegal. In 10-23% of the questions, participants indicated that they did not know whether the described action was legal or not. In addition, an asymmetry between the effects of aggressiveness and politeness priming was found. The results show that the legality judgments and mind-sets interact with consumer ethics perceptions. Thus, they portray consumer ethical judgments as dynamical processes which are inseparable from other cognitive processes and situational variables. They highlight that legal and ethical education, as well as adequate situational cues at the service place, could have a positive effect on consumer ethics perceptions. Theoretical contribution is discussed.

Keywords: consumer ethics, legality judgments, mind-set, priming, aggressiveness

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Mohammadreza Nematollahi, Bahareh Mosadegh Sedghy, Alireza Tajbakhsh

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The last two decades have witnessed substantial attention to organic and sustainable agricultural supply chains. Motivated by real-world practices, this paper aims to address two main challenges observed in organic product supply chains: decentralized decision-making process between farmers and their retailers, and competition between organic products and their conventional counterparts. To this aim, an agricultural supply chain consisting of two farmers, a conventional farmer and an organic farmer who offers an organic version of the same product, is considered. Both farmers distribute their products through a single retailer, where there exists competition between the organic and the conventional product. The retailer, as the market leader, sets the wholesale price, and afterward, the farmers set their production quantity decisions. This paper first models the demand functions of the conventional and organic products by incorporating the effect of asymmetric brand equity, which captures the fact that consumers usually pay a premium for organic due to positive perceptions regarding their health and environmental benefits. Then, profit functions with consideration of some characteristics of organic farming, including crop yield gap and organic cost factor, are modeled. Our research also considers both economies and diseconomies of scale in farming production as well as the effects of organic subsidy paid by the government to support organic farming. This paper explores the investigated supply chain in three scenarios: decentralized, centralized, and coordinated decision-making structures. In the decentralized scenario, the conventional and organic farmers and the retailer maximize their own profits individually. In this case, the interaction between the farmers is modeled under the Bertrand competition, while analyzing the interaction between the retailer and farmers under the Stackelberg game structure. In the centralized model, the optimal production strategies are obtained from the entire supply chain perspective. Analytical models are developed to derive closed-form optimal solutions. Moreover, analytical sensitivity analyses are conducted to explore the effects of main parameters like the crop yield gap, organic cost factor, organic subsidy, and percent price premium of the organic product on the farmers’ and retailer’s optimal strategies. Afterward, a coordination scenario is proposed to convince the three supply chain members to shift from the decentralized to centralized decision-making structure. The results indicate that the proposed coordination scenario provides a win-win-win situation for all three members compared to the decentralized model. Moreover, our paper demonstrates that the coordinated model respectively increases and decreases the production and price of organic produce, which in turn motivates the consumption of organic products in the market. Moreover, the proposed coordination model helps the organic farmer better handle the challenges of organic farming, including the additional cost and crop yield gap. Last but not least, our results highlight the active role of the organic subsidy paid by the government as a means of promoting sustainable organic product supply chains. Our paper shows that although the amount of organic subsidy plays a significant role in the production and sales price of organic products, the allocation method of subsidy between the organic farmer and retailer is not of that importance.

Keywords: analytical game-theoretic model, product competition, supply chain coordination, sustainable organic supply chain

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Authors: B. Van Kessel, P. T. Kockelkorn, T. R. Speelman, T. C. Wierikx, C. Mai Van, T. A. Bogaard

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Saltwater intrusion is a common problem in estuaries around the world, as it could hinder the freshwater supply of coastal zones. This problem is likely to grow due to climate change and sea-level rise. The influence of these factors on the saltwater intrusion was investigated for the Cai River in the Khanh Hoa province in Vietnam. In addition, the Cai River has high seasonal fluctuations in discharge, leading to increased saltwater intrusion during the dry season. Sea level rise, river discharge changes, river mouth widening and a proposed saltwater intrusion prevention dam can have influences on the saltwater intrusion but have not been quantified for the Cai River estuary. This research used both an analytical and numerical model to investigate the effect of the aforementioned factors. The analytical model was based on a model proposed by Savenije and was calibrated using limited in situ data. The numerical model was a 3D hydrodynamic model made using the Delft3D4 software. The analytical model and numerical model agreed with in situ data, mostly for tidally average data. Both models indicated a roughly similar dependence on discharge, also agreeing that this parameter had the most severe influence on the modeled saltwater intrusion. Especially for discharges below 10 m/s3, the saltwater was predicted to reach further than 10 km. In the models, both sea-level rise and river widening mainly resulted in salinity increments up to 3 kg/m3 in the middle part of the river. The predicted sea-level rise in 2070 was simulated to lead to an increase of 0.5 km in saltwater intrusion length. Furthermore, the effect of the saltwater intrusion dam seemed significant in the model used, but only for the highest position of the gate.

