Search results for: behavior against washing machine parameters

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: A. El Amri, M. El Yakhloufi Haddou, A. Khamlichi

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The numerical simulation based on the Finite Element Method (FEM) is widely used in academic institutes and in the industry. It is a useful tool to predict many phenomena present in the classical manufacturing forming processes such as fracture. But, the results of such numerical model depend strongly on the parameters of the constitutive behavior model. The influences of thermal and mechanical loads cause damage. The temperature and strain rate dependent materials’ properties and their modelling are discussed. A Johnson-Cook Model of damage has been selected for the numerical simulations. Virtual software called the ABAQUS 6.11 is used for finite element analysis. This model was introduced in order to give information concerning crack initiation during thermal and mechanical loads.

Keywords: thermo-mechanical fatigue, failure, numerical simulation, fracture, damage

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Authors: Pedro M. Velasco, Cecilia C. Alday, Oscar C. Avello, Ximena T. Faundez, Luis M. Velasco

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Anaerobic digestion (AD) plants of food waste (FW) produced by agro-industry, have been widely developed from last decade to nowadays, because of the advantages over aerobic active sludge systems. Despite several bioreactor configurations and operation modes have been successfully improved and implemented at industrial scale in a wide range of applications, effluent behavior, after AD, does not commonly meet requirements for direct disposal into the environment without further treatments. In addition, literature has rarely shown AD of food waste at psychrophilic range. This temperature range may be of interest for making AD plant operation easier and increasing the stability of digestion. In spite of literature shows several methods for post-treatment, such as the use of microalgae, these have not been cultivated on effluents from AD at psychrophilic range. Hence, with the aim of showing the potential use of AD of FW at the psychrophilic range (25ºC) and the viability of microalgae post-treatment, single batch reactors have been used for methane potential tests at laboratory scale. Afterwards, digestates, derived from this AD of FW sludge, were diluted with fresh water at different ratios (1:0, 1:1; 1:4) and used as culture media for photoautotrophic microalgae. Several parameters, such as pH, biogas production, and chemical oxygen demand, were measured periodically over several months. Results show that methane potential is 150 ml g-1 per volatile solid with up to 57.7 % of methane content. Moreover, microalgae has been successfully cultivated on all tested effluents and in case of 1:1 and 1:4 rates, the resulting effluents meet the quality levels required for irrigation water.

Keywords: anaerobic digestion, biogas, food waste, microalgae, psychrophilic range

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Authors: Rishabh Ambavanekar

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Expressive Aphasia (EA) is an oral disability, common among stroke victims, in which the Broca’s area of the brain is damaged, interfering with verbal communication abilities. EA currently has no technological solutions and its only current viable solutions are inefficient or only available to the affluent. This prompts the need for an affordable, innovative solution to facilitate recovery and assist in speech generation. This project proposes a novel concept: using a wearable low-cost electroencephalography (EEG) device-based brain-computer interface (BCI) to translate a user’s inner dialogue into words. A low-cost EEG device was developed and found to be 10 to 100 times less expensive than any current EEG device on the market. As part of the BCI, a machine learning (ML) model was developed and trained using the EEG data. Two stages of testing were conducted to analyze the effectiveness of the device: a proof-of-concept and a final solution test. The proof-of-concept test demonstrated an average accuracy of above 90% and the final solution test demonstrated an average accuracy of above 75%. These two successful tests were used as a basis to demonstrate the viability of BCI research in developing lower-cost verbal communication devices. Additionally, the device proved to not only enable users to verbally communicate but has the potential to also assist in accelerated recovery from the disorder.

Keywords: neurotechnology, brain-computer interface, neuroscience, human-machine interface, BCI, HMI, aphasia, verbal disability, stroke, low-cost, machine learning, ML, image recognition, EEG, signal analysis

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Authors: G. Khamooshian

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One of the main points of application of the mechanisms of the series and parallel is the subject of managing them. The control of this mechanism and similar mechanisms is one that has always been the intention of the scholars. On the other hand, modeling the behavior of the system is difficult due to the large number of its parameters, and it leads to complex equations that are difficult to solve and eventually difficult to control. In this paper, a six-linkage robot has been presented that could be used in different areas such as medical robots. Using these robots needs a robust control. In this paper, the system equations are first found, and then the system conversion function is written. A new controller has been designed for this robot which could be used in other parallel robots and could be very useful. Parallel robots are so important in robotics because of their stability, so methods for control of them are important and the robust controller, especially in parallel robots, makes a sense.

