Search results for: sequential extraction process

Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Evgeni Baratashvili, Nino Pailodze, Ana Bolkvadze, Giorgi Sulashvili

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At the present stage in our country intensifies cooperation with different business cultures, actively developing the process of implementation of Georgia in the global business system that requires us to develop a specific concept, including in the field of management. With the entry of Georgia into the international community, exchange of experience will only intensify. It is clear that the achievement of goals such as the doubling of the National Product increase the competitiveness of Georgian enterprises can’t be recorded without foreign management experience. On the other hand, knowledge of the areas of comparative management can be used in the process of choosing the path of socio-economic development of Georgia.

Keywords: business cultures, comparative management, corporate culture, Georgian business, Anglo-Saxon model, Georgian civilization, anti-capitalist mentality, culture management

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Authors: Hyunsun Lee, Yi Zhu

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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.

Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles

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Authors: Abigaba Sophia

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Artificial intelligence (AI) has transformed the education sector by revolutionizing educational frameworks by providing new opportunities and innovative advanced platforms for language translation during the teaching and learning process. In today's education sector, the primary key to scholarly communication is language; therefore, translation between different languages becomes vital in the process of communication. KIU-T being an International University, admits students from different nations speaking different languages, and English is the official language; some students find it hard to grasp a word during teaching and learning. This paper explores the practical aspect of using artificial intelligence technologies in an advanced language translation manner during teaching and learning. The impact of this technology is reflected in the education strategies to equip students with the necessary knowledge and skills for professional activity in the best way they understand. The researcher evaluated the demand for this practice since students have to apply the knowledge they acquire in their native language to their countries in the best way they understand. The main objective is to improve student's language competence and lay a solid foundation for their future professional development. A descriptive-analytic approach was deemed best for the study to investigate the phenomena of language translation intelligence alongside Microsoft Office during the teaching and learning process. The study analysed the responses of 345 students from different academic programs. Based on the findings, the researcher recommends using the artificial intelligence language translation technique during teaching, and this requires the wisdom of human content designers and educational experts. Lecturers and students will be trained in the basic knowledge of this technique to improve the effectiveness of teaching and learning to meet the student’s needs.

Keywords: artificial intelligence, language translation technique, teaching and learning process, Microsoft Office

Procedia PDF Downloads 79

Authors: Andualem Workie

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In this study, we examined the sinterability and bioactivity of MgO-SiO₂-P₂O₅-CaO-CaF₂ glass compositions created through spray pyrolysis. We evaluated the bioactivity of the materials by immersing them for varying periods of time in simulated bodily fluid (SBF) and found that bioactivity was related to the sintering temperature and soaking time. The material's pH value during immersion in SBF was within the range of 7.4-8.2, which is below 8.5 and improves compatibility and reduces toxicity in biological applications. We used X-ray diffraction and scanning electron microscopy to determine the phase compositions and morphologies of the samples and found that the 1100°C sintered A-W GC sample exhibited the highest bioactivity after soaking in SBF. This sample was dominated by fluorapatite, wollastonite, and whitlockite crystals scattered throughout the glass matrix. The crystallinity (%) of the A-W GC increased as its bioactivity improved, making it more suitable for use in pharmaceutical applications. We also conducted a cytotoxicity test on A-W GC samples sintered at different temperatures and found that the glass-ceramics were non-toxic to MC3T3-E1 cells at all extraction concentrations, except for those sintered at 700°C at concentrations of 250, 200, and 150 mg/ml where cell viability (%) was below the threshold of 70%.

Keywords: apatite wollastonite glass ceramics, bioactivity, calcination, cell viability

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Authors: Maitree Thamma, Witchupong Wiboonjaroen, Thanat Suknuan, Karan Homchat

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First order plus dead time (FOPDT) is a high dynamic system. Therefore, the controller must be intelligent. This paper presents the development and implementation of self-tuning Fuzzy-PID controller for controlling the FOPDT system. The water level process used represented FOPDT system and the mathematical model of the system was approximated by using System Identification toolbox in Matlab. The control programming and Fuzzy-PID algorithm used Matlab/Simulink and run on Microcontroller STM32.

