Search results for: maintenance optimization

Authors: M. Firoz Kalam, Wilfried Schuetz, Jan Hendrik Bredehoeft

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Thiophene sulfur compounds' removal from diesel oil by batch adsorption process using commercial powdered activated carbon was designed and optimized in two-level factorial design method. This design analysis was used to find out the effects of operating parameters directing the adsorption process, such as amount of adsorbent, temperature and stirring time. The desulfurization efficiency was considered the response or output variable. Results showed that the stirring time had the largest effects on sulfur removal efficiency as compared with other operating parameters and their interactions under the experimental ranges studied. A regression model was generated to observe the closeness between predicted and experimental values. The three-dimensional plots and contour plots of main factors were generated according to the regression results to observe the optimal points.

Keywords: activated carbon, adsorptive desulfurization, factorial design, process optimization

Procedia PDF Downloads 162

Authors: Roghieh A. Biroon, Zoleikha Abdollahi

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The recent growth of renewables, e.g., solar panels, batteries, and electric vehicles (EVs) in residential and small commercial sectors, has potential impacts on the stability and operation of power grids. Considering approximately 50 percent share of the residential and the commercial sectors in the electricity demand market, the significance of these impacts, and the necessity of addressing them are more highlighted. Utilities and power system operators should manage the renewable electricity sources integration with power systems in such a way to extract the most possible advantages for the power systems. The most common effect of high penetration level of the renewables is the reverse power flow in the distribution feeders when the customers generate more power than their needs. The reverse power flow causes voltage rise and thermal issues in the power grids. To overcome the voltage rise issues in the distribution system, several techniques have been proposed including reducing transformers short circuit resistance and feeder impedance, installing autotransformers/voltage regulators along the line, absorbing the reactive power by distributed generators (DGs), and limiting the PV and battery sizes. In this study, we consider a medium-scale battery energy storage to manage the power energy and address the aforementioned issues on voltage deviation and power loss increase. We propose an optimization algorithm to find the optimum size and location for the battery. The optimization for the battery location and size is so that the battery maintains the feeder voltage deviation and power loss at a certain desired level. Moreover, the proposed optimization algorithm controls the charging/discharging profile of the battery to absorb the negative power flow from residential and commercial customers in the feeder during the peak time and sell the power back to the system during the off-peak time. The proposed battery regulates the voltage problem in the distribution system while it also can play frequency regulation role in islanded microgrids. This battery can be regulated and controlled by the utilities or a third-party ancillary service provider for the utilities to reduce the power system loss and regulate the distribution feeder voltage and frequency in standard level.

Keywords: ancillary services, battery, distribution system and optimization

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Authors: Michal Friedrich

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Cross-border investment has never been easier than in today’s global economy. Despite recent initiatives tightening the international tax landscape, profit shifting and tax optimization by multinational entities (MNEs) in the context of cross-border M&A remain persistent and complex phenomena that warrant in-depth exploration. By synthesizing the outcomes of existing research, this study aims to first provide a methodological framework for identifying MNEs’ profit-shifting behavior and quantifying its fiscal impacts via various macroeconomic and microeconomic approaches. The study also proposes additional methods and qualitative/quantitative measures for extracting insight into the profit shifting behavior of MNEs in the context of their M&A activities at industry and entity levels. To develop the proposed methods, this study applies the knowledge of international tax laws and known profit shifting conduits (incl. dividends, interest, and royalties) on several model cases/types of cross-border acquisitions and post-acquisition integration activities by MNEs and highlights important factors that encourage or discourage tax optimization. Follow-up research is envisaged to apply the methods outlined in this study on published data on real-world M&A transactions to gain practical country-by-country, industry and entity-level insights. In conclusion, this study seeks to contribute to the ongoing discourse on profit shifting by providing a methodological toolkit for exploring profit shifting tendencies MNEs in connection with their M&A activities and to serve as a backbone for further research. The study is expected to provide valuable insight to policymakers, tax authorities, and tax professionals alike.

