Search results for: behavior against washing machine parameters

Authors: Li-Ju Chen, Hsiang-Lin Chan, Hsin-Yi Kathy Cheng, Hui-Ju Chen

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Stereotype behavior is one of the maladaptive syndromes of the individuals with autism. Most of the previous researches focused on the stereotype behavior with stimulating type, while less on the stereotype behavior about cognition (This research names it cognitive stereotype behavior; CSB). This research explored CSB and the rationality to explain CSB with imprinting phenomenon. After excluding the samples without CSB described, the data that came from 271 individuals with autism were recruited and analyzed with quantitative and qualitative analyses. This research discovers that : (1) Most of the individuals with autism originally came out CSB at 3 years old and more than a half of them appeared before 4 years old; The average age which firstly came out CSB was 6.10 years old, the average time insisting or ossifying CSB was 31.71 minutes each time and the average longest time which they last was 358.35 minutes (5.97 hours). (2) CSB demonstrates various aspects, this research classified them into 4 fields with 26 categories. They were categorized into sudden CSB or habitual CSB by imprinting performance. (3) Most of the autism commented that their CSBs were not necessary but they could not control them well. One-third of them appeared CSB suddenly and the first occurrence accompanied a strong emotional or behavioral response. (4) Whether respondent is the person with autism himself/herself or not was the critical element: on the awareness of the severity degree, disturbance degree, and the emotional /behavioral intensity at the first-time CSB happened. This study concludes imprinting could reasonably explain the phenomenon CSB forms. There are implications leading the individuals with autism and their family to develop coping strategies to promote individuals with autism having a better learning accomplishment and life quality in their future.

Keywords: autism, cognitive stereotype behavior, constructivism, imprinting, stereotype

Procedia PDF Downloads 130

Authors: Dijana Sulejmanović

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Cognitive-behavioral modification (CBM) is a combination of cognitive and behavioral learning principles to shape and encourage the desired behaviors. A crucial element of cognitive-behavioral modification is that a change the behavior precedes awareness of how it affects others. CBM is oriented toward changing inner speech and learning to control behaviors through self-regulation techniques. It aims to teach individuals how to develop the ability to recognize, monitor and modify their thoughts, feelings, and behaviors. The review of literature emphasizes the efficiency the CBM approach in the treatment of children's hyperactivity and negative emotions such as anger. The results of earlier research show how impulsive and hyperactive behavior, agitation, and aggression may slow down and block the child from being able to actively monitor and participate in regular classes, resulting in the disruption of the classroom and the teaching process, and the children may feel rejected, isolated and develop long-term poor image of themselves and others. In this article, we will provide how the use of CBM, adapted to child's age, can incorporate measures of cognitive and emotional functioning which can help us to better understand the children’s cognitive processes, their cognitive strengths, and weaknesses, and to identify factors that may influence their behavioral and emotional regulation. Such a comprehensive evaluation can also help identify cognitive and emotional risk factors associated with aggressive behavior, specifically the processes involved in modulating and regulating cognition and emotions.

Keywords: aggressive behavior, cognitive behavioral modification, cognitive behavioral theory, modification

Procedia PDF Downloads 326

Authors: Noriyuki Suyama

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This paper discusses purchasing behavior in retail stores, with a particular focus on the impact of weather changes on customers' purchasing behavior. Weather conditions are one of the factors that greatly affect the management and operation of retail stores. However, there is very little research on the relationship between weather conditions and marketing from an academic perspective, although there is some importance from a practical standpoint and knowledge based on experience. For example, customers are more hesitant to go out when it rains than when it is sunny, and they may postpone purchases or buy only the minimum necessary items even if they do go out. It is not difficult to imagine that weather has a significant impact on consumer behavior. To the best of the authors' knowledge, there have been only a few studies that have delved into the purchasing behavior of individual customers. According to Hirata (2018), the economic impact of weather in the United States is estimated to be 3.4% of GDP, or "$485 billion ± $240 billion per year. However, weather data is not yet fully utilized. Representative industries include transportation-related industries (e.g., airlines, shipping, roads, railroads), leisure-related industries (e.g., leisure facilities, event organizers), energy and infrastructure-related industries (e.g., construction, factories, electricity and gas), agriculture-related industries (e.g., agricultural organizations, producers), and retail-related industries (e.g., retail, food service, convenience stores, etc.). This paper focuses on the retail industry and advances research on weather. The first reason is that, as far as the author has investigated the retail industry, only grocery retailers use temperature, rainfall, wind, weather, and humidity as parameters for their products, and there are very few examples of academic use in other retail industries. Second, according to NBL's "Toward Data Utilization Starting from Consumer Contact Points in the Retail Industry," labor productivity in the retail industry is very low compared to other industries. According to Hirata (2018) mentioned above, improving labor productivity in the retail industry is recognized as a major challenge. On the other hand, according to the "Survey and Research on Measurement Methods for Information Distribution and Accumulation (2013)" by the Ministry of Internal Affairs and Communications, the amount of data accumulated by each industry is extremely large in the retail industry, so new applications are expected by analyzing these data together with weather data. Third, there is currently a wealth of weather-related information available. There are, for example, companies such as WeatherNews, Inc. that make weather information their business and not only disseminate weather information but also disseminate information that supports businesses in various industries. Despite the wide range of influences that weather has on business, the impact of weather has not been a subject of research in the retail industry, where business models need to be imagined, especially from a micro perspective. In this paper, the author discuss the important aspects of the impact of weather on marketing strategies in the non-food retail industry.

