Search results for: stochastic matrices

Authors: Raed KOUTA, José-Alcebiades-Ernesto HLUNGUANE, Eric Châtele

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The search for and exploitation of new fossil energy resources is taking place in the context of the gradual depletion of existing deposits. Despite the adoption of international targets to combat global warming, the demand for fuels continues to grow, contradicting the movement towards an energy-efficient society. The increase in the share of offshore in global hydrocarbon production tends to compensate for the depletion of terrestrial reserves, thus constituting a major challenge for the players in the sector. Through the economic potential it represents, and the energy independence it provides, offshore exploitation is also a challenge for States such as Mozambique, which have large maritime areas and whose environmental wealth must be considered. The exploitation of new reserves on economically viable terms depends on available technologies. The development of deep and ultra-deep offshore requires significant research and development efforts. Progress has also been made in managing the multiple risks inherent in this activity. Our study proposes a reliability approach to develop products and processes designed to live at sea. Indeed, the context of an offshore platform requires highly reliable solutions to overcome the difficulties of access to the system for regular maintenance and quick repairs and which must resist deterioration and degradation processes. One of the characteristics of failures that we consider is the actual conditions of use that are considered 'extreme.' These conditions depend on time and the interactions between the different causes. These are the two factors that give the degradation process its dynamic character, hence the need to develop dynamic reliability models. Our work highlights mathematical models that can explicitly manage interactions between components and process variables. These models are accompanied by numerical resolution methods that help to structure a dynamic reliability approach in a physical and probabilistic context. The application developed makes it possible to evaluate the reliability, availability, and maintainability of a floating storage and unloading platform for liquefied natural gas production.

Keywords: dynamic reliability, offshore plateform, stochastic process, uncertainties

Procedia PDF Downloads 107

Authors: Krupali Mehta, Sandip Bhatt, Umesh Trivedi, Bhavesh Bharatiya, Mukesh Ranjan, Atindra D. Shukla

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Owing to the dominating surface forces and large-scale surface interactions, the nano-scale particles face difficulties in getting suspended in various media. Magnetic nanoparticles of iron oxide offer a great deal of promise due to their ease of preparation, reasonable magnetic properties, low cost and environmental compatibility. We intend to modify the surface of magnetic Fe₂O₃ nanoparticles with selected surface modifying agents using simple and effective single-step chemical reactions in order to enhance dispersibility of magnetic nanoparticles in non-polar media. Magnetic particles were prepared by hydrolysis of Fe²⁺/Fe³⁺ chlorides and their subsequent oxidation in aqueous medium. The dried particles were then treated with Octadecyl quaternary ammonium silane (Terrasil™), stearic acid and gallic acid ester of stearyl alcohol in ethanol separately to yield S-2 to S-4 respectively. The untreated Fe₂O₃ was designated as S-1. The surface modified nanoparticles were then analysed with Dynamic Light Scattering (DLS), Fourier Transform Infrared spectroscopy (FTIR), X-Ray Diffraction (XRD), Thermogravimetric Gravimetric Analysis (TGA) and Scanning Electron Microscopy and Energy dispersive X-Ray analysis (SEM-EDAX). Characterization reveals the particle size averaging 20-50 nm with and without modification. However, the crystallite size in all cases remained ~7.0 nm with the diffractogram matching to Fe₂O₃ crystal structure. FT-IR suggested the presence of surfactants on nanoparticles’ surface, also confirmed by SEM-EDAX where mapping of elements proved their presence. TGA indicated the weight losses in S-2 to S-4 at 300°C onwards suggesting the presence of organic moiety. Hydrophobic character of modified surfaces was confirmed with contact angle analysis, all modified nanoparticles showed super hydrophobic behaviour with average contact angles ~129° for S-2, ~139.5° for S-3 and ~151° for S-4. This indicated that surface modified particles are super hydrophobic and they are easily dispersible in non-polar media. These modified particles could be ideal candidates to be suspended in oil-based fluids, polymer matrices, etc. We are pursuing elaborate suspension/sedimentation studies of these particles in various oils to establish this conjecture.

Keywords: iron nanoparticles, modification, hydrophobic, dispersion

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Authors: Ozcan Yavas, Berrak Erol Nalbur, Sermin Gunarslan

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A materiality matrix is a tool that organizations use to prioritize their activities and adapt to the increasing sustainability requirements in recent years. For the materiality index to move from business models to the sustainability business model stage, it must be done with all partners in the raw material, supply, production, product, and end-of-life product stages. Within the scope of this study, the Materiality Matrix was used to transform the business model into a sustainability business model and to create a sustainability roadmap in a factory producing electric buses. This matrix determines the necessary roadmap for all stakeholders to participate in the process, especially in sectors that produce sustainable products, such as the electric vehicle sector, and to act together with the cradle-to-cradle approach of sustainability roadmaps. Global Reporting Initiative analysis was used in the study conducted with 1150 stakeholders within the scope of the study, and 43 questions were asked to the stakeholders under the main headings of 'Legal Compliance Level,' 'Environmental Strategies,' 'Risk Management Activities,' 'Impact of Sustainability Activities on Products and Services,' 'Corporate Culture,' 'Responsible and Profitable Business Model Practices' and 'Achievements in Leading the Sector' and Economic, Governance, Environment, Social and Other. The results of the study aimed to include five 1st priority issues and four 2nd priority issues in the sustainability strategies of the organization in the short and medium term. When the studies carried out in the short term are evaluated in terms of Sustainability and Environmental Risk Management, it is seen that the studies are still limited to the level of legal legislation (60%) and individual studies in line with the strategies (20%). At the same time, the stakeholders expect the company to integrate sustainability activities into its business model within five years (35%) and to carry out projects to become the first company that comes to mind with its success leading the sector (20%). Another result obtained within the study's scope is identifying barriers to implementation. It is seen that the most critical obstacles identified by stakeholders with climate change and environmental impacts are financial deficiency and lack of infrastructure in the dissemination of sustainable products. These studies are critical for transitioning to sustainable business models for the electric vehicle sector to achieve the EU Green Deal and CBAM targets.