Keywords: real-time control, self-tuning fuzzy-PID, FOPDT system, the water lever process

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Authors: Ola Jon Mork, Lars Andre Giske, Emil Bjørlykhaug

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Stabilization of critical processes in order to have the right quality of the products, more efficient production and smoother flow is a key issue in lean production. This paper presents how simulation of key welding processes can stabilize complicated welding processes in small scale production, and how simulation can impact the entire production concept seen from the perspective of lean production. First, a field study was made to learn the production processes in the factory, and subsequently the field study was transformed into a value stream map to get insight into each operation, the quality issues, operation times, lead times and flow of materials. Valuable practical knowledge of how the welding operations were done by operators, appropriate tools and jigs, and type of robots that could be used, was collected. All available information was then implemented into a simulation environment for further elaboration and development. Three researchers, the management of the company and skilled operators at the work floor where working on the project over a period of eight months, and a detailed description of the process was made by the researchers. The simulation showed that simulation could solve a number of technical challenges, the robot program can be tuned in off line mode, and the design and testing of the robot cell could be made in the simulator. Further on the design of the product could be optimized for robot welding and the jigs could be designed and tested in simulation environment. This means that a key issue of lean production can be solved; the welding operation will work with almost 100% performance when it is put into real production. Stabilizing of one key process is critical to gain control of the entire value chain, then a Takt Time can be established and the focus can be directed towards the next process in the production which should be stabilized. Results show that industrial parameters like welding time, welding cost and welding quality can be defined on the simulation stage. Further on, this gives valuable information for calculation of the factories business performance, like manufacturing volume and manufacturing efficiency. Industrial impact from simulation is more efficient implementation of lean manufacturing, since the welding process can be stabilized. More research should be done to gain more knowledge about simulation as a tool for implementation of lean, especially where there complex processes.

Keywords: simulation, lean, stabilization, welding process

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Authors: Yanwen Li, Shuguo Xie

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In electromagnetic imaging, because of the diffraction limited system, the pixel values could change slowly near the edge of the image targets and they also change with the location in the same target. Using traditional digital image segmentation methods to segment electromagnetic gradient images could result in lots of errors because of this change in pixel values. To address this issue, this paper proposes a novel image segmentation and extraction algorithm based on Mean-Shift and dictionary learning. Firstly, the preliminary segmentation results from adaptive bandwidth Mean-Shift algorithm are expanded, merged and extracted. Then the overlap rate of the extracted image block is detected before determining a segmentation region with a single complete target. Last, the gradient edge of the extracted targets is recovered and reconstructed by using a dictionary-learning algorithm, while the final segmentation results are obtained which are very close to the gradient target in the original image. Both the experimental results and the simulated results show that the segmentation results are very accurate. The Dice coefficients are improved by 70% to 80% compared with the Mean-Shift only method.

Keywords: gradient image, segmentation and extract, mean-shift algorithm, dictionary iearning

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Authors: Celeste E. van Rinsum, Sanne M. P. L. Gerards, Geert M. Rutten, Ien A. M. van de Goor, Stef P. J. Kremers

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Combined lifestyle interventions have shown to be effective in changing and maintaining behavioral lifestyle changes and reducing overweight and obesity. A lifestyle coach is expected to promote lifestyle changes in adults related to physical activity and diet. The present Coaching on Lifestyle (CooL) study examined participants’ physical activity level, dietary behavioral, and motivational changes immediately after the intervention and at 1.5 years after baseline. In CooL intervention a lifestyle coach coaches individuals from eighteen years and older with (a high risk of) obesity in group and individual sessions. In addition a process evaluation was conducted in order to examine the implementation process and to be able to interpret the changes within the participants. This action-oriented research has a pre-post design. Participants of the CooL intervention (N = 200) completed three questionnaires: at baseline, immediately after the intervention (on average after 44 weeks), and at 1.5 years after baseline. T-tests and linear regressions were conducted to test self-reported changes in physical activity (IPAQ), dietary behaviors, their quality of motivation for physical activity (BREQ-3) and for diet (REBS), body mass index (BMI), and quality of life (EQ-5D-3L). For the process evaluation, we used individual and group interviews, observations and document analyses to gain insight in the implementation process (e.g. the recruitment) and how the intervention was valued by the participants, lifestyle coaches, and referrers. The study is currently ongoing and therefore the results presented here are preliminary. On average, the participants that finished the intervention and those that have completed the long-term measurement improved their level of vigorous-intense physical activity, sedentary behavior, sugar-sweetened beverage consumption and BMI. Mixed results were observed in motivational regulation for physical activity and nutrition. Moreover, an improvement on the quality of life dimension anxiety/depression was found, also in the long-term. All the other constructs did not show significant change over time. The results of the process evaluation have shown that recruitment of clients was difficult. Participants evaluated the intervention positively and the lifestyle coaches have continuously adapted the structure and contents of the intervention throughout the study period, based on their experiences and feedback from research. Preliminary results indicate that the CooL-intervention may have beneficial effects on overweight and obese participants in terms of energy balance-related behaviors, weight reduction, and quality of life. Recruitment of participants and embedding the position of the lifestyle coach in traditional care structures is challenging.

