Search results for: networked model predictive control

Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: J. Rhodes, V. Cheng, P. Lok

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The aim of this study is to develop a parsimonious model that explains the effect of different stimulus on a tourist’s intention to visit a new destination. The model consists of destination trust and interest as the mediating variables. The model was tested using two different types of stimulus; both studies empirically supported the proposed model. Furthermore, the first study revealed that advertising has a stronger effect than positive online reviews. The second study found that the peripheral route of the elaboration likelihood model has a stronger influence power than the central route in this context.

Keywords: advertising, electronic word-of-mouth, elaboration likelihood model, intention to visit, trust

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Vahid Bayrami Rad

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In this research, common methods and techniques have been investigated with a focus on intelligent fault finding and monitoring systems in the oil industry. In fact, remote and intelligent control methods are considered a necessity for implementing various operations in the oil industry, but benefiting from the knowledge extracted from countless data generated with the help of data mining algorithms. It is a avoid way to speed up the operational process for monitoring and troubleshooting in today's big oil companies. Therefore, by comparing data mining algorithms and checking the efficiency and structure and how these algorithms respond in different conditions, The proposed (Bayesian) algorithm using data clustering and their analysis and data evaluation using a colored Petri net has provided an applicable and dynamic model from the point of view of reliability and response time. Therefore, by using this method, it is possible to achieve a dynamic and consistent model of the remote control system and prevent the occurrence of leakage in oil pipelines and refineries and reduce costs and human and financial errors. Statistical data The data obtained from the evaluation process shows an increase in reliability, availability and high speed compared to other previous methods in this proposed method.

Keywords: wireless sensors, petroleum equipment troubleshooting, Bayesian algorithm, colored Petri net, rapid miner, data mining-reliability

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Authors: A. Brahma

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Analysis of deferred deformations of concrete under sustained load shows that the creep has a leading role on deferred deformations of concrete structures. Knowledge of the creep characteristics of concrete is a Necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable deformation in pre-stressed concrete or reinforced and the appropriate steps can be taken in design to accommodate this movement. In this study, we propose a prediction model that involves the acting principal parameters on the deferred behaviour of concrete structures. For the estimation of the model parameters Levenberg-Marquardt method has proven very satisfactory. A confrontation between the experimental results and the predictions of models designed shows that it is well suited to describe the evolution of the creep of concrete structures.

Keywords: concrete structure, creep, modelling, prediction

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Authors: Keiju Matsui, Mikio Yasubayashi, Masayoshi Umeno

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Consumption of energy is increasing every year, and yet does not the decline at all. The main energy source is fossil fuels such as petroleum and natural gas. Since it is the finite resources, they will be exhausted someday. Moreover, to make the fossil fuel an energy source causes an environment problem. In such way, one solution of the problems is the solar battery that is remarkable as one of the alternative energies. Under such circumstances, in this paper, we propose a novel maximum power control circuit for photovoltaic power generation system with simple and fast-response operation. In addition to an application to the solar battery, since this control system is possible to operate with simple circuit and fast-response, the polar value control like the maximum or the minimum value tracking for general application could be easily realized.

Keywords: maximum power control, inter-connection, photovoltaic power generation, PI controller, multiplier, exclusive-or, power system

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Ahmad Sarfaraz, Raiyad Herwies

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In this paper, a multi-criteria decision making analysis is used to help any organization selects the best KM tool that fits and serves its needs. The AHP model is used based on a previous study to highlight and identify the main criteria and sub-criteria that are incorporated in the selection process. Different KM tools alternatives with different criteria are compared and weighted accurately to be incorporated in the GP model. The main goal is to combine the GP model with the AHP model to ensure that selecting the KM tool considers the resource constraints. Two important issues are discussed in this paper: how different factors could be taken into consideration in forming the AHP model, and how to incorporate the AHP results into the GP model for better results.