Keywords: BEPS, cross-border M&A, international taxation, profit shifting, tax optimization

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Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira

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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.

Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing

Procedia PDF Downloads 143

Authors: Abdallah Abu Madi, Dongmei Cao, Alexeis Garcia-Perez, Qile He

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The failure of Enterprise Resource Planning (ERP) implementation in higher education institutions (HEIs) worldwide is much higher in comparison to other sectors, such as banking or manufacturing, to our knowledge limited research has been conducted on this issue. To date, prior literature has identified some key success factors (KSFs) mostly either in the domain of information and system (IS) or in the industrial context. However, evidence of ERP implementation in the higher education sector has had little attention in the extant literature. Hence, this paper identifies and categories KSFs of ERP implementation in HEIs. Semi-structured face-to-face interviews were conducted with technicians and managers from three Jordanian HEIs. From these case studies, three new sector- and context-specific KSFs were identified and categorized according to two dimensions: organizational and technical. The first new KSF is the selection of the ERP system, which is an influential factor in the organizational dimension. Results show that an ERP solution that is suitable to one context may be disastrous in another. The second new KSF, which falls under the technical dimension, is the relationship between vendors and HEIs. This must be fair and impartial to enable successful decision-making and thus the achievement of pre-defined goals. Also within the technical dimension is the third factor: in-house maintenance. Once an appropriate system is selected and a strong relationship is established, the ERP system requires continuous maintenance for effective operation. HEIs should ensure that qualified IT support is in place and in-house to avoid excessive running expenses.

Keywords: Enterprise Resource Planning (ERP)implementation, key success factors, higher education institutions, Jordanian higher education

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Authors: Anusuya Ghosh, Vishnu Narayanan

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The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming.

Keywords: semidefinite programming, semidefinite representable set, compactly semidefinite representable set, approximation

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Ritsuko Kawasaki (Aiba), Takeshi Hiromatsu

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Management is required to understand all information security risks within an organization, and to make decisions on which information security risks should be treated in what level by allocating how much amount of cost. However, such decision-making is not usually easy, because various measures for risk treatment must be selected with the suitable application levels. In addition, some measures may have objectives conflicting with each other. It also makes the selection difficult. Moreover, risks generally have trends and it also should be considered in risk treatment. Therefore, this paper provides the extension of the model proposed in the previous study. The original model supports the selection of measures by applying a combination of weighted average method and goal programming method for multi-objective analysis to find an optimal solution. The extended model includes the notion of weights to the risks, and the larger weight means the priority of the risk.

Keywords: information security risk treatment, selection of risk measures, risk acceptance, multi-objective optimization

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Authors: Dong-Woo Kang

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This paper presents an optimal magnet shape of a spoke-shaped interior permanent magnet synchronous motor by using ferrite magnets. Generally, the permanent magnet motor used the ferrite magnets has lower output power and efficiency than a rare-earth magnet motor, because the ferrite magnet has lower magnetic energy than the rare-earth magnet. Nevertheless, the ferrite magnet motor is used to many industrial products owing to cost effectiveness. In this paper, the authors propose a high power density design of the ferrite permanent magnet synchronous motor. Furthermore, because the motor design has to be taken a manufacturing process into account, the design is simulated by using the finite element method for analyzing the demagnetization, the magnetizing, and the structure stiffness. Especially, the magnet shape and dimensions are decided for satisfying these properties. Finally, the authors design an optimal motor for applying our system. That final design is manufactured and evaluated from experimentations.