Keywords: consumer behavior, weather marketing, marketing science, big data, retail marketing

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Authors: J. Ikome, M. Ndeley

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When a machine breakdown forces a Modified Flow Shop (MFS) out of the prescribed state, the proposed strategy reschedules part of the initial schedule to match up with the preschedule at some point. The objective is to create a new schedule that is consistent with the other production planning decisions like material flow, tooling and purchasing by utilizing the time critical decision making concept. We propose a new rescheduling strategy and a match-up point determination procedure through a feedback mechanism to increase both the schedule quality and stability. The proposed approach is compared with alternative reactive scheduling methods under different experimental settings.

Keywords: advanced critical task methods modified flow shop (MFS), Manufacturing, experiment, determination

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Authors: Rama Seshu Doguparti

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This paper presents the experimental investigation on the bond behavior of geo polymer concrete. The bond behavior of geo polymer concrete cubes of grade M35 reinforced with 16 mm TMT rod is analyzed. The results indicate that the bond performance of reinforced geo polymer concrete is good and thus proves its application for construction.

Keywords: geo-polymer, concrete, bond strength, behaviour

Procedia PDF Downloads 509

Authors: Sonveer Singh, Sanjay Agrawal, D. V. Avasthi, Jayant Shekhar

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The aim of this paper is to improve the efficiency of photovoltaic thermal (PVT) system with the help of Genetic Algorithms with multi-objective function. There are some parameters that affect the efficiency of PVT system like depth and length of the channel, velocity of flowing fluid through the channel, thickness of the tedlar and glass, temperature of inlet fluid i.e. all above parameters are considered for optimization. An attempt has been made to the model and optimizes the parameters of glazed hybrid single channel PVT module when two objective functions have been considered separately. The two objective function for optimization of PVT module is overall electrical and thermal efficiency. All equations for PVT module have been derived. Using genetic algorithms (GAs), above two objective functions of the system has been optimized separately and analysis has been carried out for two cases. Two cases are: Case-I; Improvement in electrical and thermal efficiency when overall electrical efficiency is optimized, Case-II; Improvement in electrical and thermal efficiency when overall thermal efficiency is optimized. All the parameters that are used in genetic algorithms are the parameters that could be changed, and the non-changeable parameters, like solar radiation, ambient temperature cannot be used in the algorithm. It has been observed that electrical efficiency (14.08%) and thermal efficiency (19.48%) are obtained when overall thermal efficiency was an objective function for optimization. It is observed that GA is a very efficient technique to estimate the design parameters of hybrid single channel PVT module.

Keywords: genetic algorithm, energy, exergy, PVT module, optimization

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Authors: Peter M. Schneider, Batyr Garlyyev, Sebastian A. Watzele, Aliaksandr S. Bandarenka

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Functional nanostructures such as nanoparticles are a promising class of materials for energy applications due to their unique properties. Bottom-up synthetic routes for nanostructured materials often involve multiple synthesis steps and the use of surfactants, reducing agents, or stabilizers. This results in complex and extensive synthesis protocols. In recent years, a novel top-down synthesis approach to form metal nanoparticles has been established, in which bulk metal wires are immersed in an electrolyte (primarily alkali earth metal based) and subsequently subjected to a high alternating potential. This leads to the generation of nanoparticles dispersed in the electrolyte. The main advantage of this facile top-down approach is that there are no reducing agents, surfactants, or precursor solutions. The complete synthesis can be performed in one pot involving one main step with consequent washing and drying of the nanoparticles. More recent studies investigated the effect of synthesis parameters such as potential amplitude, frequency, electrolyte composition, and concentration on the size and shape of the nanoparticles. Here, we investigate the electrochemical erosion of various metal wires such as Ti, Pt, Pd, and Sn in various electrolyte compositions via this facile top-down technique and its experimental optimization to successfully synthesize nanostructured materials for various energy applications. As an example, for Pt and Pd, homogeneously distributed nanoparticles on carbon support can be obtained. These materials can be used as electrocatalyst materials for the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER), respectively. In comparison, the top-down erosion of Sn wires leads to the formation of nanoparticles, which have great potential as oxygen evolution reaction (OER) support materials. The application of the technique on Ti wires surprisingly leads to the formation of nanowires, which show a high surface area and demonstrate great potential as an alternative support material to carbon.

