Search results for: coupled Markov random field (MRF)

Authors: Yaseen Muhammad, Zhenxia Zhao, Zhangfa Tong

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A key problem with hydrodesulfurization (HDS) process of fuel oils is the application of severe operating conditions. In this study, we proposed the catalytic HDS of dibenzothiophene (DBT) integrated with ionic liquids (ILs) application at mild temperature and pressure over low loaded (0.5 wt.%) Pd promoted Co-Mo@Al₂O₃ and Ni-Mo@Al₂O₃ catalysts. Among the thirteen ILs tested, [BMIM]BF₄, [(CH₃)₄N]Cl, [EMIM]AlCl₄, and [(C₈H₁₇)(C₃H₇)₃P]Br enhanced the catalytic HDS efficiency while the latest ranked the top of activity list as confirmed by DFT studies as well. Experimental results revealed that Pd incorporation greatly enhanced the HDS activity of classical Co or Ni based catalysts. At mild optimized experimental conditions of 1 MPa H₂ pressure, 120 oC, IL:oil ratio of 1:3 and 4 h reaction time, the % DBT conversion (21 %) by Ni-Mo@Al₂O₃ was enhanced to 69 % (over Pd-Ni-Mo@ Al₂O₃) using [(C₈H₁₇) (C₃H₇)₃P]Br. The fresh and spent catalysts were characterized for textural properties using XPS, SEM, EDX, XRD and BET surface area techniques. An overall catalytic HDS activity followed the order of: Pd-Ni-Mo@Al₂O₃ > Pd-Co-Mo@Al₂O₃ > Ni-Mo@Al₂O₃ > Co-Mo@Al₂O₃. [(C₈H₁₇) (C₃H₇)₃P]Br.could be recycled four times with minimal decrease in HDS activity. Reaction products were analyzed by GC-MS which helped in proposing reaction mechanism for the IL coupled HDS process. The present approach attributed to its cost-effective nature, ease of operation with less mechanical requirements in terms of mild operating conditions, and high efficiency could be deemed as an alternative approach for the HDS of DBT on industrial level applications.

Keywords: DFT simulation, GC-MS and reaction mechanism, Ionic liquid coupled HDS of DBT, low Pd loaded catalyst, mild operating condition

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Authors: S. Zhang, L. Zhang, X. Chen

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Due to the large uncertainty on the mechanical properties of FRP (fibre reinforced plastic), the reliability evaluation of FRP structures are currently receiving much attention in industry. However, possible statistical correlation between ply mechanical properties has been so far overlooked, and they are mostly assumed to be independent random variables. In this study, the statistical correlation between ply mechanical properties of uni-directional and plain weave composite is firstly analyzed by a combination of Monte-Carlo simulation and finite element modeling of the FRP unit cell. Large linear correlation coefficients between the in-plane mechanical properties are observed, and the correlation coefficients are heavily dependent on the uncertainty of the fibre volume ratio. It is also observed that the correlation coefficients related to Poisson’s ratio are negative while others are positive. To experimentally achieve the statistical correlation coefficients between in-plane mechanical properties of FRP, all concerned in-plane mechanical properties of the same specimen needs to be known. In-plane shear modulus of FRP is experimentally derived by the approach suggested in the ASTM standard D5379M. Tensile tests are conducted using the same specimens used for the shear test, and due to non-uniform tensile deformation a modification factor is derived by a finite element modeling. Digital image correlation is adopted to characterize the specimen non-uniform deformation. The preliminary experimental results show a good agreement with the numerical analysis on the statistical correlation. Then, failure probability of laminate plates is calculated in cases considering and not considering the statistical correlation, using the Monte-Carlo and Markov Chain Monte-Carlo methods, respectively. The results highlight the importance of accounting for the statistical correlation between ply mechanical properties to achieve accurate failure probability of laminate plates. Furthermore, it is found that for the multi-layer laminate plate, the statistical correlation between the ply elastic properties significantly affects the laminate reliability while the effect of statistical correlation between the ply strength is minimal.

Keywords: failure probability, FRP, reliability, statistical correlation

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Authors: Arzu K. Kamberli

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This article examines the relationship between economics and physics, starting with Adam Smith, with a new econophysics approach in Economics-Physics with the Gauss Law model proposal using for the Electric Field calculation, which will allow us to anticipate the Global Financial Crisis. For this purpose, the similarities between the Gauss Law using the electric field calculations and the global financial crisis have been explained on the formula, and a model has been suggested to predict the risks of the financial systems from the electricity field calculations. Thus, this study is expected to help for preventing the Global Financial Crisis with the contribution of the science of economics and physics from the aspect of econophysics.