Keywords: combined lifestyle intervention, effect evaluation, lifestyle coaching, process evaluation, overweight, the Netherlands

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Authors: Samir Ranjbar, Nasrullah Istanekzai

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According to a report published in The Guardian, Kabul, the capital city of Afghanistan is the fifth fastest growing city in the world, whose population has increased fourfold since 2001 from 1.2 million to 4.8 million people. The main reason for this increment is identified as the return of Afghans migrated during the civil war. In addition to the return of immigrants, a steep economic growth due to foreign assistance in last decade creating lots of job opportunities in Kabul resulted in the attraction of individuals from the neighboring provinces as well. However, the development of urban facilities such as water supply system, housing transportation and waste management systems has yet to catch up with this rapid increase in population. Since Kabul city has developed traditionally and municipal governance had very limited capacity to implement municipal bylaws. As an unwanted consequence of this growth 70% of Kabul citizens contributed to developing informal settlement for which we can say that around three million people living in informally settled areas, lacking the very vital social and physical infrastructures of livelihood. This research focuses on a region with 30 ha area and 2100 people residents in the center of Kabul city. A comprehensive land readjustment concept plan has been formulated for this area. Through this concept plan, physical and social infrastructure has been demonstrated and analyzed. Findings of this paper propose a solution for the problems of this unplanned area in Kabul which is readjusting of unplanned area by a self-supporting process. This process does not need governmental budget and can be applied by government, private sectors and landowner associations. Furthermore, by implementing the Land Readjustment process, conceptual plans can be built for unplanned areas, maximum facilities can be brought to the residents’ urban life, improve the environment for the users’ benefit, promote the culture and sense of cooperation, participation and coexistence in the mind of people, improving the transport system, improvement in economic status (the value of land increases due to infrastructure availability and land legalization). In addition to all these benefits for the public, we can raise the revenue of government by collecting the taxes from landowners. This process is implemented in most of countries of the world, it was implemented for the first time in Germany and after that in most cities of Japan as well, and is known as one of the effective processes for infrastructural development. To sum up, the notable characteristic of the Land readjustment process is that it works on the concept of mutual interest in which both landowners and the government take advantage. However, in this process, the engagement of community is very important and without public cooperation, this process can face the failure.

Keywords: land readjustment, informal settlement, Kabul, Afghanistan

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Authors: Lucia Antonela Mitidieri

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This research analyzes the application of a model of settlements management based on networks of territorial integration that advocates planning as a cyclical and participatory process that engages early on with civic society, the private sector and the state. Through qualitative methods such as participant observation, interviews with snowball technique and an active research on territories, concrete results of community empowerment are obtained from the promotion of productive enterprises and community spaces of encounter and exchange. Studying the cultural and organizational dimensions of empowerment allows building indicators such as increase of capacities or community cohesion that can lead to support local governments in achieving sustainable urban development for a reduction of urban poverty.

Keywords: community spaces, empowerment, network urbanism, participatory process

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Authors: Shane David van Zyl, A. J. Burger

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The global green energy shift has placed significant value on the production of green hydrogen and its derivatives. The study examines the impact on capital expenditure (CAPEX), operational expenditure (OPEX), levelized cost, and environmental impact, depending on the relationship between various production capacities of potable water, green hydrogen, and green ammonia. A model-based sensitivity analysis approach was used to determine the relevance of various process parameters in the production of potable water combined with green hydrogen or green ammonia production. The effects of changes on CAPEX, OPEX and levelized costs of the products were determined. Furthermore, a qualitative environmental impact analysis was done to determine the effect on the environment. The findings indicated the individual process unit contribution to the overall CAPEX and OPEX while also determining the major contributors to changes in the levelized costs of products. The results emphasize the difference in costs associated with potable water, green hydrogen, and green ammonia production, indicating the extent to which potable water production costs become insignificant in the complete process, which, therefore, can have a large social benefit through increased potable water production resulting in decreased water scarcity in the south-west African region.

Keywords: CAPEX and OPEX, desalination, green hydrogen and green ammonia, sensitivity analysis

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Authors: Hoda Ghavamipoor, S. Alireza Hashemi Golpayegani

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An approach to model the customer interaction with e-commerce websites is presented. Considering the service quality level as a predictive feature, we offer an improved method based on the Customer Behavior Model Graph (CBMG), a state-transition graph model. To derive the Quality of Service sensitive-CBMG (QoS-CBMG) model, process-mining techniques is applied to pre-processed website server logs which are categorized as ‘buy’ or ‘visit’. Experimental results on an e-commerce website data confirmed that the proposed method outperforms CBMG based method.

