Search results for: fundamental models

Authors: Claudia Martínez-Araneda, Mariella Gutiérrez, Pedro Gómez, Diego Maldonado, Alejandra Segura, Christian Vidal-Castro

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The purpose of this document is to showcase the preliminary work in developing an EduChatbot-type tool and measuring the effects of its use aimed at providing effective feedback to students in programming courses. This bot, hereinafter referred to as tutorBot+, was constructed based on chatGPT and is tasked with assisting and delivering timely positive feedback to students in the field of computer science at the Universidad Católica de Concepción. The proposed working method consists of four stages: (1) Immersion in the domain of Large Language Models (LLMs), (2) Development of the tutorBot+ prototype and integration, (3) Experiment design, and (4) Intervention. The first stage involves a literature review on the use of artificial intelligence in education and the evaluation of intelligent tutors, as well as research on types of feedback for learning and the domain of chatGPT. The second stage encompasses the development of tutorBot+, and the final stage involves a quasi-experimental study with students from the Programming and Database labs, where the learning outcome involves the development of computational thinking skills, enabling the use and measurement of the tool's effects. The preliminary results of this work are promising, as a functional chatBot prototype has been developed in both conversational and non-conversational versions integrated into an open-source online judge and programming contest platform system. There is also an exploration of the possibility of generating a custom model based on a pre-trained one tailored to the domain of programming. This includes the integration of the created tool and the design of the experiment to measure its utility.

Keywords: assessment, chatGPT, learning strategies, LLMs, timely feedback

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Authors: Bassem Nassar, Wessam Hussein, Medhat Mokhtar

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The crucial concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems in order to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important in order to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the aforementioned problem coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions and the results shows that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: space telemetry monitoring, multivariate analysis, PCA algorithm, space operations

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Authors: Jennifer M. Glennon, Hana D'Souza, Luke Mason, Annette Karmiloff-Smith, Michael S. C. Thomas

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Genetic syndrome groups that feature high rates of autism comorbidity, like Down syndrome (DS) and fragile X syndrome (FXS), have been presented as useful models for understanding risk and protective factors involved in the emergence of autistic traits. Yet despite reaching clinical thresholds, these ‘syndromic’ forms of autism appear to differ in important ways from the idiopathic or ‘non-syndromic’ autism phenotype. To uncover the true nature of these comorbidities, it is necessary to extend definitions of autism to include the cognitive characteristics of the disorder and to then apply this broadened conceptualisation to the study of syndromic autism profiles. The current study employs a variety of well-established eye-tracking paradigms to assess visual attentional performance in children with DS and FXS who reach thresholds for autism on the Social Communication Questionnaire. It investigates whether autism profiles in these children are accompanied by visual orienting difficulties (‘sticky attention’), decreased social attention, and enhanced visual search performance, all of which are characteristic of the idiopathic autism phenotype. Data is collected from children with DS and FXS aged between 6 and 10 years, in addition to two control groups matched on age and intellectual ability (i.e., children with idiopathic autism and neurotypical controls). Cross-sectional developmental trajectory analyses are conducted to enable visuo-attentional profile comparisons. Significant differences in the visuo-attentional processes underpinning autism presentations in children with FXS and DS are hypothesised, supporting notions of syndrome specificity. The study provides insight into the complex heterogeneity associated with syndromic autism presentations and autism per se, with clinical implications for the utility of autism intervention programmes in DS and FXS populations.

Keywords: autism, down syndrome, fragile X syndrome, eye tracking

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Authors: Houda Zouar, Hanane Sarnou

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In the 21st, century the concept of learners' autonomy is crucial. The concept of self-regulated learning has come forward as a result of enabling learners to direct their learning with autonomy towards academic goals achievement. Academic self-regulation is defined as the process by which learners systematically plan, monitor and asses their learning to achieve their academic established goals. In the field of English as a foreign language, teachers emphasise the role of learners’ autonomy to foster the process of English language learning. Consequently, academic self-regulation is considered as a vehicle to enhance autonomy among English language learners. However, not all learners can be equally self-regulators if not well assisted, mainly those novice pupils of basic education. For this matter, understanding the role of teachers in fostering academic self- regulation must be among the preliminary objectives in searching and developing this area. The present research work targets the role of the Algerian middle school teachers in enhancing academic self-regulation and teaching pupils how to learn, besides their role as models in the trajectory of teaching their pupils to become self-regulators. Despite the considerable endeavours in the field of educational setting on Self-Regulated Learning, the literature of the Algerian context indicates confined endeavours to undertake and divulge this notion. To go deeper into this study, a mixed method approach was employed to confirm our hypothesis. For data collection, teachers were observed and addressed by a questionnaire on their role in enhancing academic self- regulation among their pupils. The result of the research indicates that the attempts of middle school Algerian teachers are implicit and limited. This study emphasises the need to prepare English language teachers with the necessary skills to promote autonomous and self-regulator English learners.