Keywords: knowledge management, analytical hierarchy process, goal programming, multi-criteria decision making

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Authors: Ryan B. Simpson, Margaret A. Waskow, Aishwarya Venkat, Elena N. Naumova

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The Centers for Disease Control and Prevention (CDC) estimate that 31 known foodborne pathogens cause 9.4 million cases of these illnesses annually in US. Over 90% of these illnesses are associated with exposure to Campylobacter, Cryptosporidium, Cyclospora, Listeria, Salmonella, Shigella, Shiga-Toxin Producing E.Coli (STEC), Vibrio, and Yersinia. Contaminated products contain parasites typically causing an intestinal illness manifested by diarrhea, stomach cramping, nausea, weight loss, fatigue and may result in deaths in fragile populations. Since 1998, the National Outbreak Reporting System (NORS) has allowed for routine collection of suspected and laboratory-confirmed cases of food poisoning. While retrospective analyses have revealed common pathogen-specific seasonal patterns, little is known concerning the stability of those patterns over time and whether they can be used for preventative forecasting. The objective of this study is to construct a calendar of foodborne outbreaks of nine infections based on the peak timing of outbreak incidence in the US from 1996 to 2017. Reported cases were abstracted from FoodNet for Salmonella (135115), Campylobacter (121099), Shigella (48520), Cryptosporidium (21701), STEC (18022), Yersinia (3602), Vibrio (3000), Listeria (2543), and Cyclospora (758). Monthly counts were compiled for each agent, seasonal peak timing and peak intensity were estimated, and the stability of seasonal peaks and synchronization of infections was examined. Negative Binomial harmonic regression models with the delta-method were applied to derive confidence intervals for the peak timing for each year and overall study period estimates. Preliminary results indicate that five infections continue to lead as major causes of outbreaks, exhibiting steady upward trends with annual increases in cases ranging from 2.71% (95%CI: [2.38, 3.05]) in Campylobacter, 4.78% (95%CI: [4.14, 5.41]) in Salmonella, 7.09% (95%CI: [6.38, 7.82]) in E.Coli, 7.71% (95%CI: [6.94, 8.49]) in Cryptosporidium, and 8.67% (95%CI: [7.55, 9.80]) in Vibrio. Strong synchronization of summer outbreaks were observed, caused by Campylobacter, Vibrio, E.Coli and Salmonella, peaking at 7.57 ± 0.33, 7.84 ± 0.47, 7.85 ± 0.37, and 7.82 ± 0.14 calendar months, respectively, with the serial cross-correlation ranging 0.81-0.88 (p < 0.001). Over 21 years, Listeria and Cryptosporidium peaks (8.43 ± 0.77 and 8.52 ± 0.45 months, respectively) have a tendency to arrive 1-2 weeks earlier, while Vibrio peaks (7.8 ± 0.47) delay by 2-3 weeks. These findings will be incorporated in the forecast models to predict common paths of the spread, long-term trends, and the synchronization of outbreaks across etiological agents. The predictive modeling of foodborne outbreaks should consider long-term changes in seasonal timing, spatiotemporal trends, and sources of contamination.

Keywords: foodborne outbreak, national outbreak reporting system, predictive modeling, seasonality

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Authors: K. T. Sun, Y. H. Tai, H. W. Yang, H. T. Lin

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This paper presented the technique of robot control by event-related potentials (ERPs) of brain waves. Based on the proposed technique, severe physical disabilities can free browse outside world. A specific component of ERPs, N2P3, was found and used to control the movement of robot and the view of camera on the designed brain-computer interface (BCI). Users only required watching the stimuli of attended button on the BCI, the evoked potentials of brain waves of the target button, N2P3, had the greatest amplitude among all control buttons. An experimental scene had been constructed that the robot required walking to a specific position and move the view of camera to see the instruction of the mission, and then completed the task. Twelve volunteers participated in this experiment, and experimental results showed that the correct rate of BCI control achieved 80% and the average of execution time was 353 seconds for completing the mission. Four main contributions included in this research: (1) find an efficient component of ERPs, N2P3, for BCI control, (2) embed robot's viewpoint image into user interface for robot control, (3) design an experimental scene and conduct the experiment, and (4) evaluate the performance of the proposed system for assessing the practicability.