Keywords: sustainability business model, materiality matrix, electricity bus, carbon neutrality, sustainability management

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Authors: Eyup Dogan

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The number of research articles analyzing the integration properties of energy variables has rapidly increased in the energy literature for about a decade. The stochastic behaviors of energy variables are worth knowing due to several reasons. For instance, national policies to conserve or promote energy consumption, which should be taken as shocks to energy consumption, will have transitory effects in energy consumption if energy consumption is found to be stationary in one country. Furthermore, it is also important to know the order of integration to employ an appropriate econometric model. Despite being an important subject for applied energy (economics) and having a huge volume of studies, several known limitations still exist with the existing literature. For example, many of the studies use aggregate energy consumption and national level data. In addition, a huge part of the literature is either multi-country studies or solely focusing on the U.S. This is the first study in the literature that considers a form of energy consumption by sectors at sub-national level. This research study aims at investigating unit root properties of electricity consumption for 12 regions of Turkey by four sectors in addition to total electricity consumption for the purpose of filling the mentioned limits in the literature. In this regard, we analyze stationarity properties of 60 cases . Because the use of multiple unit root tests make the results robust and consistent, we apply Dickey-Fuller unit root test based on Generalized Least Squares regression (DFGLS), Phillips-Perron unit root test (PP) and Zivot-Andrews unit root test with one endogenous structural break (ZA). The main finding of this study is that electricity consumption is trend stationary in 7 cases according to DFGLS and PP, whereas it is stationary process in 12 cases when we take into account the structural change by applying ZA. Thus, shocks to electricity consumption have transitory effects in those cases; namely, agriculture in region 1, region 4 and region 7, industrial in region 5, region 8, region 9, region 10 and region 11, business in region 4, region 7 and region 9, total electricity consumption in region 11. Regarding policy implications, policies to decrease or stimulate the use of electricity have a long-run impact on electricity consumption in 80% of cases in Turkey given that 48 cases are non-stationary process. On the other hand, the past behavior of electricity consumption can be used to predict the future behavior of that in 12 cases only.

Keywords: unit root, electricity consumption, sectoral data, subnational data

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Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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The relation between stock price volatility and trading volume represents a controversial issue which has received a remarkable attention over the past decades. In fact, an extensive literature shows a positive relation between price volatility and trading volume in the financial markets, but the causal relationship which originates such association is an open question, from both a theoretical and empirical point of view. In this regard, various models, which can be considered as complementary rather than competitive, have been introduced to explain this relationship. They include the long debated Mixture of Distributions Hypothesis (MDH); the Sequential Arrival of Information Hypothesis (SAIH); the Dispersion of Beliefs Hypothesis (DBH); the Noise Trader Hypothesis (NTH). In this work, we analyze whether stock market efficiency can explain the diversity of results achieved during the years. For this purpose, we propose an alternative measure of market efficiency, based on the pointwise regularity of a stochastic process, which is the Hurst–H¨older dynamic exponent. In particular, we model the stock market by means of the multifractional Brownian motion (mBm) that displays the property of a time-changing regularity. Mostly, such models have in common the fact that they locally behave as a fractional Brownian motion, in the sense that their local regularity at time t0 (measured by the local Hurst–H¨older exponent in a neighborhood of t0 equals the exponent of a fractional Brownian motion of parameter H(t0)). Assuming that the stock price follows an mBm, we introduce and theoretically justify the Hurst–H¨older dynamical exponent as a measure of market efficiency. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. This approach is applied to financial markets; using data for the SP500 index from 1978 to 2017, on the one hand we find that when efficiency is not accounted for, a positive contemporaneous relationship emerges and is stable over time. Conversely, it disappears as soon as efficiency is taken into account. In particular, this association is more pronounced during time frames of high volatility and tends to disappear when market becomes fully efficient.

Keywords: volume–volatility relationship, efficient market hypothesis, martingale model, Hurst–Hölder exponent

Procedia PDF Downloads 66

Authors: Ayda Maadani Mallak, Amir lakzian, Elham Khodaverdi, Gholam Hossein Haghnia

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The use of pharmaceuticals and personal care products such as antibiotics and antibacterials has been increased in recent years. Since the major part of consumed compounds remains unchanged in the wastewater treatment plant, they will easily find their way into the human food chain following the land use of sewage sludge (SS). Biological treatment of SS is one the most effective methods for expunging contaminants. White rot fungi, due to their ligninolytic enzymes, are extensively used to degrade organic compounds. Among all three different morphological forms and growth patterns of filamentous fungi (mycelia, clumps, and pellets), fungal pellet formation has been the subject of interest in industrial bioprocesses. Therefore this study was aimed to investigate the uptake of tetracycline (TC) and triclosan (TCS) by radish plant (Raphanus sativus) from soil amended with untreated and pretreated SS by P. ostreatus fungal pellets under greenhouse conditions. The experimental soil was amended with 1) Contaminated SS with TC at a concentration of 100 mgkg-1 and pretreated by fungal pellets, 2) Contaminated SS with TC at 100 mgkg-1 and untreated with fungal pellets, 3) Contaminated SS with TCS at a concentration of 50 mgkg-1 and pretreated by fungal pellets, 4) contaminated SS with TCS at 50 mgkg-1 and untreated with fungal pellets. An uncontaminated and untreated SS-amended soil also was considered as control treatment. An AB SCIEX 3200 QTRAP LC-MS/MS system was used in order to analyze the concentration of TC and TCS in plant tissues and soil medium. Results of this study revealed that the presence of TC and TCS in SS-amended soil decreased the radish biomass significantly. The reduction effect of TCS on dry biomass of shoot and root was 39 and 45% compared to controls, whereas for TC, the reduction percentage for shoot and root was 27 and 40.6%, respectively. However, fungal treatment of SS by P. ostreatus pellets reduced the negative effect of both compounds on plant biomass remarkably, as no significant difference was observed compared to control treatments. Pretreatment of SS with P. ostreatus also caused a significant reduction in translocation factor (concentration in shoot/root), especially for TC compound up to 32.3%, whereas this reduction for TCS was less (8%) compared to untreated SS. Generally, the results of this study confirmed the positive effect of using fungal pellets in SS amendment to decrease TC and TCS uptake by radish plants. In conclusion, P. ostreatus fungal pellets might provide future insights into bioaugmentation to remove antibiotics from environmental matrices.