Keywords: Algeria, English as a foreign language, middle school, self-regulation, Teachers' role

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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

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Authors: Amir Reza Heydari, Yaser Jenab

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Coronary artery disease, a common cardiovascular disease, is attributed to the accumulation of cholesterol-based plaques in the coronary arteries, leading to atherosclerosis. This disease is associated with risk factors such as smoking, hypertension, diabetes, and elevated cholesterol levels, contributing to severe clinical consequences, including acute coronary syndromes and myocardial infarction. Treatment approaches such as from lifestyle interventions to surgical procedures like percutaneous coronary intervention and coronary artery bypass surgery. These interventions often employ stents, including bare-metal stents (BMS), drug-eluting stents (DES), and bioresorbable vascular scaffolds (BVS), each with its advantages and limitations. Computational tools have emerged as critical in optimizing stent designs and assessing their performance. The aim of this study is to provide an overview of the computational methods of studies based on the finite element (FE) method in the field of coronary stenting and discuss the potential for development and clinical application of stent devices. Additionally, the importance of assessing the ability of computational models is emphasized to represent real-world phenomena, supported by recent guidelines from the American Society of Mechanical Engineers (ASME). Validation processes proposed include comparing model performance with in vivo, ex-vivo, or in vitro data, alongside uncertainty quantification and sensitivity analysis. These methods can enhance the credibility and reliability of in silico simulations, ultimately aiding in the assessment of coronary stent designs in various clinical contexts.

Keywords: atherosclerosis, materials, restenosis, review, validation

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Authors: Monika Sogani, Zainab Syed, Adrian C. Fisher

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Pollution of estrogenic compounds has caught the attention of researchers as the slight increase of estrogens in the water bodies has a significant impact on the aquatic system. They belong to a class of endocrine disrupting compounds (EDCs) and are able to mimic hormones or interfere with the action of endogenous hormones. The microbial electrochemical remediation system (MERS) is employed here for exploiting an electrophototrophic bacterium for evaluating the capacity of biodegradation of ethinylestradiol hormone (EE2) under anaerobic conditions with power generation. MERS using electro-phototrophic bacterium offers a tailored solution of wastewater treatment in a developing country like India which has a huge solar potential. It is a clean energy generating technology as they require only sunlight, water, nutrients, and carbon dioxide to operate. Its main feature that makes it superior over other technologies is that the main fuel for this MERS is sunlight which is indefinitely present. When grown in light with organic compounds, these photosynthetic bacteria generate ATP by cyclic photophosphorylation and use carbon compounds to make cell biomass (photoheterotrophic growth). These cells showed EE2 degradation and were able to generate hydrogen as part of the process of nitrogen fixation. The two designs of MERS were studied, and a maximum of 88.45% decrease in EE2 was seen in a total period of 14 days in the better design. This research provides a better insight into microbial electricity generation and self-sustaining wastewater treatment facilities. Such new models of waste treatment aiming waste to energy generation needs to be followed and implemented for building a resource efficient and sustainable economy.

Keywords: endocrine disrupting compounds, ethinylestradiol, microbial electrochemical remediation systems, wastewater treatment

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Authors: Abdolhamid Anazadehsayed, Jamal Naser

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An integrated computational fluid dynamics model is developed for a combined simulation of Plateau borders, films, and transitional areas between the film and the Plateau borders to reduce the simplifications and shortcomings of available models for foam drainage in micro-scale. Additionally, the counter-flow related to the Marangoni effect in the transitional area is investigated. The results of this combined model show the contribution of the films, the exterior Plateau borders, and Marangoni flow in the drainage process more accurately since the inter-influence of foam's elements is included in this study. The exterior Plateau borders flow rate can be four times larger than the interior ones. The exterior bubbles can be more prominent in the drainage process in cases where the number of the exterior Plateau borders increases due to the geometry of container. The ratio of the Marangoni counter-flow to the Plateau border flow increases drastically with an increase in the mobility of air-liquid interface. However, the exterior bubbles follow the same trend with much less intensity since typically, the flow is less dependent on the interface of air-liquid in the exterior bubbles. Moreover, the Marangoni counter-flow in a near-wall transition area is less important than an internal one. The influence of air-liquid interface mobility on the average velocity of interior foams is attained with more accuracy with more realistic boundary condition. Then it has been compared with other numerical and analytical results. The contribution of films in the drainage is significant for the mobile foams as the velocity of flow in the film has the same order of magnitude as the velocity in the Plateau border. Nevertheless, for foams with rigid interfaces, film's contribution in foam drainage is insignificant, particularly for the films near the wall of the container.