Keywords: ORR, electrochemistry, electrocatalyst, synthesis

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Authors: Maria Bercea, Bernhard A. Wolf

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The thermodynamic behavior for solutions of poly (methyl methacrylate-ran-t-butyl methacrylate) of variable composition as compared with the corresponding homopolymers was investigated by light scattering measurements carried out for dilute solutions and vapor pressure measurements of concentrated solutions. The complex dependencies of the Flory Huggins interaction parameter on concentration and copolymer composition in solvents of different polarity (toluene and chloroform) can be understood by taking into account the ability of the polymers to rearrange in a response to changes in their molecular surrounding. A recent unified thermodynamic approach was used for modeling the experimental data, being able to describe the behavior of the different solutions by means of two adjustable parameters, one representing the effective number of solvent segments and another one accounting for the interactions between the components. Thus, it was investigated how the solvent quality changes with the composition of the copolymers through the Gibbs energy of mixing as a function of polymer concentration. The largest reduction of the Gibbs energy at a given composition of the system was observed for the best solvent. The present investigation proves that the new unified thermodynamic approach is a general concept applicable to homo- and copolymers, independent of the chain conformation or shape, molecular and chemical architecture of the components and of other dissimilarities, such as electrical charges.

Keywords: random copolymers, Flory Huggins interaction parameter, Gibbs energy of mixing, chemical architecture

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Authors: V. Bezverbny

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The actuality of the project deals with stable growing of deviant behavior’ statistics among Moscow citizens. During the recent years the socioeconomic health, wealth and life expectation of Moscow residents is regularly growing up, but the limits of crime and drug addiction have grown up seriously. Another serious Moscow problem has been economical stratification of population. The cost of identical residential areas differs at 2.5 times. The project is aimed at complex research and the development of methodology for main factors and reasons evaluation of deviant behavior growing in Moscow. The main project objective is finding out the links between the urban environment quality and dynamics of citizens’ deviant behavior in regional and municipal aspect using the statistical research methods and GIS modeling. The conducted research allowed: 1) to evaluate the dynamics of deviant behavior in Moscow different administrative districts; 2) to describe the reasons of crime increasing, drugs addiction, alcoholism, suicides tendencies among the city population; 3) to develop the city districts classification based on the level of the crime rate; 4) to create the statistical database containing the main indicators of Moscow population deviant behavior in 2010-2015 including information regarding crime level, alcoholism, drug addiction, suicides; 5) to present statistical indicators that characterize the dynamics of Moscow population deviant behavior in condition of expanding the city territory; 6) to analyze the main sociological theories and factors of deviant behavior for concretization the deviation types; 7) to consider the main theoretical statements of the city sociology devoted to the reasons for deviant behavior in megalopolis conditions. To explore the level of deviant behavior’ factors differentiation, the questionnaire was worked out, and sociological survey involved more than 1000 people from different districts of the city was conducted. Sociological survey allowed to study the socio-economical and psychological factors of deviant behavior. It also included the Moscow residents’ open-ended answers regarding the most actual problems in their districts and reasons of wish to leave their place. The results of sociological survey lead to the conclusion that the main factors of deviant behavior in Moscow are high level of social inequality, large number of illegal migrants and bums, nearness of large transport hubs and stations on the territory, ineffective work of police, alcohol availability and drug accessibility, low level of psychological comfort for Moscow citizens, large number of building projects.