Keywords: antibiotic, fungal pellet, sewage sludge, white-rot fungi

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 247

Authors: Irati Barandiaran, Xabier Velasco-Iza, Galder Kortaberria

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Hybrid inorganic/organic nanostructured materials based on block copolymers are of considerable interest in the field of Nanotechnology, taking into account that these nanocomposites combine the properties of polymer matrix and the unique properties of the added nanoparticles. The use of block copolymers as templates offers the opportunity to control the size and the distribution of inorganic nanoparticles. This research is focused on the surface modification of inorganic nanoparticles to reach a good interface between nanoparticles and polymer matrices which hinders the nanoparticle aggregation. The aim of this work is to obtain a good and selective dispersion of Fe3O4 magnetic nanoparticles into different types of block copolymers such us, poly(styrene-b-methyl methacrylate) (PS-b-PMMA), poly(styrene-b-ε-caprolactone) (PS-b-PCL) poly(isoprene-b-methyl methacrylate) (PI-b-PMMA) or poly(styrene-b-butadiene-b-methyl methacrylate) (SBM) by using different grafting strategies. Fe3O4 magnetic nanoparticles have been surface-modified with polymer or block copolymer brushes following different grafting methods (grafting to, grafting from and grafting through) to achieve a selective location of nanoparticles into desired domains of the block copolymers. Morphology of fabricated hybrid nanocomposites was studied by means of atomic force microscopy (AFM) and with the aim to reach well-ordered nanostructured composites different annealing methods were used. Additionally, nanoparticle amount has been also varied in order to investigate the effect of the nanoparticle content in the morphology of the block copolymer. Nowadays different characterization methods were using in order to investigate magnetic properties of nanometer-scale electronic devices. Particularly, two different techniques have been used with the aim of characterizing synthesized nanocomposites. First, magnetic force microscopy (MFM) was used to investigate qualitatively the magnetic properties taking into account that this technique allows distinguishing magnetic domains on the sample surface. On the other hand, magnetic characterization by vibrating sample magnetometer and superconducting quantum interference device. This technique demonstrated that magnetic properties of nanoparticles have been transferred to the nanocomposites, exhibiting superparamagnetic behavior similar to that of the maghemite nanoparticles at room temperature. Obtained advanced nanostructured materials could found possible applications in the field of dye-sensitized solar cells and electronic nanodevices.

Keywords: atomic force microscopy, block copolymers, grafting techniques, iron oxide nanoparticles

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Authors: Masoud Sakhinia, Sajjad Ahmad

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Introduction: Though knowledge of the compositional makeup and structure of the limbal niche has progressed exponentially during the past decade, much is yet to be understood. Identifying the precise profile and role of the stromal makeup which spans the ocular surface may inform researchers of the most optimum conditions needed to effectively expand LESCs in vitro, whilst preserving their differentiation status and phenotype. Limbal fibroblasts, as opposed to corneal fibroblasts are thought to form an important component of the microenvironment where LESCs reside. Methods: The corneal stroma was tissue engineered in vitro using both limbal and corneal fibroblasts embedded within a tissue engineered 3D collagen matrix. The effect of these two different fibroblasts on LESCs and hTCEpi corneal epithelial cell line were then subsequently determined using phase contrast microscopy, histolological analysis and PCR for specific stem cell markers. The study aimed to develop an in vitro model which could be used to determine whether limbal, as opposed to corneal fibroblasts, maintained the stem cell phenotype of LESCs and hTCEpi cell line. Results: Tissue culture analysis was inconclusive and required further quantitative analysis for remarks on cell proliferation within the varying stroma. Histological analysis of the tissue-engineered cornea showed a comparable structure to that of the human cornea, though with limited epithelial stratification. PCR results for epithelial cell markers of cells cultured on limbal fibroblasts showed reduced expression of CK3, a negative marker for LESC’s, whilst also exhibiting a relatively low expression level of P63, a marker for undifferentiated LESCs. Conclusion: We have shown the potential for the construction of a tissue engineered human cornea using a 3D collagen matrix and described some preliminary results in the analysis of the effects of varying stroma consisting of limbal and corneal fibroblasts, respectively, on the proliferation of stem cell phenotype of primary LESCs and hTCEpi corneal epithelial cells. Although no definitive marker exists to conclusively illustrate the presence of LESCs, the combination of positive and negative stem cell markers in our study were inconclusive. Though it is less traslational to the human corneal model, the use of conditioned medium from that of limbal and corneal fibroblasts may provide a more simple avenue. Moreover, combinations of extracellular matrices could be used as a surrogate in these culture models.

Keywords: cornea, Limbal Stem Cells, tissue engineering, PCR

Procedia PDF Downloads 262

Authors: Gaoyang Meng, Philip Harrison

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When manufacturing composite laminates, the fibre directions within the laminate are never perfectly straight and inevitably contain some degree of stochastic in-plane waviness or ‘meandering’. In this work we aim to understand the relationship between the degree of meandering of the fibre paths, and the resulting uncertainty in the laminate’s final mechanical properties. To do this, a numerical tool is developed to automatically generate meandering fibre paths in each of the laminate's 8 plies (using Matlab) and after mapping this information into finite element simulations (using Abaqus), the statistical variability of the tensile mechanical properties of a [45°/90°/-45°/0°]s carbon/epoxy (IM7/8552) laminate is predicted. The stiffness, first ply failure strength and ultimate failure strength are obtained. Results are generated by inputting the degree of variability in the fibre paths and the laminate is then examined in all directions (from 0° to 359° in increments of 1°). The resulting predictions are output as flower (polar) plots for convenient analysis. The average fibre orientation of each ply in a given laminate is determined by the laminate layup code [45°/90°/-45°/0°]s. However, in each case, the plies contain increasingly large amounts of in-plane waviness (quantified by the standard deviation of the fibre direction in each ply across the laminate. Four different amounts of variability in the fibre direction are tested (2°, 4°, 6° and 8°). Results show that both the average tensile stiffness and the average tensile strength decrease, while the standard deviations increase, with an increasing degree of fibre meander. The variability in stiffness is found to be relatively insensitive to the rotation angle, but the variability in strength is sensitive. Specifically, the uncertainty in laminate strength is relatively low at orientations centred around multiples of 45° rotation angle, and relatively high between these rotation angles. To concisely represent all the information contained in the various polar plots, rotation-angle dependent Weibull distribution equations are fitted to the data. The resulting equations can be used to quickly estimate the size of the errors bars for the different mechanical properties, resulting from the amount of fibre directional variability contained within the laminate. A longer term goal is to use these equations to quickly introduce realistic variability at the component level.

Keywords: advanced composite laminates, FE simulation, in-plane waviness, tensile properties, uncertainty quantification

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Authors: Cagri Mollamahmutoglu, Aykut Levent, Ali Mercan

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Micro-beams are one of the most common components of Nano-Electromechanical Systems (NEMS) and Micro Electromechanical Systems (MEMS). For this reason, static bending, buckling, and free vibration analysis of micro-beams have been the subject of many studies. In addition, micro-beams restrained with elastic type foundations have been of particular interest. In the analysis of microstructures, closed-form solutions are proposed when available, but most of the time solutions are based on numerical methods due to the complex nature of the resulting differential equations. Thus, a robust and efficient solution method has great importance. In this study, a mixed finite element formulation is obtained for a functionally graded Timoshenko micro-beam resting on two-parameter elastic foundation. In the formulation modified couple stress theory is utilized for the micro-scale effects. The equation of motion and boundary conditions are derived according to Hamilton’s principle. A functional, derived through a scientific procedure based on Gateaux Differential, is proposed for the bending and buckling analysis which is equivalent to the governing equations and boundary conditions. Most important advantage of the formulation is that the mixed finite element formulation allows usage of C₀ type continuous shape functions. Thus shear-locking is avoided in a built-in manner. Also, element matrices are sparsely populated and can be easily calculated with closed-form integration. In this framework results concerning the effects of micro-scale length parameter, power-law parameter, aspect ratio and coefficients of partially or fully continuous elastic foundation over the static bending, buckling, and free vibration response of FG-micro-beam under various boundary conditions are presented and compared with existing literature. Performance characteristics of the presented formulation were evaluated concerning other numerical methods such as generalized differential quadrature method (GDQM). It is found that with less computational burden similar convergence characteristics were obtained. Moreover, formulation also includes a direct calculation of the micro-scale related contributions to the structural response as well.