Keywords: severe physical disabilities, robot control, event-related potentials (ERPs), brain-computer interface (BCI), brain waves

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Authors: Hamza Javar Magnier, Robin Curtis

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There have been rapid advances in the upstream processing of protein therapeutics, which has shifted the bottleneck to downstream purification and formulation. Finding liquid formulations with shelf lives of up to two years is increasingly difficult for some of the newer therapeutics, which have been engineered for activity, but their formulations are often viscous, can phase separate, and have a high propensity for irreversible aggregation1. We explore means to develop improved predictive ability from a better understanding of how protein-protein interactions on formulation conditions (pH, ionic strength, buffer type, presence of excipients) and how these impact upon the initial steps in protein self-association and aggregation. In this work, we study the initial steps in the aggregation pathways using a minimal protein model based on square-well potentials and discontinuous molecular dynamics. The effect of model parameters, including range of interaction, stiffness, chain length, and chain sequence, implies that protein models fold according to various pathways. By reducing the range of interactions, the folding- and collapse- transition come together, and follow a single-step folding pathway from the denatured to the native state2. After parameterizing the model interaction-parameters, we developed an understanding of low-temperature conformational properties and fluctuations, and the correlation to the folding transition of proteins in isolation. The model fluctuations increase with temperature. We observe a low-temperature point, below which large fluctuations are frozen out. This implies that fluctuations at low-temperature can be correlated to the folding transition at the melting temperature. Because proteins “breath” at low temperatures, defining a native-state as a single structure with conserved contacts and a fixed three-dimensional structure is misleading. Rather, we introduce a new definition of a native-state ensemble based on our understanding of the core conservation, which takes into account the native fluctuations at low temperatures. This approach permits the study of a large range of length and time scales needed to link the molecular interactions to the macroscopically observed behaviour. In addition, these models studied are parameterized by fitting to experimentally observed protein-protein interactions characterized in terms of osmotic second virial coefficients.

Keywords: protein folding, native-ensemble, conformational fluctuation, aggregation

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Authors: Kai Chun Yang, Shao-Wen Chen, Jong-Rong Wang, Chunkuan Shih, Jung-Hua Yang, Hsiung-Chih Chen, Wen-Sheng Hsu

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In this study, we build RADionuclide Transport, Removal And Dose Estimation/Symbolic Nuclear Analysis Package (SNAP/RADTRAD) model of Kuosheng Nuclear Power Plant which is based on the Final Safety Evaluation Report (FSAR) and other data of Kuosheng Nuclear Power Plant. It is used to estimate the radiation dose of the Exclusion Area Boundary (EAB), the Low Population Zone (LPZ), and the control room following ‘release from the containment’ case in Loss Of Coolant Accident (LOCA). The RADTRAD analysis result shows that the evaluation dose at EAB, LPZ, and the control room are close to the FSAR data, and all of the doses are lower than the regulatory limits. At last, we do a sensitivity analysis and observe that the evaluation doses increase as the intake rate of the control room increases.

Keywords: RADTRAD, radionuclide transport, removal and dose estimation, snap, symbolic nuclear analysis package, boiling water reactor, NPP, kuosheng

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: Paolo Grasso, Sai Kalyan Yelike, Federico Benzi, Mathieu Le Cam

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The cabin air quality (CAQ) in commercial aircraft is of prime interest, especially in the context of the COVID-19 pandemic. Current Environmental Control Systems (ECS) rely on a prescribed fresh airflow per passenger to dilute contaminants. An adaptive ECS strategy is proposed, leveraging air sensing and filtration technologies to ensure a better CAQ. This paper investigates the CAQ level achieved in commercial aircraft’s cabin during various flight scenarios. The modeling and simulation analysis is performed in a Modelica-based environment describing the dynamic behavior of the system. The model includes the following three main systems: cabin, recirculation loop and air-conditioning pack. The cabin model evaluates the thermo-hygrometric conditions and the air quality in the cabin depending on the number of passengers and crew members, the outdoor conditions and the conditions of the air supplied to the cabin. The recirculation loop includes models of the recirculation fan, ordinary and novel filtration technology, mixing chamber and outflow valve. The air-conditioning pack includes models of heat exchangers and turbomachinery needed to condition the hot pressurized air bled from the engine, as well as selected contaminants originated from the outside or bled from the engine. Different ventilation control strategies are modeled and simulated. Currently, a limited understanding of contaminant concentrations in the cabin and the lack of standardized and systematic methods to collect and record data constitute a challenge in establishing a causal relationship between CAQ and passengers' comfort. As a result, contaminants are neither measured nor filtered during flight, and the current sub-optimal way to avoid their accumulation is their dilution with the fresh air flow. However, the use of a prescribed amount of fresh air comes with a cost, making the ECS the most energy-demanding non-propulsive system within an aircraft. In such a context, this study shows that an ECS based on a reduced and adaptive fresh air flow, and relying on air sensing and filtration technologies, provides promising results in terms of CAQ control. The comparative simulation results demonstrate that the proposed adaptive ECS brings substantial improvements to the CAQ in terms of both controlling the asymptotic values of the concentration of the contaminant and in mitigating hazardous scenarios, such as fume events. Original architectures allowing for adaptive control of the inlet air flow rate based on monitored CAQ will change the requirements for filtration systems and redefine the ECS operation.