Keywords: demagnetization, design optimization, magnetic analysis, permanent magnet motors

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Authors: Maria Ulfa, Nur Awaliyah Putri

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Madurese is known as Malayo-Sumbawan Austronesian language which is used by Madurese people in Madura Island, Indonesia. However, there was a massive migration of Madurese people due to Dutch colonization. The Madurese people were brought by force for cultivation system to the eastern salient north coast or called as Tapal Kuda that spread in region covers the regencies of Probolinggo, Lumajang, Jember, Situbondo, Bondowoso, and Banyuwangi, the eastern part of the Pasuruan Regency, as well as the city of Probolinggo. The city of Probolinggo has unique characteristic regarding the ethnic and language variation. Several ethnics can be found in this city, such as Madurese, Javanese, Tengger, Arabic, Mandhalungan, Osing, and Chinese. Hence, the hybrid culture happens in Probolinggo, they called the culture as Pendhalungan which is the combination of culture among Madurese and Javanese. Among those ethnics, Madurese is the strongest ethnic that still maintains their identity, such as their ethnic language. The massive growth of Madurese in Probolinggo city, East Java is interesting to be analyzed. The object of this study is to discover language ideology and literacy of Madurese to maintain their ethnic language in Probolinggo city, East Java. The researchers used the theory of language maintenance practice based on three types of practices social language, social literacy, and peripheral ritualized practices. The approach of this study was qualitative research with ethnography method. In order to collect the data, researchers used observation and interview techniques. The amount of informants were 20 families which consist of mother, father and children in 5 sub-districts in Probolinggo city and they were interviewed regarding language ideology and literacy of Madurese. In supporting the data, researchers employed the Madurese speakers outside family scope like in school, office, and market. The result of the study revealed that Madurese has been preserved heritably to young generations by ethnics of Madura in Probolinggo city. Primarily the language is being taught in the earlier age of their children as L1 and used as ethnic identity. The parents teach them with simple sentences that grammatically correct. This language literacy is applied to maintain ethnic language as their ethnicity marker since they inhabit in Javanese ethnic area. In fact, it is not the only ideology of Madurese ethnic but also the influence of economic situation like in trading communication. The usage of Madurese in the trading scope is very beneficial since people can bargain the goods cheaper and easier because most of the traders are from Madurese ethnic. In this situation, linguistic phenomena such as code mixing and code switching between Madurese and Javanese are emerged as the trading communication. From the result, it can be concluded that solidarity exists among Madurese people in many scopes.

Keywords: language literacy, language maintenance, Madurese, Probolinggo City

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Authors: Georges Bou Ghantous

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This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: artificial intelligence, infrastructure as code, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making

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Authors: Francesco Ruscio, Paolo Paoletti, Jens Thomas, Paul Myers, Sebastiano Fichera

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This paper presents the development of a low-cost monitoring system for a hydroponic urban vertical farm, enabling its automation and a quantitative assessment of the farm performance. Urban farming has seen increasing interest in the last decade thanks to the development of energy efficient and affordable LED lights; however, the optimal configuration of such systems (i.e. amount of nutrients, light-on time, ambient temperature etc.) is mostly based on the farmers’ experience and empirical guidelines. Moreover, even if simple, the maintenance of such systems is labor intensive as it requires water to be topped-up periodically, mixing of the nutrients etc. To unlock the full potential of urban farming, a quantitative understanding of the role that each variable plays in the growth of the plants is needed, together with a higher degree of automation. The low-cost monitoring system proposed in this paper is a step toward filling this knowledge and technological gap, as it enables collection of sensor data related to water and air temperature, water level, humidity, pressure, light intensity, pH and electric conductivity without requiring any human intervention. More sensors and actuators can also easily be added thanks to the modular design of the proposed platform. Data can be accessed remotely via a simple web interface. The proposed platform can be used both for quantitatively optimizing the setup of the farms and for automating some of the most labor-intensive maintenance activities. Moreover, such monitoring system can also potentially be used for high-level decision making, once enough data are collected.

Keywords: automation, hydroponics, internet of things, monitoring system, urban farming

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Authors: Mahmoud M. Othman, Almoataz Y. Abdelaziz, Yasser G. Hegazy

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This paper presents a load control strategy based on modification of the Big Bang Big Crunch optimization method. The proposed strategy aims to determine the optimal load to be controlled and the corresponding time of control in order to minimize the energy purchased from substation. The presented strategy helps the distribution network operator to rely on the renewable energy sources in supplying the system demand. The renewable energy sources used in the presented study are modeled using the diagonal band Copula method and sequential Monte Carlo method in order to accurately consider the multivariate stochastic dependence between wind power, photovoltaic power and the system demand. The proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder. Several case studies are done and the subsequent discussions show the effectiveness of the proposed algorithm.

