Search results for: factor models

Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Taru Hakanen, Mervi Murtonen

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This study aims to increase understanding of the transition of business models in servitization. The significance of service in all business has increased dramatically during the past decades. Service-dominant logic (SDL) describes this change in the economy and questions the goods-dominant logic on which business has primarily been based in the past. A business model canvas is one of the most cited and used tools in defining end developing business models. The starting point of this paper lies in the notion that the traditional business model canvas is inherently goods-oriented and best suits for product-based business. However, the basic differences between goods and services necessitate changes in business model representations when proceeding in servitization. Therefore, new knowledge is needed on how the conception of business model and the business model canvas as its representation should be altered in servitized firms in order to better serve business developers and inter-firm co-creation. That is to say, compared to products, services are intangible and they are co-produced between the supplier and the customer. Value is always co-created in interaction between a supplier and a customer, and customer experience primarily depends on how well the interaction succeeds between the actors. The role of service experience is even stronger in service business compared to product business, as services are co-produced with the customer. This paper provides business model developers with a service business model canvas, which takes into account the intangible, interactive, and relational nature of service. The study employs a design science approach that contributes to theory development via design artifacts. This study utilizes qualitative data gathered in workshops with ten companies from various industries. In particular, key differences between Goods-dominant logic (GDL) and SDL-based business models are identified when an industrial firm proceeds in servitization. As the result of the study, an updated version of the business model canvas is provided based on service-dominant logic. The service business model canvas ensures a stronger customer focus and includes aspects salient for services, such as interaction between companies, service co-production, and customer experience. It can be used for the analysis and development of a current service business model of a company or for designing a new business model. It facilitates customer-focused new service design and service development. It aids in the identification of development needs, and facilitates the creation of a common view of the business model. Therefore, the service business model canvas can be regarded as a boundary object, which facilitates the creation of a common understanding of the business model between several actors involved. The study contributes to the business model and service business development disciplines by providing a managerial tool for practitioners in service development. It also provides research insight into how servitization challenges companies’ business models.

Keywords: boundary object, business model canvas, managerial tool, service-dominant logic

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Authors: Ritesh Watharkar, Sourabh Chakraborty, Brijesh Srivastava

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Reduction of water from the fruits and vegetables using different drying techniques is widely employed to prolong the shelf life of these food commodities. Heat transfer occurs inside the sample by conduction and mass transfer takes place by diffusion in accordance with temperature and moisture concentration gradient respectively during drying. This study was undertaken to study and model the thin layer drying behavior of Bhimkol pulp. The drying was conducted in a tray drier at 500c temperature with 5, 10 and 15 % concentrations of added maltodextrin. The drying experiments were performed at 5mm thickness of the thin layer and the constant air velocity of 0.5 m/s.Drying data were fitted to different thin layer drying models found in the literature. Comparison of fitted models was based on highest R2(0.9917), lowest RMSE (0.03201), and lowest SSE (0.01537) revealed Middle equation as the best-fitted model for thin layer drying with 10% concentration of maltodextrin. The effective diffusivity was estimated based on the solution of Fick’s law of diffusion which is found in the range of 3.0396 x10-09 to 5.0661 x 10-09. There was a reduction in drying time with the addition of maltodextrin as compare to the raw pulp.

Keywords: Bhimkol, diffusivity, maltodextrine, Midilli model

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Authors: Tarek M. Alguhane, Ayman H. Khalil, M. N. Fayed, Ayman M. Ismail

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The retrofitting of existing buildings to resist the seismic loads is very important to avoid losing lives or financial disasters. The aim at retrofitting processes is increasing total structure strength by increasing stiffness or ductility ratio. In addition, the response modification factors (R) have to satisfy the code requirements for suggested retrofitting types. In this study, two types of jackets are used, i.e. full reinforced concrete jackets and surrounding steel plate jackets. The study is carried out on an existing building in Madinah by performing static pushover analysis before and after retrofitting the columns. The selected model building represents nearly all-typical structure lacks structure built before 30 years ago in Madina City, KSA. The comparison of the results indicates a good enhancement of the structure respect to the applied seismic forces. Also, the response modification factor of the RC building is evaluated for the studied cases before and after retrofitting. The design of all vertical elements (columns) is given. The results show that the design of retrofitted columns satisfied the code's design stress requirements. However, for some retrofitting types, the ductility requirements represented by response modification factor do not satisfy KSA design code (SBC- 301).

