Search results for: agent based model

Authors: Hamid Maidat, Khedidja Bouhadef, Djamel Eddine Ameziani, Azzedine Abdedou

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This work consists of a numerical simulation of convective heat transfer in a vertical plane channel filled with a heat generating porous medium, in the absence of local thermal equilibrium. The walls are maintained to a constant temperature and the inlet velocity is uniform. The dynamic range is described by the Darcy-Brinkman model and the thermal field by two energy equations model. A dimensionless formulation is developed for performing a parametric study based on certain dimensionless groups such as, the Biot interstitial number, the thermal conductivity ratio and the volumetric heat generation. The governing equations are solved using the finite volume method, gave rise to a multitude of results concerning in particular the thermal field in the porous channel and the existence or not of the local thermal equilibrium.

Keywords: local thermal non equilibrium model, mixed convection, porous medium, power generation

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

Procedia PDF Downloads 152

Authors: Milad Keshvardoost, Amir Khanlari, Nader Khalesi

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Background: Any firm in the pharmaceutical industry requires efficient and effective management information systems (MIS) to support managerial functions. Purpose: The aim of this study is to investigate the impact of Task-Technology Fit on user effectiveness, efficiency, and creativity in Iranian pharmaceutical companies. Methodology: 345 reliable and validate questionnaires were distributed among selected samples, through the cluster method, to Information system users of eight leading Iranian pharmaceutical companies, based on the likert scale. The proposed model of the article is based on a model with Task technology fit, on user performance with the definition of efficiency, effectiveness, and creativity through mediation effects of perceived usefulness and ease of use. Results: This study confirmed that TTF with definitions of adequacy and compatibility has positive impacts on user performance Conclusion: We concluded that pharmaceutical users of IS, utilizing a system with a precise and intense observation of users' demands, may make facilitation for them to design an exclusive IS framework.

Keywords: information systems, user performance, pharmaceuticals, task technology fit

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Authors: Adriana Duarte, Sandra Sampaio, Catia Ferreira, Jaime I. N. R. Gomes

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With the fashion of faded worn garments the textile industry has moved from indigo and pigments to dyes that are fixed by cationization, with products that can be toxic, and that can show this effect after washing down the dye with friction and/or treating with enzymes in a subsequent operation. Increasingly they are treated with bleaches, such as hypochlorite and permanganate, both toxic substances. An alternative process is presented in this work for both garment and jet dyeing processes, without the use of pre-cationization and the alternative use of “particle-dyes”. These are hybrid products, made up by an inorganic particle and an organic dye. With standard soluble dyes, it is not possible to avoid diffusion into the inside of the fiber unless using previous cationization. Only in this way can diffusion be avoided keeping the centre of the fibres undyed so as to produce the faded effect by removing the surface dye and showing the white fiber beneath. With “particle-dyes”, previous cationization is avoided. By applying low temperatures, the dye does not diffuse completely into the inside of the fiber, since it is a particle and not a soluble dye, being then able to give the faded effect. Even though bleaching can be used it can also be avoided, by the use of friction and enzymes they can be used just as for other dyes. This fashion brought about new ways of applying reactive dyes by the use of previous cationization of cotton, lowering the salt, and temperatures that reactive dyes usually need for reacting and as a side effect the application of a more environmental process. However, cationization is a process that can be problematic in applying it outside garment dyeing, such as jet dyeing, being difficult to obtain level dyeings. It also should be applied by a pad-fix or Pad-batch process due to the low affinity of the pre-cationization products making it a more expensive process, and the risk of unlevelness in processes such as jet dyeing. Wit particle-dyes, since no pre-cationizartion is necessary, they can be applied in jet dyeing. The excess dye is fixed by a fixing agent, fixing the insoluble dye onto the surface of the fibers. By applying the fixing agent only one to 1-3 rinses in water at room temperature are necessary, saving water and improving the washfastness.