Keywords: customer behavior model, electronic commerce, quality of service, customer behavior model graph, process mining

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Authors: Golnoosh Torabian, P. Valtchev, F. Dehghani

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The need for efficient nutraceutical products has been dramatically changing the approach of the industrial processes. The development of novel mild processes is highly demanded for the production of such products; especially when both quality and safety need to be guaranteed during their long shelf life. Within this research, for the first time, we investigated the effect of supercritical carbon dioxide treatment for the inactivation of microbes and enzymes in a berry juice possessing therapeutic effect. We demonstrated that a complete inactivation of microbes can be achieved at optimized conditions of treatment. However, the bottle neck of the process was represented by the unpromising inactivation of the degradative enzyme by supercritical carbon dioxide treatment. However, complete enzyme inactivation was achieved by applying two strategies: the first was optimizing juicing method by adding a mechanical step and the second strategy was addition of natural inhibitors to the juice. Overall these results demonstrate that our hybrid process has a significant effect on the inactivation of microorganism and enzymes in the fresh juice. The developed process opens the possibility for the evolution of new products with optimal nutritional and sensorial characteristics, as well as offering a competitive cost and an environmentally friendly alternative for pasteurization and extension of shelf life in a wide range of natural therapeutic products.

Keywords: hybrid method, berry juice, pasteurization, enzymes inactivation

Procedia PDF Downloads 193

Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Narendra Akhadkar, Silvestre Cano, Christophe Gourru

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Injection-molded parts are widely used in power system protection products. One of the biggest challenges in an injection molding process is shrinkage and warpage of the molded parts. All these geometrical variations may have an adverse effect on the quality of the product, functionality, cost, and time-to-market. The situation becomes more challenging in the case of intricate shapes and in mass production using multi-cavity tools. To control the effects of shrinkage and warpage, it is very important to correctly find out the input parameters that could affect the product performance. With the advances in the computer-aided engineering (CAE), different tools are available to simulate the injection molding process. For our case study, we used the MoldFlow insight tool. Our aim is to predict the spread of the functional dimensions and geometrical variations on the part due to variations in the input parameters such as material viscosity, packing pressure, mold temperature, melt temperature, and injection speed. The input parameters may vary during batch production or due to variations in the machine process settings. To perform the accurate product assembly variation simulation, the first step is to perform an individual part variation simulation to render realistic tolerance ranges. In this article, we present a method to simulate part variations coming from the input parameters variation during batch production. The method is based on computer simulations and experimental validation using the full factorial design of experiments (DoE). The robustness of the simulation model is verified through input parameter wise sensitivity analysis study performed using simulations and experiments; all the results show a very good correlation in the material flow direction. There exists a non-linear interaction between material and the input process variables. It is observed that the parameters such as packing pressure, material, and mold temperature play an important role in spread on functional dimensions and geometrical variations. This method will allow us in the future to develop accurate/realistic virtual prototypes based on trusted simulated process variation and, therefore, increase the product quality and potentially decrease the time to market.

Keywords: correlation, molding process, tolerance, sensitivity analysis, variation simulation

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Authors: Oscar F. Toro, Alexia Pardo Figueroa, Brigitte M. Larico

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The development of new processes in the textile industry entails designing methodologies to select adequate supplies that fit these new processes requirements. This paper presents a methodology to select chemicals that fulfill a new process technical specifications. The proposed methodology involves three major phases: (1) Data collection of chemical products, (2) Qualitative pre-selection and (3) Laboratory tests. We have applied this methodology to the selection of a binder which will form a protective film above the textile fibers and bond them. Our findings were that, there exist five possible products that can be used in our new process: Arkofil, Elvanol, Size plus A, Size plus AC and Starch. This new methodology has both qualitative and experimental variables, and can be used to select supplies for new textile processes.

Keywords: binder, chemical products, selection methodology, textile supplies, textile fiber

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Authors: Marko Živanović, Jovana Bjekić, Maša Vukčević Marković