Keywords: concrete jackets, steel jackets, RC buildings, pushover analysis, non-Linear analysis

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Authors: Sujata Chaturvedi, Ishita Pant, Deepak Kumar Jha, Vinod Kumar Singh Gautam, Chandra Bhushan Tripathi

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Objective: To describe clinical, histopathological and immunohistochemical profile of glioblastoma in patients and to correlate these findings with patient survival. Material and methods: 30 cases of histopathologically diagnosed glioblastomas were included in this study. These cases were analysed in detail for certain clinical and histopathological parameters. Immunohistochemical staining for p53, epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), mouse double minute 2 homolog (MDM2) and Ki67 was done and scores were calculated. Results of these findings were correlated with patient survival. Results: A retrospective analysis of the histopathology records and clinical case files was done in 30 cases of glioblastoma (WHO grade IV). The mean age of presentation was 50.6 years with a male predilection. The most common involved site was the frontal lobe. Amongst the clinical parameters, age of the patient and extent of surgical resection showed a significant correlation with the patient survival. Histopathological parameters showed no significant correlation with the patient survival, while amongst the immunohistochemical parameters expression of MDM2 showed a significant correlation with the patient survival. Conclusion: In this study incorporating clinical, histopathological and basic panel of immunohistochemistry, age of the patient, extent of the surgical resection and expression of MDM2 showed significant correlation with the patient survival.

Keywords: glioblastoma, p53, EGFR, VEGF, MDM2, Ki67

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Authors: Xuewen Chen, Aizhen Liang, Xiaoping Zhang

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To better understand the spatial variability of soil penetration resistance (SPR) and soil water content (SWC) induced by different tillage and crop rotation in a Mollisol of Northeast China, the soil was sampled from the tillage experiment which was established in Dehui County, Jilin Province, Northeast China, in 2001. Effect of no-tillage (NT), moldboard plow (MP) and ridge tillage (RT) under corn-soybean rotation (C-S) and continuous corn (C-C) system on SPR and SWC were compared with horizontal and vertical variations. The results showed that SPR and SWC spatially varied across the ridge. SPR in the rows was higher than inter-rows, especially in topsoil (2.5-15 cm) of NT and RT plots. SPR of MP changed in the trend with the curve-shaped ridge. In contrast to MP, NT, and RT resulted in average increment of 166.3% and 152.3% at a depth of 2.5-17.5 cm in the row positions, respectively. The mean SPR in topsoil in the rows means soil compaction is not the main factor limiting plant growth and crop yield. SPR in the row of RT soil was lower than NT at a depth of 2.5-12.5 cm. The SWC in NT and RT soil was highest in the inter-rows and least in the rows or shoulders, respectively. However, the lateral variation trend of MP was opposite to NT. From the profile view of SWC, MP was greater than NT and RT in 0-20 cm of the rows. SWC in RT soil was higher than NT in the row of 0-20 cm. Crop rotation did not have a marked impact on SPR and SWC. In addition to the tillage practices, the factor which affects SPR greatly was depth but not position. These two factors have significant effects on SWC. These results indicated that the adoption of RT was a more suitable conservation tillage practices than NT in the black soil of Northeast China.

Keywords: row, soil penetration resistance, spatial variability, tillage practice

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Authors: Hadia Abdel Aziz, Raghda El Ebrashi

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Business models are shaped by their design space or the environment they are designed to be implemented in. The rapidly changing economic, technological, political, regulatory and market external environment severely affects business logic. This is particularly true for social enterprises whose core mission is to transform their environments, and thus, their whole business logic revolves around the interchange between the enterprise and the environment. The context in which social business operates imposes different business design constraints while at the same time, open up new design opportunities. It is also affected to a great extent by the impact that successful enterprises generate; a continuous loop of interaction that needs to be managed through a dynamic capability in order to generate a lasting powerful impact. This conceptual research synthesizes and analyzes literature on social enterprise, social enterprise business models, business model innovation, business model design, and the open system view theory to propose a new business model design process for social enterprises that takes into account the critical role of environmental factors. This process would help the social enterprise develop a dynamic capability that ensures the alignment of its business model to its environmental context, thus, maximizing its probability of success.

Keywords: social enterprise, business model, business model design, business model environment

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Authors: Biruk Wolde Eremacho

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The Kombolcha to Desse road, linking Addis Ababa with Northern Ethiopia towns traverses through one of the most difficult mountainous ranges in Ethiopia. The presence of loose unconsolidated materials (colluvium materials), highly weathered and fractured basalt rocks high relief, steep natural slopes, nature of geologic formations exposed along the road section, poor drainage conditions, occurrence of high seasonal rains, and seismically active nature of the region created favorable condition for slope instability in the area. Thus, keeping in mind all above points the present study was conceived to study in detail the slope stability condition of the area. It was realized that detailed slope stability studies along this road section are very necessary to identify critical slopes and to provide the best remedial measures to minimize the slope instability problems which frequently disrupt and endanger the traffic movement on this important road. For the present study based on the field manifestation of instability two most critical slope sections were identified for detailed slope stability analysis. The deterministic slope stability analysis approach was followed to perform the detailed slope stability analysis of the selected slope sections. Factor of safety for the selected slope sections was determined for the different anticipated conditions (i.e., static and dynamic with varied water saturations) using Slope/W and Slide software. Both static and seismic slope stability analysis were carried out and factor of safety was deduced for each anticipated conditions. In general, detailed slope stability analysis of the two critical slope sections reveals that for only static dry condition both the slopes sections would be stable. However, for the rest anticipated conditions defined by static and dynamic situations with varied water saturations both critical slope sections would be unstable. Moreover, the causes of slope instability in the study area are governed by different factors; therefore integrated approaches of remedial measures are more appropriate to mitigate the possible slope instability in the study area. Depending on site condition and slope stability analysis result four types of suitable preventive and remedial measures are recommended namely; proper managements of drainages, retaining structures, gabions, and managing steeply cut slopes.