Keywords: denim, garment dyeing, worn look, eco-fashion

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Authors: Sukwoong Kang

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Background: The application of light-emitting diode (LED)-dependent photobiomodulation (PBM) in promoting post-tendon injury healing has been recently reported. Despite the establishment of a theoretical basis for ligament restoration through PBM, the lack of any empirical evidence deems this therapeutic strategy contentious. Therefore, the aim of this study was to investigate the potency of LED-based PBM in facilitating tendon healing in a murine model. Methods: Migration kinetics were analyzed at two specific wavelengths: 630 and 880 nm. The Achilles tendon in the hind limbs of Balb/c mice was severed via Achilles tendon transection. Subsequently, the mice were randomized into LED non-irradiation and LED irradiation groups. Mice with intact tendons were employed as healthy controls. The wounds were LED-irradiated for 20 min daily for two days. Histological properties, tendon healing mediators, and inflammatory mediators were screened on day 14. Results: The roundness of the nuclei and fiber structure, indicating the degree of infiltrated inflammatory cells and severity of fiber fragmentation, respectively, were considerably lower in the LED irradiation group than in the LED non-irradiation group. Immunohistochemical analysis depicted an increase in tenocytes (SCX+ cells) and a recovery of wounds with reduced fibrosis (lower collagen 3 and TGF-β1) in the LED irradiation group during healing; conversely, the LED non-irradiation group exhibited tissue fibrosis. The ratio of M2 macrophages to total macrophages was higher in the LED irradiation group than in the injured group. Conclusion: LED-based PBM in the Achilles tendon rupture murine model effectuated a rapid restoration of histological and immunochemical outcomes. The aforementioned findings suggest that LED-based PBM presents remarkable potential as an adjunct therapeutic for tendon healing and warrants further research to standardize various parameters to advance and establish it as a reliable treatment regime.

Keywords: photobiomodulation, light-emitting diode, tendon, regeneration

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Authors: Ali Akbar, Seungho Shin, Sukkee Um

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The composition of catalyst layers (CLs) plays an important role in the overall performance and cost of the proton exchange membrane fuel cells (PEMFCs). Low platinum loading, high utilization, and more durable catalyst still remain as critical challenges for PEMFCs. In this study, a three-dimensional material network model is developed to visualize the nanostructure of carbon supported platinum Pt/C and Pt/VACNT catalysts in pursuance of maximizing the catalyst utilization. The quadruple-phase randomly generated CLs domain is formulated using quasi-random stochastic Monte Carlo-based method. This unique statistical approach of four-phase (i.e., pore, ionomer, carbon, and platinum) model is closely mimic of manufacturing process of CLs. Various CLs compositions are simulated to elucidate the effect of electrons, ions, and mass transport paths on the catalyst utilization factor. Based on simulation results, the effect of key factors such as porosity, ionomer contents and Pt weight percentage in Pt/C catalyst have been investigated at the represented elementary volume (REV) scale. The results show that the relationship between ionomer content and Pt utilization is in good agreement with existing experimental calculations. Furthermore, this model is implemented on the state-of-the-art Pt/VACNT CLs. The simulation results on Pt/VACNT based CLs show exceptionally high catalyst utilization as compared to Pt/C with different composition ratios. More importantly, this study reveals that the maximum catalyst utilization depends on the distance spacing between the carbon nanotubes for Pt/VACNT. The current simulation results are expected to be utilized in the optimization of nano-structural construction and composition of Pt/C and Pt/VACNT CLs.

Keywords: catalyst layer, platinum utilization, proton exchange membrane fuel cell, stochastic modeling

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Authors: Mohsen Goodarzi, George Berghorn

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Despite the rapid growth in the number of green building and community development projects, the long-term performance of these projects has not yet been sufficiently evaluated from the users’ points of view. This is partially due to the lack of post-occupancy evaluation tools available for this type of project. In this study, a post-construction evaluation model is developed to evaluate the relationship between the perceived performance and satisfaction of residents in LEED-certified residential buildings and communities. To develop this evaluation model, a primary five-factor model was developed based on the existing models and residential satisfaction theories. Each factor of the model included several measures that were adopted from LEED certification systems such as LEED-BD+C New Construction, LEED-BD+C Multifamily Midrise, LEED-ND, as well as the UC Berkeley’s Center for the Built Environment survey tool. The model included four predictor variables (factors), including perceived building performance (8 measures), perceived infrastructure performance (9 measures), perceived neighborhood design (6 measures), and perceived economic performance (4 measures), and one dependent variable (factor), which was residential satisfaction (6 measures). An online survey was then conducted to collect the data from the residents of LEED-certified residential communities (n=192) and the validity of the model was tested through Confirmatory Factor Analysis (CFA). After modifying the CFA model, 26 measures, out of the initial 33 measures, were retained to enter into a Structural Equation Model (SEM) and to find the relationships between the perceived buildings performance, infrastructure performance, neighborhood design, economic performance and residential Satisfaction. The results of the SEM showed that the perceived building performance was the most influential factor in determining residential satisfaction in LEED-certified communities, followed by the perceived neighborhood design. On the other hand, perceived infrastructure performance and perceived economic performance did not show any significant relationship with residential satisfaction in these communities. This study can benefit green building researchers by providing a model for the evaluation of the long-term performance of these projects. It can also provide opportunities for green building practitioners to determine priorities for future residential development projects.

