Search results for: physical traffic model

Authors: Neslihan Sahin Celik, Ali Eraslan

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Mathematical model and modeling are one of the topics that have been intensively discussed in recent years. In line with the results of the PISA studies, researchers in many countries have begun to question how much students in school-education system are prepared to solve the real-world problems they encounter in their future professional lives. As a result, many mathematics educators have begun to emphasize the importance of new skills and understanding such as constructing, Hypothesizing, Describing, manipulating, predicting, working together for complex and multifaceted problems for success in beyond the school. When students increasingly face this kind of situations in their daily life, it is important to make sure that students have enough experience to work together and interpret mathematical situations that enable them to think in different ways and share their ideas with their peers. Thus, model eliciting activities are one of main tools that help students to gain experiences and the new skills required. This research study was carried on the town center of a big city located in the Black Sea region in Turkey. The participants were eighth-grade students in a middle school. After a six-week preliminary study, three students in an eighth-grade classroom were selected using criterion sampling technique and placed in a focus group. The focus group of three students was videotaped as they worked on a model eliciting activity, the Summer Job Problem. The conversation of the group was transcribed, examined with students’ written work and then qualitatively analyzed through the lens of Blum’s (1996) modeling processing cycle. The study results showed that eighth grade students can successfully work with the model eliciting, develop a model based on the two parameters and review the whole process. On the other hand, they had difficulties to relate parameters to each other and take all parameters into account to establish the model.

Keywords: middle school, modeling, mathematical modeling, summer job problem

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Authors: Rahimi-Movaghar Vafa, Mansouri Pejman, Chardoli Mojtaba, Rezvani Samina

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Introduction: Blunt abdominal trauma is one of the leading causes of morbidity and mortality in all age groups, but diagnosis of serious intra-abdominal pathology is difficult and most of the damages are obscure in the initial investigation. There is still controversy about which patients should undergo abdomen/pelvis CT, which patients needs more observation and which patients can be discharged safely The aim of this study was to determine that is it safe to discharge patients with blunt abdominal trauma with normal initial findings. Methods: This non-randomized cross-sectional study was conducted from September 2013 to September 2014 at two levels I trauma centers, Sina hospital and Rasoul-e-Akram hospital (Tehran, Iran). Our inclusion criteria were all patients were admitted for suspicious BAT and our exclusion criteria were patients that have serious head and neck, chest, spine and limb injuries which need surgical intervention, those who have unstable vital signs, pregnant women with a gestational age over 3 months and homeless or without exact home address. 390 patients with blunt trauma abdomen examined and the necessary data, including demographic data, the abdominal examination, FAST result, patients’ lab test results (hematocrit, base deficit, urine analysis) on admission and at 6 and 12 hours after admission were recorded. Patients with normal physical examination, laboratory tests and FAST were discharged from the ED during 12 hours with the explanation of the alarm signs and were followed up after 24 hours and 1 week by a telephone call. Patients with abnormal findings in physical examination, laboratory tests, and FAST underwent abdomino-pelvic CT scan. Results: The study included 390 patients with blunt abdominal trauma between 12 and 80 years of age (mean age, 37.0 ± 13.7 years) and the mean duration of hospitalization in patients was 7.4 ± 4.1 hours. 88.6% of the patients were discharged from hospital before 12 hours. Odds ratio (OR) for having any symptoms for discharge after 6 hours was 0.160 and after 12 hours was 0.117 hours, which is statistically significant. Among the variables age, systolic and diastolic blood pressure, heart rate, respiratory rate, hematocrit and base deficit at admission, 6 hours and 12 hours after admission showed no significant statistical relationship with discharge time. From our 390 patients, 190 patients have normal initial physical examination, lab data and FAST findings that didn’t show any signs or symptoms in their next assessment and in their follow up by the phone call. Conclusion: It is recommended that patients with no symptoms at admission (completely normal physical examination, ultrasound, normal hematocrit and normal base deficit and lack of microscopic hematuria) and good family and social status can be safely discharged from the emergency department.