Keywords: factor of safety, remedial measures, slope stability analysis, static and dynamic condition

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Authors: Balthazar Temu, Zhao Yan, Bogdan-Petrin Ratiu, Sang Soon Oh, Qiang Li

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Photonic crystal surface emitting lasers (PCSELs) has recently become an area of active research because of the advantages these lasers have over the edge emitting lasers and vertical cavity surface emitting lasers (VCSELs). PCSELs can emit laser beams with high power (from the order of few milliwatts to Watts or even tens of Watts) which scales with the emission area while maintaining single mode operation even at large emission areas. Most PCSELs reported in the literature are air-hole based, with only few demonstrations of nanowire based PCSELs. We previously reported an optically pumped, nanowire based PCSEL operating in the O band by using the honeycomb lattice. The nanowire based PCSELs have the advantage of being able to grow on silicon platform without threading dislocations. It is desirable to extend their operating wavelength to C band to open more applications including eye-safe sensing, lidar and long haul optical communications. In this work we first analyze how the lattice constant , nanowire diameter, nanowire height and side length of the hexagon in the honeycomb pattern can be changed to increase the operating wavelength of the honeycomb based PCSELs to the C band. Then as an attempt to make our device electrically pumped, we present the finite-difference time-domain (FDTD) simulation results with metals on the nanowire. The results for different metals on the nanowire are presented in order to choose the metal which gives the device with the best quality factor. The metals under consideration are those which form good ohmic contact with p-type doped InGaAs with low contact resistivity and decent sticking coefficient to the semiconductor. Such metals include Tungsten, Titanium, Palladium and Platinum. Using the chosen metal we demonstrate the impact of thickness of the metal for a given nanowire height on the quality factor of the device. We also investigate how the height of the nanowire affects the quality factor for a fixed thickness of the metal. Finally, the main steps in making the practical device are discussed.

Keywords: designing nanowire PCSEL, designing PCSEL on silicon substrates, low threshold nanowire laser, simulation of photonic crystal lasers.

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Yung C. Lin, Kung D. Wu, Wei C. Shih, Jui P. Hung

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High speed and high precision machining have become the most important technology in manufacturing industry. The surface roughness of high precision components is regarded as the important characteristics of the product quality. However, machining chatter could damage the machined surface and restricts the process efficiency. Therefore, selection of the appropriate cutting conditions is of importance to prevent the occurrence of chatter. In addition, vibration of the spindle tool also affects the surface quality, which implies the surface precision can be controlled by monitoring the vibration of the spindle tool. Based on this concept, this study was aimed to investigate the influence of the machining conditions on the surface roughness and the vibration of the spindle tool. To this end, a series of machining tests were conducted on aluminum alloy. In tests, the vibration of the spindle tool was measured by using the acceleration sensors. The surface roughness of the machined parts was examined using white light interferometer. The response surface methodology (RSM) was employed to establish the mathematical models for predicting surface finish and tool vibration, respectively. The correlation between the surface roughness and spindle tool vibration was also analyzed by ANOVA analysis. According to the machining tests, machined surface with or without chattering was marked on the lobes diagram as the verification of the machining conditions. Using multivariable regression analysis, the mathematical models for predicting the surface roughness and tool vibrations were developed based on the machining parameters, cutting depth (a), feed rate (f) and spindle speed (s). The predicted roughness is shown to agree well with the measured roughness, an average percentage of errors of 10%. The average percentage of errors of the tool vibrations between the measurements and the predictions of mathematical model is about 7.39%. In addition, the tool vibration under various machining conditions has been found to have a positive influence on the surface roughness (r=0.78). As a conclusion from current results, the mathematical models were successfully developed for the predictions of the surface roughness and vibration level of the spindle tool under different cutting condition, which can help to select appropriate cutting parameters and to monitor the machining conditions to achieve high surface quality in milling operation.

