Search results for: gaussian process prior

Authors: Michael C. Nwaohia

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Whereas anecdotal evidence supports the notion that increase in information technology (IT) know-how may enhance output of professionals in the accounting sector, this has not been systematically explored in the Nigerian context. Against this background, this paper examines the correlation between knowledgeability of IT and level of performance at everyday auditing and accounting tasks. It utilizes primary and secondary data from selected business organizations in Lagos, Nigeria. Accounting staff were administered structured questionnaires which, amongst other things, sought to examine knowledge and exposure to information technology prior to joining the firms and current level of performance based on self-reporting and supervisor comments. In addition, exposure to on-the-job IT training and current level of performance was examined. The statistical analysis of the data was done using the SPSS package. The results strongly suggest that prior exposure to IT skills enabled accounting professionals to better flexibly fit into the dynamic environment in which contemporary business takes place. Ultimately, the paper attempts to explicate some of the implications of these findings for individuals and business firms.

Keywords: accounting, firms, information technology, professional behavior

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Authors: Lenka Krsnakova, Petr Jirsak

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Prior research on facility location in supply chain is mostly focused on improvement of mathematical models. It is due to the fact that supply chain design has been for the long time the area of operational research that underscores mainly quantitative criteria. Qualitative criteria are still highly neglected within the supply chain design research. Facility location in the supply chain has become multi-criteria decision-making problem rather than single criteria decision due to changes of market conditions. Thus, both qualitative and quantitative criteria have to be included in the decision making process. The aim of this study is to emphasize the importance of qualitative criteria as key parameters of relevant mathematical models. We examine which criteria are taken into consideration when Czech companies decide about their facility location. A literature review on criteria being used in facility location decision making process creates a theoretical background for the study. The data collection was conducted through questionnaire survey. Questionnaire was sent to manufacturing and business companies of all sizes (small, medium and large enterprises) with the representation in the Czech Republic within following sectors: automotive, toys, clothing industry, electronics and pharmaceutical industry. Comparison of which criteria prevail in the current research and which are considered important by companies in the Czech Republic is made. Despite the number of articles focused on supply chain design, only minority of them consider qualitative criteria and rarely process supply chain design as a multi-criteria decision making problem. Preliminary results of the questionnaire survey outlines that companies in the Czech Republic see the qualitative criteria and their impact on facility location decision as crucial. Qualitative criteria as company strategy, quality of working environment or future development expectations are confirmed to be considered by Czech companies. This study confirms that the qualitative criteria can significantly influence whether a particular location could or could not be right place for a logistic facility. The research has two major limitations: researchers who focus on improving of mathematical models mostly do not mention criteria that enter the model. Czech supply chain managers selected important criteria from the group of 18 available criteria and assign them importance weights. It does not necessarily mean that these criteria were taken into consideration when the last facility location was chosen, but how they perceive that today. Since the study confirmed the necessity of future research on how qualitative criteria influence decision making process about facility location, the authors have already started in-depth interviews with participating companies to reveal how the inclusion of qualitative criteria into decision making process about facility location influence the company´s performance.

Keywords: criteria influencing facility location, Czech Republic, facility location decision-making, qualitative criteria

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Authors: Siti Nur Izati Azmi, Zatil Afifah Omar, Kathi Swaran, Navin Kumar

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Organophilic Clays, also known as Organoclays, is used as a viscosifier in Oil based Drilling fluids. Most often, Organophilic clay are produced from modified Sodium and Calcium based Bentonite. Many studies and data show that Organophilic Clay using Hectorite based clays provide the best yield and good fluid loss properties in an oil-based drilling fluid at a higher cost. In terms of the manufacturing process, the two common methods of manufacturing organophilic clays are a Wet Process and a Dry Process. Wet process is known to produce better performance product at a higher cost while Dry Process shorten the production time. Hence, the purpose of this study is to evaluate the various formulation of an organophilic clay and its performance vs. the cost, as well as to determine the most efficient and cost-effective method of manufacturing organophilic clays.

