Search results for: e2e reliability prediction

Authors: Simon J. Wilde, David M. Herold, Michael J. Bryant

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Social media networks have become the hotbed for advertising activities due mainly to their increasing consumer/user base and, secondly, owing to the ability of marketers to accurately measure ad exposure and consumer-based insights on such networks. More than half of the world’s population (4.8 billion) now uses social media (60%), with 150 million new users having come online within the last 12 months (to June 2022). As the use of social media networks by users grows, key business strategies used for interacting with these potential customers have matured, especially social media advertising. Unlike other traditional media outlets, social media advertising is highly interactive and digital channel specific. Social media advertisements are clearly targetable, providing marketers with an extremely powerful marketing tool. Yet despite the measurable benefits afforded to businesses engaged in social media advertising, recent controversies (such as the relationship between Facebook and Cambridge Analytica in 2018) have only heightened the role trust and privacy play within these social media networks. Using a web-based quantitative survey instrument, survey participants were recruited via a reputable online panel survey site. Respondents to the survey represented social media users from all states and territories within Australia. Completed responses were received from a total of 258 social media users. Survey respondents represented all core age demographic groupings, including Gen Z/Millennials (18-45 years = 60.5% of respondents) and Gen X/Boomers (46-66+ years = 39.5% of respondents). An adapted ADTRUST scale, using a 20 item 7-point Likert scale, measured trust in social media advertising. The ADTRUST scale has been shown to be a valid measure of trust in advertising within traditional media, such as broadcast media and print media, and, more recently, the Internet (as a broader platform). The adapted scale was validated through exploratory factor analysis (EFA), resulting in a three-factor solution. These three factors were named reliability, usefulness and affect, and the willingness to rely on. Factor scores (weighted measures) were then calculated for these factors. Factor scores are estimates of the scores survey participants would have received on each of the factors had they been measured directly, with the following results recorded (Reliability = 4.68/7; Usefulness and Affect = 4.53/7; and Willingness to Rely On = 3.94/7). Further statistical analysis (independent samples t-test) determined the difference in factor scores between the factors when age (Gen Z/Millennials vs. Gen X/Boomers) was utilized as the independent, categorical variable. The results showed the difference in mean scores across all three factors to be statistically significant (p<0.05) for these two core age groupings: (1) Gen Z/Millennials Reliability = 4.90/7 vs. Gen X/Boomers Reliability = 4.34/7; (2) Gen Z/Millennials Usefulness and Affect = 4.85/7 vs Gen X/Boomers Usefulness and Affect = 4.05/7; and (3) Gen Z/Millennials Willingness to Rely On = 4.53/7 vs Gen X/Boomers Willingness to Rely On = 3.03/7. The results clearly indicate that older social media users lack trust in the quality of information conveyed in social media ads when compared to younger, more social media-savvy consumers. This is especially evident with respect to Factor 3 (Willingness to Rely On), whose underlying variables reflect one’s behavioral intent to act based on the information conveyed in advertising. These findings can be useful to marketers, advertisers, and brand managers in that the results highlight a critical need to design ‘authentic’ advertisements on social media sites to better connect with these older users in an attempt to foster positive behavioral responses from within this large demographic group – whose engagement with social media sites continues to increase year on year.

Keywords: social media advertising, trust, older consumers, internet studies

Procedia PDF Downloads 38

Authors: Dilek Saritas Atalar, Cansu Alsancak Akbulut, İrem Metin Orta, Feyza Yön, Zeynep Yenen, Joan Grusec

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Objective: The aim of this study was to develop semi-structured Domains of Socialization Interview and its coding manual and to test their psychometric properties. Domains of Socialization Interview was designed to assess maternal awareness regarding effective parenting in five socialization domains (protection, mutual reciprocity, control, guided learning, and group participation) within the framework of the domains-of-socialization approach. Method: A series of two studies were conducted to develop and validate the interview and its coding manual. The pilot study, sampled 13 mothers of preschool-aged children, was conducted to develop the assessment tools and to test their function and clarity. Participants of the main study were 82 Turkish mothers (Xage = 34.25, SD = 3.53) who have children aged between 35-76 months (Xage = 50.75, SD = 11.24). Mothers filled in a questionnaire package including Coping with Children’s Negative Emotions Questionnaire, Social Competence and Behavior Evaluation-30, Child Rearing Questionnaire, and Two Dimensional Social Desirability Questionnaire. Afterward, interviews were conducted online by a single interviewer. Interviews were rated independently by two graduate students based on the coding manual. Results: The relationships of the awareness of effective parenting scores to the other measures demonstrate convergent, discriminant, and predictive validity of the coding manual. Intra-class correlation coefficient estimates were ranged between 0.82 and 0.90, showing high interrater reliability of the coding manual. Conclusion: Taken as a whole, the results of these studies demonstrate the validity and reliability of a new and useful interview to measure maternal awareness regarding effective parenting within the framework of the domains-of-socialization approach.

Keywords: domains of socialization, parenting, interview, assessment

Procedia PDF Downloads 189

Authors: Mohd Zubairy Bin Shamsudin

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Work-related Musculoskeletal Disorder (WMSDs) is one of the occupational health problems encountered by workers over the world. In Malaysia, there is increasing in trend over the years, particularly in the manufacturing sectors. Current method to observe workplace WMSDs is self-report questionnaire, observation and direct measurement. Observational method is most frequently used by the researcher and practitioner because of the simplified, quick and versatile when it applies to the worksite. However, there are some limitations identified e.g. some approach does not cover a wide spectrum of biomechanics activity and not sufficiently sensitive to assess the actual risks. This paper elucidates the development of Work Movement Task Analysis (WMTA), which is an observational tool for industrial practitioners’ especially untrained personnel to assess WMSDs risk factors and provide a basis for suitable intervention. First stage of the development protocol involved literature reviews, practitioner survey, tool validation and reliability. A total of six themes/comments were received in face validity stage. New revision of WMTA consisted of four sections of postural (neck, back, shoulder, arms, and legs) and associated risk factors; movement, load, coupling and basic environmental factors (lighting, noise, odorless, heat and slippery floor). For inter-rater reliability study shows substantial agreement among rater with K = 0.70. Meanwhile, WMTA validation shows significant association between WMTA score and self-reported pain or discomfort for the back, shoulder&arms and knee&legs with p<0.05. This tool is expected to provide new workplace ergonomic observational tool to assess WMSDs for the next stage of the case study.