Keywords: green building, residential satisfaction, perceived performance, confirmatory factor analysis, structural equation modeling

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Authors: Liwei Liu, Shuguo Pan, Wang Gao

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In order to take full advantages of the BDS-3 penta-frequency signals in the long-baseline RTK positioning, a long-baseline RTK positioning method based on the BDS-3 penta-frequency ionospheric-reduced (IR) combinations is proposed. First, the low noise and weak ionospheric delay characteristics of the multi-frequency combined observations of BDS-3is analyzed. Second, the multi-frequency extra-wide-lane (EWL)/ wide-lane (WL) combinations with long-wavelengths are constructed. Third, the fixed IR EWL combinations are used to constrain the IR WL, then constrain narrow-lane (NL)ambiguityies and start multi-epoch filtering. There is no need to consider the influence of ionospheric parameters in the third step. Compared with the estimated ionospheric model, the proposed method reduces the number of parameters by half, so it is suitable for the use of multi-frequency and multi-system real-time RTK. The results using real data show that the stepwise fixed model of the IR EWL/WL/NL combinations can realize long-baseline instantaneous cimeter-level positioning.

Keywords: penta-frequency, ionospheric-reduced (IR), RTK positioning, long-baseline

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Authors: Mathias Marquardt, Peter Dünow, Sandra Baßler

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The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions.

Keywords: auto tuning, balanced robot, closed loop identification, subspace identification

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Authors: Jun Yuan

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Shipping is a major transportation mode all over the world, and it has a significant contribution to global carbon emissions. Electrification of ships is one of the main strategies to reduce shipping carbon emissions. The number of electric ships has continued to grow in recent years. However, charging infrastructure is still scarce, which severely restricts the development of electric ships. Therefore, it is very important to design ship charging stations reasonably by comprehensively considering charging demand and investment costs. This study aims to minimize the full life cycle cost of charging stations, considering the uncertainty of charging demand. A mixed integer programming model is developed for this optimization problem. Based on the characteristics of the mathematical model, a simulation based optimization method is proposed to find the optimal number and rated power of chargers. In addition, the impact of renewable energy and storage systems is analyzed. The results can provide decision support and a reference basis for the design of ship charging stations.

Keywords: shipping emission, electricity ship, charging station, optimal design

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Authors: Hamed Saremi, Suzan Taghavy, Seyed Mohammad Hanif Sanjari, Mostafa Kahali

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One of the challenges in manufacturing and service companies to provide a product or service is recognized Brand to consumers in target markets. They provide most of their processes under the same capacity. But the constant threat of devastating internal and external resources to prevent a rise Brands and more companies are recognizing the stages are bankrupt. This paper has tried to identify and analyze effective indicators of brand equity and focuses on indicators and presents a model of intelligent create a model to prevent possible damage. In this study, the identified indicators of brand equity are based on literature study and according to expert opinions, set of indicators By techniques DEMATEL Then to used Multi-Step Adaptive Neural-Fuzzy Inference system (ANFIS) to design a multi-stage intelligent system for assessment of brand equity.

Keywords: brand, cosmetic product, ANFIS, DEMATEL

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Authors: Jui-Chen Huang, Shou-Hsiung Cheng

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This study examined their willingness to use telecare for people who have had experience falling in the last three months in Taiwan. This study adopted convenience sampling and a structural questionnaire to collect data. It was based on the definition and the constructs related to the Health Belief Model (HBM). HBM is comprised of seven constructs: perceived benefits (PBs), perceived disease threat (PDT), perceived barriers of taking action (PBTA), external cues to action (ECUE), internal cues to action (ICUE), attitude toward using (ATT), and behavioral intention to use (BI). This study adopted Fuzzy Neural Network (FNN) to put forward an effective method. It shows the dependence of ATT on PB, PDT, PBTA, ECUE, and ICUE. The training and testing data RMSE (root mean square error) are 0.028 and 0.166 in the FNN, respectively. The training and testing data RMSE are 0.828 and 0.578 in the regression model, respectively. On the other hand, as to the dependence of ATT on BI, as presented in the FNN, the training and testing data RMSE are 0.050 and 0.109, respectively. The training and testing data RMSE are 0.529 and 0.571 in the regression model, respectively. The results show that the FNN method is better than the regression analysis. It is an effective and viable good way.