Keywords: blunt abdominal trauma, patient discharge, emergency department, FAST

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Authors: Zahra Hamedani

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Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

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Authors: S. A. Agbalajobi, W. A. Bello

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The study carried out analysis on slope stability in an excavated gneiss rock using geological strength index (GSI) in Ilorin, Kwara State, Nigeria. A kinematic analysis of planar discontinuity sets in a gneiss deposit was carried out to ascertain the degree of slope stability. Discontinuity orientations in the rock mass were mapped using compass clinometers. The average result of physical and mechanical properties such as specific gravity, unit weight, uniaxial compressive strength, point load index, and Schmidt rebound value are 2.64 g/m3, 25.95 kN/m3, 156 MPa, 6.5 MPa, and 53.12 respectively. Also, a statistical model equation relating the rock strength was developed. The analyses states that the rock face is susceptible to wedge failures having all the geometrical conditions associated with the occurrence of such failures were noticeable. It can be concluded that analyses of discontinuity orientation in relation to cut face direction in rock excavation is essential for mine planning to forestall mine accidents. Assessment of excavated slope methods was evident that one excavation method (blasting and/or use of hydraulic hammer) is applicable for the given rock strength, the ease of excavation decreases as the rock mass quality increases, thus blasting most suitable for such operation.

Keywords: slope stability, wedge failure, geological strength index (GSI), discontinuities and excavated slope

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Authors: Inese Trusina, Elita Jermolajeva

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Global challenges require a transition from the existing linear economic model to a model that will consider nature as a life support system for the developmenton the way to social well-being in the frame of the ecological economics paradigm. The article presentsbasic definitions for the development of formalized description of sustainabledevelopment monitoring. It provides examples of calculating the parameters of monitoring for the Baltic Sea region countries and their primary interpretation.

Keywords: sustainability, development, power, ecological economics, regional economic, monitoring

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Authors: Pedro B. Antunes, Paulo J. Ramísio

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Road runoff is a linear source of diffuse pollution that can cause significant environmental impacts. During rainfall events, pollutants from both stationary and mobile sources, which have accumulated on the road surface, are dragged through the superficial runoff. Road runoff in coastal zones may present high levels of salinity and chlorides due to the proximity of the sea and transported marine aerosols. Appearing to be correlated to this process, organic matter concentration may also be significant. This study assesses this phenomenon with the purpose of identifying the relationships between monitored water quality parameters and intrinsic site variables. To achieve this objective, an extensive monitoring program was conducted on a Portuguese coastal highway. The study included thirty rainfall events, in different weather, traffic and salt deposition conditions in a three years period. The evaluations of various water quality parameters were carried out in over 200 samples. In addition, the meteorological, hydrological and traffic parameters were continuously measured. The salt deposition rates (SDR) were determined by means of a wet candle device, which is an innovative feature of the monitoring program. The SDR, variable throughout the year, appears to show a high correlation with wind speed and direction, but mostly with wave propagation, so that it is lower in the summer, in spite of the favorable wind direction in the case study. The distance to the sea, topography, ground obstacles and the platform altitude seems to be also relevant. It was confirmed the high salinity in the runoff, increasing the concentration of the water quality parameters analyzed, with significant amounts of seawater features. In order to estimate the correlations and patterns of different water quality parameters and variables related to weather, road section and salt deposition, the study included exploratory data analysis using different techniques (e.g. Pearson correlation coefficients, Cluster Analysis and Principal Component Analysis), confirming some specific features of the investigated road runoff. Significant correlations among pollutants were observed. Organic matter was highlighted as very dependent of salinity. Indeed, data analysis showed that some important water quality parameters could be divided into two major clusters based on their correlations to salinity (including organic matter associated parameters) and total suspended solids (including some heavy metals). Furthermore, the concentrations of the most relevant pollutants seemed to be very dependent on some meteorological variables, particularly the duration of the antecedent dry period prior to each rainfall event and the average wind speed. Based on the results of a monitoring case study, in a coastal zone, it was proven that SDR, associated with the hydrological characteristics of road runoff, can contribute for a better knowledge of the runoff characteristics, and help to estimate the specific nature of the runoff and related water quality parameters.