Keywords: cabin air quality, commercial aircraft, environmental control system, ventilation

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Authors: Kazuma Okada, Tomoaki Hashimoto, Hirokazu Tahara

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Recently, the effectiveness of random dither quantization method for linear feedback control systems has been shown in several papers. However, the random dither quantization method has not yet been applied to nonlinear feedback control systems. The objective of this paper is to verify the effectiveness of random dither quantization method for nonlinear feedback control systems. For this purpose, we consider the attitude stabilization problem of satellites using discrete-level actuators. Namely, this paper provides a control method based on the random dither quantization method for stabilizing the attitude of satellites using discrete-level actuators.

Keywords: quantized control, nonlinear systems, random dither quantization

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Authors: Feddaoui Amina

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Income smoothing represents an attempt on the part of the company's management to reduce variations in earnings through the manipulation of the accounting principles. In this study, we aimed to measure income smoothing practices in a sample of 30 French joint stock companies during the period (2007-2009), we used Dummy variables method and “ECKEL” model to measure income smoothing practices and Binomial test accourding to SPSS program, to confirm or refute our hypothesis. This study concluded that there are no significant statistical indicators of income smoothing practices in the sample studied of French companies during the period (2007-2009), so the income series in the same sample studied of is characterized by stability and non-volatility without any intervention of management through accounting manipulation. However, this type of accounting manipulation should be taken into account and efforts should be made by control bodies to apply Eckel model and generalize its use at the global level.

Keywords: income, smoothing, 'Eckel', French companies

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Authors: Manamba Epaphra

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This paper analyzes the causal effect between domestic private investment, public investment, foreign direct investment and economic growth in Tanzania during the 1970-2014 period. The modified neo-classical growth model that includes control variables such as trade liberalization, life expectancy and macroeconomic stability proxied by inflation is used to estimate the impact of investment on economic growth. Also, the economic growth models based on Phetsavong and Ichihashi (2012), and Le and Suruga (2005) are used to estimate the crowding out effect of public investment on private domestic investment on one hand and foreign direct investment on the other hand. A correlation test is applied to check the correlation among independent variables, and the results show that there is very low correlation suggesting that multicollinearity is not a serious problem. Moreover, the diagnostic tests including RESET regression errors specification test, Breusch-Godfrey serial correlation LM test, Jacque-Bera-normality test and white heteroskedasticity test reveal that the model has no signs of misspecification and that, the residuals are serially uncorrelated, normally distributed and homoskedastic. Generally, the empirical results show that the domestic private investment plays an important role in economic growth in Tanzania. FDI also tends to affect growth positively, while control variables such as high population growth and inflation appear to harm economic growth. Results also reveal that control variables such as trade openness and life expectancy improvement tend to increase real GDP growth. Moreover, a revealed negative, albeit weak, association between public and private investment suggests that the positive effect of domestic private investment on economic growth reduces when public investment-to-GDP ratio exceeds 8-10 percent. Thus, there is a great need for promoting domestic saving so as to encourage domestic investment for economic growth.

Keywords: FDI, public investment, domestic private investment, crowding out effect, economic growth

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Authors: J. Fernandez de Canete, S. Fernandez-Calvo, I. García-Moral

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This paper presents and discusses the application of the object-oriented modelling software SIMSCAPE to hydraulic systems, with particular reference to multivariable proportional-integral-derivative (PID) control. As a result, a particular modelling approach of a double cylinder-piston coupled system is proposed and motivated, and the SIMULINK based PID tuning tool has also been used to select the proper controller parameters. The paper demonstrates the usefulness of the object-oriented approach when both physical modelling and control are tackled.