Keywords: foam, plateau border, film, Marangoni, CFD, bubble

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Authors: A. Peyvan, D. Li, J. Komperda, F. Mashayek

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Turbulence can be observed in a variety fluid motions in nature and industrial applications. Recent investment in high-speed aircraft and propulsion systems has revitalized fundamental research on turbulent flows. In these systems, capturing chaotic fluid structures with different length and time scales is accomplished through the Direct Numerical Simulation (DNS) approach since it accurately simulates flows down to smallest dissipative scales, i.e., Kolmogorov’s scales. The discontinuous spectral element method (DSEM) is a high-order technique that uses spectral functions for approximating the solution. The DSEM code has been developed by our research group over the course of more than two decades. Recently, the code has been improved to run large cases in the order of billions of solution points. Running big simulations requires a considerable amount of RAM. Therefore, the DSEM code must be highly parallelized and able to start on multiple computational nodes on an HPC cluster with distributed memory. However, some pre-processing procedures, such as determining global element information, creating a global face list, and assigning global partitioning and element connection information of the domain for communication, must be done sequentially with a single processing core. A separate code has been written to perform the pre-processing procedures on a local machine. It stores the minimum amount of information that is required for the DSEM code to start in parallel, extracted from the mesh file, into text files (pre-files). It packs integer type information with a Stream Binary format in pre-files that are portable between machines. The files are generated to ensure fast read performance on different file-systems, such as Lustre and General Parallel File System (GPFS). A new subroutine has been added to the DSEM code to read the startup files using parallel MPI I/O, for Lustre, in a way that each MPI rank acquires its information from the file in parallel. In case of GPFS, in each computational node, a single MPI rank reads data from the file, which is specifically generated for the computational node, and send them to other ranks on the node using point to point non-blocking MPI communication. This way, communication takes place locally on each node and signals do not cross the switches of the cluster. The read subroutine has been tested on Argonne National Laboratory’s Mira (GPFS), National Center for Supercomputing Application’s Blue Waters (Lustre), San Diego Supercomputer Center’s Comet (Lustre), and UIC’s Extreme (Lustre). The tests showed that one file per node is suited for GPFS and parallel MPI I/O is the best choice for Lustre file system. The DSEM code relies on heavily optimized linear algebra operation such as matrix-matrix and matrix-vector products for calculation of the solution in every time-step. For this, the code can either make use of its matrix math library, BLAS, Intel MKL, or ATLAS. This fact and the discontinuous nature of the method makes the DSEM code run efficiently in parallel. The results of weak scaling tests performed on Blue Waters showed a scalable and efficient performance of the code in parallel computing.

Keywords: computational fluid dynamics, direct numerical simulation, spectral element, turbulent flow

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

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The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: educative model, good life, professional social responsibility, values

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Authors: Kamyar Maleki Bagherabadi, Torstein Aarseth Bø, Truls Flatberg, Olve Mo

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Hydrogen fuel cells and batteries are one of the promising solutions aligned with carbon emission reduction goals for the marine sector. However, the higher installation and operation costs of hydrogen-based systems compared to conventional diesel gensets raise questions about the appropriate hydrogen tank size, energy, and fuel consumption estimations. Ship designers need methodologies and tools to calculate energy and fuel consumption for different component sizes to facilitate decision-making regarding feasibility and performance for retrofits and design cases. The aim of this work is to compare three alternative modeling approaches for the estimation of energy and fuel consumption with various hydrogen tank sizes, battery capacities, and load-sharing strategies. A fishery vessel is selected as an example, using logged load demand data over a year of operations. The modeled power system consists of a PEM fuel cell, a diesel genset, and a battery. The methodologies used are: first, an energy-based model; second, considering load variations during the time domain with a rule-based Power Management System (PMS); and third, a load variations model and dynamic PMS strategy based on optimization with perfect foresight. The errors and potentials of the methods are discussed, and design sensitivity studies for this case are conducted. The results show that the energy-based method can estimate fuel and energy consumption with acceptable accuracy. However, models that consider time variation of the load provide more realistic estimations of energy and fuel consumption regarding hydrogen tank and battery size, still within low computational time.

Keywords: fuel cell, battery, hydrogen, hybrid power system, power management system

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Authors: Benedictus Asriparusa

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In the area of the oil industry, there is accompanied by associated natural gas. The thing shows that a large amount of energy is being wasted mostly in the developing countries by contributing to the global warming process. This research represents an overview of methods in Minas area employed by these researchers in PT. Chevron Pacific Indonesia to determine ways of measuring and reducing gas flaring and its emission drastically. It provides an approximation includes analytical studies, numerical studies, modeling, computer simulations, etc. Flaring system is the controlled burning of natural gas in the course of routine oil and gas production operations. This burning occurs at the end of a flare stack or boom. The combustion process will release emissions of greenhouse gases such as NO2, CO2, SO2, etc. This condition will affect the air and environment around the industrial area. Therefore, we need a simulation to create the pattern of the dissemination of pollutant. This research paper has being made to see trends in gas flaring model and current developments to predict dominant variable which gives impact to dissemination of pollutant. Fluent models used to simulate the distribution of pollutant gas coming out of the stack. While WRF model output is used to overcome the limitations of the analysis of meteorological data and atmospheric conditions in the study area. This study condition focused on transition season in 2012 at Minas area. The goal of the simulation is looking for the exact time which is most influence towards dissemination of pollutants. The most influence factor divided into two main subjects. It is the quickest wind and the slowest wind. According to the simulation results, it can be seen that quickest wind moves to horizontal way and slowest wind moves to vertical way.