Keywords: deviant behavior, megapolis, Moscow, urban environment, social stratification

Procedia PDF Downloads 193

Authors: Shakila N. Senarath, Dinesh Asanka, Janaka Wijayanayake

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The necessity of cosmetic products is arising to fulfill consumer needs of personality appearance and hygiene. A cosmetic product consists of various chemical ingredients which may help to keep the skin healthy or may lead to damages. Every chemical ingredient in a cosmetic product does not perform on every human. The most appropriate way to select a healthy cosmetic product is to identify the texture of the body first and select the most suitable product with safe ingredients. Therefore, the selection process of cosmetic products is complicated. Consumer surveys have shown most of the time, the selection process of cosmetic products is done in an improper way by consumers. From this study, a content-based system is suggested that recommends cosmetic products for the human factors. To such an extent, the skin type, gender and price range will be considered as human factors. The proposed system will be implemented by using Machine Learning. Consumer skin type, gender and price range will be taken as inputs to the system. The skin type of consumer will be derived by using the Baumann Skin Type Questionnaire, which is a value-based approach that includes several numbers of questions to derive the user’s skin type to one of the 16 skin types according to the Bauman Skin Type indicator (BSTI). Two datasets are collected for further research proceedings. The user data set was collected using a questionnaire given to the public. Those are the user dataset and the cosmetic dataset. Product details are included in the cosmetic dataset, which belongs to 5 different kinds of product categories (Moisturizer, Cleanser, Sun protector, Face Mask, Eye Cream). An alternate approach of TF-IDF (Term Frequency – Inverse Document Frequency) is applied to vectorize cosmetic ingredients in the generic cosmetic products dataset and user-preferred dataset. Using the IF-IPF vectors, each user-preferred products dataset and generic cosmetic products dataset can be represented as sparse vectors. The similarity between each user-preferred product and generic cosmetic product will be calculated using the cosine similarity method. For the recommendation process, a similarity matrix can be used. Higher the similarity, higher the match for consumer. Sorting a user column from similarity matrix in a descending order, the recommended products can be retrieved in ascending order. Even though results return a list of similar products, and since the user information has been gathered, such as gender and the price ranges for product purchasing, further optimization can be done by considering and giving weights for those parameters once after a set of recommended products for a user has been retrieved.

Keywords: content-based filtering, cosmetics, machine learning, recommendation system

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Authors: Ashok Kumar, Shiv Brat Singh, Kalyan Kumar Ray

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There is growing awareness to design steels against fatigue damage Ferrite martensite dual-phase steels are known to exhibit favourable mechanical properties like good strength, ductility, toughness, continuous yielding, and high work hardening rate. However, dual-phase steels containing bainite as second phase are potential alternatives for ferrite martensite steels for certain applications where good fatigue property is required. Fatigue properties of dual phase steels are popularly assessed by the nature of variation of crack growth rate (da/dN) with stress intensity factor range (∆K), and the magnitude of fatigue threshold (∆Kth) for long cracks. There exists an increased emphasis to understand not only the long crack fatigue behavior but also short crack growth behavior of ferrite bainite dual phase steels. The major objective of this report is to examine the influence of microstructures on the short and long crack growth behavior of a series of developed dual-phase steels with varying amounts of bainite and. Three low carbon steels containing Nb, Cr and Mo as microalloying elements steels were selected for making ferrite-bainite dual-phase microstructures by suitable heat treatments. The heat treatment consisted of austenitizing the steel at 1100°C for 20 min, cooling at different rates in air prior to soaking these in a salt bath at 500°C for one hour, and finally quenching in water. Tensile tests were carried out on 25 mm gauge length specimens with 5 mm diameter using nominal strain rate 0.6x10⁻³ s⁻¹ at room temperature. Fatigue crack growth studies were made on a recently developed specimen configuration using a rotating bending machine. The crack growth was monitored by interrupting the test and observing the specimens under an optical microscope connected to an Image analyzer. The estimated crack lengths (a) at varying number of cycles (N) in different fatigue experiments were analyzed to obtain log da/dN vs. log °∆K curves for determining ∆Kthsc. The microstructural features of these steels have been characterized and their influence on the near threshold crack growth has been examined. This investigation, in brief, involves (i) the estimation of ∆Kthsc and (ii) the examination of the influence of microstructure on short and long crack fatigue threshold. The maximum fatigue threshold values obtained from short crack growth experiments on various specimens of dual-phase steels containing different amounts of bainite are found to increase with increasing bainite content in all the investigated steels. The variations of fatigue behavior of the selected steel samples have been explained with the consideration of varying amounts of the constituent phases and their interactions with the generated microstructures during cyclic loading. Quantitative estimation of the different types of fatigue crack paths indicates that the propensity of a crack to pass through the interfaces depends on the relative amount of the microstructural constituents. The fatigue crack path is found to be predominantly intra-granular except for the ones containing > 70% bainite in which it is predominantly inter-granular.