Keywords: coastal zones, monitoring, road runoff pollution, salt deposition

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Authors: Abdelmalek Ouannou, Adil Brouri, Laila Kadi, Tarik

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Switched reluctance motor (SRM) has a major interest in a large domain as in electric vehicle driving because of its wide range of speed operation, high performances, low cost, and robustness to run under degraded conditions. The purpose of the paper is to develop a new analytical approach for modeling SRM parameters. Then, an identification scheme is proposed to obtain the SRM parameters. Since the SRM is featured by a highly nonlinear behavior, modeling these devices is difficult. Then, it is convenient to develop an accurate model describing the SRM. Furthermore, it is always operated in the magnetically saturated mode to maximize the energy transfer. Accordingly, it is shown that the SRM can be accurately described by a generalized polynomial Hammerstein model, i.e., the parallel connection of several Hammerstein models having polynomial nonlinearity. Presently an analytical identification method is developed using a chirp excitation signal. Afterward, the parameters of the obtained model have been determined using Finite Element Method analysis. Finally, in order to show the effectiveness of the proposed method, a comparison between the true and estimate models has been performed. The obtained results show that the output responses are very close.

Keywords: switched reluctance motor, swept-sine signal, generalized Hammerstein model, nonlinear system

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Authors: N. Azouaou, H. Mokaddem, D. Senadjki, K. Kedjit, Z. Sadaoui

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Introduction: Water contamination caused by dye industries, including food, leather, textile, plastic, cosmetics, paper-making, printing and dye synthesis, has caused more and more attention, since most dyes are harmful to human being and environments. Untreated coffee grounds were used as a high-efficiency adsorbent for the removal of a cationic dye (methylene blue, MB) from aqueous solution. Characterization of the adsorbent was performed using several techniques such as SEM, surface area (BET), FTIR and pH zero charge. The effects of contact time, adsorbent dose, initial solution pH and initial concentration were systematically investigated. Results showed the adsorption kinetics followed the pseudo-second-order kinetic model. Langmuir isotherm model is in good agreement with the experimental data as compared to Freundlich and D–R models. The maximum adsorption capacity was found equal to 52.63mg/g. In addition, the possible adsorption mechanism was also proposed based on the experimental results. Experimental: The adsorption experiments were carried out in batch at room temperature. A given mass of adsorbent was added to methylene blue (MB) solution and the entirety was agitated during a certain time. The samples were carried out at quite time intervals. The concentrations of MB left in supernatant solutions after different time intervals were determined using a UV–vis spectrophotometer. The amount of MB adsorbed per unit mass of coffee grounds (qt) and the dye removal efficiency (R %) were evaluated. Results and Discussion: Some chemical and physical characteristics of coffee grounds are presented and the morphological analysis of the adsorbent was also studied. Conclusions: The good capacity of untreated coffee grounds to remove MB from aqueous solution was demonstrated in this study, highlighting its potential for effluent treatment processes. The kinetic experiments show that the adsorption is rapid and maximum adsorption capacities qmax= 52.63mg/g achieved in 30min. The adsorption process is a function of the adsorbent concentration, pH and metal ion concentration. The optimal parameters found are adsorbent dose m=5g, pH=5 and ambient temperature. FTIR spectra showed that the principal functional sites taking part in the sorption process included carboxyl and hydroxyl groups.

Keywords: adsorption, methylene blue, coffee grounds, kinetic study

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Authors: Kristopher A. Pruitt, Justin M. Hill

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The purpose of this research is to determine the pacing and nutrition strategies which minimize completion time and carbohydrate intake for athletes competing in ultramarathon races. The model formulation consists of a two-phase optimization. The first-phase mixed-integer nonlinear program (MINLP) determines the minimum completion time subject to the altitude, terrain, and distance of the race, as well as the mass and cardiovascular fitness of the athlete. The second-phase MINLP determines the minimum total carbohydrate intake required for the athlete to achieve the completion time prescribed by the first phase, subject to the flow of carbohydrates through the stomach, liver, and muscles. Consequently, the second phase model provides the optimal pacing and nutrition strategies for a particular athlete for each kilometer of a particular race. Validation of the model results over a wide range of athlete parameters against completion times for real competitive events suggests strong agreement. Additionally, the kilometer-by-kilometer pacing and nutrition strategies, the model prescribes for a particular athlete suggest unconventional approaches could result in lower completion times. Thus, the MINLP provides prescriptive guidance that athletes can leverage when developing pacing and nutrition strategies prior to competing in ultramarathon races. Given the highly-variable topographical characteristics common to many ultramarathon courses and the potential inexperience of many athletes with such courses, the model provides valuable insight to competitors who might otherwise fail to complete the event due to exhaustion or carbohydrate depletion.

