Search results for: real volume

Authors: Manuel Rosales, Francisco Agrela, Julia Rosales

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The development of concrete pavements that include recycled waste with active and predictive safety features is a possible approach to mitigate the harmful impacts of the construction industry, such as CO2 emissions and the consumption of energy and natural resources during the construction and maintenance of road infrastructure. This study establishes the basis for formulating new smart materials for concrete pavements and carrying out the in-situ implementation of an experimental road section. To this end, a comprehensive recycled pavement solution is developed that combines eco-hybrid cement made with 25% mixed recycled aggregate powder (pMRA) and biomass bottom ash powder (pBBA) and a 30% substitution of natural aggregate by MRA and BBA. This work is grouped in three lines. 1) construction materials with high rates of use of recycled material, 2) production processes with efficient consumption of natural resources and use of cleaner energies, and 3) implementation and monitoring of road section with sustainable concrete made from waste. The objective of this study is to ensure satisfactory rheology, mechanical strength, durability, and CO2 capture of pavement concrete manufactured from waste and its subsequent application in real road section as well as its monitoring to establish the optimal range of recycled material. The concrete developed during this study are aimed at the reuse of waste, promoting the circular economy. For this purpose, and after having carried out different tests in the laboratory, three mixtures were established to be applied on the experimental road.

Keywords: biomass bottom ash, construction and demolition waste, recycled concrete pavements, full-scale experimental road, monitoring

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Authors: Johannes Bibinger, Sebastian Eibl, Hans-Joachim Gudladt

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This study considers the influence of different irradiation scenarios on the thermal degradation of carbon fiber-reinforced polymers (CFRP). Real threats are simulated, such as fires with long-lasting low heat fluxes and nuclear heat flashes with short-lasting high heat fluxes. For this purpose, coated and uncoated quasi-isotropic samples of the commercially available CFRP HexPly® 8552/IM7 are thermally irradiated from one side by a cone calorimeter and a xenon short-arc lamp with heat fluxes between 5 and 175 W/cm² at varying time intervals. The specimen temperature is recorded on the front and backside as well as at different laminate depths. The CFRP is non-destructively tested with ultrasonic testing, infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), and micro-focused computed X-Ray tomography (μCT). Destructive tests are performed to evaluate the mechanical properties in terms of interlaminar shear strength (ILSS), compressive and tensile strength. The irradiation scenarios vary significantly in heat flux and exposure time. Thus, different heating rates, radiation effects, and temperature distributions occur. This leads to unequal decomposition processes, which affect the sensitivity of the strength type and damage behaviour of the specimens. However, with the use of surface coatings, thermal degradation of composite materials can be delayed.

Keywords: CFRP, one-sided thermal damage, high heat flux, heating rate, non-destructive and destructive testing

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Authors: Chen Zhang, Nan Chen

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High-dimensional process monitoring becomes increasingly important in many application domains, where usually the process distribution is unknown and much more complicated than the normal distribution, and the between-stream correlation can not be neglected. However, since the process dimension is generally much bigger than the reference sample size, most traditional nonparametric multivariate control charts fail in high-dimensional cases due to the curse of dimensionality. Furthermore, when the process goes out of control, the influenced variables are quite sparse compared with the whole dimension, which increases the detection difficulty. Targeting at these issues, this paper proposes a new nonparametric monitoring scheme for high-dimensional processes. This scheme first projects the high-dimensional process into several subprocesses using random projections for dimension reduction. Then, for every subprocess with the dimension much smaller than the reference sample size, a local nonparametric control chart is constructed based on the spatial rank test to detect changes in this subprocess. Finally, the results of all the local charts are fused together for decision. Furthermore, after an out-of-control (OC) alarm is triggered, a diagnostic framework is proposed. using the square-root LASSO. Numerical studies demonstrate that the chart has satisfactory detection power for sparse OC changes and robust performance for non-normally distributed data, The diagnostic framework is also effective to identify truly changed variables. Finally, a real-data example is presented to demonstrate the application of the proposed method.