Keywords: bainite, dual phase steel, fatigue crack growth rate, long crack fatigue threshold, short crack fatigue threshold

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

Procedia PDF Downloads 235

Authors: Dogaru-Ulieru Valentin, Sălișteanu Ioan Corneliu, Ardeleanu Mihăiță Nicolae, Broscăreanu Ștefan, Sălișteanu Bogdan, Mihai Mihail

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The SiaMOTO system is a communications and data processing platform for vehicle traffic. The human factor is the most important factor in the generation of this data, as the driver is the one who dictates the trajectory of the vehicle. Like any trajectory, specific parameters refer to position, speed and acceleration. Constant knowledge of these parameters allows complex analyses. Roadways allow many vehicles to travel through their confined space, and the overlapping trajectories of several vehicles increase the likelihood of collision events, known as road accidents. Any such event has causes that lead to its occurrence, so the conditions for its occurrence are known. The human factor is predominant in deciding the trajectory parameters of the vehicle on the road, so monitoring it by knowing the events reported by the DiaMOTO device over time, will generate a guide to target any potentially high-risk driving behavior and reward those who control the driving phenomenon well. In this paper, we have focused on detailing the communication infrastructure of the DiaMOTO device with the traffic data collection server, the infrastructure through which the database that will be used for complex AI/DLM analysis is built. The central element of this description is the data string in CODEC-8 format sent by the DiaMOTO device to the SiaMOTO collection server database. The data presented are specific to a functional infrastructure implemented in an experimental model stage, by installing on a number of 50 vehicles DiaMOTO unique code devices, integrating ADAS and GPS functions, through which vehicle trajectories can be monitored 24 hours a day.

Keywords: DiaMOTO, Codec-8, ADAS, GPS, driver monitoring

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Authors: Sadek Salah

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The determination of the strength characteristics of waste materials is essential when evaluating the stability of waste fills during initial placement and at the time of closure and rehabilitation of the landfill. Significant efforts, mostly experimental, have been deployed to date in attempts to quantify the mechanical properties of municipal wastes various stages of decomposition. Even though the studies and work done so far have helped in setting baseline parameters and characteristics for waste materials, inherent concerns remain as to the scalability of the findings between the laboratory and the field along with questions as to the suitability of the actual test conditions. These concerns are compounded by the complexity of the problem itself with significant variability in composition, placement conditions, and levels of decay of the various constituents of the waste fills. A complimentary, if not necessarily an alternative approach is to rely on field observations of behavior and instability of such materials. This paper describes an effort at obtaining relevant shear strength parameters from back-analyses of failures which have been observed at a major un-engineered waste fill along the Mediterranean shoreline. Results from the limit-equilibrium failure back-analyses are presented and compared to results from laboratory-scale testing on comparable waste materials.

Keywords: solid waste, shear strength, landfills, slope stability

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Authors: Elena Kokkoni, Prasanna Kannappan, Ashkan Zehfroosh, Effrosyni Mavroudi, Kristina Strother-Garcia, James C. Galloway, Jeffrey Heinz, Rene Vidal, Herbert G. Tanner

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Early immobility affects the motor, cognitive, and social development. Current pediatric rehabilitation lacks the technology that will provide the dosage needed to promote mobility for young children at risk. The addition of socially assistive robots in early interventions may help increase the mobility dosage. The aim of this study is to examine the feasibility of an early intervention paradigm where non-walking infants experience independent mobility while socially interacting with robots. A dynamic environment is developed where both the child and the robot interact and learn from each other. The environment involves: 1) a range of physical activities that are goal-oriented, age-appropriate, and ability-matched for the child to perform, 2) the automatic functions that perceive the child’s actions through novel activity recognition algorithms, and decide appropriate actions for the robot, and 3) a networked visual data acquisition system that enables real-time assessment and provides the means to connect child behavior with robot decision-making in real-time. The environment was tested by bringing a two-year old boy with Down syndrome for eight sessions. The child presented delays throughout his motor development with the current being on the acquisition of walking. During the sessions, the child performed physical activities that required complex motor actions (e.g. climbing an inclined platform and/or staircase). During these activities, a (wheeled or humanoid) robot was either performing the action or was at its end point 'signaling' for interaction. From these sessions, information was gathered to develop algorithms to automate the perception of activities which the robot bases its actions on. A Markov Decision Process (MDP) is used to model the intentions of the child. A 'smoothing' technique is used to help identify the model’s parameters which are a critical step when dealing with small data sets such in this paradigm. The child engaged in all activities and socially interacted with the robot across sessions. With time, the child’s mobility was increased, and the frequency and duration of complex and independent motor actions were also increased (e.g. taking independent steps). Simulation results on the combination of the MDP and smoothing support the use of this model in human-robot interaction. Smoothing facilitates learning MDP parameters from small data sets. This paradigm is feasible and provides an insight on how social interaction may elicit mobility actions suggesting a new early intervention paradigm for very young children with motor disabilities. Acknowledgment: This work has been supported by NIH under grant #5R01HD87133.