Keywords: assessment, biomechanics, musculoskeletal disorders, observational tools

Procedia PDF Downloads 469

Authors: Swati Bhomia, C. M. Kishtawal, Neeru Jaiswal

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Traditionally, monsoon forecasts have encountered many difficulties that stem from numerous issues such as lack of adequate upper air observations, mesoscale nature of convection, proper resolution, radiative interactions, planetary boundary layer physics, mesoscale air-sea fluxes, representation of orography, etc. Uncertainties in any of these areas lead to large systematic errors. Global circulation models (GCMs), which are developed independently at different institutes, each of which carries somewhat different representation of the above processes, can be combined to reduce the collective local biases in space, time, and for different variables from different models. This is the basic concept behind the multi-model superensemble and comprises of a training and a forecast phase. The training phase learns from the recent past performances of models and is used to determine statistical weights from a least square minimization via a simple multiple regression. These weights are then used in the forecast phase. The superensemble forecasts carry the highest skill compared to simple ensemble mean, bias corrected ensemble mean and the best model out of the participating member models. This approach is a powerful post-processing method for the estimation of weather forecast parameters reducing the direct model output errors. Although it can be applied successfully to the continuous parameters like temperature, humidity, wind speed, mean sea level pressure etc., in this paper, this approach is applied to rainfall, a parameter quite difficult to handle with standard post-processing methods, due to its high temporal and spatial variability. The present study aims at the development of advanced superensemble schemes comprising of 1-5 day daily precipitation forecasts from five state-of-the-art global circulation models (GCMs), i.e., European Centre for Medium Range Weather Forecasts (Europe), National Center for Environmental Prediction (USA), China Meteorological Administration (China), Canadian Meteorological Centre (Canada) and U.K. Meteorological Office (U.K.) obtained from THORPEX Interactive Grand Global Ensemble (TIGGE), which is one of the most complete data set available. The novel approaches include the dynamical model selection approach in which the selection of the superior models from the participating member models at each grid and for each forecast step in the training period is carried out. Multi-model superensemble based on the training using similar conditions is also discussed in the present study, which is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional multi-model ensemble (MME) approaches. Further, a variety of methods that incorporate a 'neighborhood' around each grid point which is available in literature to allow for spatial error or uncertainty, have also been experimented with the above mentioned approaches. The comparison of these schemes with respect to the observations verifies that the newly developed approaches provide more unified and skillful prediction of the summer monsoon (viz. June to September) rainfall compared to the conventional multi-model approach and the member models.

Keywords: multi-model superensemble, dynamical model selection, similarity criteria, neighborhood technique, rainfall prediction

Procedia PDF Downloads 139

Authors: Benjamin O. Ehigie, Aderemi Alarape, Nyitor Shenge, Sylvester A. Okhakhume, Timileyin Fashola, Fiyinfunjah Dosumu

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Organisational citizenship behaviour (OCB) is a construct in industrial and organisational behaviour that explains a person's voluntary commitment within an organisation, which is outside the scope of his or her contractual tasks. To attain organisational effectiveness the human factor of production is inevitable, hence the importance of employee behaviour. While the concept of organisational citizenship behavior is mostly discussed in the context of the workplace, it is reasoned that the idea could be reflective in relation to national commitment. Many developing countries in Africa, including Nigeria, suffer economic hardship today not necessarily due to poor resources but bad management of the resources. The mangers of their economies are not committed to the tenets of economic growth but engrossed in fraud, corruption, bribery, and other economic vices. It is this backdrop that necessitated the development and validation of the Ibadan-Nigeria Citizenship Behaviour (I-NCB) Scale. The study adopted a cross-sectional survey (online) research design, using 2404 postgraduate students in the Premier University of the country, with 99.2% being Nigerians and 0.8% non-Nigerians. Gender composition was 1,439 (60%) males and 965 (40%) females, 1201 (50%) were employed while 1203 50% unemployed, 74.2% of the employed were in public paid employment, 19.5% in private sector, and 6.3% were self-employed. Through literature review, 78 items were generated. Using 10 lecturers and 21 students, content and face validity were established respectively. Data collected were subjected to reliability and factor analytic statistics at p < .05 level of significance. Results of the content and face validity at 80% level of item acceptance resulted to 60 items; this was further reduced to 50 after item-total correlation using r=.30 criterion. Divergent validity of r= -.28 and convergent validity of r= .44 were obtained by correlating the I-NCB scale with standardized Counterproductive work behaviour (CWB) scale and Organisational Citizenship Behaviour (OCB) scale among the workers. The reliability coefficients obtained were; Cronbach alpha of internal consistency (α = 0.941) and split-half reliability of r = 0.728. Factor analyses of the I-NCB scale with principal component and varimax rotation yielded five factors when Eigenvalue above 1 were extracted. The factors which accounted for larger proportions of the total variance were given factor names as; Altruistic, Attachment, Affective, Civic responsibility and Allegiance. As much as there are vast journals on citizenship behaviour in organisations, there exists no standardized tool to measure citizenship behaviour of a country. The Ibadan-Nigeria Citizenship Behaviour (I-NCB) scale was consequently developed. The scale could be used to select personnel into political positions and senior administrative positions among career workers in Nigeria, with the aim of determining national commitment to service.