Keywords: random projection, high-dimensional process control, spatial rank, sequential change detection

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Authors: Lokesh Soni, Sushanta Kumar Sethi, Gaurav Manik

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This work attempts to use molecular simulations to create equilibrated structures of a range of commercially used polymers. Generated equilibrated structures for polyvinyl acetate (isotactic), polyvinyl alcohol (atactic), polystyrene, polyethylene, polyamide 66, poly dimethyl siloxane, poly carbonate, poly ethylene oxide, poly amide 12, natural rubber, poly urethane, and polycarbonate (bisphenol-A) and poly ethylene terephthalate are employed to estimate the correct chain length that will correctly predict the chain parameters and properties. Further, the equilibrated structures are used to predict some properties like density, solubility parameter, cohesive energy density, surface energy, and Flory-Huggins interaction parameter. The simulated densities for polyvinyl acetate, polyvinyl alcohol, polystyrene, polypropylene, and polycarbonate are 1.15 g/cm3, 1.125 g/cm3, 1.02 g/cm3, 0.84 g/cm3 and 1.223 g/cm3 respectively are found to be in good agreement with the available literature estimates. However, the critical repeating units or the degree of polymerization after which the solubility parameter showed saturation were 15, 20, 25, 10 and 20 respectively. This also indicates that such properties that dictate the miscibility of two or more polymers in their blends are strongly dependent on the chosen polymer or its characteristic properties. An attempt has been made to correlate such properties with polymer properties like Kuhn length, free volume and the energy term which plays a vital role in predicting the mentioned properties. These results help us to screen and propose a useful library which may be used by the research groups in estimating the polymer properties using the molecular simulations of chains with the predicted critical lengths. The library shall help to obviate the need for researchers to spend efforts in finding the critical chain length needed for simulating the mentioned polymer properties.

Keywords: Kuhn length, Flory Huggins interaction parameter, cohesive energy density, free volume

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Authors: Chevon Fernando, Banuka Athuraliya

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The purpose of this research is to provide a solution to the students’ time management, which usually becomes an issue because students must study and manage their personal commitments. "TimeTune," an AI-based study planner that provides an opportunity to maneuver study timeframes by incorporating modern machine learning algorithms with calendar applications, is unveiled as the ideal solution. The research is focused on the development of LSTM models that connect to the Google Calendar API in the process of developing learning paths that would be fit for a unique student's daily life experience and study history. A key finding of this research is the success in building the LSTM model to predict optimal study times, which, integrating with the real-time data of Google Calendar, will generate the timetables automatically in a personalized and customized manner. The methodology encompasses Agile development practices and Object-Oriented Analysis and Design (OOAD) principles, focusing on user-centric design and iterative development. By adopting this method, students can significantly reduce the tension associated with poor study habits and time management. In conclusion, "TimeTune" displays an advanced step in personalized education technology. The fact that its application of ML algorithms and calendar integration is quite innovative is slowly and steadily revolutionizing the lives of students. The excellence of maintaining a balanced academic and personal life is stress reduction, which the applications promise to provide for students when it comes to managing their studies.

Keywords: personalized learning, study planner, time management, calendar integration

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Authors: Pejman Taghibeikzadehbadr

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Muscle contraction stimulates a transient change of myogenic factors, partly related to the mode of contractions. Here, we assessed the response of Insulin-like growth factor 1Ea (IGF-1Ea), Insulin-like growth factor 1Eb (IGF-1Eb), Insulin-like growth factor 1Ec (IGF-1Ec), Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α-1), Peroxisome proliferator-activated receptor gamma coactivator 4-alpha (PGC1α-4), and myostatin to the eccentric Vs the concentric contraction in human skeletal muscle. Ten healthy males were performed an acute eccentric and concentric exercise bout (n = 5 per group). For each contraction type, participants performed 12 sets of 10 repetitions knee extension by the dominant leg. Baseline and post-exercise muscle biopsy were taken 4 weeks before and immediately after experimental sessions from Vastus Lateralis muscle. Genes expression was measured by real-time PCR technique. There was a significant increase in PGC1α-1, PGC1α-4, IGF-1Ea and, IGF-1Eb mRNA after concentric contraction (p ≤ 0.05), while the PGC1α-4 and IGF-1Ec significantly increased after eccentric contraction (p ≤ 0.05). It is intriguing to highlight that; no significant differences between groups were evident for changes in any variables following exercise bouts (p ≥ 0.05). Our results found that concentric and eccentric contractions presented different responses in PGC1α-1, IGF-1Ea, IGF-1Eb, and IGF-1Ec mRNA. However, a similar significant increase in mRNA content was observed in PGC1α-4. Further, no apparent differences could be found between the response of genes to eccentric and concentric contraction.