Keywords: big bang big crunch, distributed generation, load control, optimization, planning

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Authors: Ming Su, Ziqiang Mu

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This paper proposes a synthesis method for nonuniform linear antenna arrays that combine Gaussian process regression (GPR) and genetic algorithm (GA). In this method, the GPR model can be used to calculate the array radiation pattern in the presence of mutual coupling effects, and then the GA is used to optimize the excitations and locations of the elements so as to generate the desired radiation pattern. In this paper, taking a 9-element nonuniform linear array as an example and the desired radiation pattern corresponding to a Chebyshev distribution as the optimization objective, optimize the excitations and locations of the elements. Finally, the optimization results are verified by electromagnetic simulation software CST, which shows that the method is effective.

Keywords: nonuniform linear antenna arrays, GPR, GA, mutual coupling effects, active element pattern

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Authors: Javad Jamali Khouei, Mohammadreza Khoshravan

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Considering that application of functionally graded material is increasing in most industries, it seems necessary to present a methodology for designing optimal profile of structures such as plate under mechanical loading which is highly consumed in industries. Therefore, volume fraction variation profile of functionally graded circular plate which has been considered two-directional is optimized so that stress of structure is minimized. For this purpose, equilibrium equations of two-directional functionally graded circular plate are solved by applying semi analytical-numerical method under mechanical loading and support conditions. By solving equilibrium equations, deflections and stresses are obtained in terms of control variables of volume fraction variation profile. As a result, the problem formula can be defined as an optimization problem by aiming at minimization of critical von-mises stress under constraints of deflections, stress and a physical constraint relating to structure of material. Then, the related problem can be solved with help of one of the metaheuristic algorithms such as genetic algorithm. Results of optimization for the applied model under constraints and loadings and boundary conditions show that functionally graded plate should be graded only in radial direction and there is no need for volume fraction variation of the constituent particles in thickness direction. For validating results, optimal values of the obtained design variables are graphically evaluated.

Keywords: two-directional functionally graded material, single objective optimization, semi analytical-numerical solution, genetic algorithm, graphical solution with contour

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Authors: Majid Galoie, Khodadad Safavi, Abdolreza Karami Nejad, Reza Roshan

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Scour downstream of a flip bucket in a plunge pool is caused by impingement of water jet force. In order to reduce this force and consequently reduce scour hole depth, flip buckets may equip by dents. The minimum scour hole depth might be occurred by optimization of dents (number, shape, placement) on flip buckets. In this study, a comprehensive physical model has been developed and various options for dents have been investigated. The experimental data for each dent option such as scour hole depth, angle of impingement jet, piezometric pressure in tail-water and jet trajectory have been measured for various discharges. Finally, the best option can be found by analysis of the experimental results which has been expressed in this paper.

Keywords: scouring process, plunge pool, scour hole depth, physical model, flip bucket

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Authors: Ahmed Arkoazi, Hussein Znad, Ranjeet Utikar

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The synergistic impact and optimization of gas flow rate, concentration of CO₂, and light intensity on CO₂ biofixation rate were investigated using wastewater as a medium to cultivate Chlorella vulgaris under different conditions (gas flow rate 1-8 L/min), CO₂ concentration (0.03-7%), and light intensity (150-400 µmol/m².s)). Response Surface Methodology and Box-Behnken experimental Design were applied to find optimum values for gas flow rate, CO₂ concentration, and light intensity. The optimum values of the three independent variables (gas flow rate, concentration of CO₂, and light intensity) and desirability were 7.5 L/min, 3.5%, and 400 µmol/m².s, and 0.904, respectively. The highest amount of biomass produced and CO₂ biofixation rate at optimum conditions were 5.7 g/L, 1.23 gL^-1d^-1, respectively. The synergistic effect between gas flow rate and concentration of CO₂, and between gas flow rate and light intensity was significant on the three responses, while the effect between CO₂ concentration and light intensity was less significant on CO₂ biofixation rate. The results of this study could be highly helpful when using microalgae for CO₂ biofixation in wastewater treatment.