Keywords: Cai River, hydraulic models, river discharge, saltwater intrusion, tidal barriers

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Authors: K. Iu. Hrizhymalska, O. Ol. Andrushkova, I. Iu. Pshenychna

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Aim. To study and copare the side effects of myopia contact correction by soft lenses and orthokeratology lenses among medical students. Patients and methods: 34 students (68 eyes) with moderate and severe myopia, who used contact correction of myopia for 2-4 years, were examined. Some of them used soft lenses, while others - orthokeratology lenses. Methods were used: biomicroscopy of the eye surface, Schirmer's test, Norn's test, survey regarding satisfaction with use. Results. Corneal vascularization along the limbus was noted in 4 (5%) eyes of the examined students. In 8 (11%) eyes, symptoms of mild dry eye disease were detected. 2 (3%) eyes showed signs of meibomitis. Allergic conjunctivitis was observed in 4 (5%) eyes, and a purulent corneal ulcer was present in 1 eye. Surveys have shown that orthokeratology lenses unlike soft lenses don't limit everyday activity (in sports, tourism, swimming etc.), they also don't cause discomfort during temperature changes and reduce existing symptoms of dry eye disease. Conclusion. Thus, myopia contact correction is one of the optimal options among students, which allows to expand physical and mental activity. However, taking into account the frequency of side effects in users of soft contact lenses, it is necessary to carry out prevention and treatment of myopia in medical students, follow the recommendations for use, instill preservative-free tear substitutes with trehalose when symptoms of dry eye appear. Also when side reactions occur, contact correction with soft lenses should be changed to orthokeratology lenses.

Keywords: correction, myopia, soft lenses, orthokeratology, specracles, cornea, dry eye, side effects, refractive errors

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Authors: Chun-Yu Chen, Kua-Rong Wu, Yu-Cheng Chen, Perng-Jy Tsai

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Cooking fumes are known containing polycyclic aromatic hydrocarbons (PAHs) and aldehydes, and some of them have been proven carcinogenic or possibly carcinogenic to humans. Considering their chronic health effects, long-term exposure data is required for assessing cooking workers’ lifetime health risks. Previous exposure assessment studies, due to both time and cost constraints, mostly were based on the cross-sectional data. Therefore, establishing a long-term exposure data has become an important issue for conducting health risk assessment for cooking workers. An approach was proposed in this study. Here, the generation rates of both PAHs and aldehydes from a cooking process were determined by placing a sampling train exactly under the under the exhaust fan under the both the total enclosure condition and normal operating condition, respectively. Subtracting the concentration collected by the former (representing the total emitted concentration) from that of the latter (representing the hood collected concentration), the fugitive emitted concentration was determined. The above data was further converted to determine the generation rates based on the flow rates specified for the exhaust fan. The determinations of the above generation rates were conducted in a testing chamber with a selected cooking process (deep-frying chicken nuggets under 3 L peanut oil at 200°C). The sampling train installed under the exhaust fan consisted respectively an IOM inhalable sampler with a glass fiber filter for collecting particle-phase PAHs, followed by a XAD-2 tube for gas-phase PAHs. The above was also used to sample aldehydes, however, installed with a filter pre-coated with DNPH, and followed by a 2,4-DNPH-cartridge for collecting particle-phase and gas-phase aldehydes, respectively. PAHs and aldehydes samples were analyzed by GC/MS-MS (Agilent 7890B), and HPLC-UV (HITACHI L-7100), respectively. The obtained generation rates of both PAHs and aldehydes were applied to the near-field/ far-field exposure model to estimate the exposures of cooks (the estimated near-field concentration), and helpers (the estimated far-field concentration). For validating purposes, both PAHs and aldehydes samplings were conducted simultaneously using the same sampling train at both near-field and far-field sites of the testing chamber. The sampling results, together with the use of the mixed-effect model, were used to calibrate the estimated near-field/ far-field exposures. In the present study, the obtained emission rates were further converted to emission factor of both PAHs and aldehydes according to the amount of food oil consumed. Applying the long-term food oil consumption records, the emission rates for both PAHs and aldehydes were determined, and the long-term exposure databanks for cooks (the estimated near-field concentration), and helpers (the estimated far-field concentration) were then determined. Results show that the proposed approach was adequate to determine the generation rates of both PAHs and aldehydes under various fan exhaust flow rate conditions. The estimated near-field/ far-field exposures, though were significantly different from that obtained from the field, can be calibrated using the mixed effect model. Finally, the established long-term data bank could provide a useful basis for conducting long-term exposure assessments for cooking workers exposed to PAHs and aldehydes.