Keywords: flaring system, fluent model, dissemination of pollutant, transition season

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Authors: Sepideh Nikounejad, Mostafa Ghaderi, Nematollah Azizi, Per-Olof Thang, Mohamad Reza Neyestani

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From an educational perspective, an effective and efficient relationship between university and industry can be considered as an important means by which not only both sides are improved but also it brings many advantages and benefits for both parties. It means more specifically, mutual collaboration between universities and industry can not only reduce youth unemployment, but it can improve the quality of teaching and learning in higher education settings while providing more qualified people to industrial enterprises. Indeed the lack of effective interaction between Iranian universities and industry has confronted the country and created many challenges include in increasing number of unskillful and unemployed graduates. However, in order to suggest appropriate practical strategies, it is very important to see how this issue has been tackled by Swedish universities, which have had a good background in this collaboration and how they are connected to the industry in particular and labour market in general. The research aims to study and compare the mechanisms, processes, and policies of the current model in the relationships between university and industry in Iran and Sweden. As a qualitative study, grounded theory was applied. Data were collected via semi-structured interviews. Participants were selected purposefully and by the snowball sampling method. The findings indicate that despite reported needs from both sides for close collaborations between universities and industries in Iran, current policies and practices, including internship, laboratory, and financial support, need to be revised critically. However, in light of our findings on the Swedish policies and practices for linking universities and industries, some practical strategies will be suggested for the Iranian higher education context. In conclusion, cooperation models between universities and industries in Iran and Sweden will be described.

Keywords: cooperation, higher education, industry, comparative

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Authors: R. Bakr, M. Elmeligy, A. Ibrahim

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When the building loads are relatively small, challenges are often facing the foundation design especially when inappropriate soil conditions exist. These may be represented in the existence of soft soil in the upper layers of soil while sandy soil or firm cohesive soil exist in the deeper layers. In such cases, the design becomes infeasible if the piles are extended to the deeper layers, especially when there are sandy layers existing at shallower depths underlain by stiff clayey soil. In this research, models of piles terminated in sand underlain by clay soils are numerically simulated by different modelling theories. Finite element software, Plaxis 3-D Foundation was used to evaluate the pile behavior under different loading scenarios. The standard static load test according to ASTM D-1143 was simulated and compared with the real-life loading scenario. The results showed that the pile behavior obtained from the current static load test do not realistically represent that obtained from real-life loading. Attempts were carried out to capture the proper numerical loading scenario that simulates the pile behavior in real-life loading including the long-term effect. A modified method based on this research findings is proposed for the static pile loading tests. Field loading tests were carried out to validate the new method. Results obtained from both numerical and field tests by using the modified method prove that this method is more accurate in predicting the pile behavior in sand soil underlain by clay more than the current standard static load.

Keywords: numerical simulation, static load test, pile behavior, sand underlain with clay, creep

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Authors: Dorothy Chyung

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In the psychiatric interview, the mental status exam begins with an assessment of the patient's appearance, noting aspects such as grooming and hygiene. However, it is not well established whether further examination of a patient's attire can provide further useful information. The popular assumption is that those who are mentally unwell will manifest this in unusual clothing. In the moral treatment of the 19th century, proper clothing was also seen as a pivotal therapeutic concern. This project examines assumptions about clothing, both as a reflection of and treatment for psychopathology. The methodology considers the opinions expressed in 19th century art and journals, as well as asylum rules, in comparison to contemporary psychiatric practice and research evidence. Per moral treatment in the 19th century, self-discipline and a proper environment would cure insanity. Madness was evident in the opposite of these ideals—such as ragged or ‘improper’ clothing—and rules about attire delineated the most correct (i.e. sane) ways to dress. These rules applied not only for the patients but also for staff. Despite these ideals, accusations were made that asylums, in fact, dressed patients to look more mentally unwell and further removed patients’ agency. Current practice in psychiatric hospitals retains remnants of moral treatment. Patients are expected to dress ‘appropriately’ while retaining some choice to build self-esteem, with arguments about safety being used to justify the removal of choice. Meanwhile, staff is expected to dress professionally and as role models, based on the assumption that conservative dress is least pathological. Research on this subject is limited, and there is little evidence that discrete psychiatric diagnoses manifest in the particular dress, nor that conservative dress would result in a reduction in pathology. Dressing unusually has become a privilege granted only to those without association with mental illness.