Keywords: machining parameters, machining stability, regression analysis, surface roughness

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Authors: Gizem Kodak, Alper Unal, Birsen Koldemir, Tayfun Acarer

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Turkish Straits System which consists of Istanbul Strait (Bosphorus), Canakkale Strait (Dardanelles) and the Marmara Sea has a strategical location on international maritime as it is a unique waterway between the Mediterranean Sea, Black Sea and the Aegean Sea. Thus, this area has great importance since it is the only waterway between Black Sea countries and the rest of the World. Turkish Straits System has dangerous environmental factors hosts more vessel every day through developing World trade and this situation results in expanding accident risks day by day. Today, a lot of precautions have been taken to ensure safe navigation and to prevent maritime accidents, and international standards are followed to avoid maritime accidents. Despite this, the environmental factors that affect this area, trigger the maritime accidents and threaten the vessels with new accidents risks in different months with different hazards. This descriptive study consists of temporal and spatial analyses of environmental factors causing maritime accidents. This study also aims at contributing to safety navigation including monthly and regionally characteristics of variables. In this context, two different data sets are created consisting of environmental factors and accidents. This descriptive study on the accidents between 2001 and 2017 the mentioned region also studies the months and places of the accidents with environmental factor variables. Environmental factor variables are categorized as dynamic and static factors. Dynamic factors are appointed as meteorological and oceanographical while static factors are appointed as geological factors that threaten safety navigation with geometrical restricts. The variables that form dynamic factors are approached meteorological as wind direction, wind speed, wave altitude and visibility. The circulations and properties of the water mass on the system are studied as oceanographical properties. At the end of the study, the efficient meteorological and oceanographical parameters on the region are presented monthly and regionally. By this way, we acquired the monthly, seasonal and regional distributions of the accidents. Upon the analyses that are done; The Turkish Straits System that connects the Black Sea countries with the other countries and which is one of the most important parts of the world trade; is analyzed on temporal and spatial dimensions on the reasons of the accidents and have been presented as environmental factor dynamics causing maritime accidents.

Keywords: descriptive study, environmental factors, maritime accidents, statistics

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Authors: Syazreen N. Shair, Saiful A. Ishak, Aida Y. Yusof, Azizah Murad

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In this paper, we forecast age-specific Malaysian mortality rates and life expectancy at births by gender and ethnic groups including Malay, Chinese and Indian. Two mortality forecasting models are adopted the original Lee-Carter model and its recent modified version, the product ratio coherent model. While the first forecasts the mortality rates for each subpopulation independently, the latter accounts for the relationship between sub-populations. The evaluation of both models is performed using the out-of-sample forecast errors which are mean absolute percentage errors (MAPE) for mortality rates and mean forecast errors (MFE) for life expectancy at births. The best model is then used to perform the long-term forecasts up to the year 2030, the year when Malaysia is expected to become an aged nation. Results suggest that in terms of overall accuracy, the product ratio model performs better than the original Lee-Carter model. The association of lower mortality group (Chinese) in the subpopulation model can improve the forecasts of high mortality groups (Malay and Indian).

Keywords: coherent forecasts, life expectancy at births, Lee-Carter model, product-ratio model, mortality rates

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Authors: Mehdi Behbahani, Sebastian Rible, Charles Moulinec, Yvan Fournier, Mike Nicolai, Paolo Crosetto

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Computational Fluid Dynamics blood-flow simulations are increasingly used to develop and validate blood-contacting medical devices. This study shows that numerical simulations can provide additional and accurate estimates of relevant hemodynamic indicators (e.g., recirculation zones or wall shear stresses), which may be difficult and expensive to obtain from in-vivo or in-vitro experiments. The most recent FDA (Food and Drug Administration) benchmark consisted of a simplified centrifugal blood pump model that contains fluid flow features as they are commonly found in these devices with a clear focus on highly turbulent phenomena. The FDA centrifugal blood pump study is composed of six test cases with different volumetric flow rates ranging from 2.5 to 7.0 liters per minute, pump speeds, and Reynolds numbers ranging from 210,000 to 293,000. Within the frame of this study different turbulence models were tested including RANS models, e.g. k-omega, k-epsilon and a Reynolds Stress Model (RSM) and, LES. The partitioners Hilbert, METIS, ParMETIS and SCOTCH were used to create an unstructured mesh of 76 million elements and compared in their efficiency. Computations were performed on the JUQUEEN BG/Q architecture applying the highly parallel flow solver Code SATURNE and typically using 32768 or more processors in parallel. Visualisations were performed by means of PARAVIEW. Different turbulence models including all six flow situations could be successfully analysed and validated against analytical considerations and from comparison to other data-bases. It showed that an RSM represents an appropriate choice with respect to modeling high-Reynolds number flow cases. Especially, the Rij-SSG (Speziale, Sarkar, Gatzki) variant turned out to be a good approach. Visualisation of complex flow features could be obtained and the flow situation inside the pump could be characterized.