Keywords: 3-RRS, 6 linkage, parallel robot, control

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Authors: M. Kozłowski, M. Kadela

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Foamed concrete is known for its low self-weight and excellent thermal and acoustic properties. For many years, it has been used worldwide for insulation to foundations and roof tiles, as backfill to retaining walls, sound insulation, etc. However, in the last years it has become a promising material also for structural purposes e.g. for stabilization of weak soils. Due to favorable properties of foamed concrete, many interests and studies were involved to analyze its strength, mechanical, thermal and acoustic properties. However, these studies do not cover the investigation of fracture energy which is the core factor governing the damage and fracture mechanisms. Only limited number of publications can be found in literature. The paper presents the results of experimental investigation and numerical campaign of foamed concrete based on three-point bending test of beams with initial notch. First part of the paper presents the results of a series of static loading tests performed to investigate the fracture properties of foamed concrete of varying density. Beam specimens with dimensions of 100×100×840 mm with a central notch were tested in three-point bending. Subsequently, remaining halves of the specimens with dimensions of 100×100×420 mm were tested again as un-notched beams in the same set-up with reduced distance between supports. The tests were performed in a hydraulic displacement controlled testing machine with a load capacity of 5 kN. Apart from measuring the loading and mid-span displacement, a crack mouth opening displacement (CMOD) was monitored. Based on the load – displacement curves of notched beams the values of fracture energy and tensile stress at failure were calculated. The flexural tensile strength was obtained on un-notched beams with dimensions of 100×100×420 mm. Moreover, cube specimens 150×150×150 mm were tested in compression to determine the compressive strength. Second part of the paper deals with numerical investigation of the fracture behavior of beams with initial notch presented in the first part of the paper. Extended Finite Element Method (XFEM) was used to simulate and analyze the damage and fracture process. The influence of meshing and variation of mechanical properties on results was investigated. Numerical models simulate correctly the behavior of beams observed during three-point bending. The numerical results show that XFEM can be used to simulate different fracture toughness of foamed concrete and fracture types. Using the XFEM and computer simulation technology allow for reliable approximation of load–bearing capacity and damage mechanisms of beams made of foamed concrete, which provides some foundations for realistic structural applications.

Keywords: foamed concrete, fracture energy, three-point bending, XFEM

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Authors: Soumya Sajjan

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Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

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Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Maryam Khalilzadeh Poshtegal, Seyed Ahmad Mirbagheri, Mojtaba Noury

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In this paper, by application of the input yearly data of rainfall, temperature and flow to the Urmia Lake, the simulation of water level fluctuation were applied by means of three models. According to the climate change investigation the fluctuation of lakes water level are of high interest. This study investigate data-driven models, support vector machines (SVM), SVM method which is a new regression procedure in water resources are applied to the yearly level data of Lake Urmia that is the biggest and the hyper saline lake in Iran. The evaluated lake levels are found to be in good correlation with the observed values. The results of SVM simulation show better accuracy and implementation. The mean square errors, mean absolute relative errors and determination coefficient statistics are used as comparison criteria.

Keywords: simulation, water level fluctuation, urmia lake, support vector machine

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Authors: Kyujin Shim, Soojin Kim

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This study identifies and examines the impact of factors on two types of CSR outcomes, consumers’ perceptions of corporate authenticity and their pro-firm behavior intentions. Specifically we investigated the roles of two factors - the consumers’ perceptions of CSR motives of a company (i.e. business-oriented vs. society-oriented) and their ethical orientations (i.e. deontology vs. consequentialism). A web-based survey was conducted in South Korea and the United States respectively to compare the differences of consumer reactions between the two countries. The results show that consumers in two countries behave differently to a firm’s CSR motives. In the United States, when consumers perceive a company’s CSR motive as society-oriented, they are more likely to perceive the company authentic and as a result more likely to engage in pro-firm behavior. However, when consumers’ ethical orientation is considered, only consumers’ consequential orientation led to their pro-firm behavioral intention. In South Korea, interpretation of two different CSR motives affects the valence in consumers’ perceptions of corporate authenticity (i.e. society-oriented CSR motive and positive perception of corporate authenticity vs. business-oriented CSR motive and negative perception of corporate authenticity). Korean consumers also showed same pattern in terms of relationship among society-oriented CSR motive, perception of corporate authenticity, and pro-firm behavior intention. Interestingly, Korean consumers’ consequential orientation affects both their perception of corporate authenticity and their pro-firm behavior intention positively. In addition, there was an interaction effect of business-oriented CSR motive and deontological orientation on perception of corporate authenticity. Theoretical and practical implications will be discussed.