Keywords: fall, fuzzy neural network, health belief model, telecare, willingness

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Authors: Wipassorn Vinicchayakul, Pichaya Supanakoon, Sathaporn Promwong

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A human’s hand localization is revised by using radar cross section (RCS) measurements with a minimum root mean square (RMS) error matching algorithm on a touchless keypad mock-up model. RCS and frequency transfer function measurements are carried out in an indoor environment on the frequency ranged from 3.0 to 11.0 GHz to cover federal communications commission (FCC) standards. The touchless keypad model is tested in two different distances between the hand and the keypad. The initial distance of 19.50 cm is identical to the heights of transmitting (Tx) and receiving (Rx) antennas, while the second distance is 29.50 cm from the keypad. Moreover, the effects of Rx angles relative to the hand of human factor are considered. The RCS input parameters are compared with power loss parameters at each frequency. From the results, the performance of the RCS input parameters with the second distance, 29.50 cm at 3 GHz is better than the others.

Keywords: radar cross section, fingerprint-based localization, minimum root mean square (RMS) error matching algorithm, touchless keypad model

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Authors: Debarati Bhaumik, Diptish Dey

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Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

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Authors: Yushuai Wang, Feng Xu, Junbo Tan, Xueqian Wang, Bin Liang

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In this paper, a sensor fault-tolerant control (FTC) scheme using robust model predictive control (RMPC) and set theoretic fault detection and isolation (FDI) is extended to linear parameter varying (LPV) systems. First, a group of set-valued observers are designed for passive fault detection (FD) and the observer gains are obtained through minimizing the size of invariant set of state estimation-error dynamics. Second, an input set for fault isolation (FI) is designed offline through set theory for actively isolating faults after FD. Third, an RMPC controller based on state estimation for LPV systems is designed to control the system in the presence of disturbance and measurement noise and tolerate faults. Besides, an FTC algorithm is proposed to maintain the plant operate in the corresponding mode when the fault occurs. Finally, a numerical example is used to show the effectiveness of the proposed results.

Keywords: fault detection, linear parameter varying, model predictive control, set theory

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Authors: Nikolaos Reppas, Yilin Gui

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A dual-porosity finite element-code will be presented for the stability analysis of the wellbore during CO₂ injection. An elastoplastic damage response will be considered to the model. The Finite Element Method (FEM) will be validated using experimental results from literature or from experiments that are planned to be undertaken at Newcastle University. The main target of the research paper is to present a constitutive model that can help industries to safely store CO₂ in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elastoplastic damage Thermo-Hydraulic-Mechanical (THM) model will determine the pressure and temperature of the injected CO₂ as well as the size of the radius of the wellbore that can make the Carbon Capture and Storage (CCS) procedure more efficient.

Keywords: carbon capture and storage, Wellbore stability, elastoplastic damage response for rock, constitutive THM model, fully coupled thermo-hydraulic-mechanical model

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Authors: M. A. García, J. Vinolas, A. Hernando

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Steel is nowadays one of the most important structural materials because of its outstanding mechanical properties. Therefore, in order to look for a sustainable economic model and to optimize the use of extensive resources, new methods to monitor and prevent failure of steel-based facilities are required. The classical mechanical tests, as for instance building tasting, are invasive and destructive. Moreover, for facilities where the steel element is embedded, (as reinforced concrete) these techniques are directly non applicable. Hence, non-invasive monitoring techniques to prevent failure, without altering the structural properties of the elements are required. Among them, electromagnetic methods are particularly suitable for non-invasive inspection of the mechanical state of steel-based elements. The magnetoelastic coupling effects induce a modification of the electromagnetic properties of an element upon applied stress. Since most steels are ferromagnetic because of their large Fe content, it is possible to inspect their structure and state in a non-invasive way. We present here a distinct electromagnetic method for contactless evaluation of internal stress in steel-based elements. In particular, this method relies on measuring the magnetic induction between two coils with the steel specimen in between them. We found that the alteration of electromagnetic properties of the steel specimen induced by applied stress-induced changes in the induction allowed us to detect stress well below half of the elastic limit of the material. Hence, it represents an outstanding non-invasive method to prevent failure in steel-based facilities. We here describe the theoretical model, present experimental results to validate it and finally we show a practical application for detection of stress and inhomogeneities in train railways.