Keywords: organophilic clay, viscosifier, wet process, dry process

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Authors: Jéssica Ferreira Rodrigues, Júlio Silvio De Sousa Bueno Filho, Vanessa Rios De Souza, Ana Carla Marques Pinheiro

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This study aimed to evaluate the implications of the block size and testing order on efficiency and precision of preference estimation for Dulce de leche samples. Efficiency was defined as the inverse of the average variance of pairwise comparisons among treatments. Precision was defined as the inverse of the variance of treatment means (or effects) estimates. The experiment was originally designed to test 16 treatments as a series of 8 Sudoku 16x16 designs being 4 randomized independently and 4 others in the reverse order, to yield balance in testing order. Linear mixed models were assigned to the whole experiment with 112 testers and all their grades, as well as their partially balanced subgroups, namely: a) experiment with the four initial EU; b) experiment with EU 5 to 8; c) experiment with EU 9 to 12; and b) experiment with EU 13 to 16. To record responses we used a nine-point hedonic scale, it was assumed a mixed linear model analysis with random tester and treatments effects and with fixed test order effect. Analysis of a cumulative random effects probit link model was very similar, with essentially no different conclusions and for simplicity, we present the results using Gaussian assumption. R-CRAN library lme4 and its function lmer (Fit Linear Mixed-Effects Models) was used for the mixed models and libraries Bayesthresh (default Gaussian threshold function) and ordinal with the function clmm (Cumulative Link Mixed Model) was used to check Bayesian analysis of threshold models and cumulative link probit models. It was noted that the number of samples tested in the same session can influence the acceptance level, underestimating the acceptance. However, proving a large number of samples can help to improve the samples discrimination.

Keywords: acceptance, block size, mixed linear model, testing order, testing order

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Authors: Melissa Langevin, Natalie Ward, Colleen Fitzgibbons, Christa Ramsey, Melanie Hogue, Anna Theresa Lobos

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Description: Root Cause Analysis (RCA) is used by health care teams to examine adverse events (AEs) to identify causes which then leads to recommendations for prevention Despite widespread use, RCA has limitations. Best practices have not been established for implementing recommendations or tracking the impact of interventions after AEs. During phase 1 of this study, we used simulation to analyze two fictionalized AEs that occurred in hospitalized paediatric patients to identify and understand how the errors occurred and generated recommendations to mitigate and prevent recurrences. Scenario A involved an error of commission (inpatient drug error), and Scenario B involved detecting an error that already occurred (critical care drug infusion error). Recommendations generated were: improved drug labeling, specialized drug kids, alert signs and clinical checklists. Aim: Use simulation to optimize interventions recommended post critical event analysis prior to implementation in the clinical environment. Methods: Suggested interventions from Phase 1 were designed and tested through scenario simulation in the clinical environment (medicine ward or pediatric intensive care unit). Each scenario was simulated 8 times. Recommendations were tested using different, voluntary teams and each scenario was debriefed to understand why the error was repeated despite interventions and how interventions could be improved. Interventions were modified with subsequent simulations until recommendations were felt to have an optimal effect and data saturation was achieved. Along with concrete suggestions for design and process change, qualitative data pertaining to employee communication and hospital standard work was collected and analyzed. Results: Each scenario had a total of three interventions to test. In, scenario 1, the error was reproduced in the initial two iterations and mitigated following key intervention changes. In scenario 2, the error was identified immediately in all cases where the intervention checklist was utilized properly. Independently of intervention changes and improvements, the simulation was beneficial to identify which of these should be prioritized for implementation and highlighted that even the potential solutions most frequently suggested by participants did not always translate into error prevention in the clinical environment. Conclusion: We conclude that interventions that help to change process (epinephrine kit or mandatory checklist) were more successful at preventing errors than passive interventions (signage, change in memory aids). Given that even the most successful interventions needed modifications and subsequent re-testing, simulation is key to optimizing suggested changes. Simulation is a safe, practice changing modality for institutions to use prior to implementing recommendations from RCA following AE reviews.