Keywords: eccentric contraction, concentric contraction, gene expression, PGC-1 alpha, IGF-1 Myostatin

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Authors: Pınar Karakaya, Meral Mert, Yildiz Okuturlar, Didem Acarer, Asuman Gedikbasi, Filiz Islim, Teslime Ayaz, Ozlem Soyluk, Ozlem Harmankaya, Abdulbaki Kumbasar

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Objective: To evaluate the relation of mean platelet volume (MPV) levels with serum paraoxonase-1 activity and brachial artery diameter and intima media thickness in diabetic patients with respect to obesity and diabetic complications. Methods: A total of 201 diabetic patients grouped with respect to obesity [obese (n=89) and non-obese (n=112) and diabetic complications [with (n=50) or without (n=150) microvascular complications and with (n=91) or without (n=108) macrovascular complications] groups were included. Data on demographic and lifestyle characteristics of patients, anthropometric measurements, diabetes related microvascular and macrovascular complications, serum levels for MPV, bBrachial artery diameter and intima media thickness (IMT) and serum paraoxonase and arylesterase activities were recorded. Correlation of MPV values to paraoxonase and arylesterase activities as well as to brachial artery diameter and IMT was evaluated in study groups. Results: Mean(SD) paraoxonase and arylesterase values were 119.8(37.5) U/L and 149.0(39.9) U/L, respectively in the overall population with no significant difference with respect to obesity and macrovascular diabetic complications, whereas significantly lower values for paraoxonase (107.5(30.7) vs. 123.9(38.8) U/L, p=0.007) and arylesterase (132.1(30.2) vs. 154.7(41.2) U/L, p=0.001) were noted in patients with than without diabetic microvascular complications. Mean(SD) MPV values were 9.10 (0.87) fL in the overall population with no significant difference with respect to obesity and diabetic complications. No significant correlation of MPV values to paraoxonase, arylesterase activities, to brachial artery diameter and IMT was noted in the overall study population as well as in study groups. Conclusion: In conclusion, our findings revealed a significant decrease I PON-1 activity in diabetic patients with microvascular rather than macrovascular complications, whereas regardless of obesity and diabetic complications, no increase in thrombogenic activity and no relation of thrombogenic activity with PON-1 activity and brachial artery diameter and IMK.

Keywords: atherosclerosis, diabetes mellitus, microvascular complications, macrovascular complications, obesity, paraoxonase

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Authors: Nastaran Hajiheydari, Mohammad Soltani Delgosha

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Vendor (supplier) selection is a group decision-making (GDM) process, in which, based on some predetermined criteria, the experts’ preferences are provided in order to rank and choose the most desirable suppliers. In the real business environment, our attitudes or our choices would be made in an uncertain and indecisive situation could not be expressed in a crisp framework. Intuitionistic fuzzy sets (IFSs) could handle such situations in the best way. VIKOR method was developed to solve multi-criteria decision-making (MCDM) problems. This method, which is used to determine the compromised feasible solution with respect to the conflicting criteria, introduces a multi-criteria ranking index based on the particular measure of 'closeness' to the 'ideal solution'. Until now, there has been a little investigation of VIKOR with IFS, therefore we extended the intuitionistic fuzzy (IF) VIKOR to solve vendor selection problem under IF GDM environment. The present study intends to develop an IF VIKOR method in a GDM situation. Therefore, a model is presented to calculate the criterion weights based on entropy measure. Then, the interval-valued intuitionistic fuzzy weighted geometric (IFWG) operator utilized to obtain the total decision matrix. In the next stage, an approach based on the positive idle intuitionistic fuzzy number (PIIFN) and negative idle intuitionistic fuzzy number (NIIFN) was developed. Finally, the application of the proposed method to solve a vendor selection problem illustrated.

Keywords: group decision making, intuitionistic fuzzy set, intuitionistic fuzzy entropy measure, vendor selection, VIKOR

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Authors: J. Bernasovska, I. Boronova, J. Poracova, M. Mydlarova Blascakova, V. Szabadosova, P. Ruzbarsky, E. Petrejcikova, I. Bernasovsky

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The paper presents the results of the molecular genetics analysis in sports research, with special emphasis to use genetic information in diagnosing of motoric predispositions in Roma boys from East Slovakia. The ability and move are the basic characteristics of all living organisms. The phenotypes are influenced by a combination of genetic and environmental factors. Genetic tests differ in principle from the traditional motoric tests, because the DNA of an individual does not change during life. The aim of the presented study was to examine motion abilities and to determine the frequency of ACTN3 (R577X) gene in Roma children. Genotype data were obtained from 138 Roma and 155 Slovak boys from 7 to 15 years old. Children were investigated on physical performance level in association with their genotype. Biological material for genetic analyses comprised samples of buccal swabs. Genotypes were determined using Real Time High resolution melting PCR method (Rotor-Gene 6000 Corbett and Light Cycler 480 Roche). The software allows creating reports of any analysis, where information of the specific analysis, normalized and differential graphs and many information of the samples are shown. Roma children of analyzed group legged to non-Romany children at the same age in all the compared tests. The % distribution of R and X alleles in Roma children was different from controls. The frequency of XX genotype was 9.26%, RX 46.33% and RR was 44.41%. The frequency of XX genotype was 9.26% which is comparable to a frequency of an Indian population. Data were analyzed with the ANOVA test.