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Professionals and volunteers involved in refugee protection and support are on a daily basis faced with people who have experienced numerous traumatic experiences and, as such, are subjected to secondary traumatization (ST). The aim of this study was to provide insight into risk factors for ST in helpers working with refugees in Serbia. A total of 175 participants working with refugees fulfilled: Secondary Traumatization Questionnaire, checklist of refugees’ traumatic experiences, Hopkins Symptoms Checklist (HSCL) assessing depression and anxiety symptoms, quality of life questionnaire (MANSA), HEXACO personality inventory, and COPE assessing coping mechanisms. In addition, participants provided information on work-related problems. Qualitative analysis of answers to the question about most difficult part of their job has shown that burnout-related issues are clustered around three recurrent topics that can be considered as the most prominent generators of stress, namely: ‘lack of organization and cooperation’, ‘not been able to do enough’, and ‘hard to take it and to process it’. Factor analysis (Maximum likelihood extraction, Promax rotation) have shown that ST comprises of two correlated factors (r = .533, p < .01), namely Psychological deficits and Intrusions. Results have shown that risk factor for ST could be find in three interrelated sources: 1) work-related problems; 2) personality-related risk factors and 3) clients’ traumatic experiences. Among personality related factors, it was shown that risk factor for Intrusions could be find in – high Emotionality (β = .221, p < .05), and Altruism (β = .322, p < .01), while low Extraversion (β = -.365, p < .01) represents risk factor for Psychological deficits. In addition, usage of maladaptive coping mechanisms –mental disengagement (r = .253, p < .01), behavioral disengagement (r = .274, p < .01), focusing on distress and venting of emotions (r = .220, p < .05), denial (r = .164, p < .05), and substance use (r = .232, p < .01) correlate with Psychological deficits while Intrusions corelate with Mental disengagement (r = .251, p < .01) and denial (r = .183, p < .05). Regarding clients’ traumatic experiences it was shown that both quantity of traumatic events in country of origin (for Deficits r = .226, p < .01; for Intrusions r = .174, p < .05) and in transit (for Deficits r = .288, p < .01), as well as certain content-related features of such experiences (especially experiences which are severely dislocated from ‘everyday reality’) are related to ST. In addition, Psychological deficits and Intrusions have shown to be accompanied by symptoms of depression (r = .760, p < .01; r = .552, p < .01) and anxiety (r = .740, p < .01; r = .447, p < .01) and overall lower life quality (r = -.454, p < .01; r = .256, p < .01). Results indicate that psychological vulnerability of persons who are working with traumatized individuals can be found in certain personality traits, and usage of maladaptive coping mechanisms, which disable one to deal with work-related issues, and to cope with quantity and quality of traumatic experiences they were faced with, affecting ones’ psychological well-being. Acknowledgement: This research was funded by IRC Serbia.

Keywords: mental health, refugees, secondary traumatization, traumatic experiences

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Authors: Huseyin Sinan Gunesli

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Process Safety Management is a disciplined framework for managing the integrity of systems and processes that handle hazardous substances. It relies on good design principles, well-implemented automation systems, and operating and maintenance practices. Alarm Management Systems play a critically important role in the safe and efficient operation of modern industrial plants. In that respect, Alarm Management is one of the critical factors feeding the safe operations of the plants in the manner of applying effective process safety principles. Trans Anatolian Natural Gas Pipeline (TANAP) is part of the Southern Gas Corridor, which extends from the Caspian Sea to Italy. TANAP transports Natural Gas from the Shah Deniz gas field of Azerbaijan, and possibly from other neighboring countries, to Turkey and through Trans Adriatic Pipeline (TAP) Pipeline to Europe. TANAP plays a crucial role in maintaining Energy Security for the region and Europe. In that respect, the application of Process Safety principles is vital to deliver safe, reliable and efficient Natural Gas delivery to Shippers both in the region and Europe. Effective Alarm Management is one of those Process Safety principles which feeds safe operations of the TANAP pipeline. Alarm Philosophy was designed and implemented in TANAP Pipeline according to the relevant standards. However, it is essential to manage the alarms received in the control room effectively to maintain safe operations. In that respect, TANAP has commenced Alarm Management & Rationalization program as of February 2022 after transferring to Plateau Regime, reaching the design parameters. While Alarm Rationalization started, there were more than circa 2300 alarms received per hour from one of the compressor stations. After applying alarm management principles such as reviewing and removal of bad actors, standing, stale, chattering, fleeting alarms, comprehensive review and revision of alarm set points through a change management principle, conducting alarm audits/design verification and etc., it has been achieved to reduce down to circa 40 alarms per hour. After the successful implementation of alarm management principles as specified above, the number of alarms has been reduced to industry standards. That significantly improved operator vigilance to focus on mainly important and critical alarms to avoid any excursion beyond safe operating limits leading to any potential process safety events. Following the ‟What Gets Measured, Gets Managed” principle, TANAP has identified key Performance Indicators (KPIs) to manage Process Safety principles effectively, where Alarm Management has formed one of the key parameters of those KPIs. However, review and analysis of the alarms were performed manually. Without utilizing Alarm Management Software, achieving full compliance with international standards is almost infeasible. In that respect, TANAP has started using one of the industry-wide known Alarm Management Applications to maintain full review and analysis of alarms and define actions as required. That actually significantly empowered TANAP’s process safety principles in terms of Alarm Management.