Keywords: adverse events, patient safety, pediatrics, root cause analysis, simulation

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Authors: I. Hosni, L. Bennaceur Farah, N. Saber, R Bennaceur

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Soil moisture content is a key variable in many environmental sciences. Even though it represents a small proportion of the liquid freshwater on Earth, it modulates interactions between the land surface and the atmosphere, thereby influencing climate and weather. Accurate modeling of the above processes depends on the ability to provide a proper spatial characterization of soil moisture. The measurement of soil moisture content allows assessment of soil water resources in the field of hydrology and agronomy. The second parameter in interaction with the radar signal is the geometric structure of the soil. Most traditional electromagnetic models consider natural surfaces as single scale zero mean stationary Gaussian random processes. Roughness behavior is characterized by statistical parameters like the Root Mean Square (RMS) height and the correlation length. Then, the main problem is that the agreement between experimental measurements and theoretical values is usually poor due to the large variability of the correlation function, and as a consequence, backscattering models have often failed to predict correctly backscattering. In this study, surfaces are considered as band-limited fractal random processes corresponding to a superposition of a finite number of one-dimensional Gaussian process each one having a spatial scale. Multiscale roughness is characterized by two parameters, the first one is proportional to the RMS height, and the other one is related to the fractal dimension. Soil moisture is related to the complex dielectric constant. This multiscale description has been adapted to two-dimensional profiles using the bi-dimensional wavelet transform and the Mallat algorithm to describe more correctly natural surfaces. We characterize the soil surfaces and sub-surfaces by a three layers geo-electrical model. The upper layer is described by its dielectric constant, thickness, a multiscale bi-dimensional surface roughness model by using the wavelet transform and the Mallat algorithm, and volume scattering parameters. The lower layer is divided into three fictive layers separated by an assumed plane interface. These three layers were modeled by an effective medium characterized by an apparent effective dielectric constant taking into account the presence of air pockets in the soil. We have adopted the 2D multiscale three layers small perturbations model including, firstly air pockets in the soil sub-structure, and then a vegetable canopy in the soil surface structure, that is to simulate the radar backscattering. A sensitivity analysis of backscattering coefficient dependence on multiscale roughness and new soil moisture has been performed. Later, we proposed to change the dielectric constant of the multilayer medium because it takes into account the different moisture values of each layer in the soil. A sensitivity analysis of the backscattering coefficient, including the air pockets in the volume structure with respect to the multiscale roughness parameters and the apparent dielectric constant, was carried out. Finally, we proposed to study the behavior of the backscattering coefficient of the radar on a soil having a vegetable layer in its surface structure.

Keywords: multiscale, bidimensional, wavelets, backscattering, multilayer, SPM, air pockets

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Authors: Davit Mirzoyan, Ararat Khachatryan

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A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

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Authors: Avoseh Funmilola, Osman Khalid, Wayne Nelson, Paramespri Naidoo, Deresh Ramjugernath

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The increasing global atmospheric temperature is of great concern and this has led to the development of technologies to reduce the emission of greenhouse gases into the atmosphere. Flue gas emissions from fossil fuel combustion are major sources of greenhouse gases. One of the ways to reduce the emission of CO₂ from flue gases is by post combustion capture process and this can be done by absorbing the gas into suitable chemical solvents before emitting the gas into the atmosphere. Alkanolamines are promising solvents for this capture process. Vapour liquid equilibrium of CO₂-alkanolamine systems is often represented by CO₂ loading and partial pressure of CO₂ without considering the liquid phase. The liquid phase of this system is a complex one comprising of 9 species. Online analysis of the process is important to monitor the concentrations of the liquid phase reacting and product species. Liquid phase analysis of CO₂-diethanolamine (DEA) solution was performed by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. A robust Calibration was performed for the CO₂-aqueous DEA system prior to an online monitoring experiment. The partial least square regression method was used for the analysis of the calibration spectra obtained. The models obtained were used for prediction of DEA and CO₂ concentrations in the online monitoring experiment. The experiment was performed with a newly built recirculating experimental set up in the laboratory. The set up consist of a 750 ml equilibrium cell and ATR-FTIR liquid flow cell. Measurements were performed at 400°C. The results obtained indicated that the FTIR spectroscopy combined with Partial least square method is an effective tool for online monitoring of speciation.

Keywords: ATR-FTIR, CO₂ capture, online analysis, PLS regression

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Authors: Ceren Vardar Acar, Mine Caglar

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In finance, the price of a volatile asset can be modeled using fractional Brownian motion (fBm) with Hurst parameter H>1/2. The Black-Scholes model for the values of returns of an asset using fBm is given as, 〖Y_t=Y_0 e^((r+μ)t+σB)〗_t^H, 0≤t≤T where Y_0 is the initial value, r is constant interest rate, μ is constant drift and σ is constant diffusion coefficient of fBm, which is denoted by B_t^H where t≥0. Black-Scholes model can be constructed with some Markov processes such as Brownian motion. The advantage of modeling with fBm to Markov processes is its capability of exposing the dependence between returns. The real life data for a volatile asset display long-range dependence property. For this reason, using fBm is a more realistic model compared to Markov processes. Investors would be interested in any kind of information on the risk in order to manage it or hedge it. The maximum possible loss is one way to measure highest possible risk. Therefore, it is an important variable for investors. In our study, we give some theoretical bounds on the distribution of maximum possible loss of fBm. We provide both asymptotical and strong estimates for the tail probability of maximum loss of standard fBm and fBm with drift and diffusion coefficients. In the investment point of view, these results explain, how large values of possible loss behave and its bounds.