Keywords: nutrition, optimization, pacing, ultramarathons

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Authors: Kais Ben-Ahmed, Mhamed Ali-El-Aroui

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This paper develops a Local Generalized Linear Spatial Model (LGLSM) to describe the spatial variation of Visceral Leishmaniasis (VL) infection risk in northern and central Tunisia. The response from each region is a number of affected children less than five years of age recorded from 1996 through 2006 from Tunisian pediatric departments and treated as a poison county level data. The model includes climatic factors, namely averages of annual rainfall, extreme values of low temperatures in winter and high temperatures in summer to characterize the climate of each region according to each continentality index, the pluviometric quotient of Emberger (Q2) to characterize bioclimatic regions and component for residual extra-poison variation. The statistical results show the progressive increase in the number of affected children in regions with high continentality index and low mean yearly rainfull. On the other hand, an increase in pluviometric quotient of Emberger contributed to a significant increase in VL incidence rate. When compared with the original GLSM, Bayesian locally modeling is improvement and gives a better approximation of the Tunisian VL risk estimation. According to the Bayesian approach inference, we use vague priors for all parameters model and Markov Chain Monte Carlo method.

Keywords: generalized linear spatial model, local model, extra-poisson variation, continentality index, visceral leishmaniasis, Tunisia

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Authors: Maha Sliti, Walid Abdallah, Noureddine Boudriga

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In the frame of this work, we present an optical multicasting approach based on optical code-words. Our approach associates, in the edge node, an optical code-word to a group multicast address. In the core node, a set of tunable decoders are used to send a traffic data to multiple destinations based on the received code-word. The use of code-words, which correspond to the combination of an input port and a set of output ports, allows the implementation of an optical switching matrix. At the reception of a burst, it will be delayed in an optical memory. And, the received optical code-word is split to a set of tunable optical decoders. When it matches a configured code-word, the delayed burst is switched to a set of output ports.

Keywords: optical multicast, optical burst switching networks, optical code-words, tunable decoder, virtual optical memory

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Authors: Farhad Salek, Shahaboddin Resalati

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The production of electric vehicles (EVs) featuring lithium-ion battery technology has substantially escalated over the past decade, demonstrating a steady and persistent upward trajectory. The imminent retirement of electric vehicle (EV) batteries after approximately eight years underscores the critical need for their redirection towards recycling, a task complicated by the current inadequacy of recycling infrastructures globally. A potential solution for such concerns involves extending the operational lifespan of electric vehicle (EV) batteries through their utilization in stationary energy storage systems during secondary applications. Such adoptions, however, require addressing the safety concerns associated with batteries’ knee points and thermal runaways. This paper develops an accurate mathematical model representative of the second-life battery packs from a cell-to-pack scale using an equivalent circuit model (ECM) methodology. Neural network algorithms are employed to forecast the degradation parameters based on the EV batteries' aging history to develop a degradation model. The degradation model is integrated with the ECM to reflect the impacts of the cycle aging mechanism on battery parameters during operation. The developed model is tested under real-life load profiles to evaluate the life span of the batteries in various operating conditions. The methodology and the algorithms introduced in this paper can be considered the basis for Battery Management System (BMS) design and techno-economic analysis of such technologies.