Keywords: blood flow, centrifugal blood pump, high performance computing, scalability, turbulence

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Authors: Mohammed Salahat, Steve Wade

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This paper presents and compares the SSDDD “Systematic Soft Domain Driven Design Framework” to DDD “Domain Driven Design Framework” as a soft system approach of information systems development. The framework use SSM as a guiding methodology within which we have embedded a sequence of design tasks based on the UML leading to the implementation of a software system using the Naked Objects framework. This framework has been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, a comparison between SSDDD and DDD is presented in this paper, to show how SSDDD improved DDD as an approach to modelling and implementing business domain perspectives for Information Systems Development. The comparison process, the results, and the improvements are presented in the following sections of this paper.

Keywords: domain-driven design, soft domain-driven design, naked objects, soft language

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Authors: P. Bhavya, P. R. Jayasree

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This paper presents a low cost transformer isolated boost half bridge micro-inverter for single phase grid connected PV system. Since the output voltage of a single PV panel is as low as 20~50V, a high voltage gain inverter is required for the PV panel to connect to the single-phase grid. The micro-inverter has two stages, an isolated dc-dc converter stage and an inverter stage with a dc link. To achieve MPPT and to step up the PV voltage to the dc link voltage, a transformer isolated boost half bridge dc-dc converter is used. To output the synchronised sinusoidal current with unity power factor to the grid, a pulse width modulated full bridge inverter with LCL filter is used. Variable step size Maximum Power Point Tracking (MPPT) method is adopted such that fast tracking and high MPPT efficiency are both obtained. AC voltage as per grid requirement is obtained at the output of the inverter. High power factor (>0.99) is obtained at both heavy and light loads. This paper gives the results of computer simulation program of a grid connected solar PV system using MATLAB/Simulink and SIM Power System tool.

Keywords: boost-half-bridge, micro-inverter, maximum power point tracking, grid connection, MATLAB/Simulink

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Authors: Hossein Motaghedi, Kaveh Arkani, Siavash Salamatpoor

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Usually the piles are important tools for safety and economical design of high and heavy structures. For this aim the response of single pile under dynamic load is so effective. Also, the agents which have influence on single pile response are properties of pile geometrical, soil and subjected loads. In this study the finite difference numerical method and by using FLAC 3D software is used for evaluation of single pile behavior under peak ground acceleration (PGA) of El Centro earthquake record in California (1940). The results of this models compared by experimental results of other researchers and it will be seen that the results of this models are approximately coincide by experimental data's. For example the maximum moment and displacement in top of the pile is corresponding to the other experimental results of pervious researchers. Furthermore, in this paper is tried to evaluate the effective properties between soil and pile. The results is shown that by increasing the pile diagonal, the pile top displacement will be decreased. As well as, by increasing the length of pile, the top displacement will be increased. Also, by increasing the stiffness ratio of pile to soil, the produced moment in pile body will be increased and the taller piles have more interaction by soils and have high inertia. So, these results can help directly to optimization design of pile dimensions.

Keywords: pile seismic response, interaction between soil and pile, numerical analysis, FLAC 3D

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Authors: František Šebek, Petr Kubík, Jindřich Petruška, Jiří Hůlka

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Present study is aimed on the cutting process of circular cross-section rods where the fracture is used to separate one rod into two pieces. Incorporating the phenomenological ductile fracture model into the explicit formulation of finite element method, the process can be analyzed without the necessity of realizing too many real experiments which could be expensive in case of repetitive testing in different conditions. In the present paper, the steel AISI 1045 was examined and the tensile tests of smooth and notched cylindrical bars were conducted together with biaxial testing of the notched tube specimens to calibrate material constants of selected phenomenological ductile fracture models. These were implemented into the Abaqus/Explicit through user subroutine VUMAT and used for cutting process simulation. As the calibration process is based on variables which cannot be obtained directly from experiments, numerical simulations of fracture tests are inevitable part of the calibration. Finally, experiments regarding the cutting process were carried out and predictive capability of selected fracture models is discussed. Concluding remarks then make the summary of gained experience both with the calibration and application of particular ductile fracture criteria.

Keywords: ductile fracture, phenomenological criteria, cutting process, explicit formulation, AISI 1045 steel

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Authors: Mohsen Azarmjoo, Yasaman Azarmjoo, Zahra Alikhani Koopaei

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This article introduces devices that can transfer sunlight to buildings that do not have access to direct sunlight during the day. The transmission and reflection of sunlight are done through the movement of movable mirrors. The focus of this article is on two models of sun tracker systems designed and built by the Macad team. In fact, this article will reveal the distinction between the two Macad devices and the previously built competitor device. What distinguishes the devices built by the Macad team from the competitor's device is the different mode of operation and the difference in the location of the sensors. Given that the devices have the same results, the Macad team has tried to reduce the defects of the competitor's device as much as possible. The special feature of the second type of device built by the Macad team has enabled buildings with different construction positions to use sun tracking systems. This article will also discuss diagrams of the path of sunlight transmission and more details of the device. It is worth mentioning that fixed mirrors are also placed next to the main devices. So that the light shining on the first device is reflected to these mirrors, this light is guided within the light receiver space and is transferred to the different parts around by steel sheets built in the light receiver space, and finally, these spaces benefit from sunlight.