Keywords: maximum drawdown, maximum loss, fractional brownian motion, large deviation, Gaussian process

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Authors: Ying Chuan Wang, Zhang Tao

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The aim of this article intends to explore the differences of well-being of employees in casino hotels before and after the Chinese government began to fight corruption. This researcher also attempted to find out the relationship between work pressure and well-being of employees in gambling workplaces before and after the Chinese government crackdowns the corruption. The category of well-being including life well-being, workplace well-being, and psychological well-being was included for analyzing well-being of employees in gaming workplaces. In addition, the psychological pressure classification was applied into this study and the Job Content Questionnaire (JCQ) would be adopted on investigating employees’ work pressure in terms of decision latitude, psychological demands, and workplace support. This study is a quantitative approach research and was conducted in March 2017. A purposive sampling was used in this study. A total of valid 339 responses were collected and the participants were casino hotel employees. The findings showed that decision latitude was significantly different prior to and after Chinese government crackdowns on corruption. Moreover, workplace support was strongly significantly related to employees’ well-being before Chinese government crackdowns. Decision latitude was strongly significantly related to employees’ well-being after Chinese government crackdowns. The findings suggest that employees’ work pressure affects their well being. In particular, because of workplace supports, it may alleviate employees’ work pressure and affect their perceptions of well-being but only prior to fighting the crackdowns. Importantly, decision latitude has become an essential factor affecting their well-being after the crackdown. It is finally hoped that the findings of this study provide suggestion to the managerial levels of hospitality industries. It is important to enhance employees’ decision latitude. Offering training courses to equip employees’ skills could be a possible way to reduce work pressure. In addition, establishing career path for the employees to pursuit is essential for their self-development and the improvement of well being. This would be crucial for casino hotels’ sustainable development and strengthening their competitiveness.

Keywords: well-being, work pressure, Casino hotels’ employees, gaming workplace

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Authors: Aboli Gore, Aritra Das, Sunil Sonthalia, Tanmay Mahapatra, Sridhar Srikantiah, Hemant Shah

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AMANAT is an initiative, taken in collaboration with the Government of Bihar, aimed at improving the Quality of Maternal and Neonatal care services at Bihar’s public health facilities – those offering either the Basic Emergency Obstetric and Neonatal care (BEmONC) or Comprehensive Emergency Obstetric and Neonatal care (CEmONC) services. The effectiveness of this program is evaluated by conducting cross-sectional assessments at the concerned facilities prior to (baseline) and following completion (endline) of intervention. Direct Observation of Delivery (DOD) methodology is employed for carrying out the baseline and endline assessments – through which key obstetric and neonatal care practices among the Health Care Providers (especially the nurses) are assessed quantitatively by specially trained nursing professionals. Assessment of vitals prior to delivery improved during all three phases of BEmONC and all four phases of CEmONC training with statistically significant improvement noted in: i) pulse measurement in BEmONC phase 2 (9% to 68%), 3 (4% to 57%) & 4 (14% to 59%) and CEmONC phase 2 (7% to 72%) and 3 (0% to 64%); ii) blood pressure measurement in BEmONC phase 2 (27% to 84%), 3 (21% to 76%) & 4 (36% to 71%) and CEmONC phase 2 (23% to 76%) and 3 (2% to 70%); iii) fetal heart rate measurement in BEmONC phase 2 (10% to 72%), 3 (11% to 77%) & 4 (13% to 64%) and CEmONC phase 1 (24% to 38%), 2 (14% to 82%) and 3 (1% to 73%); and iv) abdominal examination in BEmONC phase 2 (14% to 59%), 3 (3% to 59%) & 4 (6% to 56%) and CEmONC phase 1 (0% to 24%), 2 (7% to 62%) & 3 (0% to 62%). Regarding infection control, wearing of apron, mask and cap by the delivery conductors improved significantly in all BEmONC phases. Similarly, the practice of handwashing improved in all BEmONC and CEmONC phases. Even on disaggregation, the handwashing showed significant improvement in all phases but CEmONC phase-4. Not only the positive practices related to handwashing improved but also negative practices such as turning off the tap with bare hands declined significantly in the aforementioned phases. Significant decline was also noted in negative maternal care practices such as application of fundal pressure for hastening the delivery process and administration of oxytocin prior to delivery. One of the notable achievement of AMANAT is an improvement in active management of the third stage of labor (AMTSL). The overall AMTSL (including administration of oxytocin or other uterotonics uterotonic in proper dose, route and time along with controlled cord traction and uterine massage) improved in all phases of BEmONC and CEmONC mentoring. Another key area of improvement, across phases, was in proper cutting/clamping of the umbilical cord. AMANAT mentoring also led to improvement in important immediate newborn care practices such as initiation of skin-to-skin care and timely initiation of breastfeeding. The next phase of the mentoring program seeks to institutionalize mentoring across the state that could potentially perpetuate improvement with minimal external intervention.