Keywords: second life battery, electric vehicles, degradation, neural network

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Authors: Magdalena B. Kaziuk, Waldemar Kosiba, Marek J. Kuzniewski

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Introduction: Adipose tissue is a typical place for storage water-insoluble toxins in the body. It's connective tissue, where the intercellular substance consist of fat, which level in people with low physical activity should be 18-25% for women and 13-18% for men. Due to the fat distribution in the body we distinquish two types of obesity: android (visceral, abdominal) and gynoidal (gluteal-femoral, peripheral). Abdominal obesity increases the risk of complications of the cardiovascular system diseases, and impaired renal and liver function. Through the influence on disorders of metabolism, lipid metabolism, diabetes and hypertension, leading to emergence of the metabolic syndrome. So thus, obesity will especially overload kidney function in patients after transplantation. Aim: An attempt was made to estimate the impact of amount fat tissue on transplanted kidney function and excretory function of the liver in patients after Ktx. Material and Methods: The study included 108 patients (50 females, 58 male, age 46.5 +/- 12.9 years) with active kidney transplant after more than 3 months from the transplantation. An analysis of body composition was done by using electrical bioimpedance (BIA) and anthropometric measurements. Estimated basal metabolic rate (BMR), muscle mass, total body water content and the amount of body fat. Information about physical activity were obtained during clinical examination. Nutritional status, and type of obesity were determined by using indicators: Waist to Height Ratio (WHR) and Waist to Hip Ratio (WHR). Excretory functions of the transplanted kidney was rated by calculating the estimated renal glomerular filtration rate (eGFR) using the MDRD formula. Liver function was rated by total bilirubin and alanine aminotransferase levels ALT concentration in serum. In our patients haemolitic uremic syndrome (HUS) was excluded. Results: In 19.44% of patients had underweight, 22.37% of the respondents were with normal weight, 11.11% had overweight, and the rest were with obese (49.08%). People with android stature have a lower eGFR compared with those with the gynoidal stature (p = 0.004). All patients with obesity had higher amount of body fat from a few to several percent. The higher amount of body fat percentage, the lower eGFR had patients (p <0.001). Elevated ALT levels significantly correlated with a high fat content (p <0.02). Conclusion: Increased amount of body fat, particularly in the case of android obesity can be a predictor of kidney and liver damage. Due to that obese patients should have more frequent control of diagnostic functions of these organs and the intensive dietary proceedings, pharmacological and regular physical activity adapted to the current physical condition of patients after transplantation.

Keywords: obesity, body fat, kidney transplantation, glomerular filtration rate, liver function

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Fadwa Haraka, Ahmad Elouatouati, Mourad Taha Janan

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In this study, we propose a neural network based method in order to calculate the heat source temperature of isotropic plate with lower surface forced cooling. To validate the proposed model, the heat source temperatures values will be compared to the analytical method -variables separation- and finite element model. The mathematical simulation is done through 3D numerical simulation by COMSOL software considering three different materials: Aluminum, Copper, and Graphite. The proposed method will lead to a formulation of the heat source temperature based on the thermal and geometric properties of the base plate.

Keywords: thermal model, thermal resistance, finite element simulation, neural network

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Authors: Aurora Cerri, Niko Pullojani

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Long-term differential settlement between the bridge structure and the bridge embankment typically results in an abrupt grade change, causing driver discomfort, impairing driver safety, and exerting a potentially excessive impact traffic loading on the abutment. This paper has analysed a case of study showing the effect of an approaching slab realized in a bridge constructed at Tirane-Elbasan Motorway. The layer thickness under the slab is modeled as homogenous, the slab is a reinforced concrete structure and over that the asphaltic layers take place. Analysis indicates that reinforced concrete approaching slab distributes the stresses quite uniformly into the road fill layers and settlements varies in a range less than 2.50 cm in the total slab length of 6.00 m with a maximum slope of 1/240. Results taken from analytical analysis are compared with topographic measurements done on field and they carry great similarities.