Keywords: design, construction, mechatronic device, sun tracker system, sun tracker, sunlight

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Lia Gventsadze, Elguja Kutelia, David Gventsadze

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The purpose of the article is to show the possibility for the development of a new generation, eco-friendly (asbestos free) nano-porous friction materials on the basis of Georgian raw materials, along with the determination of technological parameters for their production, as well as the optimization of tribological properties and the investigation of structural aspects of wear peculiarities of elaborated materials using the scanning electron microscopy (SEM) and Auger electron spectroscopy (AES) methods. The study investigated the tribological properties of the polymer friction materials on the basis of the phenol-formaldehyde resin using the porous diatomite filler modified by silane with the aim to improve the thermal stability, while the composition was modified by iron phosphate, technical carbon and basalt fibre. As a result of testing the stable values of friction factor (0.3-0,45) were reached, both in dry and wet friction conditions, the friction working parameters (friction factor and wear stability) remained stable up to 500 OC temperatures, the wear stability of gray cast-iron disk increased 3-4 times, the soundless operation of materials without squeaking were achieved. Herewith it was proved that small amount of ingredients (5-6) are enough to compose the nano-porous friction materials. The study explains the mechanism of the action of nano-porous composition base brake lining materials and its tribological efficiency on the basis of the triple phase model of the tribo-pair.

Keywords: brake lining, friction coefficient, wear, nanoporous composite, phenolic resin

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Authors: Farida, M. Shehu, Shehu U. R. Aliyu

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Access to finance has been recognized in the literature as one of the major impediments confronting small scale businesses (SSBs). This largely arises due to high lending rate, religious inclinations, collateral, etc. Islamic mode finance operates under Profit and Loss Sharing (PLS) arrangement between a borrower (business owner) and a lender (Islamic bank). This paper empirically assesses the determinants of predisposition of small scale business operators in Fagge local government area, Kano State, Nigeria, towards the PLS. Cross-sectional data from a sample of 291 small scale business operators was analyzed using logit and probit regression models. Empirical results reveal that while awareness and religion inclination positively drive interest towards the PLS, lending rate and collateral work against it. The paper, therefore, strongly recommends more advocacy campaigns and setting up of more Islamic banks in the country to cater for the financing and religious needs of SSBs in the study area.

Keywords: Islamic finance, logit and probit models, profit and loss sharing small scale businesses, finance, commerce

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Authors: Zeynep Karacor, Rahime Hulya Ozturk

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Located in developing countries but with the successful performance in recent years have shown in emerging economies , the labor factor has undoubtedly an important place in Turkish economy. The theorists have emphasized the importance of labor and human capital factors for many years. The importance of human capital is emerging in the process of determining the labor force participation rate. It is relatively easy to employ qualified labor force but employment of unskilled labor is particularly difficult. Another factor affecting the gender differences are employment opportunities in the labor force. In our country, the employment conditions of men and women differ. Factors causing these differentials are inherent job requirements, the social structure of society, women's point of view, working hours, working conditions. Crisis in our country in recent years have significantly affect the labor force participation rates. In particular, women's labor force participation rates have shown a decrease in crisis.In crisis female laborforce leave their job and go their home. It is the sole provider of social perception of men so in crisis period it is considered that woman lost their job. In the first part of this study the current situation in the world of female participation in the labor force in Turkey will examine. In the second part of the study literature will be examined. In the third and last part of the study factors of determinants of female labor force participation rate analysis will done by Granger Causality Analysis.

Keywords: female labour force, employment, labor force, Turkey

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Authors: Shiva Rezaei, Seyed Jalal Hosseinimehr, Abbasali Karimpour Malekshah, Mansooreh Mirzaei, Fereshteh Talebpour Amiri, Mehryar Zargari

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Objective(s): Cyclophosphamide (CP), as an antineoplastic drug, is widely used in cancer patients, and liver toxicity is one of its complications. Sinapic acid (SA), as a natural phenylpropanoid, has antioxidant, anti-inflammatory, and anti-cancer properties. Materials and Methods: The purpose of the current study was to determine the protective effect of SA versus CP-induced liver toxicity. In this research, BALB/c mice were treated with SA (5 and 10 mg/kg) orally for one week, and CP (200 mg/kg) was injected on day 3 of the study. Oxidative stress markers, serum liver-specific enzymes, histopathological features, caspase-3, and nuclear factor kappa-B cells were then checked. Results: CP induced hepatotoxicity in mice and showed structural changes in liver tissue. CP significantly increased liver enzymes and lipid peroxidation and decreased glutathione. The immunoreactivity of caspase-3 and nuclear factor kappa-B cells was significantly increased. Administration of SA significantly maintained histochemical parameters and liver function enzymes in mice treated with CP. Immunohistochemical examination showed SA reduced apoptosis and inflammation. Conclusion: The data confirmed that SA with anti-apoptotic, anti-oxidative, and anti-inflammatory activities was able to preserve CP-induced liver injury in mice.