Keywords: capacity building, nurse-mentoring, quality of care, pregnancy, newborn care

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Authors: S. Kreitlein, N. Eder, J. Franke

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The importance of energy efficiency within the production process increases steadily. Unfortunately, so far no tools for a comprehensive assessment of energy efficiency within the production process exist. Therefore the Institute for Factory Automation and Production Systems of the Friedrich-Alexander-University Erlangen-Nuremberg has developed two methods with the goal of achieving transparency and a quantitative assessment of energy efficiency: EEV (Energy Efficiency Value) and EPE (Energetic Process Efficiency). This paper describes the basics and state of the art as well as the developed approaches.

Keywords: energy efficiency, energy efficiency value, energetic process efficiency, production

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Authors: Shankar M. Bakkannavar, Smitha Nayak, Vinod C. Nayak, Ravi Bagali

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Attendance rate for hospital outpatient appointments plays a pivotal role in operational efficiency of a hospital. Strategic interventions like ‘reminder systems’ prior to the scheduled appointment has proved to be an effective strategy for outpatient appointment ‘show-ups’. This study is designed with an objective to assess the effectiveness of SMS reminders as an intervention to enhance the effectiveness of hospital outpatient attendance. Method: The survey was conducted at Columbia Asia Hosiptal, Bangalore. We surveyed 60 patients who had a scheduled outpatient appointment in Department of General Medicine, Department of Obstetrics and Gynecology and the Orthopedics department, as these departments had a heavy patient flow and had higher contributions to the top line of the hospital. Results: Majority (64%) of the patients preferred to be sent an SMS reminder on the outpatient appointment schedule. 37 (61%) respondents stated that the ideally, reminders could be effective only if they are sent 24-48 hours prior to the appointment schedule. 41(68%) respondents were of the opinion that a minimum of two reminders would be necessary to ensure patients show up for the appointment. 1% level of significance. It also observed that there is strong association between age and preference on mode of reminder (P=0.002).

Keywords: reminder systems, appointment show-ups, SMS reminders, health Information

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Authors: Xiaohui Zhao, Shahan Mafuz

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Context-aware technologies provide system applications with the awareness of environmental conditions, customer behaviour, object movements, etc. Further, with such capability system applications can be smart to adapt intelligently their responses to the changing conditions. Concerning business operations, this promises businesses that their business processes can run more intelligently, adaptively and flexibly, and thereby either improve customer experience, enhance reliability of service delivery, or lower operational cost, to make the business more competitive and sustainable. Aiming at realizing such context-aware business process management, this paper firstly explores its potential benefit and then identifies some gaps between the current business process management support and the expected. In addition, some preliminary solutions are also discussed with context definition, rule-based process execution, run-time process evolution, etc. A framework is also presented to give a conceptual architecture of context-aware business process management system to guide system implementation.