Keywords: counterproductive work behaviour, CWB, Nigeria Citizenship Behaviour, organisational citizenship behaviour, OCB, Ibadan

Procedia PDF Downloads 249

Authors: Mario M. Figueroa de la Cruz, Claudia I. Solorzano, Raul Acosta, Ignacio Funes

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This technological development project seeks to create a tool that allows companies, in need of implementing a Data Center, intelligently determining factors for allocating resources support cooling and power supply (UPS) in its conception. The results should show clearly the speed, robustness and reliability of a system designed for deployment in environments where they must manage and protect large volumes of data.

Keywords: telecommunications, data center, fuzzy logic, expert systems

Procedia PDF Downloads 345

Authors: Machimontorn Promtong, Sherman C. P. Cheung, Guan H. Yeoh, Sara Vahaji, Jiyuan Tu

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The wide range of industrial applications involved with boiling flows promotes the necessity of establishing fundamental knowledge in boiling flow phenomena. For this purpose, a number of experimental and numerical researches have been performed to elucidate the underlying physics of this flow. In this paper, the improved wall boiling models, implemented on ANSYS CFX 14.5, were introduced to study subcooled boiling flow at elevated pressure. At the heated wall boundary, the Fractal model, Force balance approach and Mechanistic frequency model are given for predicting the nucleation site density, bubble departure diameter, and bubble departure frequency. The presented wall heat flux partitioning closures were modified to consider the influence of bubble sliding along the wall before the lift-off, which usually happens in the flow boiling. The simulation was performed based on the Two-fluid model, where the standard k-ω SST model was selected for turbulence modelling. Existing experimental data at around 5 bars were chosen to evaluate the accuracy of the presented mechanistic approach. The void fraction and Interfacial Area Concentration (IAC) are in good agreement with the experimental data. However, the predicted bubble velocity and Sauter Mean Diameter (SMD) are over-predicted. This over-prediction may be caused by consideration of only dispersed and spherical bubbles in the simulations. In the future work, the important physical mechanisms of bubbles, such as merging and shrinking during sliding on the heated wall will be incorporated into this mechanistic model to enhance its capability for a wider range of flow prediction.

Keywords: subcooled boiling flow, computational fluid dynamics (CFD), mechanistic approach, two-fluid model

Procedia PDF Downloads 317

Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

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1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

Procedia PDF Downloads 122

Authors: Shilpa Kumari, Ramakumar Jayachandran

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Machining of aluminium extrusion profiles in the automotive industry has gained much interest in the last decade, particularly due to the higher utilization of aluminum profiles and the weight reduction benefits it brings. Milling is the most common material removal process, where the rotary milling cutter is moved against a workpiece. The physical contact of the milling cutter to the workpiece increases the friction between them, thereby affecting the longevity of the milling tool and also the surface finish of the workpiece. To minimise this issue, the milling process uses cutting fluids or emulsions; however, the use of emulsion in the process has a negative impact on the environment ( such as consumption of water, oils and the used emulsion needs to be treated before disposal) and also on the personal ( may cause respiratory problems, exposure to microbial toxins generated by bacteria in the emulsions on prolonged use) working close to the process. Furthermore, the workpiece also needs to be cleaned after the milling process, which is not adding value to the process, and the cleaning also disperses mist of emulsion in the working environment. Hydro Extrusion is committed to improving the performance of sustainability from its operations, and with the negative impact of using emulsion in the milling process, a new innovative process- Dry Milling was developed to minimise the impact the cutting fluid brings. In this paper, the authors present one application of dry milling in the machining of tensile specimens in the laboratory. Dry milling is an innovative milling process without the use of any cooling/lubrication and has several advantages. Several million tensile tests are carried out in extrusion laboratories worldwide with the wet milling process. The machining of tensile specimens has a significant impact on the reliability of test results. The paper presents the results for different 6xxx alloys with different wall thicknesses of the specimens, which were machined by both dry and wet milling processes. For both different 6xxx alloys and different wall thicknesses, mechanical properties were similar for samples milled using dry and wet milling. Several tensile specimens were prepared using both dry and wet milling to compare the results, and the outcome showed the dry milling process does not affect the reliability of tensile test results.

Keywords: dry milling, tensile testing, wet milling, 6xxx alloy

Procedia PDF Downloads 198

Authors: Soumiya Bhattacharjee, P. K. Champati Ray, Shovan L. Chattoraj, Mrinmoy Dhara

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The entire Himalayan range is globally renowned for rainfall-induced landslides. The prime focus of the study is to determine rainfall based threshold for initiation of landslides that can be used as an important component of an early warning system for alerting stake holders. This research deals with temporal dimension of slope failures due to extreme rainfall events along the National Highway-58 from Karanprayag to Badrinath in the Garhwal Himalaya, India. Post processed 3-hourly rainfall intensity data and its corresponding duration from daily rainfall data available from Tropical Rainfall Measuring Mission (TRMM) were used as the prime source of rainfall data. Landslide event records from Border Road Organization (BRO) and some ancillary landslide inventory data for 2013 and 2014 have been used to determine Intensity Duration (ID) based rainfall threshold. The derived governing threshold equation, I= 4.738D^-0.025, has been considered for prediction of landslides of the study region. This equation was validated with an accuracy of 70% landslides during August and September 2014. The derived equation was considered for further prediction of landslides of the study region. From the obtained results and validation, it can be inferred that this equation can be used for initiation of landslides in the study area to work as a part of an early warning system. Results can significantly improve with ground based rainfall estimates and better database on landslide records. Thus, the study has demonstrated a very low cost method to get first-hand information on possibility of impending landslide in any region, thereby providing alert and better preparedness for landslide disaster mitigation.