Keywords: magnetoelastic, magnetic induction, mechanical stress, steel

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Authors: N. R. Badurally Adam, S. R. Monebhurrun, M. Z. Dauhoo, A. Khoodaruth

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Transport energy demand is vital for the economic growth of any country. Globalisation and better standard of living plays an important role in transport energy demand. Recently, transport energy demand in Mauritius has increased significantly, thus leading to an abuse of natural resources and thereby contributing to global warming. Forecasting the transport energy demand is therefore important for controlling and managing the demand. In this paper, we develop a model to predict the transport energy demand. The model developed is based on a system of five stochastic differential equations (SDEs) consisting of five endogenous variables: fuel price, population, gross domestic product (GDP), number of vehicles and transport energy demand and three exogenous parameters: crude birth rate, crude death rate and labour force. An interval of seven years is used to avoid any falsification of result since Mauritius is a developing country. Data available for Mauritius from year 2003 up to 2009 are used to obtain the values of design variables by applying genetic algorithm. The model is verified and validated for 2010 to 2012 by substituting the values of coefficients obtained by GA in the model and using particle swarm optimisation (PSO) to predict the values of the exogenous parameters. This model will help to control the transport energy demand in Mauritius which will in turn foster Mauritius towards a pollution-free country and decrease our dependence on fossil fuels.

Keywords: genetic algorithm, modeling, particle swarm optimization, stochastic differential equations, transport energy demand

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Authors: Jin Sung Kim, Hoon Huh

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The material properties of OFHC copper film was investigated with the High-Speed Material Micro Testing Machine (HSMMTM) at the high strain rates. The rate-dependent stress-strain curves from the experiment and the Johnson-Cook curve fitting showed large discrepancies as the plastic strain increases since the constitutive model implies no rate-dependent strain hardening effect. A new constitutive model was proposed in consideration of rate-dependent strain hardening effect. The strain rate hardening term in the new constitutive model consists of the strain rate sensitivity coefficients of the yield strength and strain hardening.

Keywords: rate dependent material properties, dynamic constitutive model, OFHC copper film, strain rate

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Authors: M. Ndaw, G. Mendy, S. Ouya

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Risk management in banking sector is a key issue linked to financial system stability and its importance has been elevated by technological developments and emergence of new financial instruments. In this paper, we improve the model previously defined for quantifying internal control impact on banking risks by automatizing the residual criticality estimation step of FMECA. For this, we defined three equations and a maturity coefficient to obtain a mathematical model which is tested on all banking processes and type of risks. The new model allows an optimal assessment of residual criticality and improves the correlation rate that has become 98%.

Keywords: risk, control, banking, FMECA, criticality

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Authors: Jiachen Wang, Dongxu Ji

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Geothermal power generation is a mature technology with zero carbon emission and stable power output, which could play a vital role as an optimum substitution of base load technology in China’s future decarbonization society. However, the development of geothermal power plants in China is stagnated for a decade due to the underestimation of geothermal energy and insufficient favoring policy. Lack of understanding of the potential value of base-load technology and environmental benefits is the critical reason for disappointed policy support. This paper proposed a different energy-economic model to uncover the potential benefit of developing a geothermal power plant in Puer, including the value of base-load power generation, and environmental and economic benefits. Optimization of the Organic Rankine Cycle (ORC) for maximum power output and minimum Levelized cost of electricity was first conducted. This process aimed at finding the optimum working fluid, turbine inlet pressure, pinch point temperature difference and superheat degrees. Then the optimal ORC model was sent to the energy-economic model to simulate the potential economic and environmental benefits. Impact of geothermal power plants based on the scenarios of implementing carbon trade market, the direct subsidy per electricity generation and nothing was tested. In addition, a requirement of geothermal reservoirs, including geothermal temperature and mass flow rate for a competitive power generation technology with other renewables, was listed. The result indicated that the ORC power plant has a significant economic and environmental benefit over other renewable power generation technologies when implementing carbon trading market and subsidy support. At the same time, developers must locate the geothermal reservoirs with minimum temperature and mass flow rate of 130 degrees and 50 m/s to guarantee a profitable project under nothing scenarios.