Keywords: ACTN3 gene, R577X polymorphism, Roma children, sport performance, Slovakia

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

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Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

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Authors: Fadi Eleiwi, Taous Meriem Laleg-Kirati

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This paper presents a complete dynamic modeling of a membrane distillation process. The model contains two consistent dynamic models. A 2D advection-diffusion equation for modeling the whole process and a modified heat equation for modeling the membrane itself. The complete model describes the temperature diffusion phenomenon across the feed, membrane, permeate containers and boundary layers of the membrane. It gives an online and complete temperature profile for each point in the domain. It explains heat conduction and convection mechanisms that take place inside the process in terms of mathematical parameters, and justify process behavior during transient and steady state phases. The process is monitored for any sudden change in the performance at any instance of time. In addition, it assists maintaining production rates as desired, and gives recommendations during membrane fabrication stages. System performance and parameters can be optimized and controlled using this complete dynamic model. Evolution of membrane boundary temperature with time, vapor mass transfer along the process, and temperature difference between membrane boundary layers are depicted and included. Simulations were performed over the complete model with real membrane specifications. The plots show consistency between 2D advection-diffusion model and the expected behavior of the systems as well as literature. Evolution of heat inside the membrane starting from transient response till reaching steady state response for fixed and varying times is illustrated.

Keywords: membrane distillation, dynamical modeling, advection-diffusion equation, thermal equilibrium, heat equation

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Authors: Mijoh A. Gbededo, Kapila Liyanage, Ilias Oraifige

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Decision making for sustainable manufacturing design and management requires critical considerations due to the complexity and partly conflicting issues of economic, social and environmental factors. Although there are tools capable of assessing the combination of one or two of the sustainability factors, the frameworks have not adequately integrated all the three factors. Case study and review of existing simulation applications also shows the approach lacks integration of the sustainability factors. In this paper we discussed the development of a simulation based framework for support of a holistic assessment of sustainable manufacturing design and management. To achieve this, a strategic approach is introduced to investigate the strengths and weaknesses of the existing decision supporting tools. Investigation reveals that Discrete Event Simulation (DES) can serve as a rock base for other Life Cycle Analysis frameworks. Simio-DES application optimizes systems for both economic and competitive advantage, Granta CES EduPack and SimaPro collate data for Material Flow Analysis and environmental Life Cycle Assessment, while social and stakeholders’ analysis is supported by Analytical Hierarchy Process, a Multi-Criteria Decision Analysis method. Such a common and integrated framework creates a platform for companies to build a computer simulation model of a real system and assess the impact of alternative solutions before implementing a chosen solution.

Keywords: discrete event simulation, life cycle sustainability analysis, manufacturing, sustainability

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Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decision-making problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security) by modeling the multi-job collaborative cryptographic service scheduling problem as a multi-objective optimized job flow scheduling problem and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real-time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: cloud computing, cryptography on-demand service, reinforcement learning, workflow scheduling

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Shaolin Allen Liao, Hual-Te Chien

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Various THz phase extraction algorithms have been developed for a novel THz Modulating Interferometric Doppler Radar (THz-MIDR) developed recently by the author. The THz-MIDR differs from the well-known FTIR technique in that it introduces a continuously modulating reference branch, compared to the time-consuming discrete FTIR stepping reference branch. Such change allows real-time tracking of a moving object and capturing of its Doppler signature. The working principle of the THz-MIDR is similar to the FTIR technique: the incoming THz emission from the scene is split by a beam splitter/combiner; one of the beams is continuously modulated by a vibrating mirror or phase modulator and the other split beam is reflected by a reflection mirror; finally both the modulated reference beam and reflected beam are combined by the same beam splitter/combiner and detected by a THz intensity detector (for example, a pyroelectric detector). In order to extract THz phase from the single intensity measurement signal, we have derived rigorous mathematical formulas for 3 Frequency Banded (FB) signals: 1) DC Low-Frequency Banded (LFB) signal; 2) Fundamental Frequency Banded (FFB) signal; and 3) Harmonic Frequency Banded (HFB) signal. The THz phase extraction algorithms are then developed based combinations of 2 or all of these 3 FB signals with efficient algorithms such as Levenberg-Marquardt nonlinear fitting algorithm. Numerical simulation has also been performed in Matlab with simulated THz-MIDR interferometric signal of various Signal to Noise Ratio (SNR) to verify the algorithms.