Keywords: bubble column reactor, gas holdup, hydrodynamics, sparger

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Authors: Fezzani Boubaker

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The modified activated sludge model (ASM1 or Mantis) is a generic structured model and a common platform for dynamic simulation of varieties of aerobic processes for optimization and upgrading of existing plants and for new facilities design. In this study, the modified ASM1 included in the GPS-X software was used to simulate the wastewater treatment plant (WWTP) of Beja treating domestic sewage mixed with baker‘s yeast factory effluent. The results of daily measurements and operating records were used to calibrate the model. A sensitivity and an automatic optimization analysis were conducted to determine the most sensitive and optimal parameters. The results indicated that the ASM1 model could simulate with good accuracy: the COD concentration of effluents from the WWTP of Beja for all months of the year 2012. In addition, it prevents the disruption observed at the output of the plant by injecting the baker‘s yeast factory effluent at high concentrations varied between 20 and 80 g/l.

Keywords: ASM1, activated sludge, baker’s yeast effluent, modelling, simulation, GPS-X 5.1 software

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Authors: Qusay Alghazali, Tibor Cinkler, Abdulhalim Fayad

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In modern optical networks, the flex grid spectrum usage is most widespread, where higher bit rate streams get larger spectrum slices while lower bit rate traffic streams get smaller spectrum slices. To our practice, under the ITU-T recommendation, G.694.1, spectrum slices of 50, 75, and 100 GHz are being used with central frequency at 193.1 THz. However, when these spectrum slices are not sufficient, multiple spectrum slices can use either one next to another or anywhere in the optical wavelength. In this paper, we propose the analysis of the wavelength assignment problem. We compare different algorithms for this spectrum assignment with and without protection. As a reference for comparisons, we concluded that the Integer Linear Programming (ILP) provides the global optimum for all cases. The most scalable algorithm is the greedy one, which yields results in subsequent ranges even for more significant network instances. The algorithms’ benchmark implemented using the LEMON C++ optimization library and simulation runs based on a minimum number of spectrum slices assigned to lightpaths and their execution time.

Keywords: spectrum assignment, integer linear programming, greedy algorithm, international telecommunication union, library for efficient modeling and optimization in networks

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Authors: Zahra Abdolkarimi, Naser Zouri

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The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. There is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidentally.

Keywords: particle swarm optimization, chaos theory, inference fuzzy system, simulation environment rational fuzzy system, mamdani and assilian, deffuzify

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Authors: Jelena Vucicevic

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Reliability analysis represents a very important task in different areas of work. In any industry, this is crucial for maintenance, efficiency, safety and monetary costs. There are ways to calculate reliability, unreliability, failure density and failure rate. In this paper, the results for the reliability of diesel generator fans were calculated through Extreme Value Theory. The Extreme Value Theory is not widely used in the engineering field. Its usage is well known in other areas such as hydrology, meteorology, finance. The significance of this theory is in the fact that unlike the other statistical methods it is focused on rare and extreme values, and not on average. It should be noted that this theory is not designed exclusively for extreme events, but for extreme values in any event. Therefore, this is a great opportunity to apply the theory and test if it could be applied in this situation. The significance of the work is the calculation of time to failure or reliability in a new way, using statistic. Another advantage of this calculation is that there is no need for technical details and it can be implemented in any part for which we need to know the time to fail in order to have appropriate maintenance, but also to maximize usage and minimize costs. In this case, calculations have been made on diesel generator fans but the same principle can be applied to any other part. The data for this paper came from a field engineering study of the time to failure of diesel generator fans. The ultimate goal was to decide whether or not to replace the working fans with a higher quality fan to prevent future failures. The results achieved in this method will show the approximation of time for which the fans will work as they should, and the percentage of probability of fans working more than certain estimated time. Extreme Value Theory can be applied not only for rare and extreme events, but for any event that has values which we can consider as extreme.