Keywords: aldehydes, cooking oil fumes, long-term exposure assessment, modeling, polycyclic aromatic hydrocarbons (PAHs)

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Authors: Aspasia Koutsokosta, Stefanos Katsavounis

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This paper reviews the model-based qualitative and quantitative Operations Management research in the context of Construction Supply Chain Management (CSCM). Construction industry has been traditionally blamed for low productivity, cost and time overruns, waste, high fragmentation and adversarial relationships. The construction industry has been slower than other industries to employ the Supply Chain Management (SCM) concept and develop models that support the decision-making and planning. However the last decade there is a distinct shift from a project-based to a supply-based approach of construction management. CSCM comes up as a new promising management tool of construction operations and improves the performance of construction projects in terms of cost, time and quality. Modeling the Construction Supply Chain (CSC) offers the means to reap the benefits of SCM, make informed decisions and gain competitive advantage. Different modeling approaches and methodologies have been applied in the multi-disciplinary and heterogeneous research field of CSCM. The literature review reveals that a considerable percentage of CSC modeling accommodates conceptual or process models which discuss general management frameworks and do not relate to acknowledged soft OR methods. We particularly focus on the model-based quantitative research and categorize the CSCM models depending on their scope, mathematical formulation, structure, objectives, solution approach, software used and decision level. Although over the last few years there has been clearly an increase of research papers on quantitative CSC models, we identify that the relevant literature is very fragmented with limited applications of simulation, mathematical programming and simulation-based optimization. Most applications are project-specific or study only parts of the supply system. Thus, some complex interdependencies within construction are neglected and the implementation of the integrated supply chain management is hindered. We conclude this paper by giving future research directions and emphasizing the need to develop robust mathematical optimization models for the CSC. We stress that CSC modeling needs a multi-dimensional, system-wide and long-term perspective. Finally, prior applications of SCM to other industries have to be taken into account in order to model CSCs, but not without the consequential reform of generic concepts to match the unique characteristics of the construction industry.

Keywords: construction supply chain management, modeling, operations research, optimization, simulation

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Authors: Huseyin Demir

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This article is the results of a quantitative research about the effects of using interactive whiteboards in high school mathematics classroom. The aim of this article is to investigate the effects of using interactive whiteboards in high school mathematics classrooms. During the article the following questions are answered: 'What can we do with an interactive whiteboard?' and 'Do we really need those properties of the interactive whiteboard?' For the research part of the article, two groups of lessons are executed in Private Demirel College. In the first 6 weeks, the topics are taught on a normal blackboard. Starting from seventh week, we have used interactive whiteboard in the mathematics lessons. At the end of an eight week lectures with interactive whiteboards, a questionnaire is prepared and executed for the students. In the questionnaire 10 questions were asked about the benefits and differences of using the interactive whiteboards in mathematics lessons. By looking at the conclusion of the results of questionnaire and some discussions with the students we found some useful benefits of the usage of interactive whiteboards in mathematics lessons. This article will be helpful for the high school mathematics teachers.