Keywords: activity recognition, human-robot interaction, machine learning, pediatric rehabilitation

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Authors: Dodi Ikhsanshaleh

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When subjected to dynamic load, foundation oscillates in the way that depends on the soil behaviour, the geometry and inertia of the foundation and the dynamic exctation. The practical guideline to analysis block-type foundation excitated by dynamic load from vibrating machine is presented. The analysis use Lumped Mass Parameter Method to express dynamic properties such as stiffness and damping of soil. The numerical examples are performed on design block-type foundation supporting gas turbine compressor which is important equipment package in gas processing plant

Keywords: block foundation, dynamic load, lumped mass parameter

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Authors: Sheila Shahidi, Hootan Rezaee, Abosaeed Rashidi, Mahmood Ghoranneviss

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Plasma treatment has an explosive increase in interest and use in industrial applications as for example in medical, biomedical, automobile, electronics, semiconductor and textile industry. A lot of intensive basic research has been performed in the last decade in the field of textiles along with technical textiles. Textile manufacturers and end-users alike have been searching for ways to improve the surface properties of natural and man-made fibers. Specifically, there is a need to improve adhesion and wettability. Functional groups may be introduced onto the fiber surface by using gas plasma treatments, improving fiber surface properties without affecting the fiber’s bulk properties. In this research work, ZnO nanoparticles (ZnO-NPs) were insitue synthesized by sonochemical method at room temperature on both untreated and plasma pretreated cotton woven fabric. Oxygen and nitrogen plasmas were used for pre-functionalization of cotton fabric. And the effect of oxygen and nitrogen pre-functionalization on adhesion properties between ZnO nanoparticles and cotton surface were studied. The results show that nanoparticles with average sizes of 20-100 nm with different morphologies have been created on the surface of samples. Synthesis of ZnO-NPs was varied in the morphological transformation by changes in zinc acetate dehydrate concentration. Characterizations were carried out using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Inductive coupled plasma (ICP) and Spectrophotometery. The antibacterial activities of the fabrics were assessed semi-quantitatively by the colonies count method. The results show that the finished fabric demonstrated significant antibacterial activity against S. aureus in antibacterial test. The wash fastness of both untreated and plasma pretreated samples after 30 times of washing was investigated. The results showed that the parameters of plasma reactor plays very important role for improving the antibacterial durability.

Keywords: antibacterial activity, cotton, fabric, nanoparticles, plasma

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Authors: Nidhi Bansal, Upasna Agarwal

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Innovation is a key driver for economic growth and well-being of developed as well as emerging economies like India. Very few studies examined the relationship between IWB and work-family enrichment. Therefore, the present study examines the relationship between work-family enrichment (WFE) and innovative workplace behavior (IWB) and whether it is mediated by positive emotions. Social exchange theory and broaden and build theory explain the proposed relationships. Data were collected from 250 full time dual working parents in different Indian organizations through a survey questionnaire. Snowball technique was used for approaching respondents. Mediation analysis was assessed through PROCESS macro (Hayes, 2012) in SPSS. With correlational analysis, it was explored that all three variables were significantly and positively related. Analysis suggests that work-family enrichment is significantly related to innovative workplace behavior and this relationship is partially mediated by positive emotions. A cross-sectional design, use of self-reported questions and data collected only from dual working parents are few limitations of the study. This is one of the few studies to examine the innovative workplace behavior in response to work-family enrichment and first attempt to examine the mediation effect of emotions between these two variables.

Keywords: dual working parents, emotions, innovative workplace behavior, work-family enrichment

Procedia PDF Downloads 258

Authors: Muluken Z. Getie, Francois Lanzetta, Sylvie Begot, Bimrew T. Admassu

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This study is aimed at the numerical analysis of the effects of phase angle and losses (shuttle heat loss and gas leakage to the crankcase) that could have an impact on the pressure and temperature of working fluid for a β-type Stirling cycle refrigerating machine. First, the developed numerical model incorporates into the ideal adiabatic analysis, the shuttle heat transfer (heat loss from compression space to expansion space), and gas leakage from the working space to the buffer space into the crankcase. The other losses that may not have a direct effect on the temperature and pressure of working fluid are simply incorporated in a simple analysis. The model is then validated by reversing the model to the engine model and compared with other literature results using (GPU-3) engine. After validating the model with other engine model and experiment results, analysis of the effect of phase angle, shuttle heat lose and gas leakage on temperature, pressure, and performance (power requirement, cooling capacity and coefficient of performance) of refrigerating machine considering the FEMTO 60 Stirling engine as a case study have been conducted. Shuttle heat loss has a greater effect on the temperature of working gas; gas leakage to the crankcase has more effect on the pressure of working spaces and hence both have a considerable impact on the performance of the Stirling cycle refrigerating machine. The optimum coefficient of performance exists between phase angles of 900-950, and optimum cooling capacity could be found between phase angles of 950-980.