Keywords: micro-beam, functionally graded materials, two-paramater elastic foundation, mixed finite element method

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Suleyman Ogul Ertugrul, Mustafa Turgut, Serkan Inandı, Mustafa Gorkem Coban, Mustafa Kıgılı, Ali Mert, Oguzhan Kesmez, Murat Ozancı, Caglar Uyulan

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In high-performance automotive engineering, the realistic simulation of suspension systems is crucial for enhancing vehicle dynamics and handling. This study focuses on the double wishbone suspension system, prevalent in racing vehicles due to its superior control and stability characteristics. Utilizing MATLAB and Adams Car simulation software, we conduct a comprehensive analysis of displacement behaviors and damper sizing under various dynamic conditions. The initial phase involves using MATLAB to simulate the entire suspension system, allowing for the preliminary determination of damper size based on the system's response under simulated conditions. Following this, manual calculations of wheel loads are performed to assess the forces acting on the front and rear suspensions during scenarios such as braking, cornering, maximum vertical loads, and acceleration. Further dynamic force analysis is carried out using MATLAB Simulink, focusing on the interactions between suspension components during key movements such as bumps and rebounds. This simulation helps in formulating precise force equations and in calculating the stiffness of the suspension springs. To enhance the accuracy of our findings, we focus on a detailed kinematic and dynamic analysis. This includes the creation of kinematic loops, derivation of relevant equations, and computation of Jacobian matrices to accurately determine damper travel and compression metrics. The calculated spring stiffness is crucial in selecting appropriate springs to ensure optimal suspension performance. To validate and refine our results, we replicate the analyses using the Adams Car software, renowned for its detailed handling of vehicular dynamics. The goal is to achieve a robust, reliable suspension setup that maximizes performance under the extreme conditions encountered in racing scenarios. This study exemplifies the integration of theoretical mechanics with advanced simulation tools to achieve a high-performance suspension setup that can significantly improve race car performance, providing a methodology that can be adapted for different types of racing vehicles.

Keywords: FSAE, suspension system, Adams Car, kinematic

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Authors: Syed Masood Hussain

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An effective control method, including system-level control and pulse width modulation for quasi-Z-source cascade multilevel inverter (qZS-CMI) based grid-tie photovoltaic (PV) power system is proposed. The system-level control achieves the grid-tie current injection, independent maximum power point tracking (MPPT) for separate PV panels, and dc-link voltage balance for all quasi-Z-source H-bridge inverter (qZS-HBI) modules. A recent upsurge in the study of photovoltaic (PV) power generation emerges, since they directly convert the solar radiation into electric power without hampering the environment. However, the stochastic fluctuation of solar power is inconsistent with the desired stable power injected to the grid, owing to variations of solar irradiation and temperature. To fully exploit the solar energy, extracting the PV panels’ maximum power and feeding them into grids at unity power factor become the most important. The contributions have been made by the cascade multilevel inverter (CMI). Nevertheless, the H-bridge inverter (HBI) module lacks boost function so that the inverter KVA rating requirement has to be increased twice with a PV voltage range of 1:2; and the different PV panel output voltages result in imbalanced dc-link voltages. However, each HBI module is a two-stage inverter, and many extra dc–dc converters not only increase the complexity of the power circuit and control and the system cost, but also decrease the efficiency. Recently, the Z-source/quasi-Z-source cascade multilevel inverter (ZS/qZS-CMI)-based PV systems were proposed. They possess the advantages of both traditional CMI and Z-source topologies. In order to properly operate the ZS/qZS-CMI, the power injection, independent control of dc-link voltages, and the pulse width modulation (PWM) are necessary. The main contributions of this paper include: 1) a novel multilevel space vector modulation (SVM) technique for the single phase qZS-CMI is proposed, which is implemented without additional resources; 2) a grid-connected control for the qZS-CMI based PV system is proposed, where the all PV panel voltage references from their independent MPPTs are used to control the grid-tie current; the dual-loop dc-link peak voltage control.

Keywords: Quzi-Z source inverter, Photo voltaic power system, space vector modulation, cascade multilevel inverter

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Authors: Alica Höpken, Hergen Pargmann

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The resource-efficient planning of the complex production planning processes in the meat industry and the reduction of food waste is a permanent challenge. The complexity of the production planning process occurs in every part of the supply chain, from agriculture to the end consumer. It arises from long and uncertain planning phases. Uncertainties such as stochastic yields, fluctuations in demand, and resource variability are part of this process. In the meat industry, waste mainly relates to incorrect storage, technical causes in production, or overproduction. The high amount of food waste along the complex supply chain in the meat industry could not be reduced by simple solutions until now. Therefore, resource-efficient production planning by conventional methods is currently only partially feasible. The realization of intelligent, automated production planning is basically possible through the application of machine learning algorithms, such as those of reinforcement learning. By applying the adapted design thinking method, machine learning methods (especially reinforcement learning algorithms) are used for the complex production planning process in the meat industry. This method represents a concretization to the application area. A resource-efficient production planning process is made available by adapting the design thinking method. In addition, the complex processes can be planned efficiently by using this method, since this standardized approach offers new possibilities in order to challenge the complexity and the high time consumption. It represents a tool to support the efficient production planning in the meat industry. This paper shows an elegant adaption of the design thinking method to apply the reinforcement learning method for a resource-efficient production planning process in the meat industry. Following, the steps that are necessary to introduce machine learning algorithms into the production planning of the food industry are determined. This is achieved based on a case study which is part of the research project ”REIF - Resource Efficient, Economic and Intelligent Food Chain” supported by the German Federal Ministry for Economic Affairs and Climate Action of Germany and the German Aerospace Center. Through this structured approach, significantly better planning results are achieved, which would be too complex or very time consuming using conventional methods.