Keywords: approach slab, bridge, road pavement, differential settlement

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Authors: Saif K. Al-Qaisi, Jad R. Mansour, Aseel W. Sakka, Yousef Al-Abdallat

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Handwheels are common to numerous industries, such as power generation plants, oil refineries, and chemical processing plants. The forces required to manually turn handwheels have been shown to exceed operators’ physical strengths, posing risks for injuries. Therefore, the objectives of this research were twofold: (1) to determine humans’ physical strengths on handwheels of different sizes and angles and (2) to subsequently propose recommended torque limits (RTLs) that accommodate the strengths of even the weaker segment of the population. Thirty male and thirty female participants were recruited from a university student population. Participants were asked to exert their maximum possible forces in a counter-clockwise direction on handwheels of different sizes (35 cm, 45 cm, 60 cm, and 70 cm) and angles (0°-horizontal, 45°-slanted, and 90°-vertical). The participant’s posture was controlled by adjusting the handwheel to be at the elbow level of each participant, requiring the participant to stand erect, and restricting the hand placements to be in the 10-11 o’clock position for the left hand and the 4-5 o’clock position for the right hand. A torque transducer (Futek TDF600) was used to measure the maximum torques generated by the human. Three repetitions were performed for each handwheel condition, and the average was computed. Results showed that, at all handwheel angles, as the handwheel diameter increased, the maximum torques generated also increased, while the underlying forces decreased. In controlling the handwheel diameter, the 0° handwheel was associated with the largest torques and forces, and the 45° handwheel was associated with the lowest torques and forces. Hence, a larger handwheel diameter –as large as 70 cm– in a 0° angle is favored for increasing the torque production capacities of users. Also, it was recognized that, regardless of the handwheel diameter size and angle, the torque demands in the field are much greater than humans’ torque production capabilities. As such, this research proposed RTLs for the different handwheel conditions by using the 25th percentile values of the females’ torque strengths. The proposed recommendations may serve future standard developers in defining torque limits that accommodate humans’ strengths.

Keywords: handwheel angle, handwheel diameter, humans’ torque production strengths, recommended torque limits

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Authors: C. O. Agu, C. C. Okafor

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Maize and soybean flours were produced using different methods of processing which include fermentation (FWF), roasting (RWF) and malting (MWF). Products from the different methods were mixed in the ratio 60:40 maize/soybean, respectively. These composites mixed with other ingredients such as sugar, vegetable oil, vanilla flavour and vitamin mix were analyzed for proximate composition, physical/functional, sensory and nutritional properties. The results for the protein content ranged between 6.25% and 16.65% with sample RWF having the highest value. Crude fibre values ranged from 3.72 to 10.0%, carbohydrate from 58.98% to 64.2%, ash from 1.27 to 2.45%. Physical and functional properties such as bulk density, wettability, gelation capacity have values between 0.74 and 0.76g/ml, 20.33 and 46.33 min and 0.73 to 0.93g/ml, respectively. On the sensory quality colour, flavour, taste, texture and general acceptability were determined. In terms of colour and flavour there was no significant difference (P < 0.05) while the values for taste ranged between 4.89 and 7.1 l, texture 5.50 to 8.38 and general acceptability 6.09 and 7.89. Nutritionally there is no significant difference (P < 0.05) between sample RWF and the control in all parameters considered. Samples FWF and MWF showed significantly (P < 0.5) lower values in all parameters determined. In the light of the above findings, roasting method is highly recommend in the production of weaning foods.

Keywords: fermentation, malting, ratio, roasting, wettability

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Authors: Sung-Won Seo, Jang-Young Choi

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The rare earth magnets used in synchronous generators offer many advantages, including high efficiency, greatly reduced the size, and weight. The permanent magnet linear synchronous generator (PMLSG) allows for direct drive without the need for a mechanical device. Therefore, the PMLSG is well suited to translational applications, such as wave energy converters and free piston energy converters. This manuscript compares the effects of different magnetization patterns on the characteristics of double-sided PMLSGs in slotless stator structures. The Halbach array has a higher flux density in air-gap than the Vertical array, and the advantages of its performance and efficiency are widely known. To verify the advantage of Halbach array, we apply a finite element method (FEM) and analytical method. In general, a FEM and an analytical method are used in the electromagnetic analysis for determining model characteristics, and the FEM is preferable to magnetic field analysis. However, the FEM is often slow and inflexible. On the other hand, the analytical method requires little time and produces accurate analysis of the magnetic field. Therefore, the flux density in air-gap and the Back-EMF can be obtained by FEM. In addition, the results from the analytical method correspond well with the FEM results. The model of the Halbach array reveals less copper loss than the model of the Vertical array, because of the Halbach array’s high output power density. The model of the Vertical array is lower core loss than the model of Halbach array, because of the lower flux density in air-gap. Therefore, the current density in the Vertical model is higher for identical power output. The completed manuscript will include the magnetic field characteristics and structural features of both models, comparing various results, and specific comparative analysis will be presented for the determination of the best model for application in a wave energy converting system.