Keywords: business process adaptation, business process evolution, business process modelling, and context awareness

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Authors: Marcos Devaner, Marcela Alves, Cledson Braga, Fabiano Alves, Wilton Bezerra

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This article discusses the inclusion of people with disabilities in the process of testing an accessible system solution for distance education. The accessible system, team profile, methodologies and techniques covered in the testing process are presented. The testing process shown in this paper was designed from the experience with user. The testing process emerged from lessons learned from past experiences and the end user is present at all stages of the tests. Also, lessons learned are reported and how it was possible the maturing of the team and the methods resulting in a simple, productive and effective process.

Keywords: experience report, accessible systems, software testing, testing process, systems, e-learning

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Authors: Ni Wenli, He Jing

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Repeat–Accumulate (RA) codes are subclass of LDPC codes with fast encoder structures. In this paper, we consider a nonbinary extension of binary LDPC codes over GF(q) and construct a non-binary RA code and a non-binary QC-LDPC code over GF(2^4), we construct non-binary RA codes with linear encoding method and non-binary QC-LDPC codes with algebraic constructions method. And the BER performance of RA and QC-LDPC codes over GF(q) are compared with BP decoding and by simulation over the Additive White Gaussian Noise (AWGN) channels.

Keywords: non-binary RA codes, QC-LDPC codes, performance comparison, BP algorithm

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Authors: Ali Amin

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This paper describes a flipped learning initiative which was trialed in the delivery of the course: structural analysis and modelling. A short series of interactive videos were developed, which introduced the key concepts of each topic. The purpose of the videos was to introduce concepts and give the students more time to develop their thoughts prior to the lecture. This allowed more time for face to face engagement during the lecture. As part of the initial study, videos were developed for half the topics covered. The videos included a short summary of the key concepts ( < 10 mins each) as well as fully worked-out examples (~30mins each). Qualitative feedback was attained from the students. On a scale from strongly disagree to strongly agree, students were rate statements such as 'The pre-class videos assisted your learning experience', 'I felt I could appreciate the content of the lecture more by watching the videos prior to class'. As a result of the pre-class engagement, the students formed more specific and targeted questions during class, and this generated greater comprehension of the material. The students also scored, on average, higher marks in questions pertaining to topics which had videos assigned to them.

Keywords: flipped learning, structural analysis, pre-class videos, engineering education

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Authors: L. M. López López, J. V. Montesdeoca Contreras, A. R. Cuji Fajardo, L. E. Garzón Muñoz, J. I. Fajardo Seminario

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In this article a new method of cleaning process of metal sheets for household appliances was developed, using low-pressure cold plasma. In this context, this research consist in analyze the results of metal sheets cleaning process using plasma and compare with pickling process to determinate the efficiency of each process and the level of contamination produced. Surface Cleaning was evaluated by measuring the contact angle with deionized water, diiodo methane and ethylene glycol, for the calculus of the surface free energy by means of the Fowkes theories and Wu. Showing that low-pressure cold plasma is very efficient both in cleaning process how in environment impact.

Keywords: efficient use of plasma, ecological impact of plasma, metal sheets cleaning means, plasma cleaning process.

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Authors: D. Zhang, H. Y. Du, G. W. Li, J. Zeng, D. W. Zuo, Y. P. You

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For the difficult issues of process selection, case-based reasoning technology is applied to computer aided process planning system for cold form tapping of internal threads on the basis of similarity in the process. A model is established based on the analysis of process planning. Case representation and similarity computing method are given. Confidence degree is used to evaluate the case. Rule-based reuse strategy is presented. The scheme is illustrated and verified by practical application. The case shows the design results with the proposed method are effective.

Keywords: case-based reasoning, internal thread, cold extrusion, process planning

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Authors: Heather Warfield, Brad Genereux