Keywords: change management, design thinking method, machine learning, meat industry, reinforcement learning, resource-efficient production planning

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Authors: Desplanches Maxime

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Accurate estimation of the remaining useful life of lithium-ion batteries for electronic devices is crucial. Data-driven methodologies encounter challenges related to data volume and acquisition protocols, particularly in capturing a comprehensive range of aging indicators. To address these limitations, we propose a hybrid approach that integrates an electrochemical model with state-of-the-art data analysis techniques, yielding a comprehensive database. Our methodology involves infusing an aging phenomenon into a Newman model, leading to the creation of an extensive database capturing various aging states based on non-destructive parameters. This database serves as a robust foundation for subsequent analysis. Leveraging advanced data analysis techniques, notably principal component analysis and t-Distributed Stochastic Neighbor Embedding, we extract pivotal information from the data. This information is harnessed to construct a regression function using either random forest or support vector machine algorithms. The resulting predictor demonstrates a 5% error margin in estimating remaining battery life, providing actionable insights for optimizing usage. Furthermore, the database was built from the Newman model calibrated for aging and performance using data from a European project called Teesmat. The model was then initialized numerous times with different aging values, for instance, with varying thicknesses of SEI (Solid Electrolyte Interphase). This comprehensive approach ensures a thorough exploration of battery aging dynamics, enhancing the accuracy and reliability of our predictive model. Of particular importance is our reliance on the database generated through the integration of the electrochemical model. This database serves as a crucial asset in advancing our understanding of aging states. Beyond its capability for precise remaining life predictions, this database-driven approach offers valuable insights for optimizing battery usage and adapting the predictor to various scenarios. This underscores the practical significance of our method in facilitating better decision-making regarding lithium-ion battery management.

Keywords: Li-ion battery, aging, diagnostics, data analysis, prediction, machine learning, electrochemical model, regression

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Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

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The portfolio selection problem includes the evaluation of many criteria that are difficult to compare directly and is characterized by uncertain elements. The portfolio selection problem can be modeled as a group decision problem in which several experts are invited to present their assessment. In this context, it is important to study and analyze the process of reaching a consensus among group members. Indeed, due to the various diversities among experts, reaching consensus is not necessarily always simple and easily achievable. Moreover, the concept of consensus is accompanied by the concept of false consensus, which is particularly interesting in the dynamics of group decision-making processes. False consensus can alter the evaluation and selection phase of the alternative and is the consequence of the decision maker's inability to recognize that his preferences are conditioned by subjective structures. The present work aims to investigate the dynamics of consensus attainment in a group decision problem in which equivalent portfolios are proposed. In particular, the study aims to analyze the impact of the subjective structure of the decision-maker during the evaluation and selection phase of the alternatives. Therefore, the experimental framework is divided into three phases. In the first phase, experts are sent to evaluate the characteristics of all portfolios individually, without peer comparison, arriving independently at the selection of the preferred portfolio. The experts' evaluations are used to obtain individual Analytical Hierarchical Processes that define the weight that each expert gives to all criteria with respect to the proposed alternatives. This step provides insight into how the decision maker's decision process develops, step by step, from goal analysis to alternative selection. The second phase includes the description of the decision maker's state through Markov chains. In fact, the individual weights obtained in the first phase can be reviewed and described as transition weights from one state to another. Thus, with the construction of the individual transition matrices, the possible next state of the expert is determined from the individual weights at the end of the first phase. Finally, the experts meet, and the process of reaching consensus is analyzed by considering the single individual state obtained at the previous stage and the false consensus bias. The work contributes to the study of the impact of subjective structures, quantified through the Analytical Hierarchical Process, and how they combine with the false consensus bias in group decision-making dynamics and the consensus reaching process in problems involving the selection of equivalent portfolios.

Keywords: analytical hierarchical process, consensus building, false consensus effect, markov chains, portfolio selection problem

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Authors: Ju-Hong Lee, Ching-Wei Liao

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An adaptive array beamformer is designed for automatically preserving the desired signals while cancelling interference and noise. Providing robustness against model mismatches and tracking possible environment changes calls for robust adaptive beamforming techniques. The design criterion yields the well-known generalized sidelobe canceller (GSC) beamformer. In practice, the knowledge of the desired steering vector can be imprecise, which often occurs due to estimation errors in the DOA of the desired signal or imperfect array calibration. In these situations, the SOI is considered as interference, and the performance of the GSC beamformer is known to degrade. This undesired behavior results in a reduction of the array output signal-to-interference plus-noise-ratio (SINR). Therefore, it is worth developing robust techniques to deal with the problem due to local scattering environments. As to the implementation of adaptive beamforming, the required computational complexity is enormous when the array beamformer is equipped with massive antenna array sensors. To alleviate this difficulty, a generalized sidelobe canceller (GSC) with partially adaptivity for less adaptive degrees of freedom and faster adaptive response has been proposed in the literature. Unfortunately, it has been shown that the conventional GSC-based adaptive beamformers are usually very sensitive to the mismatch problems due to local scattering situations. In this paper, we present an effective GSC-based beamformer against the mismatch problems mentioned above. The proposed GSC-based array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. We utilize the predefined steering vector and a presumed angle tolerance range to carry out the required estimation for obtaining an appropriate steering vector. A matrix associated with the direction vector of signal sources is first created. Then projection matrices related to the matrix are generated and are utilized to iteratively estimate the actual direction vector of the desired signal. As a result, the quiescent weight vector and the required signal blocking matrix required for performing adaptive beamforming can be easily found. By utilizing the proposed GSC-based beamformer, we find that the performance degradation due to the considered local scattering environments can be effectively mitigated. To further enhance the beamforming performance, a signal subspace projection matrix is also introduced into the proposed GSC-based beamformer. Several computer simulation examples show that the proposed GSC-based beamformer outperforms the existing robust techniques.

Keywords: adaptive antenna beamforming, local scattering, signal blocking, steering mismatch

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Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

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Authors: Sayed M. Ahmed, Sawsan S. Darwish, Nagib A. Elmarzugi, Mohammad A. Al-Dosari, Mahmoud A. Adam, Nadia A. Al-Mouallimi

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Historical stone surfaces and architectural heritage may undergo unwanted changes due to the exposure to many physical and chemical deterioration factors, the innovative properties of the nano - materials can have advantageous application in the restoration and conservation of the cultural heritage with relation to the tailoring of new products for protection and consolidation of stone. The current work evaluates the effectiveness of inorganic compatible treatments; based on nanosized particles of silica (SiO2) dispersed in silicon based product, commonly used as a water-repellent/ consolidation for the construction materials affected by different kinds of decay. The nanocomposites obtained by dispersing the silica nanoparticles in polymeric matrices SILRES® BS OH 100 (solventless mixtures of ethyl silicates), in order to obtain a new nanocomposite, with hydrophobic and consolidation properties, to improve the physical and mechanical properties of the stone material. The nanocomposites obtained and pure SILRES® BS OH 100 were applied by brush Experimental stone blocks. The efficacy of the treatments has been evaluated after consolidation and artificial Thermal aging, through capillary water absorption measurements, Ultraviolet-light exposure to evaluate photo-induced and the hydrophobic effects of the treated surface, Scanning electron microscopy (SEM) examination is performed to evaluate penetration depth, re-aggregating effects of the deposited phase and the surface morphology before and after artificialaging. Sterio microscopy investigation is performed to evaluate the resistant to the effects of the erosion, acids and salts. Improving of stone mechanical properties were evaluated by compressive strength tests, colorimetric measurements were used to evaluate the optical appearance. All the results get together with the apparent effect that, silica/polymer nanocomposite is efficient material for the consolidation of artistic and architectural sandstone monuments, completely compatible, enhanced the durability of sandstone toward thermal and UV aging. In addition, the obtained nanocomposite improved the stone mechanical properties and the resistant to the effects of the erosion, acids and salts compared to the samples treated with pure SILRES® BS OH 100 without silica nanoparticles.