Keywords: fashion, history of psychiatry, medical humanities, mental health treatment

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Authors: Abdullah Ijaz Hussain

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The purpose of this study was to explore the cardioprotective and anti-inflammatory effects of polyphenol-rich extracts from cucurbitaceae family members, including Cucurbita pepo, C. moschata, and C. maxima, on rat models. The initial crude extracts from these cucurbits were further separated into hexane, chloroform, ethyl acetate, butanol, and aqueous ethanol fractions, labeled as HEF, CHF, EAF, BUF, and AEF, respectively. Of these, AEF yielded the highest amount, followed by BUF, HEF, EAF, and CHF in descending order. Notably, EAF contained the greatest concentration of total phenolics, flavonoids, and flavonols. In terms of antioxidant activity, EAF demonstrated the most potent DPPH radical scavenging capability, succeeded by CHF, BUF, AEF, and HEF. EAF also exhibited the strongest reducing potential among the fractions. RP-HPLC analysis identified various phenolic acids and flavonoids across the cucurbita fractions, including ferulic acid, vanillic acid, p-coumeric acid, gallic acid, p-hydroxybenzoic acid, chlorogenic acid, catechin, rutin, quercetin, myricetin, and kaempferol. Doses of 250 and 500 mg/kg body weight of cucurbita fractions were administered orally to male WKY rats daily for 21 days. The rats' body weight, heart rate, and blood pressure were monitored bi-weekly. Oxidative status assessments were conducted using plasma samples to measure levels of malondialdehyde (MDA), superoxide dismutase (SOD), reduced glutathione (GSH), nitric oxide (NO), and total antioxidant capacity (TAC). At the study's conclusion, surgical assessments, including blood pressure, pulse wave velocity (PWV), and echocardiograms (ECG) were performed. The findings indicated that EAF from cucurbita significantly enhanced antihypertensive and antioxidant activities in the SHR rat group.

Keywords: polyphenols, chlorogenic acid, antihypertensive activity, oxidative stress, lcms

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Authors: Sina Gharevali

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Staphylococcus aureus is one of the most important factors for nosocomial infections and infections acquired in a hospital setting role as is. Drug-resistant bacteria methicillin, which in 1961 was reported in many parts of the world, Made the role as the last drug, vancomycin, in the treatment of infections caused by the Staphylococcus aureus chain be taken into consideration. The aim of this study was to evaluate the antimicrobial effects of copper nanoparticles and compared it with antibiotics on Staphylococcus aureus resistant to vancomycin in vitro and animal model. In this study, this test was performed, and the most effective antibiotic for vancomycin-resistant Staphylococcus aureus was determined by disk diffusion method. After various concentrations of copper nanoparticles and antibiotics were prepared and vancomycin resistant Staphylococcus aureus bacteria with serial dilution method for determining antibiotic ciprofloxacin. Minimum Inhibitory Concentration and Minimum Bactericidal Concentrationcopper nanoparticles was performed. The agar dilution method for bacterial growth in different concentrations of copper nanoparticles and antibiotics ciprofloxacin was performed. The agar dilution method for bacterial growth in different concentrations of copper nanoparticles and antibiotics ciprofloxacin was performed. Then the broth dilution method for the antibiotic ciprofloxacin, nano-particles, and nano-particles of copper and copper-established antibiotic synergy MIC and MBC were obtained. MBC was obtained from the experimental animal model test method, and the results were compared. The results showed that copper nanoparticles compared with the antibiotic ciprofloxacin in vitro and animal model more effective in inhibiting the growth of Staphylococcus aureus resistant to vancomycin and ciprofloxacin and extent of the impact of the Synthetic effect of lower copper nanoparticles. Which can then be used to treat clinical research as a candidate.

Keywords: nanoparticles, copper, staphylococcus, aureus

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Authors: Teresa Roco, Mario Perez Won, Roberto Lemus-Mondaca, Sebastian Pizarro

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This research presents the simultaneous application of high hydrostatic pressure application (HHP) and osmotic dehydration (DO) as a pretreatment to hot –air drying of abalone cubes. The drying time was reduced to 6 hours at 60ºC as compared to the abalone drying by only a 15% NaCl osmotic pretreatment and at an atmospheric pressure that took 10 hours to dry at the same temperature. This was due to the salt and HHP saturation since osmotic pressure increases as water loss increases, thus needing a more reduced time in a convective drying, so water effective diffusion in drying plays an important role in this research. Different working conditions as pressure (350-550 MPa), pressure time ( 5-10 min), salt concentration, NaCl 15% and drying temperature (40-60ºC) will be optimized according to kinetic parameters of each mathematical model (Table 1). The models used for drying experimental curves were those corresponding to Weibull, Logarithmic and Midilli-Kucuk, but the latest one was the best fitted to the experimental data (Figure 1). The values for water effective diffusivity varied from 4.54 – to 9.95x10-9 m2/s for the 8 curves (DO+HHP) whereas the control samples (neither DO nor HHP) varied among 4.35 and 5.60x10-9 m2/s, for 40 and 60°C, respectively and as to drying by osmotic pretreatment at 15% NaCl from 3.804 to 4.36x10-9 m2/s at the same temperatures. Finally as to energy and efficiency consumption values for drying process (control and pretreated samples) it was found that they would be within a range of 777-1815 KJ/Kg and 8.22–19.20% respectively. Therefore, a knowledge concerning the drying kinetic as well as the consumption energy, in addition to knowledge about the quality of abalones subjected to an osmotic pretreatment (DO) and a high hydrostatic pressure (HHP) are extremely important to an industrial level so that the drying process can be successful at different pretreatment conditions and/or variable processes.