Keywords: algorithm, modulation, THz phase, THz interferometry doppler radar

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Authors: Barenten Suciu

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Classical matrix calculus and Routh-Hurwitz stability conditions, applied to the snake-like motion of the conical wheel axle, lead to the conclusion that the hunting mode is inherently unstable, and its natural frequency is a complex number. In order to analytically solve such a complicated vibration model, either the inertia terms were neglected, in the model designated as geometrical, or restrictions on the creep coefficients and yawing diameter were imposed, in the so-called dynamical model. Here, an alternative solution is proposed to solve the hunting mode, based on the observation that the bullet train wheel axle is equipped with cylindrical wheels. One argues that for such wheel treads, the geometrical hunting is irrelevant, since its natural frequency becomes nil, but the dynamical hunting is significant since its natural frequency reduces to a real number. Moreover, one illustrates that the geometrical simplification of the wheel causes the stabilization of the hunting mode, since the characteristic quartic equation, derived for conical wheels, reduces to a quadratic equation of positive coefficients, for cylindrical wheels. Quite simple analytical expressions for the damping ratio and natural frequency are obtained, without applying restrictions into the model of contact. Graphs of the time-depending hunting lateral perturbation, including the maximal and inflexion points, are presented both for the critically-damped and the over-damped wheel axles.

Keywords: bullet train, creep, cylindrical wheels, damping, dynamical hunting, stability, vibration analysis

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Authors: Yang Xiao-Jun, Yu Tian-Hao, Hu Ying-Qi

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Breathing in foreign objects during the operation of the aircraft engine, impurities in the aircraft fuel and products of incomplete combustion can produce deposits on the surface of the turbine blades. These deposits reduce not only the turbine's operating efficiency but also the life of the turbine blades. Based on the small open wind tunnel, the simulation of deposits on the leading edge of the turbine has been carried out in this work. The effect of film cooling on particulate deposition was investigated. Based on the analysis, the adhesive mechanism for the molten pollutants’ reaching to the turbine surface was simulated by matching the Stokes number, TSP (a dimensionless number characterizing particle phase transition) and Biot number of the test facility and that of the real engine. The thickness distribution and growth trend of the deposits have been observed by high power microscope and infrared camera under different temperature of the main flow, the solidification temperature of the particulate objects, and the blowing ratio. The experimental results from the leading edge particulate deposition demonstrate that the thickness of the deposition increases with time until a quasi-stable thickness is reached, showing a striking effect of the blowing ratio on the deposition. Under different blowing ratios, there exists a large difference in the thickness distribution of the deposition, and the deposition is minimal at the specific blow ratio. In addition, the temperature of main flow and the solidification temperature of the particulate have a great influence on the deposition.

Keywords: deposition, experiment, film cooling, leading edge, paraffin particles

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Authors: O. M. Lemine

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The effect of milling parameters on the structural and magnetic properties of nickel powder was investigated. The samples were characterized by X-ray powder diffraction and vibrating sample magnetometer (VSM). The results did not reveal any phase change of nickel during the milling. The average crystallite size decreases with a prolongation of milling times, whereas the lattice parameters increase. The hysteresis loop reveals the intrinsic magnetic behaviour. It was observed an increase in the magnetization which can be correlated to the volume expansion showed by XRD results.

Keywords: nickel powders, nanocrystallines, XRD, VSM

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Authors: Nazari Maryam, Gorji Saman

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For most competitions, athletes usually engage in a process called rapid weight loss (RWL) and subsequent rapid weight gain (RWG) in the days preceding the event. Besides the perfection of performance, weight regulation mediates a self-image of being “a real athlete” which is mentally important as a part of the pre-competition preparation. This feeling enhances the focus and commitment of the athlete. There is a large body of evidence that weight loss, particularly in combat sports, results in several health benefits. However, intentional weight loss beyond normal levels might have unknown negative special effects on the immune system. As the results show, a high prevalence (50%) of RWL is happening among combat athletes. It seems that energy deprivation and intense exercise to reach RWL results in altered blood cell distribution through modification of body composition that, in turn, changes B and T-Lymphocyte and/or CD4 T-Helper response. Moreover, it may diminish IgG antibody levels and modulate IgG glycosylation after this course. On the other hand, some studies show suppression of signaling and regulation of IgE antibody and chemokine production are responsible for immunodeficiency following a period of low-energy availability. Some researchers hypothesize that severe glutamine depletion, which occurs during exercise and calorie restriction, is responsible for this immune system weakness. However, supplementation by this amino acid is not prescribed yet. Therefore, weight loss is achieved not only through chronic strategies (body fat losses) but also through acute manipulations prior to competition should be supervised by a sports nutritionist to minimize side effects on the immune system and other body systems.