Keywords: landslide, intensity-duration, rainfall threshold, TRMM, slope, inventory, early warning system

Procedia PDF Downloads 272

Authors: Bryce Benson, Sooin Lee, Ashwin Belle

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Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring

Procedia PDF Downloads 187

Authors: Nacera Yassa, Abdelmalek Saidoune, Ghania Ouadfel, Hamza Houassine

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The rapid advancement in electrical technologies has underscored the increasing importance of multiphase machines across various industrial sectors. These machines offer significant advantages in terms of efficiency, compactness, and reliability compared to their single-phase counterparts. However, early detection and diagnosis of electrical faults remain critical challenges to ensure the durability and safety of these complex systems. This paper presents an advanced analytical model for multiphase machines, with a particular focus on dual stator asynchronous machines. The primary objective is to develop a robust diagnostic tool capable of effectively detecting and locating electrical faults in these machines, including short circuits, winding faults, and voltage imbalances. The proposed methodology relies on an analytical approach combining electrical machine theory, modeling of magnetic and electrical circuits, and advanced signal analysis techniques. By employing detailed analytical equations, the developed model accurately simulates the behavior of multiphase machines in the presence of electrical faults. The effectiveness of the proposed model is demonstrated through a series of case studies and numerical simulations. In particular, special attention is given to analyzing the dynamic behavior of machines under different types of faults, as well as optimizing diagnostic and recovery strategies. The obtained results pave the way for new advancements in the field of multiphase machine diagnostics, with potential applications in various sectors such as automotive, aerospace, and renewable energies. By providing precise and reliable tools for early fault detection, this research contributes to improving the reliability and durability of complex electrical systems while reducing maintenance and operation costs.

Keywords: faults, diagnosis, modelling, multiphase machine

Procedia PDF Downloads 63

Authors: Alinka Molnár-Tóth, Tímea Tánczos, Regina Barna, Katalin Jakab, Péter Klivényi

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Supported by neuroscientific findings, the so-called Hub-and-Spoke model of the human semantic system is based on two subcomponents of semantic cognition, namely the semantic control process and semantic representation. Our semantic knowledge is multimodal in nature, as the knowledge system stored in relation to a conception is extensive and broad, while different aspects of the conception may be relevant depending on the purpose. The motivation of our research is to develop a new diagnostic measurement procedure based on the preservation of semantic representation, which is appropriate to the specificities of the Hungarian language and which can be used to compare the non-verbal semantic knowledge of healthy and aphasic persons. The development of the test will broaden the Hungarian clinical diagnostic toolkit, which will allow for more specific therapy planning. The sample of healthy persons (n=480) was determined by the last census data for the representativeness of the sample. Based on the concept of the Pyramids and Palm Tree Test, and according to the characteristics of the Hungarian language, we have elaborated a test based on different types of semantic information, in which the subjects are presented with three pictures: they have to choose the one that best fits the target word above from the two lower options, based on the semantic relation defined. We have measured 5 types of semantic knowledge representations: associative relations, taxonomy, motional representations, concrete as well as abstract verbs. As the first step in our data analysis, we examined the normal distribution of our results, and since it was not normally distributed (p < 0.05), we used nonparametric statistics further into the analysis. Using descriptive statistics, we could determine the frequency of the correct and incorrect responses, and with this knowledge, we could later adjust and remove the items of questionable reliability. The reliability was tested using Cronbach’s α, and it can be safely said that all the results were in an acceptable range of reliability (α = 0.6-0.8). We then tested for the potential gender differences using the Mann Whitney-U test, however, we found no difference between the two (p < 0.05). Likewise, we didn’t see that the age had any effect on the results using one-way ANOVA (p < 0.05), however, the level of education did influence the results (p > 0.05). The relationships between the subtests were observed by the nonparametric Spearman’s rho correlation matrix, showing statistically significant correlation between the subtests (p > 0.05), signifying a linear relationship between the measured semantic functions. A margin of error of 5% was used in all cases. The research will contribute to the expansion of the clinical diagnostic toolkit and will be relevant for the individualised therapeutic design of treatment procedures. The use of a non-verbal test procedure will allow an early assessment of the most severe language conditions, which is a priority in the differential diagnosis. The measurement of reaction time is expected to advance prodrome research, as the tests can be easily conducted in the subclinical phase.

Keywords: communication disorders, diagnostic toolkit, neurorehabilitation, semantic knowlegde

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Authors: Wu Peng, Anders Liljerehn, Martin Magnevall

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In metal cutting, tool wear gradually changes the micro geometry of the cutting edge. Today there is a significant gap in understanding the impact these geometrical changes have on the cutting forces which governs tool deflection and heat generation in the cutting zone. Accurate models and understanding of the interaction between the work piece and cutting tool leads to improved accuracy in simulation of the cutting process. These simulations are useful in several application areas, e.g., optimization of insert geometry and machine tool monitoring. This study aims to develop an extended Kienzle force model to account for the effect of rake angle variations and tool flank wear have on the cutting forces. In this paper, the starting point sets from cutting force measurements using orthogonal turning tests of pre-machined flanches with well-defined width, using triangular coated inserts to assure orthogonal condition. The cutting forces have been measured by dynamometer with a set of three different rake angles, and wear progression have been monitored during machining by an optical measuring collaborative robot. The method utilizes the measured cutting forces with the inserts flank wear progression to extend the mechanistic cutting forces model with flank wear as an input parameter. The adapted cutting forces model is validated in a turning process with commercial cutting tools. This adapted cutting forces model shows the significant capability of prediction of cutting forces accounting for tools flank wear and different-rake-angle cutting tool inserts. The result of this study suggests that the nonlinear effect of tools flank wear and interaction between the work piece and the cutting tool can be considered by the developed cutting forces model.