Keywords: geothermal power generation, optimization, energy model, thermodynamics

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Authors: Kefaifi Aissa, Sahraoui Tahar, Kheloufi Abdelkrim, Anas Sabiha, Hannane Farouk

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The aim of this study is to show how the Design of Experiments Method (DOE) can be put into use as a practical approach for silica sand etching behavior modeling during its primary step of leaching. In the present work, we have studied etching effect on particle size during a primary step of leaching process on Algerian silica sand with florid acid (HF) at 20% and 30 % during 4 and 8 hours. Therefore, a new purity of the sand is noted depending on the time of leaching. This study was expanded by a numerical approach using a method of experiment design, which shows the influence of each parameter and the interaction between them in the process and approved the obtained experimental results. This model is a predictive approach using hide software. Based on the measured parameters experimentally in the interior of the model, the use of DOE method can make it possible to predict the outside parameters of the model in question and can give us the optimize response without making the experimental measurement.

Keywords: acid leaching, design of experiments method(DOE), purity silica, silica etching

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Authors: Ciolomic Ioana Andreea, Farcas Teodora, Tiron-Tudor Adriana

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Investments in research and innovation are widely acknowledged as being crucial drivers for economic growth, for job-creation and to secure social and economic welfare. The aim of this article is to disseminate the results of a Leonardo da Vinci Innovation Transfer project, AdapTykes Adaptation of trainings based up on the Finnish Workplace Development Programme. This project aims to analyses the adaptability of the Finnish model to the economic and political environment of the two emergent countries Romania and Hungary, in order to develop workplace innovation. The focus of this paper is to present the adaptability of the Finnish model to the Romanian context.

Keywords: innovation, human resources, education, tools

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Authors: Masato Nishi, Tetsushi Kaburagi, Masashi Kurose, Tei Hirashima, Tetsusei Kurasiki

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The process of thermoforming a carbon fiber reinforced thermoplastic (CFRTP) has increased its presence in the automotive industry for its wide applicability to the mass production car. A non-isothermal forming for CFRTP can shorten its cycle time to less than 1 minute. In this paper, the textile reinforcement FE model which the authors proposed in a previous work is extended to the CFRTP model for non-isothermal forming simulation. The effect of thermoplastic is given by adding shell elements which consider thermal effect to the textile reinforcement model. By applying Reuss model to the stress calculation of thermoplastic, the proposed model can accurately predict in-plane shear behavior, which is the key deformation mode during forming, in the range of the process temperature. Using the proposed model, thermoforming simulation was conducted and the results are in good agreement with the experimental results.

Keywords: carbon fiber reinforced thermoplastic, finite element analysis, pre-impregnated textile composite, non-isothermal forming

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Nunuy Nuraeni, Vera Amalia Lestari, Atri Laranova, Viena Nissa Mien Fadhillah, Mutia, Muhammad Ikhlas Abdian Putra

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Introduction: Obesity is a common disease with high prevalence especially in developing countries including Indonesia. The obesitygenic lifestyle such as excessive intake of food, sedentary lifestyle is the major environmental etiologies of obesity. Obesity is also as one of burden disease with high morbidity due to its complication, such as diabetes mellitus and hypertension. The objective of this literature review is to know how the Epigallocathecin-3-Gallate of Green tea or Camellia sinensis effect as anti-obesity agent and reduce the complication of obesity. Material and Methods: This study based on the secondary data analysis complemented by primary data collection from several journal and textbook. We identified the effect of Epigallocathecin-3-Gallate of Green tea or Camellia sinensis as adjuvant therapy for management obesity and to prevent the complications of obesity. Results: Based on the result, Green tea or Camellia sinensis contain Epigallocathecin-3-Gallate (EGCG) that has anti-obesity effect such as induce apoptosis, inhibit adipogenesis, increasing lipolytic activity, increasing fat oxidation and thermogenesis. Discussion: EGCG are naturally distributed in green tea, that contains a biological activity that has a potential effect to treat obesity. Conclusion: EGCG are capable to treat obesity. By consuming EGCG can prevent obesity in normal health person and prevent complication in patient with obesity.