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Military personnel encounter a number of challenges when separating from military service to include career uncertainty, relational/family dynamics, trauma as a result of military experiences, reconceptualization of identity, and existential issues related to purpose, meaning making and framing of the military experience(s). Embedded within military culture are well-defined rites of passage and a significant sense of belonging. Consequently, transition out of the military can result in the loss of such rites of passage and belongingness. However, a pilgrimage journey can provide the time and space to engage in a new rite of passage, to construct a new pilgrim identity, and a to develop deep social relationships that lead to a sense of belongingness to a particular pilgrim community as well as to the global community of pilgrims across numerous types of pilgrimage journeys. The aims of the current paper are to demonstrate the rationale for why pilgrimage journeys are particularly significant for military veterans, provide an overview of an innovative program that facilitates the Camino de Santiago pilgrimage for military veterans, and discusses the lessons learned from the initial pilot project of a recently established program. Veterans on the Camino (VOC) is an emerging nongovernmental organization in the USA. Founded by a military veteran, after leaving his military career, the primary objective of the organization is to facilitate healing for veterans via the Camino de Santiago pilgrimage journey. As part of the program, participants complete a semi-structured interview at three time points – pre, during, and post journey. The interview items are based on ongoing research by the principal investigator and address such constructs as meaning-making, wellbeing, therapeutic benefits and transformation. In addition, program participants complete The Sources of Meaning and Meaning in Life Questionnaire (SoMe). The pilot program occurred in the spring of 2017. Five participants were selected after an extensive application process and review by a three-person selection board. The selection criteria included demonstrated compatibility with the program objectives (i.e., prior military experience, availability for a 40 day journey, and awareness of the need for a transformational intervention). The participants were connected as a group through a private Facebook site and interacted with one another for several months prior to the pilgrimage. Additionally, the participants were interviewed prior to beginning the pilgrimage, at one point during the pilgrimage and immediately following the conclusion of the pilgrimage journey. The interviews yielded themes related to loss, meaning construction, renewed hope in humanity, and a commitment to future goals. The lessons learned from this pilot project included a confirmation of the need for such a program, a need for greater focus on logistical details, and the recognition that the pilgrimage experience needs to continue in some manner once the veterans return home.

Keywords: pilgrimage, healing, military veterans, Camino de Santiago

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

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Process mining provides methods and techniques for analyzing event logs recorded in modern information systems that support real-world operations. While analyzing an event-log, state-of-the-art techniques available in process mining believe that the operational process as a static entity (stationary). This is not often the case due to the possibility of occurrence of a phenomenon called concept drift. During the period of execution, the process can experience concept drift and can evolve with respect to any of its associated perspectives exhibiting various patterns-of-change with a different pace. Work presented in this paper discusses the main aspects to consider while addressing concept drift phenomenon and proposes a method for detecting and localizing the sudden concept drifts in control-flow perspective of the process by using features extracted by processing the traces in the process log. Our experimental results are promising in the direction of efficiently detecting and localizing concept drift in the context of process mining research discipline.

Keywords: abrupt drift, concept drift, sudden drift, control-flow perspective, detection and localization, process mining

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Authors: Slwa Al-Hammad

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Adaptation studies have been closely aligned to translation studies as both deal with the process of rendering the meaning from one culture to another. These two disciplines are related to each other, but the theories are still being developed. This research aims to fill this gap and provide a contribution to the growing discipline of adaptation studies through a theoretical perspective while investigating how different cultural interpretations of adaptation influence the final literary product. This research focuses on the theoretical concepts of Barthes’s death of the author and Benjamin’s afterlife of the text in translation, which is believed to lead to the birth of the adapter in a literary work. That is, in adaptation, the ‘death’ of the author allows for the ‘birth’ of the adapter, offering them all the creative possibilities of authorship. It also explores the differences between the meanings of adaptation in the West and the Arab world through the analysis of adapted texts in Arabic initially deriving from the European and American literature of the 19th and 20th centuries. The methodology of this thesis is based upon qualitative literary analysis, in which original and adapted works are compared and contrasted, with the additional insights of literary and adaptation theories and prior scholarship. The main works discussed are the Arabic adaptations of William Faulkner’s novels. The analysis is guided by theories of adaptation studies to help in explaining the concepts of relocating, recreating, and rewriting in the process of adaptation. It draws on scholarship on adaptations to inquire into the status of the adapted texts in relation to the original texts. Also, these theories prove that adaptation is the process that is used to transfer text from source to adapted text, not some other analytical practice. Through the textual analysis, concepts of the death of the author and the birth of the adapter will be illustrated, as will the roles of the adapter and the task of rendering works for a different culture, and the understanding of adaptation and Arabization in Arabic literature.