Keywords: object-oriented modeling, multivariable hydraulic system, multivariable PID control, computer simulation

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Authors: Arnab Mondal, Sanjeev Kumar Sharma, Ranjit Kumar Upadhyay

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We study the spatiotemporal dynamics of a neuronal cable. The processes of one- dimensional (1D) and 2D diffusion are considered for a single variable, which is the membrane voltage, i.e., membrane voltage diffusively interacts for spatiotemporal pattern formalism. The recovery and other variables interact through the membrane voltage. A 3D Leech-Heart (LH) model is introduced to investigate the nonlinear responses of an excitable neuronal cable. The deterministic LH model shows different types of firing properties. We explore the parameter space of the uncoupled LH model and based on the bifurcation diagram, considering v_k2_ashift as a bifurcation parameter, we analyze the 1D diffusion dynamics in three regimes: bursting, regular spiking, and a quiescent state. Depending on parameters, it is shown that the diffusive system may generate regular and irregular bursting or spiking behavior. Further, it is explored a 2D diffusion acting on the membrane voltage, where different types of patterns can be observed. The results show that the LH neurons with different firing characteristics depending on the control parameters participate in a collective behavior of an information processing system that depends on the overall network.

Keywords: bifurcation, pattern formation, spatio-temporal dynamics, stability analysis

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Authors: Mohammad Reza Ebrahimi, Behnaz Mahdaviani

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Increasing renewable sources such photovoltaic are the reason of environmental pollution. Because photovoltaic generates power in low voltage, first, generated power should increase. Usually, distributed generation injects their power to AC-Grid, hence after voltage increasing an inverter is needed to convert DC power to AC power. This results in utilization two series converter that grows cost, complexity, and low efficiency. In this paper a single stage inverter is utilized to boost and invert in one stage. Control of this scheme is easier, and its initial cost decreases comparing to conventional double stage inverters. A simple control scheme is used to control active power as well as minimum total harmonic distortion (THD) in injected current. Simulations in MATLAB demonstrate better outputs comparing with conventional approaches.

Keywords: maximum power point tracking, boost inverter, control strategy, three phase inverter

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Authors: Garreth Kestell, Lukah Dykes, Danielle Zerk, Kyla Trewartha, Rhianon Marshall, Elena Rudnik

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Adolescent mental health is an international priority and the impact of innovative service models must be evaluated. Secondary school-based mental health services (SBMHS) involving private general practitioners and psychologists are a model of care being trialed in South Australia. Measures of depression, anxiety, and stress are routinely collected throughout psychotherapy sessions. This research set out to quantify the impact of psychotherapy for rural adolescents in a school setting and explore the importance of session frequency. Methods: Demographics, session date and DASS21 scores from students (n=65) seen in 2016 by three psychologists working at the SBMHS were recorded. Students were aged 13-18 years (M=15.43, SD= 1.24), mostly female (F=51, M=14), attended between 1 and 23 sessions with a median of 6 sessions (MAD 5.93) in one-year. The treating psychologist collected self-administered DASS21 scores. A mixed model analysis was used with age, sex, treating psychologist, months from first session, and session number as fixed effects, with response variables of DASS depression, anxiety, and stress scores. Results: 71.5% were classified as having extreme or severe anxiety and half had extreme or severe depression and/or stress scores. On average males had a greater increase in DASS scores over time but males attending more sessions benefited most from therapy. Discussion: Psychologists are treating rural adolescents in schools for severe anxiety, depression, and stress. This pilot study indicates that a predictive model combining demographics, session frequency, and DASS scores may help identify who is most likely to benefit from individual psychotherapy. Variations in DAS scores of individuals over time indicate the need for the collection of information such as living situation and exposure to alcohol. A larger sample size and additional data are currently being collected to allow for a more robust analysis.