Keywords: process safety principles, energy security, natural gas pipeline operations, alarm rationalization, alarm management, alarm management application

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Authors: Jeaha Yang, Ahmed Khan, Donna L. Rodela, Mohammed A. Qaudeer

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Traditional supply chain silos still exist today due to the difficulty of establishing trust between various partners and technological barriers across industries. Companies lose opportunities and revenue and inadvertently make poor business decisions resulting in further challenges. Blockchain technology can bring a new level of transparency through sharing information with a distributed ledger in a decentralized manner that creates a basis of trust for business. Blockchain is a loosely coupled, hub-style communication network in which trading partners can work indirectly with each other for simpler integration, but they work together through the orchestration of their supply chain operations under a coherent process that is developed jointly. A Blockchain increases efficiencies, lowers costs, and improves interoperability to strengthen and automate the supply chain management process while all partners share the risk. Blockchain ledger is built to track inventory lifecycle for supply chain transparency and keeps a journal of inventory movement for real-time reconciliation. State design patterns are used to capture the life cycle (behavior) of inventory management as a state machine for a common, transparent and coherent process which creates an opportunity for trading partners to become more responsive in terms of changes or improvements in process, reconcile discrepancies, and comply with internal governance and external regulations. It enables end-to-end, inter-company visibility at the unit level for more accurate demand planning with better insight into order fulfillment and replenishment.

Keywords: supply chain management, inventory trace-ability, perpetual inventory system, inventory lifecycle, blockchain, inventory consignment, supply chain transparency, digital thread, demand planning, hyper ledger fabric

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Authors: Leela Edwin, Rithin Joseph, P. H. Dhiju Das, K. A. Sayana, P. S. Muhammed Sherief

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The shrimp trawl fishery is considered one of the more valuable fisheries from the South east Coast of Arabian Sea. Inventory data for the shrimp were collected over 1 year period and used to carry out a life cycle assessment (LCA). LCA was performed to assess and compare the environmental impacts associated with the fishing operations related to shrimp fishery. This analysis included the operation of the vessels, together with major inputs related to the production of diesel, trawl nets, or anti-fouling paints. Data regarding vessel operation was obtained from the detailed questionnaires filled out by 180 trawlers. The analysis on environmental impacts linked to shrimp extraction on a temporal scale, showed that varying landings enhanced the environmental burdens mainly associated with activities related to diesel production, transport and consumption of the fishing vessels. Discard rates for trawlers were also identified as a major environmental impact in this fishery.

Keywords: shrimp trawling, life cycle assesment (LCA), Arabian sea, environmental impacts

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Authors: Angely Sim Jia Wun, Sasa Arsovski

Abstract:

Numerous studies have been conducted on the segmentation of medical images. Segmenting the lungs is one of the common research topics in those studies. Our research stemmed from the lack of solutions for automatic bone, airway, and vessel segmentation, despite the existence of multiple lung segmentation techniques. Consequently, currently, available software tools used for medical image segmentation do not provide automatic lung, bone, airway, and vessel segmentation. This paper presents segmentation techniques along with an interactive software tool architecture for segmenting bone, lung, airway, and vessel tissues. Additionally, we propose a method for creating binary masks from automatically generated segments. The key contribution of our approach is the technique for automatic image thresholding using adjustable Hounsfield values and binary mask extraction. Generated binary masks can be successfully used as a training dataset for deep-learning solutions in medical image segmentation. In this paper, we also examine the current software tools used for medical image segmentation, discuss our approach, and identify its advantages.

Keywords: lung segmentation, binary masks, U-Net, medical software tools

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Authors: Elahe Memari, Ahmad Aslizadeh, Ahmad Memari

Abstract:

To compile and execute a strategy for State Road Management and Transportation Organization, we need to identify and include them in the process of planning. Therefore, present research work tries to rely on experiences by managers and experts from State Road Management and Transportation Organization and other sources like books, magazines and new papers, such factors have to be identified and be applied in this important and vital process before proceeding to strategic planning. Trying to present a conceptual model from factors effective on strategic planning success in fulfillment of State Road Management and Transportation Organization, the present research figures on indicating the role of organizational factors in efficiency of the process to managers. In this research connection between six main factors studied in fulfillment of State Road Management and Transportation Organization objectives. The factors are improvement of strategic thinking in senior managers, improvement of organization business, rationalizing resource allocation in different sections of the organization, conformity of strategic planning with organization needs, conformity of organization activities with environmental changes, stabilization of organizational culture, all approved through implemented tests.

Keywords: improvement of organization business, rationalization of resource allocation in different sections of the organization, stability of organizational culture, strategic planning

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Authors: Abe D. Desta

Abstract:

This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Vivek Garg, Brajendra S. Sengar, Gaurav Siddharth, Nisheka Anadkat, Amitesh Kumar, Shailendra Kumar, Shaibal Mukherjee

Abstract:

CuInGaSe (CIGSe) is the dominant thin film solar cell technology. The band alignment of Buffer/CIGSe interface is one of the most crucial parameters for solar cell performance. In this article, the valence band offset (VBOff) and conduction band offset (CBOff) values of Cu(In0.70Ga0.30)Se/ 1 at.% Ga: Mg0.25Zn0.75O (GMZO) heterojunction, grown by dual ion beam sputtering system (DIBS), are calculated to understand the carrier transport mechanism at the heterojunction for the realization of all sputtered buffer-less solar cells. To determine the valence band offset (VBOff), ∆E_V at GMZO/CIGSe heterojunction interface, the standard method based on core-level photoemission is utilized. The value of ∆E_V can be evaluated by considering common core-level peaks. In our study, the values of (Valence band onset)VBOn, obtained by linear extrapolation method for GMZO and CIGSe films are calculated to be 2.86 and 0.76 eV. In the UPS spectra peak positions of Se 3d is observed in UPS spectra at 54.82 and 54.7 eV for CIGSe film and GMZO/CIGSe interface respectively, while the peak position of Mg 2p is observed at 50.09 and 50.12 eV for GMZO and GMZO/CIGSe interface respectively. The optical band gap of CIGSe and GMZO are obtained from absorption spectra procured from spectroscopic ellipsometry are 1.26 and 3.84 eV respectively. The calculated average values of ∆E_v and ∆E_C are estimated to be 2.37 and 0.21 eV, respectively, at room temperature. The calculated positive conduction band offset termed as a spike at the absorber junction is the required criterion for the high-efficiency solar cells for the efficient charge extraction from the junction. So we can conclude that the above study confirms GMZO thin films grown by the dual ion beam sputtering system are the suitable candidate for the CIGSe thin films based ultra-thin buffer-less solar cells. We investigated the band-offset properties at the GMZO/CIGSe heterojunction to verify the suitability of the GMZO for the realization of the buffer-less solar cells. The calculated average values of ∆E_V and ∆E_C are estimated to be 2.37 and 0.21 eV, respectively, at room temperature. The calculated positive conduction band offset termed as a spike at the absorber junction is the required criterion for the high-efficiency solar cells for the efficient charge extraction from the junction. So we can conclude that the above study confirms GMZO thin films grown by the dual ion beam sputtering system are the suitable candidate for the CIGSe thin films based ultra-thin buffer-less solar cells. Acknowledgment: We are thankful to DIBS, EDX, and XRD facility equipped at Sophisticated Instrument Centre (SIC) at IIT Indore. The authors B.S.S and A.K acknowledge CSIR and V.G acknowledge UGC, India for their fellowships. B.S.S is thankful to DST and IUSSTF for BASE Internship Award. Prof. Shaibal Mukherjee is thankful to DST and IUSSTF for BASE Fellowship and MEITY YFRF award. This work is partially supported by DAE BRNS, DST CERI, and DST-RFBR Project under India-Russia Programme of Cooperation in Science and Technology. We are thankful to Mukul Gupta for SIMS facility equipped at UGC-DAE Indore.

Keywords: CIGSe, DIBS, GMZO, solar cells, UPS

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Authors: Ryoko Yoshida, Jyunpei Yoshida, Hua Fang Yu, Yasushi Sasaki, Tetsuya Nagasaka

Abstract:

Phosphorous is an important element for agriculture and industry and is a non-renewable resource. Especially, yellow phosphorus is an essential material in advanced industrial technology, but phosphorus resources were not produced in Japan at all, and all depend on imports. It has been suggested, however, that the remaining accessible reserves of phosphate ore will be depleted within 50 years. Therefore, alternative resources for phosphate ore must be found. In this research, we have developed a process that enables the production of high-purity yellow phosphorus from domestic unused phosphorus resources such as steelmaking slags. The process consists of two parts: (1) the production of crude phosphoric acid from wastes such as steelmaking slag; (2) producing high-purity yellow phosphorus by low-temperature carbothermic reduction of phosphoric acid (H₃PO₄). The details of the carbothermic reduction of phosphoric acid are presented in this paper. Yellow phosphorus is commercially produced by carbothermic reduction of phosphate ore in an electric arc furnace at more than 1673K. In the newly developed system, gaseous P₄O₁₀ evaporated from H₃PO₄ is successfully reduced to yellow phosphorus by using carbon packed bed at less than 1273K. To meet the depletion of phosphate ore, the proposed process in this study to produce yellow phosphorus by carbothermic reduction of H₃PO₄ that are extracted from dephosphorization slags will be one of the effective and economical solutions.

Keywords: carbothermic reduction, phosphoric acid, dephosphorization slags, yellow phosphorus

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Authors: Subhayan Maity

Abstract:

Complete scenario of cosmic evolution from emergent phase to late time acceleration (i.e. non-singular ever expanding Universe) is a popular preference in the recent cosmology. Yet one can’t exclude the idea that other type of evolution pattern of the Universe may also be possible. Especially, the bouncing scenario is becoming a matter of interest now a days. The present work is an exhibition of such a different pattern of cosmic evolution where the evolution of Universe has been shown as a cyclic thermodynamic process. Under diffusion mechanism (non-equilibrium thermodynamic process), the cosmic evolution has been modelled as [ emergent - accelerated expansion - decelerated expansion - decelerated contraction - accelerated contraction - emergent] .