Keywords: extreme value theory, lifetime, reliability analysis, statistic, time to failure

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Authors: Chastiti Mediafira Wulolo, Tri Legionosuko, Suhirwan, Yusuf

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Terrorism and radicalization have become a common threat to every nation in this world. As a part of the asymmetric warfare threat, terrorism and radicalization need a complex strategy as the problem solver. One such way is by collaborating with the international community. The Our Eyes Initiative (OEI), for example, is a cooperation pact in the field of intelligence information exchanges related to terrorism and radicalization initiated by the Indonesian Ministry of Defence. The pact has been signed by Indonesia, Philippines, Malaysia, Brunei Darussalam, Thailand, and Singapore. This cooperation mostly engages military acts as a central role, but it still requires the involvement of various parties such as the police, intelligence agencies and other government institutions. This paper will use a qualitative content analysis method to address the opportunity and enhance the optimization of OEI. As the result, it will explain how OEI takes the opportunities as the strategy for counter-terrorism by building it up as the regional cooperation, building the legitimacy of government and creating the legal framework of the information sharing system.

Keywords: our eyes initiative, terrorism, counter-terrorism, ASEAN, cooperation, strategy

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Authors: K. M. Yoo, M. H. Kang

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Conventional commercial wind turbine design software is limited to large wind turbines due to not incorporating with low Reynold’s Number aerodynamic characteristics typically for small wind turbines. To extract maximum annual energy product from an intermediately designed small wind turbine associated with measured wind data, numerous simulation is highly recommended to have a best fitting planform design with proper airfoil configuration. Since depending upon wind distribution with average wind speed, an optimal wind turbine planform design changes accordingly. It is theoretically not difficult, though, it is very inconveniently time-consuming design procedure to finalize conceptual layout of a desired small wind turbine. Thus, to help simulations easier and faster, a GUI software is developed to conveniently iterate and change airfoil types, wind data, and geometric blade data as well. With magnetic generator torque curve, peak power tracking simulation is also available to better match with the magnetic generator. Small wind turbine often lacks starting torque due to blade optimization. Thus this simulation is also embedded along with yaw design. This software provides various blade cross section details at user’s design convenience such as skin thickness control with fiber direction option, spar shape, and their material properties. Since small wind turbine is under international safety regulations with fatigue damage during normal operations and safety load analyses with ultimate excessive loads, load analyses are provided with each category mandated in the safety regulations.

Keywords: GUI software, Low Reynold’s number aerodynamics, peak power tracking, safety regulations, wind turbine performance optimization

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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Authors: Caroline Atef Shoukry Tadros

Abstract:

Massive Urbanization growth threatens the sustainability of cities and the quality of city life. This raised the need for an alternate model of sustainability, so we need to plan the future cities in a smarter way with smarter mobility. Smart Mobility planning applications are solutions that use digital technologies and infrastructure advances to improve the efficiency, sustainability, and inclusiveness of urban transportation systems. They can contribute to meeting the needs of Urbanization growth by addressing the challenges of traffic congestion, pollution, accessibility, and safety in cities. Some example of a Smart Mobility planning application are Mobility-as-a-service: This is a service that integrates different transport modes, such as public transport, shared mobility, and active mobility, into a single platform that allows users to plan, book, and pay for their trips. This can reduce the reliance on private cars, optimize the use of existing infrastructure, and provide more choices and convenience for travelers. MaaS Global is a company that offers mobility-as-a-service solutions in several cities around the world. Traffic flow optimization: This is a solution that uses data analytics, artificial intelligence, and sensors to monitor and manage traffic conditions in real-time. This can reduce congestion, emissions, and travel time, as well as improve road safety and user satisfaction. Waycare is a platform that leverages data from various sources, such as connected vehicles, mobile applications, and road cameras, to provide traffic management agencies with insights and recommendations to optimize traffic flow. Logistics optimization: This is a solution that uses smart algorithms, blockchain, and IoT to improve the efficiency and transparency of the delivery of goods and services in urban areas. This can reduce the costs, emissions, and delays associated with logistics, as well as enhance the customer experience and trust. ShipChain is a blockchain-based platform that connects shippers, carriers, and customers and provides end-to-end visibility and traceability of the shipments. Autonomous vehicles: This is a solution that uses advanced sensors, software, and communication systems to enable vehicles to operate without human intervention. This can improve the safety, accessibility, and productivity of transportation, as well as reduce the need for parking space and infrastructure maintenance. Waymo is a company that develops and operates autonomous vehicles for various purposes, such as ride-hailing, delivery, and trucking. These are some of the ways that Smart Mobility planning applications can contribute to meeting the needs of the Urbanization growth. However, there are also various opportunities and challenges related to the implementation and adoption of these solutions, such as the regulatory, ethical, social, and technical aspects. Therefore, it is important to consider the specific context and needs of each city and its stakeholders when designing and deploying Smart Mobility planning applications.