Keywords: adolescent health, psychotherapy, school based mental health services, DAS21

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Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

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This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

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Authors: Bin Zhou, Tongli Lu, Jianwu Zhang, Hongtao Hao

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Dual clutch transmissions (DCT) offer a high comfort performance in terms of the gearshift. Hydraulic multi-disk clutches are the key components of DCT, its engagement determines the shifting comfort. The prefill of the clutches requests an initial engagement which the clutches just contact against each other but not transmit substantial torque from the engine, this initial clutch engagement point is called the touch point. Open-loop control is typically implemented for the clutch prefill, a lot of uncertainties, such as oil temperature and clutch wear, significantly affects the prefill, probably resulting in an inappropriate touch point. Underfill causes the engine flaring in gearshift while overfill arises clutch tying up, both deteriorating the shifting comfort of DCT. Therefore, it is important to enable an adaptive capacity for the clutch prefills regarding the uncertainties. In this paper, a dynamic model of the hydraulic actuator system is presented, including the variable force solenoid and clutch piston, and validated by a test. Subsequently, the open-loop clutch prefill is simulated based on the proposed model. Two control parameters of the prefill, fast fill time and stable fill pressure is analyzed with regard to the impact on the prefill. The former has great effects on the pressure transients, the latter directly influences the touch point. Finally, an adaptive method is proposed for the clutch prefill during gear shifting, in which clutch fill control parameters are adjusted adaptively and continually. The adaptive strategy is changing the stable fill pressure according to the current clutch slip during a gearshift, improving the next prefill process. The stable fill pressure is increased by means of the clutch slip while underfill and decreased with a constant value for overfill. The entire strategy is designed in the Simulink/Stateflow, and implemented in the transmission control unit with optimization. Road vehicle test results have shown the strategy realized its adaptive capability and proven it improves the shifting comfort.

Keywords: clutch prefill, clutch slip, dual clutch transmission, touch point, variable force solenoid

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Authors: Coskun Serefoglu

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The study aims to analyse how a traditional sector such as agri-food could be mobilized through regional innovation strategies. A principal component analysis as well as qualitative information, such as in-depth interviews, focus group and surveys, were employed to find the priority sectors. An agri-food model was developed which includes both a linear model and interactive model. The model consists of two main components, one of which is technological integration and the other one is agricultural extension which is based on Land-grant university approach of U.S. which is not a common practice in Turkey.

Keywords: regional innovation strategy, interactive model, agri-food sector, local development, planning, regional development

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Authors: Surabhi Agrawal Kohli, Sunita Gupta, Esha Khanuja, Parul Garg, P. Gupta

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Background: Adenomyosis is a common gynaecological condition that affects the menstruating women. Uterine enlargement, dysmenorrhoea, and menorrhagia are regarded as the cardinal clinical symptoms of adenomyosis. Classically it was thought, compared with ultrasonography, when adenomyosis is suspected, MRI enables more accurate diagnosis of the disease. Materials and Methods: 172 subjects were enrolled after an informed consent that had complaints of HMB, dyspareunia, dysmenorrhea, and chronic pelvic pain. Detailed history of the enrolled subjects was taken, followed by a clinical examination. These patients were then subjected to TVS where myometrial echo texture, presence of myometrial cysts, blurring of endomyometrial junction was noted. MRI was followed which noted the presence of junctional zone thickness and myometrial cysts. After hysterectomy, histopathological diagnosis was obtained. Results: 78 participants were analysed. The mean age was 44.2 years. 43.5% had parity of 4 or more. heavy menstrual bleeding (HMB) was present in 97.8% and dysmenorrhea in 93.48 % of HPE positive patient. Transvaginal sonography (TVS) and MRI had a sensitivity of 89.13% and 80.43%, specificity of 90.62% and 84.37%, positive likelihood ratio of 9.51 and 5.15, negative likelihood ratio of 0.12 and 0.23, positive predictive value of 93.18% and 88.1%, negative predictive value of 85.29% and 75% and a diagnostic accuracy of 89.74% and 82.5%. Comparison of sensitivity (p=0.289) and specificity (p=0.625) showed no statistically significant difference between TVS and MRI. Conclusion: Prevalence of 30.23%. HMB with dysmenorrhoea and chronic pelvic pain helps in diagnosis. TVS (Endomyometrial junction blurring) is both sensitive and specific in diagnosing adenomyosis without need for additional diagnostic tool. Both TVS and MRI are equally efficient, however because of certain additional advantages of TVS over MRI, it may be used as the first choice of imaging. MRI may be used additionally in difficult cases as well as in patients with existing co-pathologies.