Keywords: beta configuration, engine model, moderate cooling, stirling refrigerator, and validation

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Authors: K. Haggag, N. S. Elshemy

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Due to limited fossil resource and an increased need for environmentally friendly, sustainable technologies, the importance of using renewable feed stocks in textile industry area will increase in the decades to come. This research highlights some of the perspectives in this area. Alkyd resins for high characterization and reactive properties, completely based on commercially available renewable resources (sunflower and/or soybean oil) were prepared and characterized. In this work, we present results on the synthesis of various alkyd resins according to the alcoholysis – polyesterification process under different preparation conditions using a microwave synthesis as energy source to determine suitable reaction conditions. Effects of polymerization parameters, such as catalyst ratio, reaction temperature and microwave power level have been studied. The prepared binder was characterized via FT-IR, scanning electron microscope (SEM) and transmission electron microscope (TEM), in addition to acid value (AV), iodine value (IV), water absorbance, weight loss, and glass transition temperature. The prepared binder showed high performance physico-mechanical properties. TEM analysis showed that the polymer latex nanoparticle within range of 20–200 nm. The study involved the application of the prepared alkyd resins as binder for pigment printing process onto cotton fabric by using a flat screen technique and the prints were dried and thermal cured. The optimum curing conditions were determined, color strength and fastness properties of pigment printed areas to light, washing, perspiration and crocking were evaluated. The rheological properties and apparent viscosity of prepared binders were measured in addition roughness of the prints was also determined.

Keywords: nano-binder, microwave heating, renewable resource, alkyd resins, sunflower oil, soybean oil

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Authors: Aamir Hasan, Subhash Mishra

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The purpose of the study was to determine the key drivers which influence the shopping behavior of the customers in the retail store. In today‟s competitive world with increasing number of retail stores, the retailers need to be more customer oriented. Retail has changed and expanded in all lines of business, be it apparel,jewelry, footwear, groceries etc. The modern consumer is posing a challenging task for the Indian retailer. More aware, more confident and much more demanding, therefore the retailers are looking for ways to deliver better consumer value and to increase consumer purchase intention. Retailers tend to differentiate themselves by making their service easier to consumers. The study aims to study the key drivers that can influence shopping behavior in retail store. A survey (store intercept) method was employed to elicit primary information from 300 shoppers in different retail stores of Lucknow. The findings reveal the factors that play a greater role in influencing the shopping behavior of customers in retail store. As such, a survey of retail store customers‟ attitude towards reduced price, sales promotion, quality of the products, proximity to the home, customer service, store atmospherics were analyzed to identify the key drivers influencing shopping behavior in retail store. A questionnaire based on a five-item Likert scale, as well as random sampling, was employed for data collection. Data analysis was accomplished using SPSS software. The paper has found shopping experience, store image and value for money as three important variable out of which shopping experience emerged as a dominant factor which influences the consumer's shopping behavior in the retail store. Since the research has established empirical evidences in determining the key drivers which influences the shopping behavior of the customers in the retail store, it serves as a foundation for a deeper probe into the shopping behavior of the customers in the retail store research domain in the Indian context.

Keywords: retail, shopping, customers, questionnaire

Procedia PDF Downloads 423

Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

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Authors: Kaisa M. Herne, Hanna E. Björkstedt

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Comparisons of online and laboratory environments are important for assessing whether the environment influences behavioral results. Trust game behavior was examined in three environments: 1) The standard laboratory setting with physically present participants (laboratory), 2) An online environment with an online meeting before playing the trust game (online plus a meeting); and 3) An online environment without a meeting (online without a meeting). In laboratory, participants were present in a classroom and played the trust game anonymously via computers. Online plus a meeting mimicked the laboratory in that participants could see each other in an online meeting before sessions started, whereas online without a meeting was a standard online experiment in which participants did not see each other at any stages of the experiment. Participants were recruited through pools of student subjects at two universities. The trust game was identical in all conditions; it was played with the same software, anonymously, and with stranger matching. There were no statistically significant differences between the treatment conditions regarding trust or trustworthiness. Results suggest that conducting trust game experiments online will yield similar results to experiments implemented in a laboratory.