Keywords: corporate authenticity, corporate social responsibility, consequentialist ethics, CSR motives, deontological ethics

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Authors: Dana Cassandra Winkler, Delia Leiding, Rene Bergs, Franziska Kaiser, Ramona Kirchhart, Ute Habel

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Background: Violence is a multidimensional phenomenon, affecting people of every age, socio-economic status and gender. Nevertheless, most studies primarily focus on men perpetrating women. Aim of the present study is to identify the likelihood of mental and somatic disorders and risk-taking behavior in male violence affected. In addition, the relationship between age of violence experience and the risk for health-related problems was analyzed. Method: On the basis of current evidence, a questionnaire was developed focusing on demographic background, health status, risk-taking behavior, and active and passive violence exposure. In total, 5221 males (Mean: 56,1 years, SD: 17,6) were consulted. To account for the time of violence experience in an efficient way, age clusters ‘0-12 years’, ‘13-20 years’, ‘21-35 years’, ‘36-65 years’ and ‘over 65 years’ were defined. A binary logistic regression was calculated to reveal differences in violence-affected and non-violence affected males regarding health and risk-taking factors. Males who experienced violence on a daily/ almost daily basis vs. males who reported violence occurrence once/ several times a month/ year were compared with respect to health factors and risk-taking behavior. Data of males, who indicated active and passive violence exposure, were analyzed by a chi²-analysis, to investigate a possible relation between the age of victimization and violence perpetration. Findings: Results imply that general violence experience, independent of active and passive violence exposure increases the likelihood in favor of somatic-, psychosomatic- and mental disorders as well as risk-taking behavior in males. Experiencing violence on a daily or almost daily basis in childhood and adolescence may serve as a predictor for increased health problems and risk-taking behavior. Furthermore, the violence experience and perpetration occur significantly within the same age cluster. This underlines the importance of a near-term intervention to minimize the risk, that victims become perpetrators later. Conclusion: The present study reveals predictors concerning health risk factors as well as risk-taking behavior in males with violence exposure. The results of this study may underscore the benefit of intervention and regular health care approaches in violence-affected males and underline the importance of acknowledging the overlap of violence experience and perpetration for further research.

Keywords: health disease, male, mental health, prevalence, risk-taking behavior, violence

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Authors: Bartlomiej Przybyszewski, Rafal Kozera, Katarzyna Zolynska, Anna Boczkowska, Daria Pakula

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The build-up and accumulation of dirt, ice, and snow on structural elements and vehicles is an unfavorable phenomenon, leading to economic losses and often also posing a threat to people. This problem occurs wherever the use of polymer coatings has become a standard, among others in photovoltaic farms, aviation, wind energy, and civil engineering. The accumulated pollution on the photovoltaic modules can reduce their efficiency by several percent, and snow stops power production. Accumulated ice on the blades of wind turbines or the wings of airplanes and drones disrupts the airflow by changing their shape, leading to increased drag and reduced efficiency. This results in costly maintenance and repairs. The goal of the work is to reduce or completely eliminate the accumulation of dirt, snow, and ice build-up on polymer coatings by achieving self-cleaning and icephobic properties. It is done by the use of a multi-step surface modification of the polymer nanocoatings. For this purpose, two methods of surface structuring and the preceding volumetric modification of the chemical composition with proprietary organosilicon compounds and/or mineral additives were used. To characterize the surface topography of the modified coatings, light profilometry was utilized. Measurements of the wettability parameters (static contact angle and contact angle hysteresis) on the investigated surfaces allowed to identify their wetting behavior and determine relation between hydrophobic and anti-icing properties. Ice adhesion strength was measured to assess coatings' anti-icing behavior.