Keywords: colorimetric measurements, compressive strength, nanocomposites, porous stone consolidation, silica nanoparticles, sandstone

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Authors: Santi Maneewatchararangsri, Wanpen Chaicumpa, Patcharin Saengjaruk, Urai Chaisri

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Leptospirosis is a globally neglected disease that continues to be a significant public health and veterinary burden, with millions of cases reported each year. Early and accurate differential diagnosis of leptospirosis from other febrile illnesses and the development of a broad spectrum of leptospirosis vaccines are needed. The LipL32 outer membrane lipoprotein is a member of Leptospira adhesive matrices and has been found to exert hemolytic activity to erythrocytes in vitro. Therefore, LipL32 is regarded as a potential target for diagnosis, broad-spectrum leptospirosis vaccines, and for passive immunotherapy. In this study, we established LipL32-specific mouse monoclonal antibodies, mAbLPF1 and mAbLPF2, and their respective mouse- and humanized-engineered single chain variable fragment (ScFv). Their antibodies’ neutralizing activities against Leptospira-mediated hemolysis in vitro, and the therapeutic efficacy of mAbs against heterologous Leptospira infected hamsters were demonstrated. The epitope peptide of mAb LPF1 was mapped to a non-contiguous carboxy-terminal β-turn and amphipathic α-helix of LipL32 structure contributing to phospholipid/host cell adhesion and membrane insertion. We found that the mAbLPF2 epitope was located on the interacting loop of peptide binding groove of the LipL32 molecule responsible for interactions with host constituents. Epitope sequences are highly conserved among Leptospira spp. and are absent from the LipL32 superfamily of other microorganisms. Both epitopes are surface-exposed, readily accessible by mAbs, and immunogenic. However, they are less dominant when revealed by LipL32-specific immunoglobulins from leptospirosis-patient sera and rabbit hyperimmune serum raised by whole Leptospira. Our study also demonstrated an adhesion inhibitory activity of LipL32 protein to host membrane components and cells mediated by mAbs as well as an anti-hemolytic activity of the respective antibodies. The therapeutic antibodies, particularly the humanized-ScFv, have a potential for further development as non-drug therapeutic agent for human leptospirosis, especially in subjects allergic to antibiotics. The epitope peptides recognized by two therapeutic mAbs have potential use as tools for structure-function studies. Finally, protective peptides may be used as a target for epitope-based vaccines for control of leptospirosis.

Keywords: leptospira lipl32-specific antibodies, therapeutic epitopes, epitopes characterization, immunotherapy

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Authors: Ahmed E. Hodaib, Mohamed A. Hashem

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In engineering applications, a design has to be as fully perfect as possible in some defined case. The designer has to overcome many challenges in order to reach the optimal solution to a specific problem. This process is called optimization. Generally, there is always a function called “objective function” that is required to be maximized or minimized by choosing input parameters called “degrees of freedom” within an allowed domain called “search space” and computing the values of the objective function for these input values. It becomes more complex when we have more than one objective for our design. As an example for Multi-Objective Optimization Problem (MOP): A structural design that aims to minimize weight and maximize strength. In such case, the Pareto Optimal Frontier (POF) is used, which is a curve plotting two objective functions for the best cases. At this point, a designer should make a decision to choose the point on the curve. Engineers use algorithms or iterative methods for optimization. In this paper, we will discuss the Evolutionary Algorithms (EA) which are widely used with Multi-objective Optimization Problems due to their robustness, simplicity, suitability to be coupled and to be parallelized. Evolutionary algorithms are developed to guarantee the convergence to an optimal solution. An EA uses mechanisms inspired by Darwinian evolution principles. Technically, they belong to the family of trial and error problem solvers and can be considered global optimization methods with a stochastic optimization character. The optimization is initialized by picking random solutions from the search space and then the solution progresses towards the optimal point by using operators such as Selection, Combination, Cross-over and/or Mutation. These operators are applied to the old solutions “parents” so that new sets of design variables called “children” appear. The process is repeated until the optimal solution to the problem is reached. Reliable and robust computational fluid dynamics solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbo-machinery, and auto-motives. Coupling of Computational Fluid Dynamics “CFD” and Multi-Objective Evolutionary Algorithms “MOEA” has become substantial in aerospace engineering applications, such as in aerodynamic shape optimization and advanced turbo-machinery design.

Keywords: mathematical optimization, multi-objective evolutionary algorithms "MOEA", computational fluid dynamics "CFD", aerodynamic shape optimization

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Authors: Edvard Kokanyan, Narine Babajanyan, Ninel Kokanyan, Marco Bazzan

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Lithium niobate (LN) crystals, renowned for their exceptional nonlinear optical, electro-optical, piezoelectric, and photorefractive properties, stand as foundational materials in diverse fields of study and application. While they have long been utilized in frequency converters of laser radiation, electro-optical modulators, and holographic information recording media, LN crystals doped with rare earth ions represent a compelling frontier for modern compact devices. These materials exhibit immense potential as key components in infrared lasers, optical sensors, self-cooling systems, and radiation balanced laser setups. In this study, we present the successful synthesis of Ho-doped lithium niobate (LN:Ho) thin films on sapphire substrates employing the Sol-Gel technique. The films exhibit a strong crystallographic orientation along the perpendicular direction to the substrate surface, with X-ray diffraction analysis confirming the predominant alignment of the film's "c" axis, notably evidenced by the intense (006) reflection peak. Further characterization through Raman spectroscopy, employing a confocal Raman microscope (LabRAM HR Evolution) with exciting wavelengths of 532 nm and 785 nm, unraveled intriguing insights. Under excitation with a 785 nm laser, Raman scattering obeyed selection rules, while employing a 532 nm laser unveiled additional forbidden lines reminiscent of behaviors observed in bulk LN:Ho crystals. These supplementary lines were attributed to luminescence induced by excitation at 532 nm. Leveraging data from anti-Stokes Raman lines facilitated the disentanglement of luminescence spectra from the investigated samples. Surface scanning affirmed the uniformity of both structure and luminescence across the thin films. Notably, despite the robust orientation of the "c" axis perpendicular to the substrate surface, Raman signals indicated a stochastic distribution of "a" and "b" axes, validating the mosaic structure of the films along the mentioned axis. This study offers valuable insights into the structural properties of Ho-doped lithium niobate thin films, with the observed luminescence behavior holding significant promise for potential applications in optoelectronic devices.