Keywords: apoptosis, cyclophosphamide, liver injury, inflammation, oxidative stress, sinapic acid

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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

Procedia PDF Downloads 136

Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Andres Nieto-Leal, Victor N. Kaliakin, Tania P. Molina

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The nature of most engineering materials is quite complex. It is, therefore, difficult to devise a general mathematical model that will cover all possible ranges and types of excitation and behavior of a given material. As a result, the development of mathematical models is based upon simplifying assumptions regarding material behavior. Such simplifications result in some material idealization; for example, one of the simplest material idealization is to assume that the material behavior obeys the elasticity. However, soils are nonhomogeneous, anisotropic, path-dependent materials that exhibit nonlinear stress-strain relationships, changes in volume under shear, dilatancy, as well as time-, rate- and temperature-dependent behavior. Over the years, many constitutive models, possessing different levels of sophistication, have been developed to simulate the behavior geomaterials, particularly cohesive soils. Early in the development of constitutive models, it became evident that elastic or standard elastoplastic formulations, employing purely isotropic hardening and predicated in the existence of a yield surface surrounding a purely elastic domain, were incapable of realistically simulating the behavior of geomaterials. Accordingly, more sophisticated constitutive models have been developed; for example, the bounding surface elastoplasticity. The essence of the bounding surface concept is the hypothesis that plastic deformations can occur for stress states either within or on the bounding surface. Thus, unlike classical yield surface elastoplasticity, the plastic states are not restricted only to those lying on a surface. Elastoplastic bounding surface models have been improved; however, there is still need to improve their capabilities in simulating the response of anisotropically consolidated cohesive soils, especially the response in extension tests. Thus, in this work an improved constitutive model that can more accurately predict diverse stress-strain phenomena exhibited by cohesive soils was developed. Particularly, an improved rotational hardening rule that better simulate the response of cohesive soils in extension. The generalized definition of the bounding surface model provides a convenient and elegant framework for unifying various previous versions of the model for anisotropically consolidated cohesive soils. The Generalized Bounding Surface Model for cohesive soils is a fully three-dimensional, time-dependent model that accounts for both inherent and stress induced anisotropy employing a non-associative flow rule. The model numerical implementation in a computer code followed an adaptive multistep integration scheme in conjunction with local iteration and radial return. The one-step trapezoidal rule was used to get the stiffness matrix that defines the relationship between the stress increment and the strain increment. After testing the model in simulating the response of cohesive soils through extensive comparisons of model simulations to experimental data, it has been shown to give quite good simulations. The new model successfully simulates the response of different cohesive soils; for example, Cardiff Kaolin, Spestone Kaolin, and Lower Cromer Till. The simulated undrained stress paths, stress-strain response, and excess pore pressures are in very good agreement with the experimental values, especially in extension.

Keywords: bounding surface elastoplasticity, cohesive soils, constitutive model, modeling of geomaterials

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Authors: Anushka Naidoo, Veron Ramsuran, Maxwell Chirehwa, Paolo Denti, Kogieleum Naidoo, Helen McIlleron, Nonhlanhla Yende-Zuma, Ravesh Singh, Sinaye Ngcapu, Nesri Padayatachi