Keywords: anti-icing properties, self-cleaning, polymer coatings, icephobic coatings

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Authors: Sanjeev Kumar, Swati Choudhary

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Background: A variety of studies warn that sedentary lifestyles can contribute to many preventable causes of death. This study was taken to determine the effects of two types of aerobic training programs on erythrocytes, leukocytes, hemoglobin concentration (Hb), platelets and hematocrit of sedentary people (N=60) with age group 20 to 30 years. Methods: All the subjects were randomly divided into three groups i.e. two experiments groups (aerobic dance & cardio fitness) and control group. Each group having 10 male and 10 females. Experimental groups undergone 60 minutes of training 5 times a week for 12 weeks whereas the control group did not participate in any training program except their daily routine. The aerobic dance group was chosen to perform exercise like step –touch, side-to-side, V-step and hand and body movements, etc. The cardio fitness group was chosen to perform exercises with modern fitness equipment like treadmill, elliptical trainer, stationary bike and rowing machine. Rating of perceived exertion (RPE) scale developed by Gunner Borg was used to monitor the intensity of the workout. Aerobic programs were encompassed of low-impact (0- 4 week & perceived exertion from 6 to 12), moderate-impact (4-8 week and perceived exertion from 12 to 16) and high-impact (8- 12 week & perceived exertion from 16 to 20). Results: To test the effectiveness of training programs paired t-test was used and significant difference (p<0.05) was observed in erythrocytes, hemoglobin concentration, platelets, hematocrit but no significant effects of training was found in leukocytes (p>0.05). Paired t-test also showed that no effect of time was seen in the control group in all the cases (p>0.05). Further analysis of covariance was used to know which program was more effective and it was seen that F value was found significant in the case of erythrocytes, hemoglobin concentration, platelets, and hematocrit as their associated p-value (p<0.05) is lesser than 0.05. As F value was found significant for hematological parameters, fishers least significant difference test was used and results of post hoc mean comparison indicated that experimental groups (aerobic dance group and cardio fitness group) had significant difference with control group in erythrocytes, hemoglobin concentration, platelets and hematocrit and insignificant difference was found between aerobic dance group & cardio fitness group in all the cases. Thus, it may be concluded that in general, both the aerobic training programs had adequate effects on all the hematological parameters except leukocytes.

Keywords: aerobic dance, cardio fitness, hematological variables, rating perceived exertion scale

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Authors: Marwa Abida, Florian Gehring, Jamel Mars, Alexandre Vivet, Fakhreddine Dammak, Mohamed Haddar

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The demand for bio-based materials in semi-structural and structural applications is constantly growing to conform to new environmental policies. Among them, Plant Fiber Reinforced Composites (PFRC) are attractive for the scientific community as well as the industrial world. Due to their relatively low densities and low environmental impact, vegetal fibers appear to be suitable as reinforcing materials for polymers. However, the major issue of plant fibers and PFRC in general is their hydrophilic behavior (high affinity to water molecules). Indeed, when absorbed, water causes fiber swelling and a loss of mechanical properties. Thus, the environmental loadings (moisture, temperature, UV) can strongly affect their mechanical properties and therefore play a critical role in the service life of PFRC. In order to analyze the influence of conditioning at relative humidity on the behavior of flax fiber reinforced composites, a preliminary study on flax fabrics has been conducted. The conditioning of the fabrics in different humid atmospheres made it possible to study the influence of the water content on the hygro-mechanical behavior of flax reinforcement through mechanical tensile tests. This work shows that increasing the relative humidity of the atmosphere induces an increase of the water content in the samples. It also brings up the significant influence of water content on the stiffness and elongation at break of the fabric, while no significant change of the breaking load is detected. Non-linear decrease of flax fabric rigidity and increase of its elongation at maximal force with the increase of water content are observed. It is concluded that water molecules act as a softening agent on flax fabrics. Two kinds of typical tensile curves are identified. Most of the tensile curves of samples show one unique linear region where the behavior appears to be linear prior to the first yarn failure. For some samples in which water content is between 2.7 % and 3.7 % (regardless the conditioning atmosphere), the emergence of a two-linear region behavior is pointed out. This phenomenon could be explained by local heterogeneities of water content which could induce premature local plasticity in some regions of the flax fabric sample behavior.

Keywords: hygro-mechanical behavior, hygroscopy, flax fabric, relative humidity, mechanical properties

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Authors: Narayan Bhatta, Rodhana Pokhel