Keywords: abalone, convective drying, high pressure hydrostatic, pretreatments, diffusion coefficient

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Authors: Imtiaz Ali, Krishnan Jayaraman, Debes Bhattacharyya

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Soybean concentrate is abundant material and renewable product that is recently been explored as an alternative to conventional formaldehyde based resins in wood based products. The main goal of this study is to evaluate the technical feasibility of manufacturing environment friendly MDF panels from renewable resources. The panels are made by using kenaf bast fibers (KB) as wood substitute and soy based adhesive as bonding material. Second order response surface regression models are used to understand the effects and interactions of resin content (RC) and pressing time (PT) on the mechanical and water soaking properties of kenaf panels. The mechanical and water soaking properties are significantly improved as the RC increased and reached at the highest level at maximum resin loading (12%). The effect of pressing time is significant in the first phase when the pressing time increased from 4 to 6 min; however the effect was not as significant when pressing time further increased to 8 min. The second order regression equations further confirm that the variation in process parameters has strong relationship with the physciomechanical properties. The MDF panels the minimum requirements of internal bond strength, modulus of rupture and modulus of elasticity as recommended by US wood MDF standard specifications for G110, G120, G130 and G140 grade MDF panels. However, the thickness swelling results are considerably poorer than the recommended values of general purpose standard requirements. This deficiency can be counterbalanced by the advantage of being formaldehyde free panels made from renewable sources and by making them suitable alternative for less humid environment applications.

Keywords: kenaf, Medium density fibreboard, soy adhesive, mechanical properties, water soaking properties

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Authors: G. Stamatopoulou, M. Symeonaki, C. Michalopoulou

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In this work, we study the intergenerational social mobility in Greece, in order to provide up-to-date evidence on the changes in the mobility patterns throughout the years. An analysis for both men and women aged between 25-64 years old is carried out. Three main research objectives are addressed. First, we aim to examine the relationship between the socio-economic status of parents and their children. Secondly, we investigate the evolution of the mobility patterns between different birth cohorts. Finally, the role of education is explored in shaping the mobility patterns. For the analysis, we draw data on both parental and individuals' social outcomes from different national databases. The social class of origins and destination is measured according to the European Socio-Economic Classification (ESeC), while the respondents' educational attainment is coded into categories based on the International Standard Classification of Education (ISCED). Applying the Markov transition probability theory, and a range of measures and models, this work focuses on the magnitude and the direction of the movements that take place in the Greek labour market, as well as the level of social fluidity. Three-way mobility tables are presented, where the transition probabilities between the classes of destination and origins are calculated for different cohorts. Additionally, a range of absolute and relative mobility rates, as well as distance measures, are presented. The study covers a large time span beginning in 1940 until 1995, shedding light on the effects of the national institutional processes on the social movements of individuals. Given the evidence on the mobility patterns of the most recent birth cohorts, we also investigate the possible effects of the 2008 economic crisis.

Keywords: cohort analysis, education, Greece, intergenerational mobility, social class

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Authors: Deane Roehl, Roberto Quevedo

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Gas migration from pressurized formations is a problem reported in the oil and gas industry. This means increased risks for drilling, production, well integrity, and hydrocarbon escape. Different processes can contribute to the development of pressurized formations, particularly in High-Pressure–High-Temperature (HPHT) gas fields. Over geological time-scales, the different formations of those fields have maintained and/or developed abnormal pressures owing to low permeability and the presence of an impermeable seal. However, if this seal is broken, large volumes of gas could migrate into other less pressurized formations. Three main mechanisms for gas migration have been identified in the literature –molecular diffusion, continuous-phase flow, and continuous-phase flow coupled with mechanical effects. In relation to the latter, gas migration can occur as a consequence of the mechanical effects triggered by reservoir depletion. The compaction of the reservoir can redistribute the in-situ stresses sufficiently to induce deformations that may increase the permeability of rocks and lead to fracture processes or reactivate nearby faults. The understanding of gas flow through discontinuities is still under development. However, some models based on porosity changes and fracture aperture have been developed in order to obtain enhanced permeabilities in numerical simulations. In this work, a simple relationship to integrate fluid flow through rock matrix and discontinuities has been implemented in a fully thermo-hydro-mechanical simulator developed in-house. Numerical simulations of hydrocarbon production in an HPHT field were carried out. Results suggest that rock permeability can be considerably affected by the deformation of the field, creating preferential flow paths for the transport of large volumes of gas.