Keywords: econophysics, electric field, financial system, Gauss law, global financial crisis

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Authors: Pardeep, Yugandhar Bitla, A. K. Patra, G. A. Basheed

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The topological protected nanosized particle-like swirling spin textures, “skyrmion,” has been observed in various ferromagnets with chiral crystal structures like MnSi, FeGe, Cu₂OSeO₃ alloys, however the magnetic ordering in these systems takes place at very low temperatures. For skyrmion-based spintronics devices, the skyrmion phase is required to stabilize in a wide temperature – field (T - H) region. The equilibrium skyrmion phase (SkX) in Co₇Zn₇Mn₆ alloy exists in a narrow T – H region just below transition temperature (TC ~ 215 K) and can be quenched by field cooling as a metastable skyrmion phase (MSkX) below SkX region. To realize robust MSkX at 110 K, field sweep ac susceptibility χ(H) measurements were performed after the zero field cooling (ZFC) and field cooling (FC) process. In ZFC process, the sample was cooled from 320 K to 110 K in zero applied magnetic field and then field sweep measurement was performed (up to 2 T) in positive direction (black curve). The real part of ac susceptibility (χ′(H)) at 110 K in positive field direction after ZFC confirms helical to conical phase transition at low field HC₁ (= 42 mT) and conical to ferromagnetic (FM) transition at higher field HC₂ (= 300 mT). After ZFC, FC measurements were performed i.e., sample was initially cooled in zero fields from 320 to 206 K and then a sample was field cooled in the presence of 15 mT field down to the temperature 110 K. After FC process, isothermal χ(H) was measured in positive (+H, red curve) and negative (-H, blue curve) field direction with increasing and decreasing field upto 2 T. Hysteresis behavior in χ′(H), measured after ZFC and FC process, indicates the stabilization of MSkX at 110 K which is in close agreement with literature. Also, the asymmetry between field-increasing curves measured after FC process in both sides confirm the stabilization of MSkX. In the returning process from the high field polarized FM state, helical state below HC₁ is destroyed and only the conical state is observed. Thus, the robust MSkX state is stabilized below its SkX phase over a much wider T - H region by FC in polycrystalline Co₇Zn₇Mn₆ alloy.

Keywords: skyrmions, magnetic susceptibility, metastable phases, topological phases

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Authors: A. Ramalho, L. Romeiro, R. Ventura, A. Suleman

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A real-time path planning framework for Unmanned Air Vehicles, and in particular multi-rotors is proposed. The framework is designed to provide feasible trajectories from the current UAV position to a goal state, taking into account constraints such as obstacle avoidance, problem kinematics, and vehicle limitations such as maximum speed and maximum acceleration. The framework computes feasible paths online, allowing to avoid new, unknown, dynamic obstacles without fully re-computing the trajectory. These features are achieved using an iterative process in which the robot computes and optimizes the trajectory while performing the mission objectives. A first trajectory is computed using a modified Rapidly-Exploring Random Tree (RRT) algorithm, that provides trajectories that respect a maximum curvature constraint. The trajectory optimization is accomplished using the Interior Point Optimizer (IPOPT) as a solver. The framework has proven to be able to compute a trajectory and optimize to a locally optimal with computational efficiency making it feasible for real-time operations.

Keywords: interior point optimization, multi-rotors, online path planning, rapidly exploring random trees, trajectory optimization

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Authors: S. Srednyak

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We study a class of functional partial differential equations(FPDEs). This class is suggested by Quantum Field Theory. We derive general properties of solutions to such equations. In particular, we demonstrate that they lead to systems of coupled integral equations with singular kernels. We show that solutions to such hierarchies can be sought among functions with regular singularities at a countable set of subvarieties of the physical space. We also develop a formal analogy of basic constructions of differential geometry on functional manifolds, as this is necessary for in depth study of FPDEs. We also consider the case of linear overdetermined systems of functional differential equations and show that it can be completely solved in terms of formal solutions of a functional equation that is a functional analogy of a system of determined algebraic equations. This development leads us to formally define the functional analogy of algebraic geometry, which we call functional algebraic geometry. We study basic properties of functional algebraic varieties. In particular, we investigate the case of a formally discrete set of solutions. We also define and study functional analogy of discriminants. In the case of fully determined systems such that the defining functionals have regular singularities, we demonstrate that formal solutions can be sought in the class of functions with regular singularities. This case provides a practical way to apply our results to physics problems.