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Introduction: The link between alcohol and violence is well documented, but there is a paucity of research on alcohol use by husbands and sexual violence during pregnancy in Nepal. The aim of the study is to describe the relationship between alcohol use by the husband and sexual violence during pregnancy in Nepalese women from the Kathmandu valley. Method: A cross-sectional study was conducted using a consecutive sampling design in one government hospital. Pregnant women (N = 495) attending the antenatal clinic of Paropakar Maternity and Women’s Hospital (PMWH) were recruited. Results: Approximately one-fifth (19%) of pregnant women had experienced sexual violence. Women in the first trimester of pregnancy were more likely to suffer sexual violence (35.2%) than in the second (30.7%) and third trimester of pregnancy (34%). The most common type of sexual violence against women was a physical force for sexual intercourse (91.5%), followed by sexual intercourse without the women’s consent (26.6%) and forcing them to engage in humiliating sexual activities (10.6%). Women who belong to other ethnicities like Janajatis, Dalits, and religious minorities (AOR = 0.3), women who live outside Kathmandu (AOR = 3.73), women who are illiterate (AOR = 4.67), and women whose husband has alcohol-drinking behavior (AOR = 1.68) increased the odds of experiencing sexual violence during pregnancy. Conclusion: The study concludes that a husband’s drinking behavior is an important risk factor for sexual violence against pregnant women attending the antenatal clinic. It indicates a need for routine screening during the antenatal visit to identify the violence and alcohol use of both the husband and wife.

Keywords: husband alcohol drinking behavior, Kathmandu, pregnency, sexual violence

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Authors: Mohammad Ehsani, Iman Zarei, Soudabeh Moazemigoudarzi

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The aim of this study is to determine Pro-Environmental Behavioral Intention of Mountain Hikers to the Theory of Planned Behavior. According to many researchers nature-based recreation activities play a significant role in the tourism industry and have provided myriad opportunities for the protection of natural areas. It is essential to investigate individuals' behavior during such activities to avoid further damage to precious and dwindling natural resources. This study develops a robust model that provides a comprehensive understanding of the formation of pro-environmental behavioral intentions among climbers of Mount Damavand National Park in Iran. To this end, we combined the theory of planned behavior (TPB), value-belief-norm theory (VBN), and a hierarchical model of leisure constraints to predict individuals’ pro-environmental hiking behavior during outdoor recreation. It was used structural equation modeling to test the theoretical framework. A sample of 787 climbers was analyzed. Among the theory of planned behavior variables, perceived behavioral control showed the strongest association with behavioral intention (β = .57). This relationship indicates that if people feel they can have fewer negative impacts on national resources while hiking, it will result in more environmentally acceptable behavior. Subjective norms had a moderate positive impact on behavioral intention, indicating the importance of other people on the individual's behavior. Attitude had a small positive effect on intention. Ecological worldview positively influenced attitude and personal belief. Personal belief (awareness of consequences and ascribed responsibility) showed a positive association with TPB variables. Although the data showed a high average score in awareness of consequences (mean = 4.219 out of 5), evidence from Damavand Mount shows that there are many environmental issues that need addressing (e.g., vast amounts of garbage). National park managers need to make sure that their solutions result in awareness about proenvironmental behavior (PEB). Findings showed that negative relationship between constraints and all TPB predictors. Providing proper restrooms and parking spaces in campgrounds, strategies controlling limiting capacity and solutions for removing waste from high altitudes are helpful to decrease the negative impact of structural constraints. In order to address intrapersonal constraints, managers should provide opportunities to interest individuals in environmental activities, such as environmental celebrations or making documentaries about environmental issues. Moreover, promoting a culture of environmental protection in the Damavand Mount area would reduce interpersonal constraints. Overall, the proposed model improved the explanatory power of the TPB by predicting 64.7% of intention compared to the original TPB that accounted for 63.8% of the variance in intention.

Keywords: theory of planned behavior, pro-environmental behavior, national park, constraints

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Authors: Regina Tedjasulaksana, Maria Martina Nahak, A. A. Gede Agung, Ni Made Widhiasti