Keywords: cutting force, kienzle model, predictive model, tool flank wear

Procedia PDF Downloads 108

Authors: Amani Kappi, Ana Maria Linares, Gia Mudd-Martin

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Children with Attention Deficit Hyperactivity Disorder (ADHD) are more likely to develop social skills deficits that can lead to academic underachievement, peer rejection, and maladjustment. Surveying teachers about children's social skills with ADHD will become a significant factor in identifying whether the children will be diagnosed with social skills deficits. The teacher-specific version of the Social Skills Rating System scale (SSRS-T) has been used as a screening tool for children's social behaviors. The psychometric properties of the SSRS-T have been evaluated in various populations and settings, such as when used by teachers to assess social skills for children with learning disabilities. However, few studies have been conducted to examine the psychometric properties of the SSRS-T when used to assess children with ADHD. The purpose of this study was to examine the psychometric properties of the SSRS-T and two SSRS-T subscales, Social Skills and Problem Behaviors. This was a secondary analysis of longitudinal data from the Fragile Families and Child Well-Being Study. This study included a sample of 194 teachers who used the SSRS-T to assess the social skills of children aged 8 to 10 years with ADHD. Exploratory principal components factor analysis was used to assess the construct validity of the SSRS-T scale. Cronbach’s alpha value was used to assess the internal consistency reliability of the total SSRS-T scale and the subscales. Item analyses included item-item intercorrelations, item-to-subscale correlations, and Cronbach’s alpha value changes with item deletion. The results of internal consistency reliability for both the total scale and subscales were acceptable. The results of the exploratory factor analysis supported the five factors of SSRS-T (Cooperation, Self-control, Assertion, Internalize behaviors, and Externalize behaviors) reported in the original version. Findings indicated that SSRS-T is a reliable and valid tool for assessing the social behaviors of children with ADHD.

Keywords: ADHD, children, social skills, SSRS-T, psychometric properties

Procedia PDF Downloads 131

Authors: Rishi Agarwal

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Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

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Authors: Fangliang Chen, Yihe Huang, Tejav Deganyar, Anselm Boehm, Hamid Batoul

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With growing population density and further urbanization, the average noise level in cities is increasing. Excessive noise is not only annoying but also leads to a negative impact on human health. To deal with the increasing city noise, environmental regulations bring up higher standards on acoustic comfort in buildings by mitigating the noise transmission from building envelope exterior to interior. Framed window, door and façade systems are the leading choice for modern fenestration construction, which provides demonstrated quality of weathering reliability, environmental efficiency, and installation ease. The overall sound insulation of such systems depends both on glasses and frames, where glass usually covers the majority of the exposed surfaces, thus it is the main source of sound energy transmission. While frames in modern façade systems become slimmer for aesthetic appearance, which contribute to a minimal percentage of exposed surfaces. Nevertheless, frames might provide substantial transmission paths for sound travels through because of much less mass crossing the path, thus becoming more critical in limiting the acoustic performance of the whole system. There are various methodologies and numerical programs that can accurately predict the acoustic performance of either glasses or frames. However, due to the vast variance of size and dimension between frame and glass in the same system, there is no satisfactory theoretical approach or affordable simulation tool in current practice to access the over acoustic performance of a whole façade system. For this reason, laboratory test turns out to be the only reliable source. However, laboratory test is very time consuming and high costly, moreover different lab might provide slightly different test results because of varieties of test chambers, sample mounting, and test operations, which significantly constrains the early phase design of framed façade systems. To address this dilemma, this study provides an effective analytical methodology to predict the acoustic performance of framed façade systems, based on vast amount of acoustic test results on glass, frame and the whole façade system consist of both. Further test results validate the current model is able to accurately predict the overall sound transmission loss of a framed system as long as the acoustic behavior of the frame is available. Though the presented methodology is mainly developed from façade systems with aluminum frames, it can be easily extended to systems with frames of other materials such as steel, PVC or wood.

Keywords: city noise, building facades, sound mitigation, sound transmission loss, framed façade system

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Authors: Vipul M. Patel, Hemantkumar B. Mehta

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Technological innovations in electronic world demand novel, compact, simple in design, less costly and effective heat transfer devices. Closed Loop Pulsating Heat Pipe (CLPHP) is a passive phase change heat transfer device and has potential to transfer heat quickly and efficiently from source to sink. Thermal performance of a CLPHP is governed by various parameters such as number of U-turns, orientations, input heat, working fluids and filling ratio. The present paper is an attempt to predict the thermal performance of a CLPHP using Artificial Neural Network (ANN). Filling ratio and heat input are considered as input parameters while thermal resistance is set as target parameter. Types of neural networks considered in the present paper are radial basis, generalized regression, linear layer, cascade forward back propagation, feed forward back propagation; feed forward distributed time delay, layer recurrent and Elman back propagation. Linear, logistic sigmoid, tangent sigmoid and Radial Basis Gaussian Function are used as transfer functions. Prediction accuracy is measured based on the experimental data reported by the researchers in open literature as a function of Mean Absolute Relative Deviation (MARD). The prediction of a generalized regression ANN model with spread constant of 4.8 is found in agreement with the experimental data for MARD in the range of ±1.81%.

Keywords: ANN models, CLPHP, filling ratio, generalized regression, spread constant

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Authors: Takuro Kida, Yuichi Kida

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We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

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Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan

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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.