Keywords: wave energy converter, permanent magnet linear synchronous generator, finite element method, analytical method

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Authors: R. Hellmuth

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The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

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Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

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Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

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Authors: Liu Mingxin

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Streets act as the organizers of each image element on the urban spatial route, and the spatial continuity of urban streets is the basis for people to perceive the overall image of the city. This paper takes the walking space of Zhengzhou city as the research object, conducts investigation and analysis through questionnaire interviews, and selects typical walking space for in-depth study. Through the analysis of questionnaire data, the investigation and analysis of the current situation of walking space, and the analysis of pedestrian psychological behavior activities, the paper summarizes the construction suggestions of urban walking space continuity from the three aspects of the composition of walking street, the bottom interface and side interface, and the service facilities of walking space. The walking space is not only the traffic space but also the comfortable experience and the continuity of the space.

Keywords: walking space, spatial continuity, walking psychology, space reconstruction

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Mohaya Farzin, Parvin Babaei, Mohammad Rostampour

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Ghrelin plays a considerable role in important neurological effects related to food intake and energy homeostasis. As was found, regular physical activity may make available significant improvements to cognitive functions in various behavioral situations. Anxiety is one of the main concerns of the modern world, affecting millions of individuals’ health. There are contradictory results regarding ghrelin's effects on anxiety-like behavior, and the plasma level of this peptide is increased during physical activity. Here we aimed to evaluate the coincident effects of exogenous ghrelin and aerobic exercise on anxiety-like behavior and passive avoidance memory in Wistar rats. Forty-five male Wistar rats (250 ± 20 g) were divided into 9 groups (n=5) and received intra-hippocampal injections of 3.0 nmol ghrelin and performed aerobic exercise training for 8 weeks. Control groups received the same volume of saline and diazepam as negative and positive control groups, respectively. Learning and memory were estimated using a shuttle box apparatus, and anxiety-like behavior was recorded by an elevated plus-maze test (EPM). Data were analyzed by ANOVA test, and p<0.05 was considered significant. Our findings showed that the combined effect of ghrelin and aerobic exercise improves the acquisition, consolidation, and retrieval of passive avoidance memory in Wistar rats. Furthermore, it is supposed that the ghrelin receiving group spent less time in open arms and fewer open arms entries compared with the control group (p<0.05). However, exercising Wistar rats spent more time in the open arm zone in comparison with the control group (p<0.05). The exercise + Ghrelin administration established reduced anxiety (p<0.05). The results of this study demonstrate that aerobic exercise contributes to an increase in the endogenous production of ghrelin, and physical activity alleviates anxiety-related behaviors induced by intra-hippocampal injection of ghrelin. In general, exercise and ghrelin can reduce anxiety and improve memory.

Keywords: anxiety, ghrelin, aerobic exercise, learning, passive avoidance memory

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Authors: Davoud Maleki, Neda Zamani

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The present study aims at exploring the effective accountability model of Islamic Azad University using a qualitative approach of grounded theory. The data of this study were obtained from semi-structured interviews with 25 professors and scholars in Islamic Azad University of Tehran who were selected by theoretical sampling method. In the data analysis, the stepwise method and Strauss and Corbin analytical methods (1992) were used. After identification of the main component (balanced response to stakeholders’ needs) and using it to bring the categories together, expressions and ideas representing the relationships between the main and subcomponents, and finally, the revealed components were categorized into six dimensions of the paradigm model, with the relationships among them, including causal conditions (7 components), main component (balanced response to stakeholders’ needs), strategies (5 components), environmental conditions (5 components), intervention features (4 components), and consequences (3 components). Research findings show an exploratory model for describing the relationships between causal conditions, main components, accountability strategies, environmental conditions, university environmental features, and that consequences.