Keywords: functional equations, quantum field theory, holomorphic functions, Yang Mills mass gap problem, quantum chaos

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Authors: Sogol Zarafshan

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The present study aimed to identify important factors affecting the effectiveness of quality control circles in a hospital, as well as rank them using a combination of fuzzy VIKOR and Grey Relational Analysis (GRA). The study population consisted of five academic members and five experts in the field of nursing working in a hospital, who were selected using a purposive sampling method. Also, a sample of 107 nurses was selected through a simple random sampling method using their employee codes and the random-number table. The required data were collected using a researcher-made questionnaire which consisted of 12 factors. The validity of this questionnaire was confirmed through giving the opinions of experts and academic members who participated in the present study, as well as performing confirmatory factor analysis. Its reliability also was verified (α=0.796). The collected data were analyzed using SPSS 22.0 and LISREL 8.8, as well as VIKOR–GRA and IPA methods. The results of ranking the factors affecting the effectiveness of quality control circles showed that the highest and lowest ranks were related to ‘Managers’ and supervisors’ support’ and ‘Group leadership’. Also, the highest hospital performance was for factors such as ‘Clear goals and objectives’ and ‘Group cohesiveness and homogeneity’, and the lowest for ‘Reward system’ and ‘Feedback system’, respectively. The results showed that although ‘Training the members’, ‘Using the right tools’ and ‘Reward system’ were factors that were of great importance, the organization’s performance for these factors was poor. Therefore, these factors should be paid more attention by the studied hospital managers and should be improved as soon as possible.

Keywords: Quality control circles, Fuzzy VIKOR, Grey Relational Analysis, Importance–Performance Analysis

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: M. Gomaa Abdoelatef, Robert M. Field, Lee, Yong-Kwan

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Growing human populations have placed increased demands on water supplies and a heightened interest in desalination infrastructure. Key elements of the economics of desalination projects are thermal and electrical inputs. With growing concerns over the use of fossil fuels to (indirectly) supply these inputs, coupling of desalination with nuclear power production represents a significant opportunity. Individually, nuclear and desalination technologies have a long history and are relatively mature. For desalination, Reverse Osmosis (RO) has the lowest energy inputs. However, the economically driven output quality of the water produced using RO, which uses only electrical inputs, is lower than the output water quality from thermal desalination plants. Therefore, modern desalination projects consider that RO should be coupled with thermal desalination technologies (MSF, MED, or MED-TVC) with attendant steam inputs to permit blending to produce various qualities of water. A large nuclear facility is well positioned to dispatch large quantities of both electrical and thermal power. This paper considers the supply of thermal energy to a large desalination facility to examine heat balance impact on the nuclear steam cycle. The APR1400 nuclear plant is selected as prototypical from both a capacity and turbine cycle heat balance perspective to examine steam supply and the impact on electrical output. Extraction points and quantities of steam are considered parametrically along with various types of thermal desalination technologies to form the basis for further evaluations of economically optimal approaches to the interface of nuclear power production with desalination projects. In our study, the thermodynamic evaluation will be executed by DE-TOP which is the IAEA desalination program, it is approved to be capable of analyzing power generation systems coupled to desalination systems through various steam extraction positions, taking into consideration the isolation loop between the APR-1400 and the thermal desalination plant for safety concern.

Keywords: APR-1400, desalination, DE-TOP, IAEA, MSF, MED, MED-TVC, RO

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: J. Fryt, K. Pietras, T. Smolen