Keywords: gas migration, pressurized formations, fractured rocks, numerical modeling

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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: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Hsien Hwa Kuo, Lin Wen Chun, Lin Wen Chun, Hsien Wen Kuo

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Objective: Nurses represent an important occupational group frequently affected by wrist musculoskeletal disorders (WMSDs) due to a heavy workload, working shifts, poor posture, giving shots, making beds, lifting patients, bending their waist and insufficient rest time every day. However, lack of research reported nurses whether workload in household correlated with the severity of WMSDs. Methods: 550 nurses from a hospital in Taoyuan were interviewed using a modified standardized Nordic Musculoskeletal (NMQ) questionnaire including the demographic information, workplace condition and nine body parts of musculoskeletal disorders. Results: 17.9% and 23.9% of severity and symptoms in WMSDs among nurses with children were significant higher than among nurses without children (1​2.4% and 15.9%). Based on multiple logistic regression models adjusted for age, work duration, job title and body mass index (BMI), we found that heavy workload in hospital had higher odds ratio (OR) of the severity and symptoms of WMSD among nurses with children (OR= 8.67 and OR= 4.30, p<0.05) compared to nurses without children (OR= 1.94 and OR= 1.70). Conclusion: The severity and symptoms of WMSDs among nurses significantly correlated with workload in hospital among nurses with children. If women are at greater risk because of the combination of their work and family demands, synergistic effect of WMSDs was found among nurses. Comment: Women's domestic work, especially once they become mothers, they invest more time and energy caring for children, helping others, and doing housework. Thus domestic work, per se, may be a risk factor for wrist musculoskeletal problems, and, more importantly, it may constrain women's ability to protect themselves from the effects of their paid work. If nurses with more domestic work periodically make efforts to physical activity or modify inappropriate posture, their WMSDs symptoms will be alleviated.

Keywords: musculoskeletal disorders, nurse, NMQ, WMSDs

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Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

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Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

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Authors: Jingwei Du

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The need of optimizing air conditioning systems for existing buildings calls for control methods designed with energy-efficiency as a primary goal. The majority of current control methods boil down to two categories: empirical and model-based. To be effective, the former heavily relies on engineering expertise and the latter requires extensive historical data. Reinforcement Learning (RL), on the other hand, is a model-free approach that explores the environment to obtain an optimal control strategy often referred to as “policy”. This research adopts Proximal Policy Optimization (PPO) to improve chiller plant control, and enable the RL agent to collaborate with experienced engineers. It exploits the fact that while the industry lacks historical data, abundant operational data is available and allows the agent to learn and evolve safely under human supervision. Thanks to the development of language models, renewed interest in RL has led to modern, online, policy-based RL algorithms such as the PPO. This research took inspiration from “alignment”, a process that utilizes human feedback to finetune the pretrained model in case of unsafe content. The methodology can be summarized into three steps. First, an initial policy model is generated based on minimal prior knowledge. Next, the prepared PPO agent is deployed so feedback from both critic model and human experts can be collected for future finetuning. Finally, the agent learns and adapts itself to the specific chiller plant, updates the policy model and is ready for the next iteration. Besides the proposed approach, this study also used traditional RL methods to optimize the same simulated chiller plants for comparison, and it turns out that the proposed method is safe and effective at the same time and needs less to no historical data to start up.

Keywords: chiller plant, control methods, energy efficiency, proximal policy optimization, reinforcement learning

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Authors: Parisa Göker, Hilal Kahveci, Özlem Candan Hergül

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Humans are generating culture while being in touch with nature. Along with skills, local knowledge based on experience, and many other subjects developed within this process, 'culture' offers humans a chance to survive. For this reason, culture forms the equipment for humans, which facilitates their survival in all ecosystems. Together with technology, quick consumption of natural sources and overuse culture of humans have brought up the eco-village concept. Ecovillages are ecologically, economically, socio-culturally, and spiritually sustainable settlement models. It is known that the eco-village approach is applying a proper methodology on behalf of integrative and versatile solution generation. Today, the eco-village approach, introducing a radical criticism to the understanding of civilization and consumption culture and deeming urban solutions inadequate as a spatial reflection to civilization and consumption culture, while making a difference about integrative solution offering with multidimensional features, along with the goal of creating self-sufficient communities, is creating solutions on the subject of both reducing the ecological footprint of humans and to provide social order and also to solve the injustice seen in terms of income and life standards. In this study, environmental issues, sustainable development, and environmental sustainability topics are examined within the context of eco-tourism and eco-village. Alongside this, the natural and cultural landscape values of Kurşunlu village which are located in Bilecik province’s Gölpazarı county, and a contextual frame is created for the facilitation of sustainability in the event of dynamizing the Kurşunlu village in terms of tourism-oriented activities.