Keywords: mathematics education, interactive whiteboard, blackboard, using interactive whiteboard in mathematics lessons

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Authors: Joanna Marie A. Paulino-Morente, Vaneza Valentina L. Penolio, Grace Sabado

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Ovarian cancer is extremely hard to diagnose in its early stages, and those afflicted at the time of diagnosis are typically asymptomatic and in the late stages of the disease, with metastasis to other organs. Ovarian cancers often occur sporadically, with only 5% associated with hereditary mutations. Mutations in the BRCA1 and BRCA2 tumor suppressor genes have been found to be responsible for the majority of hereditary ovarian cancers. One type of ovarian tumor is Malignant Mixed Mullerian Tumor (MMMT), which is a very rare and aggressive type, accounting for only 1% of all ovarian cancers. Reported is a case of a 43-year-old G3P3 (3003), who came into our institution due to a 2-month history of difficulty of breathing. Family history reveals that her eldest and younger sisters both died of ovarian malignancy, with her younger sister having a histopathology report of endometrioid ovarian carcinoma, left ovary stage IIIb. She still has 2 asymptomatic sisters. Physical examination pointed to pleural effusion of right lung, and presence of bilateral ovarian new growth, which had a Sassone score of 13. Admitting Diagnosis was G3P3 (3003), Ovarian New Growth, bilateral, Malignant; Pleural effusion secondary to malignancy. BRCA was requested to establish a hereditary mutation; however, the patient had no funds. Once the patient was stabilized, TAHBSO with surgical staging was performed. Intraoperatively, the pelvic cavity was occupied by firm, irregularly shaped ovaries, with a colorectal metastasis. Microscopic sections from both ovaries and the colorectal metastasis had pleomorphic tumor cells lined by cuboidal to columnar epithelium exhibiting glandular complexity, displaying nuclear atypia and increased nuclear-cytoplasmic ratio, which are infiltrating the stroma, consistent with the features of Malignant Mixed Mullerian Tumor, since MMMT is composed histologically of malignant epithelial and sarcomatous elements. In conclusion, discussed is the clinic-pathological feature of a patient with primary ovarian Malignant Mixed Mullerian Tumor, a rare malignancy comprising only 1% of all ovarian neoplasms. Also, by understanding the hereditary ovarian cancer syndromes and its relation to this patient, it cannot be overemphasized that a comprehensive family history is really fundamental for early diagnosis. The familial association of the disease, given that the patient has two sisters who were diagnosed with an advanced stage of ovarian cancer and succumbed to the disease at a much earlier age than what is reported in the general population, points to a possible hereditary syndrome which occurs in only 5% of ovarian neoplasms. In a low-resource setting, being in a third world country, the following will be recommended for monitoring and/or screening women who are at high risk for developing ovarian cancer, such as the remaining sisters of the patient: 1) Physical examination focusing on the breast, abdomen, and rectal area every 6 months. 2) Transvaginal sonography every 6 months. 3) Mammography annually. 4) CA125 for postmenopausal women. 5) Genetic testing for BRCA1 and BRCA2 will be reserved for those who are financially capable.

Keywords: BRCA, hereditary breast-ovarian cancer syndrome, malignant mixed mullerian tumor, ovarian cancer

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Authors: Neha Awasthi, Ujjwal Kumar

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Density of large carnivores is primarily dictated by the density of their prey. Therefore, optimal management of ungulates populations permits harbouring of viable large carnivore populations within protected areas. Ungulate density is likely to respond to regimes of protection and vegetation types. This has generated the need among conservation practitioners to obtain strata specific seasonal species densities for habitat management. Kanha Tiger Reserve (KTR) of 2074 km2 area comprises of two distinct management strata: The core (940 km2), devoid of human settlements and buffer (1134 km2) which is a multiple use area. In general, four habitat strata, grassland, sal forest, bamboo-mixed forest and miscellaneous forest are present in the reserve. Stratified sampling approach was used to access a) impact of human use and b) effect of habitat and season on ungulate densities. Since 2013 to 2016, ungulates were surveyed in winter and summer of each year with an effort of 1200 km walk in 200 spatial transects distributed throughout Kanha Tiger Reserve. We used a single detection function for each species within each habitat stratum for each season for estimating species specific seasonal density, using program DISTANCE. Our key results state that the core area had 4.8 times higher wild ungulate biomass compared with the buffer zone, highlighting the importance of undisturbed area. Chital was found to be most abundant, having a density of 30.1(SE 4.34)/km2 and contributing 33% of the biomass with a habitat preference for grassland. Unlike other ungulates, Gaur being mega herbivore, showed a major seasonal shift in density from bamboo-mixed and sal forest in summer to miscellaneous forest in winter. Maximum diversity and ungulate biomass were supported by grassland followed by bamboo-mixed habitat. Our study stresses the importance of inviolate core areas for achieving high wild ungulate densities and for maintaining populations of endangered and rare species. Grasslands accounts for 9% of the core area of KTR maintained in arrested stage of succession, therefore enhancing this habitat would maintain ungulate diversity, density and cater to the needs of only surviving population of the endangered barasingha and grassland specialist the blackbuck. We show the relevance of different habitat types for differential seasonal use by ungulates and attempt to interpret this in the context of nutrition and cover needs by wild ungulates. Management for an optimal habitat mosaic that maintains ungulate diversity and maximizes ungulate biomass is recommended.