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Intergenerational similarities in values may be effect of value transmission within families or socio-cultural trends prevailing at a specific point in time. According to salience hypothesis, salient family values may be transmitted more frequently. On the other hand, many value studies reveal that generational shift from social values (conservation and self-transcendence) to more individualistic values (openness to change and self-enhancement) suggest that value transmission and value change are two different processes. The first aim of our study was to describe similarities and differences in values of young women and their parents. The second aim was to determine which value similarities may be due to transmission within families. Ninety seven Polish women aged 19-25 and both their mothers and fathers filled in the Portrait Value Questionaire. Intergenerational similarities in values between women were found in strong preference for benevolence, universalism and self-direction as well as low preference for power. Similarities between younger women and older men were found in strong preference for universalism and hedonism as well as lower preference for security and tradition. Young women differed from older generation in strong preference for stimulation and achievement as well as low preference for conformity. To identify the origin of intergenerational similarities (whether they are the effect of value transmission within families or not), we used the comparison between correlations of values in family dyads (mother-daughter, father-daughter) and distribution of correlations in random intergenerational dyads (random mother-daughter, random father-daughter) as well as peer dyads (random daughter-daughter). Values representing conservation (security, tradition and conformity) as well as benevolence and power were transmitted in families between women. Achievement, power and security were transmitted between fathers and daughters. Similarities in openness to change (self-direction, stimulation and hedonism) and universalism were not stronger within families than in random intergenerational and peer dyads. Taken together, our findings suggest that despite noticeable generation shift from social to more individualistic values, we can observe transmission of parents’ salient values such as security, tradition, benevolence and achievement.

Keywords: value transmission, value change, intergenerational similarities, differences in values

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Authors: Nasir Baba Ahmed, Nicolas Daclin, Marc Olivaux, Gilles Dusserre

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The Mobile field hospital is critical in terms of managing emergencies in crisis. It is a sub-section of the main hospitals and the health sector, tasked with delivering responsive, immediate, and efficient medical services during a crisis. With the aim to prevent further crisis, the assessment of the cyber assets follows different methods, to distinguish its strengths and weaknesses, and in turn achieve cyber resiliency. The work focuses on assessments of cyber resilience in field hospitals with trends growing in both the field hospital and the health sector in general. This creates opportunities for the adverse attackers and the response improvement objectives for attaining cyber resilience, as the assessments allow users and stakeholders to know the level of risks with regards to its cyber assets. Thus, the purpose is to show the possible threat vectors which open up opportunities, with contrast to current trends in the assessment of the mobile field hospitals’ cyber assets.

Keywords: assessment framework, cyber resilience, cyber security, mobile field hospital

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Akinwale S. O., Koya O. A.

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The need to provide ecologically-friendly and effective lawn mowing solution is crucial for the well-being of humans. This study involved the modification of a human-powered lawn mower designed to cut tall grasses in residential areas. This study designed and fabricated a reel-type mower blade system and a pedal-powered test rig for the blade system. It also evaluated the performance of the machine. The machine was tested on some overgrown grass plots at College of Education Staff School Ilesa. Parameters such as theoretical field capacity, field efficiency and effective field capacity were determined from the data gathered. The quality of cut achieved by the unit was also documented. Test results showed that the fabricated cutting system produced a theoretical field capacity of 0.11 ha/h and an effective field capacity of 0.08ha/h. Moreover, the unit’s cutting system showed a substantial improvement over existing reel mower designs in its ability to cut on both the forward and reverse phases of its motion. This study established that the blade system described herein has the capacity to cut tall grasses. Hence, this device can therefore eliminate the need for powered mowers entirely on small residential lawns.

Keywords: effective field capacity, field efficiency, theoretical field capacity, quality of cut

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Authors: Inês N. Peça , A. Bicho, Rui Gardner, M. Margarida Cardoso

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Inorganic/organic nanocomplexes offer tremendous scope for future biomedical applications, including imaging, disease diagnosis and drug delivery. The combination of Fe3O4 with biocompatible polymers to produce smart drug delivery systems for use in pharmaceutical formulation present a powerful tool to target anti-cancer drugs to specific tumor sites through the application of an external magnetic field. In the present study, we focused on the evaluation of the effect of the magnetic field application time on the rate of drug release from iron oxide polymeric nanoparticles. Doxorubicin, an anticancer drug, was selected as the model drug loaded into the nanoparticles. Nanoparticles composed of poly(d-lactide-co-glycolide (PLGA), a biocompatible polymer already approved by FDA, containing iron oxide nanoparticles (MNP) for magnetic targeting and doxorubicin (DOX) were synthesized by the o/w solvent extraction/evaporation method and characterized by scanning electron microscopy (SEM), by dynamic light scattering (DLS), by inductively coupled plasma-atomic emission spectrometry and by Fourier transformed infrared spectroscopy. The produced particles yielded smooth surfaces and spherical shapes exhibiting a size between 400 and 600 nm. The effect of the magnetic doxorubicin loaded PLGA nanoparticles produced on cell viability was investigated in mammalian CHO cell cultures. The results showed that unloaded magnetic PLGA nanoparticles were nontoxic while the magnetic particles without polymeric coating show a high level of toxicity. Concerning the therapeutic activity doxorubicin loaded magnetic particles cause a remarkable enhancement of the cell inhibition rates compared to their non-magnetic counterpart. In vitro drug release studies performed under a non-permanent magnetic field show that the application time and the on/off cycle duration have a great influence with respect to the final amount and to the rate of drug release. In order to determine the mechanism of drug release, the data obtained from the release curves were fitted to the semi-empirical equation of the the Korsmeyer-Peppas model that may be used to describe the Fickian and non-Fickian release behaviour. Doxorubicin release mechanism has shown to be governed mainly by Fickian diffusion. The results obtained show that the rate of drug release from the produced magnetic nanoparticles can be modulated through the magnetic field time application.