Keywords: adjuvant therapy, anti-obesity effect, complication, epigallocathecin-3-gallate, obesity

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Authors: Ci Lin, Tet Yeap, Iluju Kiringa

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This paper introduces the subspace rotation algorithm (SRA) to train the Restricted Hopfield Network (RHN) as an auto-associative memory. Subspace rotation algorithm is a gradient-free subspace tracking approach based on the singular value decomposition (SVD). In comparison with Backpropagation Through Time (BPTT) on training RHN, it is observed that SRA could always converge to the optimal solution and BPTT could not achieve the same performance when the model becomes complex, and the number of patterns is large. The AUTS case study showed that the RHN model trained by SRA could achieve a better structure of attraction basin with larger radius(in general) than the Hopfield Network(HNN) model trained by Hebbian learning rule. Through learning 10000 patterns from MNIST dataset with RHN models with different number of hidden nodes, it is observed that an several components could be adjusted to achieve a balance between recovery accuracy and noise resistance.

Keywords: hopfield neural network, restricted hopfield network, subspace rotation algorithm, hebbian learning rule

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Authors: Wadea Ameen

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Rapid prototyping (RP) technology, such as fused deposition modeling (FDM), is gaining popularity because it can produce functioning components with intricate geometric patterns in a reasonable amount of time. A multitude of process variables influences the quality of manufactured parts. In this study, four important process parameters such as layer thickness, model interior fill style, support fill style and orientation are considered. Their influence on three responses, such as build time, model material, and support material, is studied. Experiments are conducted based on factorial design, and the results are presented.

Keywords: fused deposition modeling, factorial design, optimization, 3D printing

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Authors: Davies Obaromi, Qin Yongsong, James Ndege, Azeez Adeboye, Akinwumi Odeyemi

Abstract:

The basic model usually used in disease mapping, is the Besag, York and Mollie (BYM) model and which combines the spatially structured and spatially unstructured priors as random effects. Bayesian Conditional Autoregressive (CAR) model is a disease mapping method that is commonly used for smoothening the relative risk of any disease as used in the Besag, York and Mollie (BYM) model. This model (CAR), which is also usually assigned as a prior to one of the spatial random effects in the BYM model, successfully uses information from adjacent sites to improve estimates for individual sites. To our knowledge, there are some unrealistic or counter-intuitive consequences on the posterior covariance matrix of the CAR prior for the spatial random effects. In the conventional BYM (Besag, York and Mollie) model, the spatially structured and the unstructured random components cannot be seen independently, and which challenges the prior definitions for the hyperparameters of the two random effects. Therefore, the main objective of this study is to construct and utilize an extended Bayesian spatial CAR model for studying tuberculosis patterns in the Eastern Cape Province of South Africa, and then compare for flexibility with some existing CAR models. The results of the study revealed the flexibility and robustness of this alternative extended CAR to the commonly used CAR models by comparison, using the deviance information criteria. The extended Bayesian spatial CAR model is proved to be a useful and robust tool for disease modeling and as a prior for the structured spatial random effects because of the inclusion of an extra hyperparameter.

Keywords: Besag2, CAR models, disease mapping, INLA, spatial models

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Authors: Rufus Temidayo Akinnubi, M. O. Adeniyi

Abstract:

Existing diurnal air temperature models simulate night time air temperature over Nigeria with high biases. An improved parameterization is presented for modeling the diurnal pattern of air temperature (Ta) which is applicable in the calculation of turbulent heat fluxes in Global climate models, based on Nigeria Micrometeorological Experimental site (NIMEX) surface layer observations. Five diurnal Ta models for estimating hourly Ta from daily maximum, daily minimum, and daily mean air temperature were validated using root-mean-square error (RMSE), Mean Error Bias (MBE) and scatter graphs. The original Fourier series model showed better performance for unstable air temperature parameterizations while the stable Ta was strongly overestimated with a large error. The model was improved with the inclusion of the atmospheric cooling rate that accounts for the temperature inversion that occurs during the nocturnal boundary layer condition. The MBE and RMSE estimated by the modified Fourier series model reduced by 4.45 oC and 3.12 oC during the transitional period from dry to wet stable atmospheric conditions. The modified Fourier series model gave good estimation of the diurnal weather patterns of Ta when compared with other existing models for a tropical environment.

Keywords: air temperature, mean bias error, Fourier series analysis, surface energy balance,

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