Keywords: athletes, immune system, rapid weight loss, weight loss strategies

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Authors: A. Abbad, W. Benstaali

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Using first-principles calculations based on the density functional theory and generalize gradient approximation, we predict electronic and magnetic properties of CeFeO3 orthorhombic perovskite. The calculated densities of states presented in this study identify the metallic behavior CeFeO3 when we use the GGA scheme, whereas when we use the GGA+U, we see that its exhibits half-metallic character with an integer magnetic moment of 24μB per formula unit at its equilibrium volume which makes this compound promising candidate for applications in spintronics.

Keywords: CeFeO3, magnetic moment, half-metallic, electronic properties

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Authors: Vineesh Amin, Ananya Agrawal

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In a prolonged period of uncertainty and disruptions in the pre-said normal order, non-cognitive skills, especially entrepreneurial mindsets, have become a pillar that can reform the educational models to inform the economy. Dreamverse Learning Lab’s IVR-based storytelling program -Call-a-Kahaani- is an evolving experiment with an aim to kindle entrepreneurial mindsets in the remotest locations of India in an accessible and engaging manner. At the heart of this experiment is the belief that at every phase in our life’s story, we have a choice which brings us closer to achieving our true potential. This interactive program is thus designed using real-time storytelling principles to empower learners, ages 24 and below, to make choices and take decisions as they become more self-aware, practice grit, try new things through stories, guided activities, and interactions, simply over a phone call. This research paper highlights the framework behind an ongoing scalable, data-oriented, low-tech program to kindle entrepreneurial mindsets in media dark zones supported by iterative design and prototyping to reach 13700+ unique learners who made 59000+ calls for 183900+min listening duration to listen to content pieces of around 3 to 4 min, with the last monitored (March 2022) record of 34% serious listenership, within one and a half years of its inception. The paper provides an in-depth account of the technical development, content creation, learning, and assessment frameworks, as well as mobilization models which have been leveraged to build this end-to-end system.

Keywords: non-cognitive skills, entrepreneurial mindsets, speech interface, remote learning, storytelling

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Authors: Seyilnan Hannah Wadak, Dangway Monica Clement

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This theoretical study explores distinct teaching methods and practices for integrating sustainable development principles into vocational education. It examines how vocational institutions can prepare students for a sustainability-oriented workforce while addressing environmental and social challenges. The research analyzes current literature, case studies, and emerging trends to identify effective strategies for incorporating sustainability across various vocational disciplines. Key approaches discussed include experiential learning, green skills training, and interdisciplinary projects that simulate real-world sustainability challenges. The study also investigates the role of technology, such as virtual reality and online collaboration tools, in enhancing sustainability education. Additionally, it addresses the importance of industry partnerships and community engagement in creating relevant, practical learning experiences. The paper highlights potential barriers to implementation and proposes solutions for overcoming them, including professional development for educators and curriculum redesign. Findings suggest that integrating sustainability into vocational education not only enhances students’ employability but also contributes to broader societal goals of sustainable development. This research provides a comprehensive framework for educational institutions and policymakers to transform vocational programs, ensuring they meet the evolving demands of a sustainable future.

Keywords: vocational education, sustainable development, teaching methods, experiential learning, green skills, curriculum integration, industry partnerships, educational technology

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Authors: Kristopher A. Pruitt, Justin M. Hill

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The purpose of this research is to determine the pacing and nutrition strategies which minimize completion time and carbohydrate intake for athletes competing in ultramarathon races. The model formulation consists of a two-phase optimization. The first-phase mixed-integer nonlinear program (MINLP) determines the minimum completion time subject to the altitude, terrain, and distance of the race, as well as the mass and cardiovascular fitness of the athlete. The second-phase MINLP determines the minimum total carbohydrate intake required for the athlete to achieve the completion time prescribed by the first phase, subject to the flow of carbohydrates through the stomach, liver, and muscles. Consequently, the second phase model provides the optimal pacing and nutrition strategies for a particular athlete for each kilometer of a particular race. Validation of the model results over a wide range of athlete parameters against completion times for real competitive events suggests strong agreement. Additionally, the kilometer-by-kilometer pacing and nutrition strategies, the model prescribes for a particular athlete suggest unconventional approaches could result in lower completion times. Thus, the MINLP provides prescriptive guidance that athletes can leverage when developing pacing and nutrition strategies prior to competing in ultramarathon races. Given the highly-variable topographical characteristics common to many ultramarathon courses and the potential inexperience of many athletes with such courses, the model provides valuable insight to competitors who might otherwise fail to complete the event due to exhaustion or carbohydrate depletion.