Keywords: adaptation, Arabization, authorship, recreating, relocating

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Authors: Ayse Ozturk

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The study extends the prior research on modeling competencies by positioning students’ cognitive and language resources as the fundamentals for pursuing their own inquiry and expression lines through mathematical modeling. This strategy aims to answer the question that guides this study, “How do students’ group approaches to modeling tasks affect their modeling competencies over a unit of instruction?” Six bilingual tenth-grade students worked on open-ended modeling problems along with the content focused on quantities over six weeks. Each group was found to have a unique cognitive approach for solving these problems. Three different problem-solving strategies affected how the groups’ modeling competencies changed. The results provide evidence that the discussion around groups’ solutions, coupled with their reflections, advances group interpreting and validating competencies in the mathematical modeling process

Keywords: cognition, collective learning, mathematical modeling competencies, problem-solving

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Autcha Araveeporn

Abstract:

This paper is to compare the parameter estimation of the mean in normal distribution by Maximum Likelihood (ML), Bayes, and Markov Chain Monte Carlo (MCMC) methods. The ML estimator is estimated by the average of data, the Bayes method is considered from the prior distribution to estimate Bayes estimator, and MCMC estimator is approximated by Gibbs sampling from posterior distribution. These methods are also to estimate a parameter then the hypothesis testing is used to check a robustness of the estimators. Data are simulated from normal distribution with the true parameter of mean 2, and variance 4, 9, and 16 when the sample sizes is set as 10, 20, 30, and 50. From the results, it can be seen that the estimation of MLE, and MCMC are perceivably different from the true parameter when the sample size is 10 and 20 with variance 16. Furthermore, the Bayes estimator is estimated from the prior distribution when mean is 1, and variance is 12 which showed the significant difference in mean with variance 9 at the sample size 10 and 20.

Keywords: Bayes method, Markov chain Monte Carlo method, maximum likelihood method, normal distribution

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Authors: Julius Onyancha, Valentina Plekhanova

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One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

Procedia PDF Downloads 240

Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Luca Garbarino, Urbano Tancredi, Domenico Accardo, Michele Grassi, Giancarmine Fasano, Anna Elena Tirri

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The development of guidance, navigation and control algorithms and avionic procedures requires the disposability of suitable analysis and verification tools, such as simulation environments, which support the design process and allow detecting potential problems prior to the flight test, in order to make new technologies available at reduced cost, time and risk. This paper presents a simulation environment for avionic software development and qualification, especially aimed at equipment for general aviation aircrafts and unmanned aerial systems. The simulation environment includes models for short and medium-range radio-navigation aids, flight assistance systems, and ground control stations. All the software modules are able to simulate the modeled systems both in fast-time and real-time tests, and were implemented following component oriented modeling techniques and requirement based approach. The paper describes the specific models features, the architectures of the implemented software systems and its validation process. Performed validation tests highlighted the capability of the simulation environment to guarantee in real-time the required functionalities and performance of the simulated avionics systems, as well as to reproduce the interaction between these systems, thus permitting a realistic and reliable simulation of a complete mission scenario.

Keywords: ADS-B, avionics, NAVAIDs, real-time simulation, TCAS, UAS ground control station

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Authors: Unchalee Inkampa, Tuanjai Somboonwiwat

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Painting process of automobiles and automobile parts, which is a continuous process based on EDP (Electrode position paint, EDP). Through EDP, all work pieces will be continuously sent to the painting process. Work process can be divided into 2 groups based on the running time: Painting Room 1 and Painting Room 2. This leads to continuous operation. The problem that arises is waiting for workloads onto Painting Room. The grading process EDP to Painting Room is a major problem. Therefore, this paper aim to develop production sequencing method by applying EDP to painting process. It also applied fixed rate launching for painting room and earliest due date (EDD) for EDP process and swap pairwise interchange for waiting time to a minimum of machine. The result found that the developed method could improve painting reduced waiting time, on time delivery, meeting customers wants and improved productivity of painting unit.

Keywords: sequencing, mixed model lines, painting process, electrode position paint

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Authors: M. V. Manoj Kumar, Likewin Thomas, Annappa

Abstract:

Process mining research discipline bridges the gap between data mining and business process modeling and analysis, it offers the process-centric and end-to-end methods/techniques for analyzing information of real-world process detailed in operational event-logs. In this paper, we have proposed a notation called trace logo for graphically representing control-flow perspective (order of execution of activities) of process. A trace logo consists of a stack of activity names at each position, sizes of the activity name indicates their frequency in the traces and the total height of the activity depicts the information content of the position. A trace logo created from a set of aligned traces generated using Multiple Trace Alignment technique.

Keywords: consensus trace, process mining, multiple trace alignment, trace logo

Procedia PDF Downloads 331