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Strategic effort to realize the empowerment of community in school is through the peer group approach so that it needs to choose the students who are trained as the’ little dentist’ in order to have the cognitive and skills to participate in the school dental health effort (UKGS) program, such as providing oral health education to the other students. Aim: To assessed the effectiveness of peer group approach to enhance the oral health knowledge level of schoolchildren at primary school in Klungkung, Bali. Methods: Experimental study using the pre-post test without control group design. The differences of knowledge levels, tooth brushing behavior and oral hygiene status (using PHP-M index) of 10 students before and after trained as the little dentists were analyzed using paired t-test. The correlations between knowledge level and tooth brushing behavior and correlations between tooth brushing behavior and oral hygiene before and after trained as the little dentists were analyzed using Spearman. Furthermore, the trained little dentists provide oral health education to 102 students of grade 1 to 5 at their school once a week for 3 months. The students’ knowledge level scores of each grade were taken every 21 days as many as three times The difference of it was analyzed using Repeated Measured. Result: The mean scores among all little dentists before and after training for each of knowledge level were each 63.05 + 5.62 and 85.00 + 7.81, tooth brushing behavior were each 31.00 + 14.49 and 100.00 + 0.00 and oral hygiene status using PHP-M index were each 32.80 + 10.17 and 11.40 + 8.01. The knowledge level, tooth brushing behavior and oral hygiene status of 10 students before and after trained as the little dentists were different significantly (p<0.05). Before and after trained as the little dentists it showed that significant correlations between knowledge level with tooth brushing behavior (p<0.05) and significant correlations between tooth brushing behavior and oral hygiene (p<0.05). The mean scores of knowledge level among all students before (pre-test) and after (post-test (1),(2),(3)) getting oral health education from little dentists for each, of grade 1 were 40.00 + 17.97; 67.85 + 18.88; 81.72 +26.48 and 70.00 + 22.87, grade 2 were 40.00 + 17.97; 67.85 + 18.88; 81.72 + 26.48 and 70.00 + 22.87, grade 3 were 65.83 + 23.94; 72.50 + 26.08; 80.41 + 24.93 and 83.75 + 19.74, grade 4 were 88.57 + 12.92; 90.71 + 9.97; 92.85 + 10.69 and 93.57 + 6.33 and grade 5 were 86.66 + 13.40; 93.33 + 9.16; 94.16 + 10.17 and 98.33 + 4.81. The students’ knowledge level of grade 1,2 and 3 before and after getting oral health education from little dentists showed significant different (p<0.05), meanwhile there was no significant different on grade 4 and 5 (p<0.05) although mean scores showed an increase. Conclusion: Peer group approach can be used to enhance the oral health knowledge level of schoolchildren at primary school in Klungkung, Bali.

Keywords: small dentists, oral health, peer group approach, school children

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Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Renata Silva-Mann, Sheila Valéria Álvares Carvalho, Robério Anastácio Ferreira, Laura Jane Gomes

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There is growing interest in defining indicators of sustainability, which are important for monitoring the conservation of native forests, particularly in areas of permanent protection. These indicators are references for assessing the state of the forest and the status of the depredated area and its ability to maintain species populations. The aim of the present study was to select genetic parameters as indicators of sustainability for Schinus terebinthifolius Raddi. Fragments located in riparian areas between the Sergipe and Alagoas States in Brazil. This species has been exploited for traditional communities, which represent 20% of the incoming. This study was carried out using the indicators suggested by the Organization for Economic Cooperation and Development, which were identified as Driving-Pressure-State-Impact-Response (DPSIR) factors. The genetic parameters were obtained in five populations located on the shores and islands of the São Francisco River, one of the most important rivers in Brazil. The framework for Schinus conservation suggests seventeen indicators of sustainability. In accordance with genetic parameters, the populations are isolated, and these genetic parameters can be used to monitor the sustainability of those populations in riparian area with the aim of defining strategies for forest restoration.

Keywords: alleles, molecular markers, genetic diversity, biodiversity

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Authors: Onan Kilicaslan, Anil Kabaklarli, Levent Subasi, Erdem Bektas, Rifat Yilmaz

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The Aluminum-Copper casting alloys are well known for their high mechanical strength, especially when compared to more commonly used Aluminum-Silicon alloys. A201 is one of the best in terms of strength vs. weight ratio among other aluminum alloys, which makes it suitable for premium quality casting applications in aerospace and automotive industries. It is reported that A201 has low castability, but it is easy to machine. However, there is a need to specifically determine the process window for feasible machining. This research investigates the machinability of A201 alloy after T7 heat treatment in terms of chip/burr formation, surface roughness, hardness, and microstructure. The samples are cast with low-pressure sand casting method and milling experiments are performed with uncoated carbide tools using different cutting speeds and feeds. Statistical analysis is used to correlate the machining parameters to surface integrity. It is found that there is a strong dependence of the cutting conditions on machinability and a process window is determined.

Keywords: A201-T7, machinability, milling, surface integrity

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: Fuyuan Ma, Ying Wang, Xin Wang

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Social networks have become breeding grounds for the rapid spread of rumors and malicious information, posing threats to societal stability and causing significant public harm. Existing research focuses on simulating the spread of information and its impact on users through propagation dynamics and applies methods such as greedy approximation strategies to approximate the optimal control solution at the global scale. However, the greedy strategy at the global scale may fall into locally optimal solutions, and the approximate simulation of information spread may accumulate more errors. Therefore, we propose a multi-scale control model for network group behavior, introducing individual and group scales on top of the greedy strategy’s global scale. At the individual scale, we calculate the propagation influence of nodes based on their structural attributes to alleviate the issue of local optimality. At the group scale, we conduct precise propagation simulations to avoid introducing cumulative errors from approximate calculations without increasing computational costs. Experimental results on three real-world datasets demonstrate the effectiveness of our proposed multi-scale model in controlling network group behavior.