Keywords: smart mobility planning, smart mobility applications, smart mobility techniques, smart mobility tools, smart transportation, smart cities, urbanization growth, future smart cities, intelligent cities, ICT information and communications technologies, IoT internet of things, sensors, lidar, digital twin, ai artificial intelligence, AR augmented reality, VR virtual reality, robotics, cps cyber physical systems, citizens design science

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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In recent years, new techniques for solving complex problems in engineering are proposed. One of these techniques is JPSO algorithm. With innovative changes in the nature of the jump algorithm JPSO, it is possible to construct a graph-based solution with a new algorithm called G-JPSO. In this paper, a new algorithm to solve the optimal control problem Fletcher-Powell and optimal control of pumps in water distribution network was evaluated. Optimal control of pumps comprise of optimum timetable operation (status on and off) for each of the pumps at the desired time interval. Maximum number of status on and off for each pumps imposed to the objective function as another constraint. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The proposed algorithm results were compared well with the ant colony algorithm, genetic and JPSO results. This shows the robustness of proposed algorithm in finding near optimum solutions with reasonable computational cost.

Keywords: G-JPSO, operation, optimization, pumping station, water distribution networks

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Authors: Alev Yüksel Aydar, Tuncay Yılmaz, Melisa Özçeli̇k, Tuba Aydın, Elif Karabaş

Abstract:

In this study, response surface methodology (RSM) was used to obtain the optimum conditions for the freeze-drying of Gemlik variety olive seeds of to achieve the desired quality characteristics. The Box Behnken Design (BBD) was applied with three-variable and three replications in the center point. The effects of the different drying parameters including initial temperature of olive seed, pressure and time for freezing on the DPPH activity, total phenolic contents, and oleuropein absorbance value of the samples were investigated. Temperature (50 – 82 °C), pressure (0.2-0.5 mbar), time (6-10 hours) were chosen as independent variables. The analysis revealed that, while the temperature of the product prior to lyophilization and the drying time had no statistically significant effect on DPPH activity (p>0.05), the pressure was more important than the other two variables , and the quadratic effect of pressure had a significant effect on DPPH activity (p<0.05). The R2 and Adj-R2 values of the DPPH activity model were calculated to be 0.8962 and 0.7045, respectively.

Keywords: olive seed, gemlik variety, DPPH, phenolics, optimization

Procedia PDF Downloads 87

Authors: Igna Brajevic-Gizdic, Magda Pletikosa Pavić

Abstract:

Introduction: Research in opiate addiction is increasingly indicating the importance of substitution therapy in opiate addicts. Opiate addiction is a chronic relapsing disease that includes craving as a criterion. Craving has been considered a predictor of a relapse, which is defined as a strong desire with an excessive need to take a substance. The study aimed to measure the intensity of craving using the VAS (visual analog scale) in opioid addicts taking Opioid Substitution Therapy (OST). Method: The total sample compromised of 30 participants in outpatient treatment. Two groups of opiate addicts were considered: Methadone-maintenance and buprenorphine-maintenance addicts. The participants completed the survey questionnaire during the outpatient treatment. Results: The results indicated high levels of cravings in patients during the treatment of OST, which is considered an important destabilization factor in abstinence. Thus, the use of methadone/buprenorphine dose should be considered. Conclusion: These findings provided an objective measurement of methadone /buprenorphine dosage and therapy options. The underdoes of OST can put patients at high risk of relapse, resulting in high levels of craving. Thus, when determining the therapeutic dose of OST, it is crucial to consider patients' cravings. This would achieve stabilization more quickly and avoid relapse in abstinence. Subjective physician assessment and patients' statement are the main criteria to determine OST dosage. Future studies should use larger sample sizes and focus on the importance of intensity craving measurement on OST to objectify methadone /buprenorphine dosage.