Keywords: nutrition, optimization, pacing, ultramarathons

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Authors: Rudolf Hela, Michala Hubertova

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The paper focuses on research of durability and lifetime of dense light-weight concrete with artificial light-weight aggregate Liapor exposed to various types of aggressive environment. Experimental part describes testing of designed concrete of various strength classes and volume weights exposed to cyclical freezing, frost and chemical de-icers and various types of chemically aggressive environment.

Keywords: aggressive environment, durability, physical-mechanical properties, light-weight concrete

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Authors: Farhad Salek, Shahaboddin Resalati

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The production of electric vehicles (EVs) featuring lithium-ion battery technology has substantially escalated over the past decade, demonstrating a steady and persistent upward trajectory. The imminent retirement of electric vehicle (EV) batteries after approximately eight years underscores the critical need for their redirection towards recycling, a task complicated by the current inadequacy of recycling infrastructures globally. A potential solution for such concerns involves extending the operational lifespan of electric vehicle (EV) batteries through their utilization in stationary energy storage systems during secondary applications. Such adoptions, however, require addressing the safety concerns associated with batteries’ knee points and thermal runaways. This paper develops an accurate mathematical model representative of the second-life battery packs from a cell-to-pack scale using an equivalent circuit model (ECM) methodology. Neural network algorithms are employed to forecast the degradation parameters based on the EV batteries' aging history to develop a degradation model. The degradation model is integrated with the ECM to reflect the impacts of the cycle aging mechanism on battery parameters during operation. The developed model is tested under real-life load profiles to evaluate the life span of the batteries in various operating conditions. The methodology and the algorithms introduced in this paper can be considered the basis for Battery Management System (BMS) design and techno-economic analysis of such technologies.

Keywords: second life battery, electric vehicles, degradation, neural network

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Authors: Rasmila Kawan, Sanjay Juvekar, Sandeep Salvi, Gufran Beig, Rainer Sauerborn

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Background: Indoor air pollution especially from the use of biomass fuels, remains a potentially large global health threat. The inefficient use of such fuels in poorly ventilated conditions results in high levels of indoor air pollution, most seriously affecting women and young children. Objectives: The main aim of this study was to measure and compare the lung function of the women exposed in the biomass fuels and LPG fuels and relate it to the indoor emission measured using a structured questionnaire, spirometer and filter based low volume samplers respectively. Methodology: This cross-sectional comparative study was conducted among the women (aged > 18 years) living in rural villages of Pune district who were not diagnosed of chronic pulmonary diseases or any other respiratory diseases and using biomass fuels or LPG for cooking for a minimum period of 5 years or more. Data collection was done from April to June 2017 in dry season. Spirometer was performed using the portable, battery-operated ultrasound Easy One spirometer (Spiro bank II, NDD Medical Technologies, Zurich, Switzerland) to determine the lung function over Forced expiratory volume. The primary outcome variable was forced expiratory volume in 1 second (FEV1). Secondary outcome was chronic obstruction pulmonary disease (post bronchodilator FEV1/ Forced Vital Capacity (FVC) < 70%) as defined by the Global Initiative for Obstructive Lung Disease. Potential confounders such as age, height, weight, smoking history, occupation, educational status were considered. Results: Preliminary results showed that the lung function of the women using Biomass fuels (FEV1/FVC = 85% ± 5.13) had comparatively reduced lung function than the LPG users (FEV1/FVC = 86.40% ± 5.32). The mean PM 2.5 mass concentration in the biomass user’s kitchen was 274.34 ± 314.90 and 85.04 ± 97.82 in the LPG user’s kitchen. Black carbon amount was found higher in the biomass users (black carbon = 46.71 ± 46.59 µg/m³) than LPG users (black carbon=11.08 ± 22.97 µg/m³). Most of the houses used separate kitchen. Almost all the houses that used the clean fuel like LPG had minimum amount of the particulate matter 2.5 which might be due to the background pollution and cross ventilation from the houses using biomass fuels. Conclusions: Therefore, there is an urgent need to adopt various strategies to improve indoor air quality. There is a lacking of current state of climate active pollutants emission from different stove designs and identify major deficiencies that need to be tackled. Moreover, the advancement in research tools, measuring technique in particular, is critical for researchers in developing countries to improve their capability to study the emissions for addressing the growing climate change and public health concerns.