Keywords: accountability, effectiveness, Islamic Azad University, grounded theory

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Authors: Song Zhang, Hui Wang, Johannes Henrich Schleifenbaum

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Due to the increasing demand for quality assurance and reliability for additive manufacturing, the development of an advanced in-situ monitoring system is required to monitor the process anomalies as input for further process control. Optical-based monitoring systems, such as CMOS cameras and NIR cameras, are proved as effective ways to monitor the geometrical distortion and exceptional thermal distribution. Therefore, many studies and applications are focusing on the availability of the optical-based monitoring system for detecting varied types of defects. However, the capability of the monitoring setup is not quantified. In this study, a quantification model to evaluate the capability of the monitoring setups for the LPBF machine based on acquired monitoring data of a designed test artifact is presented, while the design of the relevant test artifacts is discussed. The monitoring setup is evaluated based on its hardware properties, location of the integration, and light condition. Methodology of data processing to quantify the capacity for each aspect is discussed. The minimal capability of the detectable size of the monitoring set up in the application is estimated by quantifying its resolution and accuracy. The quantification model is validated using a CCD camera-based monitoring system for LPBF machines in the laboratory with different setups. The result shows the model to quantify the monitoring system's performance, which makes the evaluation of monitoring systems with the same concept but different setups possible for the LPBF process and provides the direction to improve the setups.

Keywords: data processing, in-situ monitoring, LPBF process, optical system, quantization model, test artifact

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Ramesh Adsul, Vikas Minchekar

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The present study attempts to explore the differences in academic achievement and psychological well being and its components – satisfaction, efficiency, sociability, mental health, interpersonal relations in low and moderate level of hemoglobin of college female students. It also tries to find out how hemoglobin, psychological well –being and academic achievement correlate to each other. For this study 200 (100 low hemoglobin level and 100 moderate hemoglobin level) college female students were selected by random sampling method. This sample is collected from the project ‘Health awareness and hemoglobin improvement programme’, which is being collaboratively conducted by ‘Akshyabhasha, MESA, U.S.A. and Smt. M.G. Kanya Mahavidyalaya, Sangli, Maharashtra, India. Psychological Well-Being Scale was used to collect the data. Students’ academic achievement was collected through college record, and hemoglobin level of female students was collected from project record. Data was analyzed by using independent ‘t’ test and Pearson’s correlation coefficient. The finding of the study revealed significant differences between low hemoglobin and moderate hemoglobin groups regarding efficiency and mental health. No significant difference was observed on satisfaction, sociability and interpersonal relations. It is also found that there is significant difference between low hemoglobin and moderate hemoglobin groups on academic achievement. The study revealed positive correlation between hemoglobin and academic achievement and psychological well-being and academic achievement. Moderate hemoglobin level create more efficiency, better mental health and good academic achievement in female students. One could say that there is significant role hemoglobin plays in psychological well being and academic achievement of college female students. Anemia is widely prevalent in all the states if India among all age groups. In India, college girls contribute major portion of population. It has been reported that 80% female population has hemoglobin deficiency, due to illiteracy of female, family structure, status of women, diet habits, gender discrimination and various superstitions. The deficiency of hemoglobin affects physical and mental health, general behavior and academic performance of students. This study is useful to educational managements, counselors, parents, students and Government also. In the development of personality physical as well as psychological health is essential. This research findings will create awareness about physical and mental health among people and society.

Keywords: academic achievement, college female students, hemoglobin, psychological well-being

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: J. Tamosaitiene

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The article focuses on the identification and classification of key risk management criteria that represent the most important sustainability aspects of the construction industry. The construction sector is one of the most important sectors in Lithuania. Nowadays, the assessment of the risk level of a construction project is especially important for the quality of construction projects, the growth of enterprises and the sector. To establish the most important criteria for successful growth of the sector, a questionnaire for experts was developed. The analytic hierarchy process (AHP), the expert judgement method and other multicriteria decision making (MCDM) methods were used to develop the hybrid model. The results were used to develop an integrated knowledge system for the measurement of a risk level particular to construction projects. The article presents a practical case that details the developed system, sustainable aspects, and risk assessment.

Keywords: risk, system, model, construction

Procedia PDF Downloads 158