Keywords: distance sampling, habitat management, ungulate biomass, diversity

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Authors: Sandor Levai, Valentin Juhasz, Miklos Gasz

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Rising damp is an extremely complex phenomenon that is of great practical interest to the field of building conservation due to the irreversible damages it can make to old and historic structures. The electrical effects occurring in damp masonry have been scarcely researched and are a largely unknown aspect of rising damp. Present paper describes the typical electrical patterns occurring in porous brickwork during a wetting and drying cycle. It has been found that in contrast with dry masonry, where electrical phenomena are virtually non-existent, damp masonry exhibits a wide array of electrical effects. Long-term real-time measurements performed in the lab on small-scale brick structures, using an array of embedded micro-sensors, revealed significant voltage, current, capacitance and resistance variations which can be linked to the movement of moisture inside porous materials. The same measurements performed on actual old buildings revealed a similar behaviour, the electrical effects being more significant in areas of the brickwork affected by rising damp. Understanding these electrical phenomena contributes to a better understanding of the driving mechanisms of rising damp, potentially opening new avenues of dealing with it in a less invasive manner.

Keywords: brick masonry, electrical phenomena in damp brickwork, porous building materials, rising damp, spontaneous electrical potential, wetting-drying cycle

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Authors: Jesika A. Walker, Mohammed Aswad, Guy Lacroix, Denis Cousineau

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There is a fundamental disconnect between the experience of time passing and the chronometric units by which time is quantified. Specifically, there appears to be no relationship between the passage of time judgments (PoTJs) and verbal duration estimates at short durations (e.g., < 2000 milliseconds). When a duration is longer than several minutes, however, evidence suggests that a slower feeling of time passing is predictive of overestimation. Might the length of a task moderate the relation between PoTJs and duration estimates? Similarly, the estimation paradigm (prospective vs. retrospective) and the mental effort demanded of a task (task demand) have both been found to influence duration estimates. However, only a handful of experiments have investigated these effects for tasks of long durations, and the results have been mixed. Thus, might the length of a task also moderate the effects of the estimation paradigm and task demand on duration estimates? To investigate these questions, 273 participants performed either an easy or difficult visual and memory search task for either eight or 58 minutes, under prospective or retrospective instructions. Afterward, participants provided a duration estimate in minutes, followed by a PoTJ on a Likert scale (1 = very slow, 7 = very fast). A 2 (prospective vs. retrospective) × 2 (eight minutes vs. 58 minutes) × 2 (high vs. low difficulty) between-subjects ANOVA revealed a two-way interaction between task demand and task duration on PoTJs, p = .02. Specifically, time felt faster in the more challenging task, but only in the eight-minute condition, p < .01. Duration estimates were transformed into RATIOs (estimate/actual duration) to standardize estimates across durations. An ANOVA revealed a two-way interaction between estimation paradigm and task duration, p = .03. Specifically, participants overestimated the task more if they were given prospective instructions, but only in the eight-minute task. Surprisingly, there was no effect of task difficulty on duration estimates. Thus, the demands of a task may influence ‘feeling of time’ and ‘estimation time’ differently, contributing to the existing theory that these two forms of time judgement rely on separate underlying cognitive mechanisms. Finally, a significant main effect of task duration was found for both PoTJs and duration estimates (ps < .001). Participants underestimated the 58-minute task (m = 42.5 minutes) and overestimated the eight-minute task (m = 10.7 minutes). Yet, they reported the 58-minute task as passing significantly slower on a Likert scale (m = 2.5) compared to the eight-minute task (m = 4.1). In fact, a significant correlation was found between PoTJ and duration estimation (r = .27, p <.001). This experiment thus provides evidence for a compensatory effect at longer durations, in which people underestimate a ‘slow feeling condition and overestimate a ‘fast feeling condition. The results are discussed in relation to heuristics that might alter the relationship between these two variables when conditions range from several minutes up to almost an hour.

Keywords: duration estimates, long durations, passage of time judgements, task demands

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