Keywords: eco village, sustainability, rural landscape, cultural landscape

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Authors: Ebrahim Ghasemi Ardi, Ai Bing Yu, Run Yu Yang

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Current study is aimed to develop an available in-house discrete element method (DEM) code and link it with direct breakage event. So, it became possible to determine the particle breakage and then its fragments size distribution, simultaneous with DEM simulation. It directly applies the particle breakage inside the DEM computation algorithm and if any breakage happens the original particle is replaced with daughters. In this way, the calculation will be followed based on a new updated particles list which is very similar to the real grinding environment. To validate developed model, a grinding ball impacting an unconfined particle bed was simulated. Since considering an entire ball mill would be too computationally demanding, this method provided a simplified environment to test the model. Accordingly, a representative volume of the ball mill was simulated inside a box, which could emulate media (ball)–powder bed impacts in a ball mill and during particle bed impact tests. Mono, binary and ternary particle beds were simulated to determine the effects of granular composition on breakage kinetics. The results obtained from the DEM simulations showed a reduction in the specific breakage rate for coarse particles in binary mixtures. The origin of this phenomenon, commonly known as cushioning or decelerated breakage in dry milling processes, was explained by the DEM simulations. Fine particles in a particle bed increase mechanical energy loss, and reduce and distribute interparticle forces thereby inhibiting the breakage of the coarse component. On the other hand, the specific breakage rate of fine particles increased due to contacts associated with coarse particles. Such phenomenon, known as acceleration, was shown to be less significant, but should be considered in future attempts to accurately quantify non-linear breakage kinetics in the modeling of dry milling processes.

Keywords: particle bed, breakage models, breakage kinetic, discrete element method

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Authors: Lidimma Benjamin, Nimmyel Gwakzing, Wuyep Nanyi

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Technical and Vocational Education and Training (TVET) has many stakeholders, each with their own values and interests. This study will focus on the diversity of the values and interests within and across groups of stakeholders by quantifying the value that stakeholders attached to several quality attributes of TVET, and also find out to what extent TVET stakeholders differ in their values. The quality of TVET therefore, depends on how well it aligns with the values and interests of these stakeholders. The five stakeholders are parents, students, teachers, policy makers, and work place training supervisors. The 9 attributes are employer appreciation of students, graduation rate, obtained computer skills of students, mentoring hours in workplace learning/Students Industrial Work Experience Scheme (SIWES), challenge, structure, students’ appreciation of teachers, schooling hours, and attention to civic education. 346 respondents (comprising Parents, Students, Teachers, Policy Makers, and Workplace Training Supervisors) were repeatedly asked to rank a set of 4 programs, each with a specific value on the nine quality indicators. Conjoint analysis was used to obtain the values that the stakeholders assigned to the 9 attributes when evaluating the quality of TVET programs. Rank-ordered logistic regression was the statistical/tool used for ranking the respondents values assign to the attributes. The similarities and diversity in values and interests of the different stakeholders will be of use by both Nigerian government and TVET colleges, to improve the overall quality of education and the match between vocational programs and their stakeholders simultaneous evaluation and combination of information in product attributes. Such approach models the decision environment by confronting a respondent with choices that are close to real-life choices. Therefore, it is more realistically than traditional survey methods.

Keywords: TVET, vignette study, conjoint analysis, quality perception, educational stakeholders

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Authors: Kanchan Mondal, Dasharath Koulage, Dattatray Manerikar, Asmita Ghate

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This paper presents an additive reliability model using warranty data and Finite Element Analysis (FEA) data. Warranty data for any product gives insight to its underlying issues. This is often used by Reliability Engineers to build prediction model to forecast failure rate of parts. But there is one major limitation in using warranty data for prediction. Warranty periods constitute only a small fraction of total lifetime of a product, most of the time it covers only the infant mortality and useful life zone of a bathtub curve. Predicting with warranty data alone in these cases is not generally provide results with desired accuracy. Failure rate of a mechanical part is driven by random issues initially and wear-out or usage related issues at later stages of the lifetime. For better predictability of failure rate, one need to explore the failure rate behavior at wear out zone of a bathtub curve. Due to cost and time constraints, it is not always possible to test samples till failure, but FEA-Fatigue analysis can provide the failure rate behavior of a part much beyond warranty period in a quicker time and at lesser cost. In this work, the authors proposed an Additive Weibull Model, which make use of both warranty and FEA fatigue analysis data for predicting failure rates. It involves modeling of two data sets of a part, one with existing warranty claims and other with fatigue life data. Hazard rate base Weibull estimation has been used for the modeling the warranty data whereas S-N curved based Weibull parameter estimation is used for FEA data. Two separate Weibull models’ parameters are estimated and combined to form the proposed Additive Weibull Model for prediction.

Keywords: bathtub curve, fatigue, FEA, reliability, warranty, Weibull

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