Keywords: buprenorphine, craving, heroin addicts, methadone, OST

Procedia PDF Downloads 88

Authors: Irsha Dhotre

Abstract:

Cymbopogon citratus is generally known as Indian Lemongrass and is widely applicable in the cosmetic, pharmaceutical, dairy puddings, and food industries. To enhance the quality of extraction, microwave-oven-aided hydro distillation processes were implemented. The basic parameter which influences the rate of extraction is considered, such as the temperature of extraction, the time required for extraction, and microwave-oven power applied. Locally available CKP 25 Cymbopogon citratus was used for the extraction of essential oil. Optimization of Extractions Parameters and full factorial Box–Behnken design (BBD) evaluated by using Design expert 13 software. The regression model revealed that the optimum parameters required for extractions are a temperature of 35℃, a time of extraction of 130 minutes, and microwave-oven power of 700 W. The extraction efficiency of yield is 4.76%. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis confirmed the significant components present in the extraction of lemongrass oil.

Keywords: Box–Behnken design, Cymbopogon citratus, hydro distillation, microwave-oven, response surface methodology

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Authors: Bahar Salavati Vie Le Sage, Ismaïl Ben Hariz, Flavien Viguier, Sirine Noura Kahil, Audrey Jacquin, Maxime Convert

Abstract:

The ballast layer plays a key role in railroad maintenance and the geometry of the track structure. Ballast also holds the track in place as the trains roll over it. Track ballast is packed between the sleepers and on the sides of railway tracks. An imbalance in ballast volume on the tracks can lead to safety issues as well as a quick degradation of the overall quality of the railway segment. If there is a lack of ballast in the track bed during the summer, there is a risk that the rails will expand and buckle slightly due to the high temperatures. Furthermore, the knowledge of the ballast quantities that will be excavated during renewal works is important for efficient ballast management. The volume of excavated ballast per meter of track can be calculated based on excavation depth, excavation width, volume of track skeleton (sleeper and rail) and sleeper spacing. Since 2012, SNCF has been collecting 3D points cloud data covering its entire railway network by using 3D laser scanning technology (LiDAR). This vast amount of data represents a modelization of the entire railway infrastructure, allowing to conduct various simulations for maintenance purposes. This paper aims to present an automated method for ballast volume estimation based on the processing of LiDAR data. The estimation of abnormal volumes in ballast on the tracks is performed by analyzing the cross-section of the track. Further, since the amount of ballast required varies depending on the track configuration, the knowledge of the ballast profile is required. Prior to track rehabilitation, excess ballast is often present in the ballast shoulders. Based on 3D laser scans, a Digital Terrain Model (DTM) was generated and automatic extraction of the ballast profiles from this data is carried out. The surplus in ballast is then estimated by performing a comparison between this ballast profile obtained empirically, and a geometric modelization of the theoretical ballast profile thresholds as dictated by maintenance standards. Ideally, this excess should be removed prior to renewal works and recycled to optimize the output of the ballast renewal machine. Based on these parameters, an application has been developed to allow the automatic measurement of ballast profiles. We evaluated the method on a 108 kilometers segment of railroad LiDAR scans, and the results show that the proposed algorithm detects ballast surplus that amounts to values close to the total quantities of spoil ballast excavated.

Keywords: ballast, railroad, LiDAR , cloud point, track ballast, 3D point

Procedia PDF Downloads 109