Keywords: adenomyosis, heavy menstrual bleeding, MRI, TVS

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Authors: benabbess anja

Abstract:

This research presents the design and implementation of a real-time course recommendation system for online learning platforms, leveraging user competencies and expertise levels. The system begins by extracting and classifying the complexity levels of courses from Udemy datasets using semantic enrichment techniques and resources such as WordNet and BERT. A predictive model assigns complexity levels to each course, adding columns that represent the course category, sub-category, and complexity level to the existing dataset. Simultaneously, user profiles are constructed through questionnaires capturing their skills, sub-skills, and proficiency levels. The recommendation process involves generating embeddings with BERT, followed by calculating cosine similarity between user profiles and courses. Courses are ranked based on their relevance, with the BERT model delivering the most accurate results. To enable real-time recommendations, Apache Kafka is integrated to track user interactions (clicks, comments, time spent, completed courses, feedback) and update user profiles. The embeddings are regenerated, and similarities with courses are recalculated to reflect users' evolving needs and behaviors, incorporating a progressive weighting of interactions for more personalized suggestions. This approach ensures dynamic and real-time course recommendations tailored to user progress and engagement, providing a more personalized and effective learning experience. This system aims to improve user engagement and optimize learning paths by offering courses that precisely match users' needs and current skill levels.

Keywords: recommendation system, online learning, real-time, user skills, expertise level, personalized recommendations, dynamic suggestions

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Authors: B. Neethu, Diptesh Das

Abstract:

The present study investigates the performance of a semi-active controller using magneto-rheological dampers (MR) for seismic response reduction of a multi-span bridge. The application of structural control to the structures during earthquake excitation involves numerous challenges such as proper formulation and selection of the control strategy, mathematical modeling of the system, uncertainty in system parameters and noisy measurements. These problems, however, need to be tackled in order to design and develop controllers which will efficiently perform in such complex systems. A control algorithm, which can accommodate un-certainty and imprecision compared to all the other algorithms mentioned so far, due to its inherent robustness and ability to cope with the parameter uncertainties and imprecisions, is the sliding mode algorithm. A sliding mode control algorithm is adopted in the present study due to its inherent stability and distinguished robustness to system parameter variation and external disturbances. In general a semi-active control scheme using an MR damper requires two nested controllers: (i) an overall system controller, which derives the control force required to be applied to the structure and (ii) an MR damper voltage controller which determines the voltage required to be supplied to the damper in order to generate the desired control force. In the present study a sliding mode algorithm is used to determine the desired optimal force. The function of the voltage controller is to command the damper to produce the desired force. The clipped optimal algorithm is used to find the command voltage supplied to the MR damper which is regulated by a semi active control law based on sliding mode algorithm. The main objective of the study is to propose a robust semi active control which can effectively control the responses of the bridge under real earthquake ground motions. Lumped mass model of the bridge is developed and time history analysis is carried out by solving the governing equations of motion in the state space form. The effectiveness of MR dampers is studied by analytical simulations by subjecting the bridge to real earthquake records. In this regard, it may also be noted that the performance of controllers depends, to a great extent, on the characteristics of the input ground motions. Therefore, in order to study the robustness of the controller in the present study, the performance of the controllers have been investigated for fourteen different earthquake ground motion records. The earthquakes are chosen in such a way that all possible characteristic variations can be accommodated. Out of these fourteen earthquakes, seven are near-field and seven are far-field. Also, these earthquakes are divided into different frequency contents, viz, low-frequency, medium-frequency, and high-frequency earthquakes. The responses of the controlled bridge are compared with the responses of the corresponding uncontrolled bridge (i.e., the bridge without any control devices). The results of the numerical study show that the sliding mode based semi-active control strategy can substantially reduce the seismic responses of the bridge showing a stable and robust performance for all the earthquakes.