Keywords: lithium niobate, Sol-Gel, luminescence, Raman spectroscopy

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Authors: Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Rachid Oulad Haj Thami, Sana El Fkihi

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Color and texture are highly discriminant visual cues that provide an essential information in many types of images. Color texture representation and classification is therefore one of the most challenging problems in computer vision and image processing applications. Color textures can be represented in different color spaces by using multiple image descriptors which generate a high dimensional set of texture features. In order to reduce the dimensionality of the feature set, feature selection techniques can be used. The goal of feature selection is to find a relevant subset from an original feature space that can improve the accuracy and efficiency of a classification algorithm. Traditionally, feature selection is focused on removing irrelevant features, neglecting the possible redundancy between relevant ones. This is why some feature selection approaches prefer to use feature clustering analysis to aid and guide the search. These techniques can be divided into two categories. i) Feature clustering-based ranking algorithm uses feature clustering as an analysis that comes before feature ranking. Indeed, after dividing the feature set into groups, these approaches perform a feature ranking in order to select the most discriminant feature of each group. ii) Feature clustering-based subset search algorithms can use feature clustering following one of three strategies; as an initial step that comes before the search, binded and combined with the search or as the search alternative and replacement. In this paper, we propose a new feature clustering-based sequential selection approach for the purpose of color texture representation and classification. Our approach is a three step algorithm. First, irrelevant features are removed from the feature set thanks to a class-correlation measure. Then, introducing a new automatic feature clustering algorithm, the feature set is divided into several feature clusters. Finally, a sequential search algorithm, based on a filter model and a separability measure, builds a relevant and non redundant feature subset: at each step, a feature is selected and features of the same cluster are removed and thus not considered thereafter. This allows to significantly speed up the selection process since large number of redundant features are eliminated at each step. The proposed algorithm uses the clustering algorithm binded and combined with the search. Experiments using a combination of two well known texture descriptors, namely Haralick features extracted from Reduced Size Chromatic Co-occurence Matrices (RSCCMs) and features extracted from Local Binary patterns (LBP) image histograms, on five color texture data sets, Outex, NewBarktex, Parquet, Stex and USPtex demonstrate the efficiency of our method compared to seven of the state of the art methods in terms of accuracy and computation time.

Keywords: feature selection, color texture classification, feature clustering, color LBP, chromatic cooccurrence matrix

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Authors: Kevin De Castro Cogle, Mirian Kubo, Maria Anastasiadi, Fady Mohareb, Claire Rossi

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Background: The bioaccessibility of bioactive compounds is a critical determinant of the nutritional quality of various food products. Despite its importance, there is a limited number of comprehensive studies aimed at assessing how the composition of a food matrix influences the bioaccessibility of a compound of interest. This knowledge gap has prompted a growing need to investigate the intricate relationship between food matrix formulations and the bioaccessibility of bioactive compounds. One such class of bioactive compounds that has attracted considerable attention is curcuminoids. These naturally occurring phytochemicals, extracted from the roots of Curcuma longa, have gained popularity owing to their purported health benefits and also well known for their poor bioaccessibility Project aim: The primary objective of this research project is to systematically assess the influence of matrix composition on the bioaccessibility of curcuminoids. Additionally, this study aimed to develop a series of predictive models for bioaccessibility, providing valuable insights for optimising the formula for functional foods and provide more descriptive nutritional information to potential consumers. Methods: Food formulations enriched with curcuminoids were subjected to in vitro digestion simulation, and their bioaccessibility was characterized with chromatographic and spectrophotometric techniques. The resulting data served as the foundation for the development of predictive models capable of estimating bioaccessibility based on specific physicochemical properties of the food matrices. Results: One striking finding of this study was the strong correlation observed between the concentration of macronutrients within the food formulations and the bioaccessibility of curcuminoids. In fact, macronutrient content emerged as a very informative explanatory variable of bioaccessibility and was used, alongside other variables, as predictors in a Bayesian hierarchical model that predicted curcuminoid bioaccessibility accurately (optimisation performance of 0.97 R2) for the majority of cross-validated test formulations (LOOCV of 0.92 R2). These preliminary results open the door to further exploration, enabling researchers to investigate a broader spectrum of food matrix types and additional properties that may influence bioaccessibility. Conclusions: This research sheds light on the intricate interplay between food matrix composition and the bioaccessibility of curcuminoids. This study lays a foundation for future investigations, offering a promising avenue for advancing our understanding of bioactive compound bioaccessibility and its implications for the food industry and informed consumer choices.

Keywords: bioactive bioaccessibility, food formulation, food matrix, machine learning, probabilistic modelling

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Authors: Elham Kiyani

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In this paper, we first introduce the concept of Q-efficient solutions in a real linear space not necessarily endowed with a topology, where Q is some nonempty (not necessarily convex) set. We also used the scalarization technique including the Gerstewitz function generated by a nonconvex set to characterize these Q-efficient solutions. The algebraic concepts of interior and closure are useful to study optimization problems without topology. Studying nonconvex vector optimization is valuable since topological interior is equal to algebraic interior for a convex cone. So, we use the algebraic concepts of interior and closure to define Q-weak efficient solutions and Q-Henig proper efficient solutions of set-valued optimization problems, where Q is not a convex cone. Optimization problems with set-valued maps have a wide range of applications, so it is expected that there will be a useful analytical tool in optimization theory for set-valued maps. These kind of optimization problems are closely related to stochastic programming, control theory, and economic theory. The paper focus on nonconvex problems, the results are obtained by assuming generalized non-convexity assumptions on the data of the problem. In convex problems, main mathematical tools are convex separation theorems, alternative theorems, and algebraic counterparts of some usual topological concepts, while in nonconvex problems, we need a nonconvex separation function. Thus, we consider the Gerstewitz function generated by a general set in a real linear space and re-examine its properties in the more general setting. A useful approach for solving a vector problem is to reduce it to a scalar problem. In general, scalarization means the replacement of a vector optimization problem by a suitable scalar problem which tends to be an optimization problem with a real valued objective function. The Gerstewitz function is well known and widely used in optimization as the basis of the scalarization. The essential properties of the Gerstewitz function, which are well known in the topological framework, are studied by using algebraic counterparts rather than the topological concepts of interior and closure. Therefore, properties of the Gerstewitz function, when it takes values just in a real linear space are studied, and we use it to characterize Q-efficient solutions of vector problems whose image space is not endowed with any particular topology. Therefore, we deal with a constrained vector optimization problem in a real linear space without assuming any topology, and also Q-weak efficient and Q-proper efficient solutions in the senses of Henig are defined. Moreover, by means of the Gerstewitz function, we provide some necessary and sufficient optimality conditions for set-valued vector optimization problems.