Keywords: drug delivery, magnetic nanoparticles, PLGA nanoparticles, controlled release rate

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: Rajendra Kumar

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We study the Electronic spectral density of a double coupled quantum dots sandwich between superconducting leads, where one of the superconducting leads (QD1) are connected with left superconductor lead and (QD1) also connected right superconductor lead. (QD1) and (QD2) are coupling to each other. The electronic spectral density through a quantum dots between superconducting leads having s-wave symmetry of the superconducting order parameter. Such junction is called superconducting –quantum dot (S-QD-S) junction. For this purpose, we have considered a renormalized Anderson model that includes the double coupled of the superconducting leads with the quantum dots level and an attractive BCS-type effective interaction in superconducting leads. We employed the Green’s function technique to obtain superconducting order parameter with the BCS framework and Ambegaoker-Baratoff formalism to analyze the electronic spectral density through such (S-QD-S) junction. It has been pointed out that electronic spectral density through such a junction is dominated by the attractive the paring interaction in the leads, energy of the level on the dot with respect to Fermi energy and also on the coupling parameter of the two in an essential way. On the basis of numerical analysis we have compared the theoretical results of electronic spectral density with the recent transport existing theoretical analysis. QDs is the charging energy that may give rise to effects based on the interplay of Coulomb repulsion and superconducting correlations. It is, therefore, an interesting question to ask how the discrete level spectrum and the charging energy affect the DC and AC Josephson transport between two superconductors coupled via a QD. In the absence of a bias voltage, a finite DC current can be sustained in such an S-QD-S by the DC Josephson effect.

Keywords: quantum dots, S-QD-S junction, BCS superconductors, Anderson model

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Authors: Madiha Tariq, Hena Rabbani

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Recently, cavity-optomechanics becomes an extensive research field that has manipulated the mechanical effects of light for coupling of the optical field with other physical objects specifically with regards to dynamical localization. We investigate the dynamical localization (both in momentum and position space) for a vibrational mirror in a Fabry-Pérot cavity driven by a single mode optical field and a transverse probe field. The weak probe field phenomenon results in classical chaos in phase space and spatio temporal dynamics in position |ψ(x)²| and momentum space |ψ(p)²| versus time show quantum localization in both momentum and position space. Also, we discuss the parametric dependencies of dynamical localization for a designated set of parameters to be experimentally feasible. Our work opens an avenue to manipulate the other optical phenomena and applicability of proposed work can be prolonged to turn-able laser sources in the future.

Keywords: dynamical localization, cavity optomechanics, Hamiltonian chaos, probe field

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Authors: Y. L. Hor, H. S. Chu, V. P. Bui

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Composite material is widely used to replace conventional material, especially in the aerospace industry to reduce the weight of the devices. It is formed by combining reinforced materials together via adhesive bonding to produce a bulk material with alternated macroscopic properties. In bulk composites, degradation may occur in microscopic scale, which is in each individual reinforced fiber layer or especially in its matrix layer such as delamination, inclusion, disbond, void, cracks, and porosity. In this paper, we focus on the detection of defect in matrix layer which the adhesion between the composite plies is in contact but coupled through a weak bond. In fact, the adhesive defects are tested through various nondestructive methods. Among them, pulsed phase thermography (PPT) has shown some advantages providing improved sensitivity, large-area coverage, and high-speed testing. The aim of this work is to develop an efficient numerical model to study the application of PPT to the nondestructive inspection of weak bonding in composite material. The resulting thermal evolution field is comprised of internal reflections between the interfaces of defects and the specimen, and the important key-features of the defects presented in the material can be obtained from the investigation of the thermal evolution of the field distribution. Computational simulation of such inspections has allowed the improvement of the techniques to apply in various inspections, such as materials with high thermal conductivity and more complex structures.