Keywords: non-equilibrium thermodynamics, non singular evolution of universe, cyclic evolution, diffusive fluid

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Authors: Lily Farris, Chantel E. Canessa, Rena Heathcote, Susan Shumay, Suzanna V. McRae, Alissa Collingridge, Minna K. Miller

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Objective: To describe how the Lean approach can be applied to improve access, quality and safety of care in an ambulatory pediatric primary care setting. Background: Lean was originally developed by Toyota manufacturing in Japan, and subsequently adapted for use in the healthcare sector. Lean is a systematic approach, focused on identifying and reducing waste within organizational processes, improving patient-centered care and efficiency. Limited literature is available on the implementation of the Lean methodologies in a pediatric ambulatory care setting. Methods: A strategic continuous improvement event or Rapid Process Improvement Workshop (RPIW) was launched with the aim evaluating and structurally supporting clinic workflow, capacity building, sustainability, and ultimately improving access to care and enhancing the patient experience. The Lean process consists of five specific activities: Current state/process assessment (value stream map); development of a future state map (value stream map after waste reduction); identification, quantification and prioritization of the process improvement opportunities; implementation and evaluation of process changes; and audits to sustain the gains. Staff engagement is a critical component of the Lean process. Results: Through the implementation of the RPIW and shifting workload among the administrative team, four hours of wasted time moving between desks and doing work was eliminated from the Administrative Clerks role. To streamline clinic flow, the Nursing Assistants completed patient measurements and vitals for Nurse Practitioners, reducing patient wait times and adding value to the patients visit with the Nurse Practitioners. Additionally, through the Nurse Practitioners engagement in the Lean processes a need was recognized to articulate clinic vision, mission and the alignment of NP role and scope of practice with the agency and Ministry of Health strategic plan. Conclusions: Continuous improvement work in the Pediatric Primary Care NP Clinic has provided a unique opportunity to improve the quality of care delivered and has facilitated further alignment of the daily continuous improvement work with the strategic priorities of the Ministry of Health.

Keywords: ambulatory care, lean, pediatric primary care, system efficiency

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Authors: Ruth Lorivi, Jianzheng Li, John J. Ambuchi, Kaiwen Deng

Abstract:

Nitrogen removal from wastewater is accomplished by nitrification and denitrification processes. Successful denitrification requires carbon, therefore, if placed after biochemical oxygen demand (BOD) and nitrification process, a carbon source has to be re-introduced into the water. To avoid adding a carbon source, denitrification is usually placed before BOD and nitrification processes. This process however involves recycling the nitrified effluent. In this study wood chips were used as internal carbon source which enabled placement of denitrification after BOD and nitrification process without effluent recycling. To investigate the efficiency of a wood packed aerobic-anaerobic baffled reactor on carbon and nutrients removal from domestic wastewater, a three compartment baffled reactor was presented. Each of the three compartments was packed with 329 g wood chips 1x1cm acting as an internal carbon source for denitrification. The proposed mode of operation was aerobic-anoxic-anaerobic (OAA) with no effluent recycling. The operating temperature, hydraulic retention time (HRT), dissolved oxygen (DO) and pH were 24 ± 2 ℃, 24 h, less than 4 mg/L and 7 ± 1 respectively. The removal efficiencies of chemical oxygen demand (COD), ammonia nitrogen (NH₄⁺-N) and total nitrogen (TN) attained was 99, 87 and 83% respectively. TN removal rate was limited by nitrification as 97% of ammonia converted into nitrate and nitrite was denitrified. These results show that application of wood chips in wastewater treatment processes is an efficient internal carbon source. 

Keywords: aerobic-anaerobic baffled reactor, denitrification, nitrification, wood chip

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Authors: Qiang Zhang, Xiaojian Hu1

Abstract:

Intersection is the place where vehicles and pedestrians must pass through, turn and evacuate. Obtaining the motion data of vehicles near the intersection is of great significance for transportation research. Since there are usually many targets and there are more conflicts between targets, this makes it difficult to obtain vehicle motion parameters in traffic videos of intersections. According to the characteristics of traffic videos, this paper applies video technology to realize the automated track, count and trajectory extraction of vehicles to collect traffic data by roadside surveillance cameras installed near the intersections. Based on the video recognition method, the vehicles in each lane near the intersection are tracked with extracting trajectory and counted respectively in various degrees of occlusion and visibility. The performances are compared with current recognized CPU-based algorithms of real-time tracking-by-detection. The speed of the presented system is higher than the others and the system has a better real-time performance. The accuracy of direction has reached about 94.99% on average, and the accuracy of classification and statistics has reached about 75.12% on average.

Keywords: tracking and statistics, vehicle, signalized intersection, motion parameter, trajectory

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