Keywords: influence blocking maximization, competitive linear threshold model, social networks, network group behavior

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Authors: Pranee Pathomchaiwat

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This research aims to study tourism data and behavior of foreign tourists visited Wat Phrachetuponwimolmangkalaram (Wat Po) Sample groups are tourists who visited inside the temple, during February, March, April and May 2013. Tools used in the research are questionnaires constructed by the researcher, and samples are dawn by Convenience sampling. There are 207 foreign tourists who are willing to be respondents. Statistics used are percentage, average mean and standard deviation. The results of the research reveal that: A. General Data of Respondents: The foreign tourists who visited the temple are mostly female (57.5 %), most respondents are aged between 20-29 years (37.2%). Most respondents live in Europe (62.3%), most of them got the Bachelor’s degree (40.1%), British are mostly found (16.4%), respondents who are students are also found (23.2%), and Christian are mostly found (60.9%). B. Tourists’ Behavior While Visiting the Temple Compound: The result shows that the respondents came with family (46.4%), have never visited the temples (40.6%), and visited once (42 %). It is found that the foreign tourists’ inappropriate behavior are wearing revealing attires (58.9%), touching or getting closed to the monks (55.1%), and speaking loudly (46.9%) respectively. The respondents’ outstanding objectives are to visit inside the temple (57.5%), to pay respect to the Reclining Buddha Image in the Viharn (44.4%) and to worship the Buddha image in the Phra Ubosod (37.7%) respectively. C. The Respondents’ Self-evaluation of Performance: It is found that over all tourists evaluated themselves in the highest level averaged 4.40. When focusing on each item, it is shown that they evaluated themselves in the highest level on obeying the temple staff averaged 4.57, and cleanness concern of the temple averaged 4.52, well-behaved performance during the temple visit averaged 4.47 respectively.

Keywords: deportment, traveler, foreign tourists, temple

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Authors: Salah Brahim Belakhdar, Tari Mohammed Amin, Rafai Abderrahmen, Amalsi Bilal

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Shear strength of sandy soils has been considered as the important parameter to study the stability of different civil engineering structures when subjected to monotonic, cyclic, and earthquake loading conditions. The proposed research investigated the effect of grading characteristics on the shear strength and mechanical behaviour of granular classes of sands mixed with salt in loose and dense states (Dr=15% and 90%). The laboratory investigation aimed at understanding the extent or degree at which shear strength of sand-silt mixture soil is affected by its gradation under static loading conditions. For the purpose of clarifying and evaluating the shear strength characteristics of sandy soils, a series of Casagrande shear box tests were carried out on different reconstituted samples of sand-silt mixtures with various gradations. The soil samples were tested under different normal stresses (100, 200, and 300 kPa). The results from this laboratory investigation were used to develop insight into the shear strength response of sand and sand-silt mixtures under monotonic loading conditions. The analysis of the obtained data revealed that the grading characteristics (D10, D50, Cu, ESR, and MGSR) have a significant influence on the shear strength response. It was found that shear strength can be correlated to the grading characteristics for the sand-silt mixture. The effective size ratio (ESR) and mean grain size ratio (MGSR) appear as pertinent parameters to predict the shear strength response of the sand-silt mixtures for soil gradation under study.

Keywords: mechanical behavior, silty sand, friction angle, cohesion, fines content

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Authors: Forouzan Salehi Fergeni

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Converting the movement intents of a person into commands for action employing brain signals like electroencephalogram signals is a brain-computer interface (BCI) system. When left or right-hand motions are imagined, different patterns of brain activity appear, which can be employed as BCI signals for control. To make better the brain-computer interface (BCI) structures, effective and accurate techniques for increasing the classifying precision of motor imagery (MI) based on electroencephalography (EEG) are greatly needed. Subject dependency and non-stationary are two features of EEG signals. So, EEG signals must be effectively processed before being used in BCI applications. In the present study, after applying an 8 to 30 band-pass filter, a car spatial filter is rendered for the purpose of denoising, and then, a method of analysis of variance is used to select more appropriate and informative channels from a category of a large number of different channels. After ordering channels based on their efficiencies, a sequential forward channel selection is employed to choose just a few reliable ones. Features from two domains of time and wavelet are extracted and shortlisted with the help of a statistical technique, namely the t-test. Finally, the selected features are classified with different machine learning and neural network classifiers being k-nearest neighbor, Probabilistic neural network, support-vector-machine, Extreme learning machine, decision tree, Multi-layer perceptron, and linear discriminant analysis with the purpose of comparing their performance in this application. Utilizing a ten-fold cross-validation approach, tests are performed on a motor imagery dataset found in the BCI competition III. Outcomes demonstrated that the SVM classifier got the greatest classification precision of 97% when compared to the other available approaches. The entire investigative findings confirm that the suggested framework is reliable and computationally effective for the construction of BCI systems and surpasses the existing methods.