Keywords: pulsed phase thermography, weak bond, composite, CFRP, computational modelling, optimization

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Authors: Djarot B. Darmadi

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The wider growing Finite Element Method (FEM) application is caused by its benefits of cost saving and environment friendly. Also, by using FEM a deep understanding of certain phenomenon can be achieved. This paper observed the role of material properties and volumetric change when Solid State Phase Transformation (SSPT) takes place in residual stress formation due to a welding process of ferritic steels through coupled Thermo-Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by experiment. From parametric study of the FEM model, it can be concluded that the material properties change tend to over-predicts residual stress in the weld center whilst volumetric change tend to underestimates it. The best final result is the compromise of both by incorporates them in the model which has a better result compared to a model without SSPT.

Keywords: residual stress, ferritic steels, SSPT, coupled-TMM

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Authors: Nursel Koyuncu, Nihal Ata Tutkun

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Frailty model is a survival model that takes into account the unobserved heterogeneity for exploring the relationship between the survival of an individual and several covariates. In the recent years, proposed survival models become more complex and this feature causes convergence problems especially in large data sets. Therefore selection of sample from these big data sets is very important for estimation of parameters. In sampling literature, some authors have defined new sampling schemes to predict the parameters correctly. For this aim, we try to see the effect of sampling design in semi-parametric frailty model. We conducted a simulation study in R programme to estimate the parameters of semi-parametric frailty model for different sample sizes, censoring rates under classical simple random sampling and ranked set sampling schemes. In the simulation study, we used data set recording 17260 male Civil Servants aged 40–64 years with complete 10-year follow-up as population. Time to death from coronary heart disease is treated as a survival-time and age, systolic blood pressure are used as covariates. We select the 1000 samples from population using different sampling schemes and estimate the parameters. From the simulation study, we concluded that ranked set sampling design performs better than simple random sampling for each scenario.

Keywords: frailty model, ranked set sampling, efficiency, simple random sampling

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Authors: The Nan Chang, Ping-Tang Yu, Jyun-Ming Lin

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A triple band circularly polarized antenna covering 1.17, 1.22, and 1.57 GHz is presented. To extend to the triple-band operation, we need to add one more ring while maintaining the mechanism to independently control each ring. The inset-part in the feeding scheme is used to excite the band at 1.22 GHz, while the proximate-part of the feeding scheme is used to excite not only the band at 1.57 GHz but also the band at 1.17 GHz. This is achieved by up-vertically coupled with one ring to radiate at 1.57 GHz and down-vertically coupled another ring to radiate at 1.17 GHz. It is also noted that the inset-part in our feeding scheme is by horizontal coupling. Furthermore, to increase the gain at all three bands, three air-layers are added to make the total height of the antenna be 7.8 mm. The total thickness of the three air-layers is 3 mm. The gains of the three bands are all greater than 5 dBiC after adding the air-layers.

Keywords: circular polarization, global position system, high gain, triband antenna

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Authors: Leyla Noroozbabaee, David Nickerson

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We introduce a modular, consistent, reusable bond graph model of the renal nephron’s proximal convoluted tubule (PCT), which can reproduce biological behaviour. In this work, we focus on ion and volume transport in the proximal convoluted tubule of the renal nephron. Modelling complex systems requires complex modelling problems to be broken down into manageable pieces. This can be enabled by developing models of subsystems that are subsequently coupled hierarchically. Because they are based on a graph structure. In the current work, we define two modular subsystems: the resistive module representing the membrane and the capacitive module representing solution compartments. Each module is analyzed based on thermodynamic processes, and all the subsystems are reintegrated into circuit theory in network thermodynamics. The epithelial transport system we introduce in the current study consists of five transport membranes and four solution compartments. Coupled dissipations in the system occur in the membrane subsystems and coupled free-energy increasing, or decreasing processes appear in solution compartment subsystems. These structural subsystems also consist of elementary thermodynamic processes: dissipations, free-energy change, and power conversions. We provide free and open access to the Python implementation to ensure our model is accessible, enabling the reader to explore the model through setting their simulations and reproducibility tests.