Keywords: black carbon, biomass fuels, indoor air pollution, lung function, particulate matter

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Authors: Ahmet Erten, Adil Mustafa, Ayşenur Eser, Özlem Yalçın

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We present a new viscometer based on a microfluidic chip with elastic high aspect ratio micropillar arrays. The displacement of pillar tips in flow direction can be used to analyze viscosity of liquid. In our work, Computational Fluid Dynamics (CFD) is used to analyze pillar displacement of various micropillar array configurations in flow direction at different viscosities. Following CFD optimization, micro-CNC based rapid prototyping is used to fabricate molds for microfluidic chips. Microfluidic chips are fabricated out of polydimethylsiloxane (PDMS) using soft lithography methods with molds machined out of aluminum. Tip displacements of micropillar array (300 µm in diameter and 1400 µm in height) in flow direction are recorded using a microscope mounted camera, and the displacements are analyzed using image processing with an algorithm written in MATLAB. Experiments are performed with water-glycerol solutions mixed at 4 different ratios to attain 1 cP, 5 cP, 10 cP and 15 cP viscosities at room temperature. The prepared solutions are injected into the microfluidic chips using a syringe pump at flow rates from 10-100 mL / hr and the displacement versus flow rate is plotted for different viscosities. A displacement of around 1.5 µm was observed for 15 cP solution at 60 mL / hr while only a 1 µm displacement was observed for 10 cP solution. The presented viscometer design optimization is still in progress for better sensitivity and accuracy. Our microfluidic viscometer platform has potential for tailor made microfluidic chips to enable real time observation and control of viscosity changes in biological or chemical reactions.

Keywords: Computational Fluid Dynamics (CFD), high aspect ratio, micropillar array, viscometer

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Authors: Hui Luo, Zhenhong Li, Zhen Dong

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The analysis of multi-temporal InSAR (MTInSAR) such as persistent scatterer (PS) and small baseline subset (SBAS) techniques usually relies on temporal/spatial phase unwrapping (PU). Unfortunately, it always fails to unwrap the phase for two reasons: 1) spatial phase jump between adjacent pixels larger than π, such as layover and high discontinuous terrain; 2) temporal phase discontinuities such as time varied atmospheric delay. To overcome these limitations, a least-square based PU method is introduced in this paper, which incorporates baseline-combination interferograms and adjacent phase gradient network. Firstly, permanent scatterers (PS) are selected for study. Starting with the linear baseline-combination method, we obtain equivalent 'small baseline inteferograms' to limit the spatial phase difference. Then, phase different has been conducted between connected PSs (connected by a specific networking rule) to suppress the spatial correlated phase errors such as atmospheric artifact. After that, interval phase difference along arcs can be computed by least square method and followed by an outlier detector to remove the arcs with phase ambiguities. Then, the unwrapped phase can be obtained by spatial integration. The proposed method is tested on real data of TerraSAR-X, and the results are also compared with the ones obtained by StaMPS(a software package with 3D PU capabilities). By comparison, it shows that the proposed method can successfully unwrap the interferograms in urban areas even when high discontinuities exist, while StaMPS fails. At last, precise DEM errors can be got according to the unwrapped interferograms.

Keywords: phase unwrapping, time series, InSAR, urban areas

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Authors: Derrick Mirindi, Frederic Mirindi, Oluwakemi Oshineye

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The ongoing high rate of construction project failures worldwide is often blamed on the difficulties of managing stakeholders. This highlights the crucial role of strategic management (SM) in achieving project success. This study investigates how integrating digital tools into the SM framework can effectively address stakeholder-related challenges. This work specifically focuses on the impact of evolving digital tools, such as Project Management Software (PMS) (e.g., Basecamp and Wrike), Building Information Modeling (BIM) (e.g., Tekla BIMsight and Autodesk Navisworks), Virtual and Augmented Reality (VR/AR) (e.g., Microsoft HoloLens), drones and remote monitoring, and social media and Web-Based platforms, in improving stakeholder engagement and project outcomes. Through existing literature with examples of failed projects, the study highlights how the evolution of digital tools will serve as facilitators within the strategic management process. These tools offer benefits such as real-time data access, enhanced visualization, and more efficient workflows to mitigate stakeholder challenges in construction projects. The findings indicate that integrating digital tools with SM principles effectively addresses stakeholder challenges, resulting in improved project outcomes and stakeholder satisfaction. The research advocates for a combined approach that embraces both strategic management and digital innovation to navigate the complex stakeholder landscape in construction projects.

Keywords: strategic management, digital tools, virtual and augmented reality, stakeholder management, building information modeling, project management software

Procedia PDF Downloads 83