Keywords: brain-computer interface, channel selection, motor imagery, support-vector-machine

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Authors: Abdel Hadi N. Ebraheim, Eman Azomi, Nefisa A. Fahmy

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This paper designs and implements a computer-aided system (CAS) to help detect and diagnose liver cirrhosis in patients with Chronic Hepatitis C. Our system reduces the required features (tests) the patient is asked to do to tests to their minimal best most informative subset of tests, with a diagnostic accuracy above 99%, and hence saving both time and costs. We use the Support Vector Machine (SVM) with cross-validation, a Multilayer Perceptron Neural Network (MLP), and a Generalized Regression Neural Network (GRNN) that employs a base of radial functions for functional approximation, as classifiers. Our system is tested on 199 subjects, of them 99 Chronic Hepatitis C.The subjects were selected from among the outpatient clinic in National Herpetology and Tropical Medicine Research Institute (NHTMRI).

Keywords: liver cirrhosis, artificial neural network, support vector machine, multi-layer perceptron, classification, accuracy

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Authors: Roshanak Farjad, Amirhossein Hosseini

Abstract:

Fecal incontinence (FI) is a stressful condition for children and their parents that may affect the patient’s psychological well-being. Evaluating the patients’ psychological status may help physicians manage the disease effectively. This study aimed to assess the emotional and behavioral disturbances in children with FI who were referred to the pediatric gastroenterology clinic in Mofid Children’s Hospital from April 2021 to 2022. This cross-sectional study included children (over four years old) with chronic constipation and fecal incontinence. The diagnosis of chronic constipation and FI were made according to Rome-IV criteria. The Child Behavior Checklist (CBCL) evaluated patients’ emotional, behavioral, and social problems. One hundred one patients with a mean age of 7.96 years were enrolled in the study; 67.32% were males. According to CBCL, 12% (12 patients) indicated emotional and behavioral problems, with CBCL scores in the clinical or at-risk range. We detected anxious/depressed problems in five (4.95%), withdrawn/depressed problems in eight (7.92%), somatic complaints in seven (6.93%), social problems in eight (7.92%), thought problems in nine (8.91%), attention problems in seven (6.93%), rule-breaking behavior in two (1.98%), and aggressive behavior in nine (8.91%) patients. The risk of internalizing and externalizing disorders was reported in four (3.96%) and five (4.95%) patients. Also, eight (7.92%) and seven (6.93%) patients had clinical symptoms of internalizing and externalizing disorders, respectively. There was no significant relationship between patients’ age and gender with the CBCL scores in any subscales. However, there was a significant difference in the total score among the age groups (P = 0.04). The relatively high prevalence of emotional, behavioral, and social problems in our study corroborates the importance of psychological screening of children with FI during the treatment process.

Keywords: chronic constipation, child behavior checklist (CBCL), fecal incontinence, rome-IV criteria

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Authors: Doseong Eom, Jeongmin Kim, Kwang Ryel Ryu

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Large exhibition halls require a lot of energy to maintain comfortable atmosphere for the visitors viewing inside. One way of reducing the energy cost is to have thermal energy storage systems installed so that the thermal energy can be stored in the middle of night when the energy price is low and then used later when the price is high. To minimize the overall energy cost, however, we should be able to decide how much energy to save during which time period exactly. If we can foresee future energy load and the corresponding cost, we will be able to make such decisions reasonably. In this paper, we use machine learning technique to obtain models for predicting weather conditions and the number of visitors on hourly basis for the next day. Based on the energy load thus predicted, we build a cost-optimal daily operation plan for the thermal energy storage systems and cooling and heating facilities through simulation-based optimization.

Keywords: building energy management, machine learning, operation planning, simulation-based optimization

Procedia PDF Downloads 323