Keywords: laboratory vs. online experiment, trust behavior, trust game, trustworthiness behavior

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Authors: Hung-Jen Lin, Chien-Hao Wang, Chien-Peng Huang, Yu-Sheng Tseng, En-Cheng Yang, Joe-Air Jiang

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Bees’ incoming and outgoing behavior is a decisive index which can indicate the health condition of a colony. Traditional methods for monitoring the behavior of honey bees (Apis mellifera) take too much time and are highly labor-intensive, and the lack of automation and synchronization disables researchers and beekeepers from obtaining real-time information of beehives. To solve these problems, this study proposes to use an Internet of Things (IoT)-based system for counting honey bees’ incoming and outgoing activities using an infrared interruption technique, while environmental factors are recorded simultaneously. The accuracy of the established system is verified by comparing the counting results with the outcomes of manual counting. Moreover, this highly -accurate device is appropriate for providing quantitative information regarding honey bees’ incoming and outgoing behavior. Different statistical analysis methods, including one-way ANOVA and two-way ANOVA, are used to investigate the influence of environmental factors, such as temperature, humidity, illumination and ambient pressure, on bees’ incoming and outgoing behavior. With the real-time data, a standard model is established using the outcomes from analyzing the relationship between environmental factors and bees’ incoming and outgoing behavior. In the future, smart control systems, such as a temperature control system, can also be combined with the proposed system to create an appropriate colony environment. It is expected that the proposed system will make a considerable contribution to the apiculture and researchers.

Keywords: ANOVA, environmental factors, honey bee, incoming and outgoing behavior

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: John Eric C. Bargas, Maria Cecilia M. Marcos

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One of the most common ground improvement techniques is Deep Soil Mixing (DSM). As the technique progresses, there is still lack in the development when it comes to depth control. This was the issue experienced during the installation of DSM in one of the National projects in the Philippines. This study assesses the feasibility of using equipment drilling parameters such as hydraulic pressure, drilling speed and rotational speed in determining the Standard Penetration Test N-value of a specific soil. Hydraulic pressure and drilling speed with a constant rotational speed of 30 rpm have a positive correlation with SPT N-value for cohesive soil and sand. A linear trend was observed for cohesive soil. The correlation of SPT N-value and hydraulic pressure yielded a R²=0.5377 while the correlation of SPT N-value and drilling speed has a R²=0.6355. While the best fitted model for sand is polynomial trend. The correlation of SPT N-value and hydraulic pressure yielded a R²=0.7088 while the correlation of SPT N-value and drilling speed has a R²=0.4354. The low correlation may be attributed to the behavior of sand when the auger penetrates. Sand tends to follow the rotation of the auger rather than resisting which was observed for very loose to medium dense sand. Specific Energy and the product of hydraulic pressure and drilling speed yielded same R² with a positive correlation. Linear trend was observed for cohesive soil while polynomial trend for sand. Cohesive soil yielded a R²=0.7320 which has a strong relationship. Sand also yielded a strong relationship having a coefficient of determination, R²=0.7203. It is feasible to use hydraulic pressure and drilling speed to estimate the SPT N-value of the soil. Also, the product of hydraulic pressure and drilling speed can be a substitute to specific energy when estimating the SPT N-value of a soil. However, additional considerations are necessary to account for other influencing factors like ground water and physical and mechanical properties of soil.

Keywords: ground improvement, equipment drilling parameters, standard penetration test, deep soil mixing

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

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Authors: Ayad Al-Mahturi, Herman Wahid

Abstract:

This paper presents an optimal state feedback controller based on Linear Quadratic Regulator (LQR) for a two-rotor aero-dynamical system (TRAS). TRAS is a highly nonlinear multi-input multi-output (MIMO) system with two degrees of freedom and cross coupling. There are two parameters that define the behavior of LQR controller: state weighting matrix and control weighting matrix. The two parameters influence the performance of LQR. Particle Swarm Optimization (PSO) is proposed to optimally tune weighting matrices of LQR. The major concern of using LQR controller is to stabilize the TRAS by making the beam move quickly and accurately for tracking a trajectory or to reach a desired altitude. The simulation results were carried out in MATLAB/Simulink. The system is decoupled into two single-input single-output (SISO) systems. Comparing the performance of the optimized proportional, integral and derivative (PID) controller provided by INTECO, results depict that LQR controller gives a better performance in terms of both transient and steady state responses when PSO is performed.

Keywords: LQR controller, optimal control, particle swarm optimization (PSO), two rotor aero-dynamical system (TRAS)

Procedia PDF Downloads 323