Keywords: bridge, semi active control, sliding mode control, MR damper

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Authors: Sasiporn Rattanasupha, Settapat Chinviriyasit

Abstract:

The treatment function adopts a continuous and differentiable function which can describe the effect of delayed treatment when the number of infected individuals increases and the medical condition is limited. In this paper, the SEIR epidemic model with treatment function is studied to investigate the dynamics of the model due to the effect of treatment. It is assumed that the treatment rate is proportional to the number of infective patients. The stability of the model is analyzed. The model is simulated to illustrate the analytical results and to investigate the effects of treatment on the spread of infection.

Keywords: basic reproduction number, local stability, SEIR epidemic model, treatment function

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Authors: Abirami Baleswaran, Christel Couderc, Loubnah Belahcen, Jean Dayde, Hélène Tormo, Gwénaëlle Jard

Abstract:

Cheeses are often reported to be spoiled by Pseudomonas spp., responsible for defects in appearance, texture, taste, and smell, leading to their non-marketing and even their destruction. Despite preventive actions, problems linked to Pseudomonas spp. are difficult to control by the lack of knowledge and control of these contaminants during the cheese manufacturing. Lactic goat cheese producers are not spared by this problem and are looking for solutions to decrease the number of spoiled cheeses. To explore different hypotheses, experiments are needed. However, cheese-making experiments at the pilot scale are expensive and time consuming. Thus, there is a real need to develop a miniature cheeses model system under controlled conditions. In a previous study, several miniature cheese models corresponding to different type of commercial cheeses have been developed for different purposes. The models were, for example, used to study the influence of milk, starters cultures, pathogen inhibiting additives, enzymatic reactions, microflora, freezing process on cheese. Nevertheless, no miniature model was described on the lactic goat cheese. The aim of this work was to develop a miniature cheese model system under controlled laboratory conditions which resembles commercial lactic goat cheese to study Pseudomonas spp. spoilage during the manufacturing and ripening process. First, a protocol for the preparation of miniature cheeses (3.5 times smaller than a commercial one) was designed based on the cheese factorymanufacturing process. The process was adapted from “Rocamadour” technology and involves maturation of pasteurized milk, coagulation, removal of whey by centrifugation, moulding, and ripening in a little scale cellar. Microbiological (total bacterial count, yeast, molds) and physicochemical (pH, saltinmoisture, moisture in fat-free)analyses were performed on four key stages of the process (before salting, after salting, 1st day of ripening, and end of ripening). Factory and miniature cheeses volatilomewere also obtained after full scan Sift-MS cheese analysis. Then, Pseudomonas spp. strains isolated from contaminated cheeses were selected on their origin, their ability to produce pigments, and their enzymatic activities (proteolytic, lecithinasic, and lipolytic). Factory and miniature curds were inoculated by spotting selected strains on the cheese surface. The expression of cheese spoilage was evaluated by counting the level of Pseudomonas spp. during the ripening and by visual observation and under UVlamp. The physicochemical and microbiological compositions of miniature cheeses permitted to assess that miniature process resembles factory process. As expected, differences involatilomes were observed, probably due to the fact that miniature cheeses are made usingpasteurized milk to better control the microbiological conditions and also because the little format of cheese induced probably a difference during the ripening even if the humidity and temperature in the cellar were quite similar. The spoilage expression of Pseudomonas spp. was observed in miniature and factory cheeses. It confirms that the proposed model is suitable for the preparation of miniature cheese specimens in the spoilage study of Pseudomonas spp. in lactic cheeses. This kind of model could be deployed for other applications and other type of cheese.

Keywords: cheese, miniature, model, pseudomonas spp, spoilage

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Authors: Piotr Jerzy Rugor, Julia Wajoras

Abstract:

The presented work regards a single-stage aerodynamically controlled solid propulsion rocket. Steering a rocket to fly along a predetermined trajectory can be beneficial for minimizing aerodynamic losses and achieved by implementing an active control system on board. In this particular case, a canard configuration has been chosen, although other methods of control have been considered and preemptively analyzed, including non-aerodynamic ones. The objective of this work is to create a system capable of guiding the rocket, focusing on roll stabilization. The paper describes initial analysis of the problem, covers the main challenges of missile guidance and presents data acquired during the experimental study.

Keywords: active canard control system, rocket design, numerical simulations, flight optimization

Procedia PDF Downloads 195