Keywords: algebraic interior, Gerstewitz function, vector closure, vector optimization

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Authors: Gulnara Galeeva, Yulia Kasperskaya

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The study deals with the relevant problem related to the formation of the efficient investment portfolio of an enterprise. The structure of the investment portfolio is connected to the degree of influence of intangible assets on the enterprise’s income. This determines the importance of research on the content of intangible assets. However, intangible assets studies do not take into consideration how the enterprise state can affect the content and the importance of intangible assets for the enterprise`s income. This affects accurateness of the calculations. In order to study this problem, the research was divided into several stages. In the first stage, intangible assets were classified based on their synergies as the underlying intangibles and the additional intangibles. In the second stage, this classification was applied. It showed that the lifecycle model and the theory of abrupt development of the enterprise, that are taken into account while designing investment projects, constitute limit cases of a more general theory of bifurcations. The research identified that the qualitative content of intangible assets significant depends on how close the enterprise is to being in crisis. In the third stage, the author developed and applied the Wide Pairwise Comparison Matrix method. This allowed to establish that using the ratio of the standard deviation to the mean value of the elements of the vector of priority of intangible assets makes it possible to estimate the probability of a full-blown crisis of the enterprise. The author has identified a criterion, which allows making fundamental decisions on investment feasibility. The study also developed an additional rapid method of assessing the enterprise overall status based on using the questionnaire survey with its Director. The questionnaire consists only of two questions. The research specifically focused on the fundamental role of stochastic resonance in the emergence of bifurcation (crisis) in the economic development of the enterprise. The synergetic approach made it possible to describe the mechanism of the crisis start in details and also to identify a range of universal ways of overcoming the crisis. It was outlined that the structure of intangible assets transforms into a more organized state with the strengthened synchronization of all processes as a result of the impact of the sporadic (white) noise. Obtained results offer managers and business owners a simple and an affordable method of investment portfolio optimization, which takes into account how close the enterprise is to a state of a full-blown crisis.

Keywords: analytic hierarchy process, bifurcation, investment portfolio, intangible assets, wide matrix

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Authors: Deni̇z Ezgi̇ Gülmez, Mesut Kirca

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Nanoporous (np) metals have attracted considerable attention owing to their cellular morphological features at atomistic scale which yield ultra-high specific surface area awarding a great potential to be employed in diverse applications such as catalytic, electrocatalytic, sensing, mechanical and optical. As one of the carbon based nanostructures, fullerenes are also another type of outstanding nanomaterials that have been extensively investigated due to their remarkable chemical, mechanical and optical properties. In this study, the idea of improving the mechanical behavior of nanoporous metals by inclusion of the fullerenes, which offers a new metal-carbon nanocomposite material, is examined and discussed. With this motivation, tensile mechanical behavior of nanoporous metals reinforced with carbon fullerenes is investigated by classical molecular dynamics (MD) simulations. Atomistic models of the nanoporous metals with ultrathin ligaments are obtained through a stochastic process simply based on the intersection of spherical volumes which has been used previously in literature. According to this technique, the atoms within the ensemble of intersecting spherical volumes is removed from the pristine solid block of the selected metal, which results in porous structures with spherical cells. Following this, fullerene units are added into the cellular voids to obtain final atomistic configurations for the numerical tensile tests. Several numerical specimens are prepared with different number of fullerenes per cell and with varied fullerene sizes. LAMMPS code was used to perform classical MD simulations to conduct uniaxial tension experiments on np models filled by fullerenes. The interactions between the metal atoms are modeled by using embedded atomic method (EAM) while adaptive intermolecular reactive empirical bond order (AIREBO) potential is employed for the interaction of carbon atoms. Furthermore, atomic interactions between the metal and carbon atoms are represented by Lennard-Jones potential with appropriate parameters. In conclusion, the ultimate goal of the study is to present the effects of fullerenes embedded into the cellular structure of np metals on the tensile response of the porous metals. The results are believed to be informative and instructive for the experimentalists to synthesize hybrid nanoporous materials with improved properties and multifunctional characteristics.

Keywords: fullerene, intersecting spheres, molecular dynamic, nanoporous metals

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Authors: Huseyin Guler, Cigdem Kosar

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The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

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Authors: V. Samulionis, J. Banys, A. Sánchez-Ferrer

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Inorganic nanoparticles are used for fabrication of various composites based on polymer materials because they exhibit a good homogeneity and solubility of the composite material. Multifunctional materials based on composites of a polymer containing inorganic nanotubes are expected to have a great impact on industrial applications in the future. An emerging family of such composites are polyurea elastomers with inorganic MoS2 nanotubes or MoSI nanowires. Polyurea elastomers are a new kind of materials with higher performance than polyurethanes. The improvement of mechanical, chemical and thermal properties is due to the presence of hydrogen bonds between the urea motives which can be erased at high temperature softening the elastomeric network. Such materials are the combination of amorphous polymers above glass transition and crosslinkers which keep the chains into a single macromolecule. Polyurea exhibits a phase separated structure with rigid urea domains (hard domains) embedded in a matrix of flexible polymer chains (soft domains). The elastic properties of polyurea can be tuned over a broad range by varying the molecular weight of the components, the relative amount of hard and soft domains, and concentration of nanoparticles. Ultrasonic methods as non-destructive techniques can be used for elastomer composites characterization. In this manner, we have studied the temperature dependencies of the longitudinal ultrasonic velocity and ultrasonic attenuation of these new polyurea elastomers and composites with inorganic nanoparticles. It was shown that in these polyurea elastomers large ultrasonic attenuation peak and corresponding velocity dispersion exists at 10 MHz frequency below room temperature and this behaviour is related to glass transition Tg of the soft segments in the polymer matrix. The relaxation parameters and Tg depend on the segmental molecular weight of the polymer chains between crosslinking points, the nature of the crosslinkers in the network and content of MoS2 nanotubes or MoSI nanowires. The increase of ultrasonic velocity in composites modified by nanoparticles has been observed, showing the reinforcement of the elastomer. In semicrystalline polyurea elastomer matrices, above glass transition, the first order phase transition from quasi-crystalline to the amorphous state has been observed. In this case, the sharp ultrasonic velocity and attenuation anomalies were observed near the transition temperature TC. Ultrasonic attenuation maximum related to glass transition was reduced in quasicrystalline polyureas indicating less influence of soft domains below TC. The first order phase transition in semicrystalline polyurea elastomer samples has large temperature hysteresis (> 10 K). The impact of inorganic MoS2 nanotubes resulted in the decrease of the first order phase transition temperature in semicrystalline composites.

Keywords: inorganic nanotubes, polyurea elastomer composites, ultrasonic velocity, ultrasonic attenuation

Procedia PDF Downloads 288