Keywords: forensic odontology, age estimation, North India, teeth

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Authors: Sandra Cecilia Muhirirwe, Bart Van Der Bruggen, Violet Kisakye

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Economic analysis of Rainwater harvesting (RWH) systems is vital in search of a cost-effective solution to water unreliability, especially in low-income countries. There is little literature focusing on the financial aspects of RWH for dairy farmers. The main purpose was to assess the economic viability of rainwater harvesting for diary framers in the Rwenzori region. The study focused on the use of rainwater harvesting systems from the rooftop and collection in above surface tanks. Daily rainfall time series for 12 years was obtained across nine gauging stations. The daily water balance equation was used for optimal sizing of the tank. Economic analysis of the investment was carried out based on the life cycle costs and the accruing benefits for the period of 15 years. Roof areas were varied from 75m2 as the minimum required area to 500m2 while maintaining the same number of cattle and keeping the daily water demand constant. The results show that the required rainwater tank sizes are very large and may be impractical to install due to the strongly varying terrain and the initial cost of investment. In all districts, there is a significant reduction of the volume of the required tank with an increasing collection area. The results further show that increasing the collection area has a minor effect on reducing the required tank size. Generally, for all rainfall areas, the reliability increases with an increase in the roof area. The results indicate that 100% reliability can only be realized with very large collection areas that are impractical to install. The estimated benefits outweigh the cost of investment. The Present Net Value shows that the investment is economically viable and investment with a short payback of a maximum of 3 years for all the time series in the study area.

Keywords: dairy cattle, optimisation, rainwater harvesting, economic analysis

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Natalia Feruleva

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The goal of this paper is to develop the fraud risk assessment model basing on both relational and financial data and test the impact of the relationships between Russian non-public companies on the likelihood of financial fraud commitment. Relationships mean various linkages between companies such as parent-subsidiary relationship and person-related relationships. These linkages may provide additional opportunities for committing fraud. Person-related relationships appear when firms share a director, or the director owns another firm. The number of companies belongs to CEO and managed by CEO, the number of subsidiaries was calculated to measure the relationships. Moreover, the dummy variable describing the existence of parent company was also included in model. Control variables such as financial leverage and return on assets were also implemented because they describe the motivating factors of fraud. To check the hypotheses about the influence of the chosen parameters on the likelihood of financial fraud, information about person-related relationships between companies, existence of parent company and subsidiaries, profitability and the level of debt was collected. The resulting sample consists of 160 Russian non-public firms. The sample includes 80 fraudsters and 80 non-fraudsters operating in 2006-2017. The dependent variable is dichotomous, and it takes the value 1 if the firm is engaged in financial crime, otherwise 0. Employing probit model, it was revealed that the number of companies which belong to CEO of the firm or managed by CEO has significant impact on the likelihood of financial fraud. The results obtained indicate that the more companies are affiliated with the CEO, the higher the likelihood that the company will be involved in financial crime. The forecast accuracy of the model is about is 80%. Thus, the model basing on both relational and financial data gives high level of forecast accuracy.

Keywords: financial fraud, fraud prediction, non-public companies, regression analysis, relational data

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Authors: Sreto Boljevic

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In the absence of considerable investment in electricity generation, transmission and distribution network (DN) capacity, the demand for electrical energy will quickly strain the capacity of the existing electrical power network. With anticipated growth and proliferation of Electric vehicles (EVs) and Heat pump (HPs) identified the likelihood that the additional load from EV changing and the HPs operation will require capital investment in the DN. While an area-wide implementation of EVs and HPs will contribute to the decarbonization of the energy system, they represent new challenges for the existing low-voltage (LV) network. Distributed energy resources (DER), operating both as part of the DN and in the off-network mode, have been offered as a means to meet growing electricity demand while maintaining and ever-improving DN reliability, resiliency and power quality. DN planning has traditionally been done by forecasting future growth in demand and estimating peak load that the network should meet. However, new problems are arising. These problems are associated with a high degree of proliferation of EVs and HPs as load imposes on DN. In addition to that, the promotion of electricity generation from renewable energy sources (RES). High distributed generation (DG) penetration and a large increase in load proliferation at low-voltage DNs may have numerous impacts on DNs that create issues that include energy losses, voltage control, fault levels, reliability, resiliency and power quality. To mitigate negative impacts and at a same time enhance positive impacts regarding the new operational state of DN, CHP system integration can be seen as best action to postpone/reduce capital investment needed to facilitate promotion and maximize benefits of EVs, HPs and RES integration in low-voltage DN. The aim of this paper is to generate an algorithm by using an analytical approach. Algorithm implementation will provide a way for optimal placement of the CHP system in the DN in order to maximize the integration of RES and increase in proliferation of EVs and HPs.

Keywords: combined heat & power (CHP), distribution networks, EVs, HPs, RES

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Authors: Lin Fu-Gong

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Background: As WHO announced, people with intellectual disability (ID) were vulnerable to mental health problems. And there were few custom-made mental health scales for those people to monitor their mental health. Those people with mental problems often accompanied worse prognosis and usually became to be a heavier burden on the caregivers. Purpose: In this study, we intend to develop a psychopathy scale as a practical tool for monitoring the mental health for people with ID living in institute. Methods: In this study, we adopt the Psychopathology Inventory for Mentally Retarded Adults developed by professor Matson with certified reliability and validity in Western countries with Dr. Matson’s agreement in advance. We first translated the inventory into Chinese validated version considering the domestic culture background in the past year. And the validity and reliability evaluation of mental health status using this inventory among the people with intellectual living in the institute were done. Results: The inventory includes eight psychiatric disorder scales as schizophrenic, affective, psychosexual, adjustment, anxiety, somatoform, personality disorders and inappropriate mental adjustment. Around 83% of 40 invested people, who randomly selected from the institute, were found to have at least one disorder who were recommended with medical help by two evaluators. Among the residents examined, somatoform disorder and inappropriate mental adjustment were most popular with 60% and 78% people respectively. Conclusion: The result showed the prevalence psychiatric disorders were relatively high among people with ID in institute and the mental problems need to be further cared and followed for their mental health. The results showed that the psychopathology inventory was a useful tool for institute caregiver, manager and for long-term care policy to the government. In the coming stage, we plan to extend the use of the valid Chinese version inventory among more different type institutes for people with ID to establish their dynamic mental health status including medical need, relapse and rehabilitation to promote their mental health.