Keywords: Bond Graph, Epithelial Transport, Water Transport, Mathematical Modeling

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Authors: Peiming Li

Abstract:

This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: Hamouda M. Mousa, M. Obaid, Chan Hee Park, Cheol Sang Kim

Abstract:

The rate of copper cementation on iron rod was investigated. The study was mainly dedicated to illustrate the effect of application of electromagnetic field (EMF) on the rate of cementation. The magnetic flux was placed parallel to the iron rod and different magnetic field strength was studied. The results showed that without EMF, the rate of mass transfer was correlated by the equation: Sh= 1.36 Re0. 098 Sc0.33. The application of EMF enhanced the time required to reach high percentage copper cementation by 50%. The rate of mass transfer was correlated by the equation: Sh= 2.29 Re0. 95 Sc0.33, with applying EMF. This work illustrates that the enhancement of copper recovery in presence of EMF is due to the induced motion of Fe+n in the solution which is limited in the range of rod rotation speed of 300~900 rpm. The calculation of power consumption of EMF showed that although the application of EMF partially reduced the cementation time, the reduction of power consumption due to utilization of magnetic field is comparable to the increase in power consumed by introducing magnetic field of 2462 A T/m.

Keywords: copper cementation, electromagnetic field, copper ions, iron cylinder

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Authors: Halil Ibrahim Demir, Tarık Cakar, Ibrahim Cil, Muharrem Dugenci, Caner Erden

Abstract:

Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic algorithm, random search

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Authors: Azita Ramezani, Atousa Ramezani

Abstract:

In the ever-escalating landscape of windows malware the necessity for pioneering defense strategies turns into undeniable this study introduces an avant-garde approach fusing the capabilities of clustering random forests and support vector machines SVM to combat the intricate web of cyber threats our fusion model triumphs with a staggering accuracy of 98.67 and an equally formidable f1 score of 98.68 a testament to its effectiveness in the realm of windows malware defense by deciphering the intricate patterns within malicious code our model not only raises the bar for detection precision but also redefines the paradigm of cybersecurity preparedness this breakthrough underscores the potential embedded in the fusion of diverse analytical methodologies and signals a paradigm shift in fortifying against the relentless evolution of windows malicious threats as we traverse through the dynamic cybersecurity terrain this research serves as a beacon illuminating the path toward a resilient future where innovative fusion models stand at the forefront of cyber threat defense.

Keywords: fusion models, cyber threat defense, windows malware, clustering, random forests, support vector machines (SVM), accuracy, f1-score, cybersecurity, malicious code detection

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Authors: Chaoyan Huang, Hongxia Zheng

Abstract:

For deep space exploration and long duration interplanetary manned missions, protection of astronauts from cosmic radiation is an unavoidable problem. However, passive shielding can be little effective for protecting particles which energies are greater than 1GeV/nucleon. In this study, active magnetic protection method is adopted. Taking into account the structure and size of the end-cap, eight shielding magnetic field configurations are designed based on the Hoffman configuration. The shielding effect of shielding magnetic field structure, intensity B and thickness L on H particles with 2GeV energy is compared by test particle simulation. The result shows that the shielding effect is better with the linear type magnetic field structure in the end-cap region. Furthermore, two magnetic field configurations with better shielding effect are investigated through H and He galactic cosmic spectra. And the shielding effect of the linear type configuration adopted in the barrel and end-cap regions is best.

Keywords: galactic cosmic rays, active protection, shielding magnetic field configuration, shielding effect

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Authors: Mokhtar Labiod, Nabil Ikhlef, Keltoum Bouherine, Olivier Leroy

Abstract:

A numerical model for the radio-frequency (RF) Argon discharge chamber is developed to simulate the low pressure low temperature inductively coupled plasma. This model will be of fundamental importance in the design of the plasma magnetic control system. Electric and magnetic fields inside the discharge chamber are evaluated by solving a magnetic vector potential equation. To start with, the equations of the ideal magnetohydrodynamics theory will be presented describing the basic behaviour of magnetically confined plasma and equations are discretized with finite element method in cylindrical coordinates. The discharge chamber is assumed to be axially symmetric and the plasma is treated as a compressible gas. Plasma generation due to ionization is added to the continuity equation. Magnetic vector potential equation is solved for the electromagnetic fields. A strong dependence of the plasma properties on the discharge conditions and the gas temperature is obtained.

Keywords: direct-coupled model, magnetohydrodynamic, modelling, plasma torch simulation

Procedia PDF Downloads 433