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Background: Despite efforts to introduce new drugs and shorter drug regimens for drug-susceptible tuberculosis (TB), the standard first-line treatment has not changed in over 50 years. Rifampicin, isoniazid, and pyrazinamide are critical components of the current standard treatment regimens. Some studies suggest that microbiologic failure and acquired drug resistance are primarily driven by low drug concentrations that result from pharmacokinetic (PK) variability independent of adherence to treatment. Wide between-patient pharmacokinetic variability for rifampin, isoniazid, and pyrazinamide has been reported in prior studies. There may be several reasons for this variability. However, genetic variability in genes coding for drug metabolizing and transporter enzymes have been shown to be a contributing factor for variable tuberculosis drug exposures. Objective: We describe the pharmacokinetics of first-line TB drugs rifampicin, isoniazid, and pyrazinamide and assess the effect of genetic variability in relevant selected drug metabolizing and transporter enzymes on pharmacokinetic parameters of isoniazid and rifampicin. Methods: We conducted the randomized-controlled Improving retreatment success TB trial in Durban, South Africa. The drug regimen included rifampicin, isoniazid, and pyrazinamide. Drug concentrations were measured in plasma, and concentration-time data were analysed using nonlinear-mixed-effects models to quantify the effects of relevant covariates and single nucleotide polymorphisms (SNP’s) of drug metabolizing and transporter genes on rifampicin, isoniazid and pyrazinamide exposure. A total of 25 SNP’s: four NAT2 (used to determine acetylator status), four SLCO1B1, three Pregnane X receptor (NR1), six ABCB1 and eight UGT1A, were selected for analysis in this study. Genotypes were determined for each of the SNP’s using a TaqMan® Genotyping OpenArray™. Results: Among fifty-eight patients studied; 41 (70.7%) were male, 97% black African, 42 (72.4%) HIV co-infected and 40 (95%) on efavirenz-based ART. Median weight, fat-free mass (FFM), and age at baseline were 56.9 kg (interquartile range, IQR: 51.1-65.2), 46.8 kg (IQR: 42.5-50.3) and 37 years (IQR: 31-42), respectively. The pharmacokinetics of rifampicin and pyrazinamide was best described using one-compartment models with first-order absorption and elimination, while for isoniazid two-compartment disposition was used. The median (interquartile range: IQR) AUC (h·mg/L) and Cmax (mg/L) for rifampicin, isoniazid, and pyrazinamide were; 25.62 (23.01-28.53) and 4.85 (4.36-5.40), 10.62 (9.20-12.25) and 2.79 (2.61-2.97), 345.74 (312.03-383.10) and 28.06 (25.01-31.52), respectively. Eighteen percent of patients were classified as rapid acetylators, and 34% and 43% as slow and intermediate acetylators, respectively. Rapid and intermediate acetylator status based on NAT 2 genotype resulted in 2.3 and 1.6 times higher isoniazid clearance than slow acetylators. We found no effects of the SLCO1B1 genotypes on rifampicin pharmacokinetics. Conclusion: Plasma concentrations of rifampicin, isoniazid, and pyrazinamide were low overall in our patients. Isoniazid clearance was high overall and as expected higher in rapid and intermediate acetylators resulting in lower drug exposures. In contrast to reports from previous South African or Ugandan studies, we did not find any effects of the SLCO1B1 or other genotypes tested on rifampicin PK. However, our findings are in keeping with more recent studies from Malawi and India emphasizing the need for geographically diverse and adequately powered studies. The clinical relevance of the low tuberculosis drug concentrations warrants further investigation.

Keywords: rifampicin, isoniazid pharmacokinetics, genetics, NAT2, SLCO1B1, tuberculosis

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Authors: Treder Natalia, Siemiński Mariusz

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Takotsubo cardiomyopathy (TC) is a recently defined clinical entity. First described by Japanese researchers, today is diagnosed worldwide in 1-2% of patients admitted with the preliminary diagnosis of Acute Coronary Syndrome. The etiology of takotsubo cardiomyopathy remains still largely unknown. Currently, the most likely cause of takotsubo is direct cytotoxicity caused by catecholamine surge triggered by emotional stress. There is a strong relation between recent severe emotional stress and TC. The aim of this study was to analysis the role of stress and personality as a risk factor of TT. The presented research involves 35 people who are diagnosed TC. All patients were women, mean age 60 years. The methods used in the research are popular psychological tests: Perceived Stress Scale, DS14 scale to measure type D personality, The Neo-Five Factor Inventory of Personality and psychological interview. The obtained results prove that stress events may directly precede or even release TC. The stressful events occurred directly before the symptoms in 75% examined. 65% assessed their family life as very stressful. Examiners have also a high level of experienced stress. Only 25% of the TC were classified as having type D personality but they have a high level of negative affectivity. The subjects had a high level of extraversion, openness to experiences and an average level of neuroticism. The results suggested that such a type of personality profile may predispose to the development of takotsubo cardiomyopathy. Patients with TT are the individuals who reveal joint tendency to the experience of negative emotions and very stressful family life.

Keywords: stress, personality trails, familiar problems, Takotsubo cardiomyopathy

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Authors: Mojtaba Ahmadabadi, Akbar Masoudi, Morteza Rezai

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In this paper, using the finite element method, the effect of soil and wall characteristics was investigated. Thirty and two different models were studied by different parameters. These studies could calculate displacement at any height of the wall for frictional-cohesive soils. The main purpose of this research is to determine the most effective soil characteristics in reducing the wall displacement. Comparing different models showed that the overall increase in internal friction angle, angle of friction between soil and wall and modulus of elasticity reduce the replacement of the wall. In addition, increase in special weight of soil will increase the wall displacement. Based on results, it can be said that all wall displacements were overturning and in the backfill, soil was bulging. Results show that the highest impact is seen in reducing wall displacement, internal friction angle, and the angle friction between soil and wall. One of the advantages of this study is taking into account all the parameters of the soil and walls replacement distribution in wall and backfill soil. In this paper, using the finite element method and considering all parameters of the soil, we investigated the impact of soil parameter in wall displacement. The aim of this study is to provide the best conditions in reducing the wall displacement and displacement wall and soil distribution.

Keywords: retaining wall, fem, soil and wall interaction, angle of internal friction of the soil, wall displacement

Procedia PDF Downloads 387