Keywords: intellectual disability, psychiatric disorder, psychopathology inventory, mental health, the institute

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Authors: Annika J. Meyer, Tom Piechotta

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Unmanned Aerial Vehicles (UAVs) have increasingly become vital tools in various applications, including surveillance, search and rescue, and environmental monitoring. One common approach to ensure redundant communication systems when flying beyond visual line of sight is for UAVs to employ multiple mobile data modems by different providers. Although widely adopted, this approach suffers from several drawbacks, such as high costs, added weight and potential increases in signal interference. In light of these challenges, this paper proposes a communication framework intermeshing mobile networks and LoRa (Long Range) technology—a low-power, long-range communication protocol. LoRaWAN (Long Range Wide Area Network) is commonly used in Internet of Things applications, relying on stationary gateways and Internet connectivity. This paper, however, utilizes the underlying LoRa protocol, taking advantage of the protocol’s low power and long-range capabilities while ensuring efficiency and reliability. Conducted in collaboration with the Potsdam Fire Department, the implementation of mobile network technology in combination with the LoRa protocol in small UAVs (take-off weight < 0.4 kg), specifically designed for search and rescue and area monitoring missions, is explored. This research aims to test the viability of LoRa as an additional redundant communication system during UAV flights as well as its intermeshing with the primary, mobile network-based controller. The methodology focuses on direct UAV-to-UAV and UAV-to-ground communications, employing different spreading factors optimized for specific operational scenarios—short-range for UAV-to-UAV interactions and long-range for UAV-to-ground commands. This explored use case also dramatically reduces one of the major drawbacks of LoRa communication systems, as a line of sight between the modules is necessary for reliable data transfer. Something that UAVs are uniquely suited to provide, especially when deployed as a swarm. Additionally, swarm deployment may enable UAVs that have lost contact with their primary network to reestablish their connection through another, better-situated UAV. The experimental setup involves multiple phases of testing, starting with controlled environments to assess basic communication capabilities and gradually advancing to complex scenarios involving multiple UAVs. Such a staged approach allows for meticulous adjustment of parameters and optimization of the communication protocols to ensure reliability and effectiveness. Furthermore, due to the close partnership with the Fire Department, the real-world applicability of the communication system is assured. The expected outcomes of this paper include a detailed analysis of LoRa's performance as a communication tool for UAVs, focusing on aspects such as signal integrity, range, and reliability under different environmental conditions. Additionally, the paper seeks to demonstrate the cost-effectiveness and operational efficiency of using a single type of communication technology that reduces UAV payload and power consumption. By shifting from traditional cellular network communications to a more robust and versatile cellular and LoRa-based system, this research has the potential to significantly enhance UAV capabilities, especially in critical applications where reliability is paramount. The success of this paper could pave the way for broader adoption of LoRa in UAV communications, setting a new standard for UAV operational communication frameworks.

Keywords: LoRa communication protocol, mobile network communication, UAV communication systems, search and rescue operations

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Authors: Kibrom Hadush

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Countries like Ethiopia, whose economy is mainly rain-fed dependent agriculture, are highly vulnerable to climate variability and weather extremes. Sub-seasonal (monthly) and dekadal forecasts are hence critical for crop production and water resource management. Therefore, this paper was conducted to study the predictability and variability of Kiremt rainfall over the northern half of Ethiopia on monthly and dekadal time scales in association with global Sea Surface Temperature (SST) at different lag time. Trends in rainfall have been analyzed on annual, seasonal (Kiremt), monthly, and dekadal (June–September) time scales based on rainfall records of 36 meteorological stations distributed across four homogenous zones of the northern half of Ethiopia for the period 1992–2017. The results from the progressive Mann–Kendall trend test and the Sen’s slope method shows that there is no significant trend in the annual, Kiremt, monthly and dekadal rainfall total at most of the station's studies. Moreover, the rainfall in the study area varies spatially and temporally, and the distribution of the rainfall pattern increases from the northeast rift valley to northwest highlands. Methods of analysis include graphical correlation and multiple linear regression model are employed to investigate the association between the global SSTs and Kiremt rainfall over the homogeneous rainfall zones and to predict monthly and dekadal (June-September) rainfall using SST predictors. The results of this study show that in general, SST in the equatorial Pacific Ocean is the main source of the predictive skill of the Kiremt rainfall variability over the northern half of Ethiopia. The regional SSTs in the Atlantic and the Indian Ocean as well contribute to the Kiremt rainfall variability over the study area. Moreover, the result of the correlation analysis showed that the decline of monthly and dekadal Kiremt rainfall over most of the homogeneous zones of the study area are caused by the corresponding persistent warming of the SST in the eastern and central equatorial Pacific Ocean during the period 1992 - 2017. It is also found that the monthly and dekadal Kiremt rainfall over the northern, northwestern highlands and northeastern lowlands of Ethiopia are positively correlated with the SST in the western equatorial Pacific, eastern and tropical northern the Atlantic Ocean. Furthermore, the SSTs in the western equatorial Pacific and Indian Oceans are positively correlated to the Kiremt season rainfall in the northeastern highlands. Overall, the results showed that the prediction models using combined SSTs at various ocean regions (equatorial and tropical) performed reasonably well in the prediction (With R2 ranging from 30% to 65%) of monthly and dekadal rainfall and recommends it can be used for efficient prediction of Kiremt rainfall over the study area to aid with systematic and informed decision making within the agricultural sector.

Keywords: dekadal, Kiremt rainfall, monthly, Northern Ethiopia, sea surface temperature

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Authors: Demet Ozgur-Unluakin, Busenur Turkali, S. Caglar Aksezer

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Since the age of industrialization, maintenance has always been a very crucial element for all kinds of factories and plants. With today’s increasingly developing technology, the system structure of such facilities has become more complicated, and even a small operational disruption may return huge losses in profits for the companies. In order to reduce these costs, effective maintenance planning is crucial, but at the same time, it is a difficult task because of the complexity of systems. The most important aspect of correct maintenance planning is to understand the structure of the system, not to ignore the dependencies among the components and as a result, to model the system correctly. In this way, it will be better to understand which component improves the system more when it is maintained. Undoubtedly, proactive maintenance at a scheduled time reduces costs because the scheduled maintenance prohibits high losses in profits. But the necessity of corrective maintenance, which directly affects the situation of the system and provides direct intervention when the system fails, should not be ignored. When a fault occurs in the system, if the problem is not solved immediately and proactive maintenance time is awaited, this may result in increased costs. This study proposes various maintenance methods with different efficiency measures under corrective maintenance strategy on a subsystem of a thermal power plant. To model the dependencies between the components, dynamic Bayesian Network approach is employed. The proposed maintenance methods aim to minimize the total maintenance cost in a planning horizon, as well as to find the most appropriate component to be attacked on, which improves the system reliability utmost. Performances of the methods are compared under corrective maintenance strategy. Furthermore, sensitivity analysis is also applied under different cost values. Results show that all fault effect methods perform better than the replacement effect methods and this conclusion is also valid under different downtime cost values.

Keywords: dynamic Bayesian networks, maintenance, multi-component systems, reliability

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Authors: Léa Caban, Lucile Soudani, Julien Berger, Armelle Nouviaire, Emilio Bastidas-Arteaga

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Improvement of the thermal performance of buildings is a high concern for the construction industry. With the increase in environmental issues, new types of construction materials are being developed. These include bio-based insulation materials. They capture carbon dioxide, can be produced locally, and have good thermal performance. However, their behavior with respect to moisture transfer is still facing some issues. With a high porosity, the mass transfer is more important in those materials than in mineral insulation ones. Therefore, they can be more sensitive to moisture disorders such as mold growth, condensation risks or decrease of the wall energy efficiency. For this reason, the initial moisture content on the construction site is a piece of crucial knowledge. Measuring moisture content in a laboratory is a mastered task. Diverse methods exist but the easiest and the reference one is gravimetric. A material is weighed dry and wet, and its moisture content is mathematically deduced. Non-destructive methods (NDT) are promising tools to determine in an easy and fast way the moisture content in a laboratory or on construction sites. However, the quality and reliability of the measures are influenced by several factors. Classical NDT portable devices usable on-site measure the capacity or the resistivity of materials. Water’s electrical properties are very different from those of construction materials, which is why the water content can be deduced from these measurements. However, most moisture meters are made to measure wooden materials, and some of them can be adapted for construction materials with calibration curves. Anyway, these devices are almost never calibrated for insulation materials. The main objective of this study is to determine the reliability of moisture meters in the measurement of biobased insulation materials. The determination of which one of the capacitive or resistive methods is the most accurate and which device gives the best result is made. Several biobased insulation materials are tested. Recycled cotton, two types of wood fibers of different densities (53 and 158 kg/m3) and a mix of linen, cotton, and hemp. It seems important to assess the behavior of a mineral material, so glass wool is also measured. An experimental campaign is performed in a laboratory. A gravimetric measurement of the materials is carried out for every level of moisture content. These levels are set using a climatic chamber and by setting the relative humidity level for a constant temperature. The mass-based moisture contents measured are considered as references values, and the results given by moisture meters are compared to them. A complete analysis of the uncertainty measurement is also done. These results are used to analyze the reliability of moisture meters depending on the materials and their water content. This makes it possible to determine whether the moisture meters are reliable, and which one is the most accurate. It will then be used for future measurements on construction sites to assess the initial hygrothermal state of insulation materials, on both new-build and renovation projects.

Keywords: capacitance method, electrical resistance method, insulation materials, moisture transfer, non-destructive testing

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Authors: Selvam M., Vadthya Poornachandar, Surender Singh

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These Reclaimed Asphalt Pavement (RAP) aggregates are generally dumped in the open area after the demolition of Asphalt Pavements. The utilization of RAP aggregates in cement concrete pavements may provide several socio-economic-environmental benefits and could embrace the circular economy. The cross recycling of RAP aggregates in the concrete pavement could reduce the consumption of virgin aggregates and saves the fertile land. However, the structural, as well as functional properties of RAP-concrete could be significantly lower than the conventional Pavement Quality Control (PQC) pavements. This warrants judicious selection of RAP fraction (coarse and fine aggregates) along with the accurate proportion of the same for PQC highways. Also, the selection of the RAP fraction and its proportion shall not be solely based on the mechanical properties of RAP-concrete specimens but also governed by the structural and functional behavior of the pavement system. In this study, an effort has been made to predict the optimum RAP fraction and its corresponding proportion for cement concrete pavements by considering the low-volume and high-volume roads. Initially, the effect of inclusions of RAP on the fresh and mechanical properties of concrete pavement mixes is mapped through an extensive literature survey. Almost all the studies available to date are considered for this study. Generally, Indian Roads Congress (IRC) methods are the most widely used design method in India for the analysis of concrete pavements, and the same has been considered for this study. Subsequently, fatigue damage analysis is performed to evaluate the required safe thickness of pavement slab for different fractions of RAP (coarse RAP). Consequently, the performance of RAP-concrete is predicted by employing the AASHTO-1993 model for the following distresses conditions: faulting, cracking, and smoothness. The performance prediction and total cost analysis of RAP aggregates depict that the optimum proportions of coarse RAP aggregates in the PQC mix are 35% and 50% for high volume and low volume roads, respectively.

Keywords: concrete pavement, RAP aggregate, performance prediction, pavement design

Procedia PDF Downloads 158